[et_pb_section fb_built=”1″ admin_label=”Courses Hero” _builder_version=”3.22″ use_background_color_gradient=”on” background_color_gradient_start=”#474ab6″ background_color_gradient_end=”#9271f6″ background_image=”http://chan.higher-ed.org/wp-content/uploads/2020/08/coding-background-texture.jpg” background_blend=”overlay” custom_padding=”144px|0px|146px|0px||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”2%” locked=”off”][et_pb_row _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” custom_width_px=”1280px”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.6.0″ header_font=”|on|||” header_font_size=”42px” header_line_height=”1.3em” background_size=”initial” background_position=”top_left” background_repeat=”repeat” text_orientation=”center” background_layout=”dark” max_width=”530px” module_alignment=”center” custom_padding=”|||” hover_enabled=”0″ header_font_size_tablet=”” header_font_size_phone=”” header_font_size_last_edited=”on|desktop” locked=”off” sticky_enabled=”0″]

Self-Regulation in a Computer Literacy Course

[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Courses” _builder_version=”3.22″ custom_padding=”0px|0px|100px|0px”][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.5.6″ _module_preset=”default” custom_padding=”46px|||||”][et_pb_column type=”3_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_text _builder_version=”4.6.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]

Mary Niemczyk, Arizona State University

Wilhelmina Savenye, Arizona State University

Mary Niemczyk is an Assistant Professor in Aeronautical Management Technology and Wilhelmina Savenye is an Associate Professor in Educational Technology at Arizona State University

Abstract

Computer Literacy courses are offered by many colleges and universities and are often taken by students from various academic majors. Since the course may be unlike others in their major, students may be unfamiliar with strategies that may be useful for learning these concepts. Participants (n=291) completed a survey consisting of course-related selected-response questions, questions adopted from the Motivated Strategies for Learning Questionnaire (MSLQ), and open-ended questions focusing on their study habits. Results indicated several strategies strongly related to success as indicated by course grade.

Introduction

Research has shown that self-regulated learning is an important aspect of student academic performance in the classroom. Students practicing self-regulation behaviors initiate and direct their own efforts to acquire knowledge and skill rather than relying on teachers, parents, or others. In general, self-regulated learning consists of three essential elements: commitment to academic goals, self-efficacy perceptions, and self-regulated learning strategies (Zimmerman, 1989).

Student academic goals are the underlying reasons for students’ learning behaviors and are most often described as either mastery or performance goals (Ames, 1992; Urdan, 1997). Students possessing mastery goals are considered to be intrinsically motivated, primarily focusing on learning or mastering course material. These students often seek out challenging assignments, put forth more effort to learn the material and tend to use more effective learning strategies while studying. In contrast, students with performance goals are considered to be extrinsically motivated and tend to focus on the outcome of their learning. They are primarily interested in earning a good grade in the course, or gaining social esteem (Pintrich, 1995). Extrinsically motivated students oftentimes use less effective learning strategies (Meece, Blumenfeld & Hoyle, 1988).

Self-efficacy is the student’s beliefs about his or her ability and not only influences the type of goals students set for themselves but can also influence an individual’s willingness to attempt a particular task, the level of effort employed, and persistence in accomplishing the task (Bandura, 1986; Pintrich, 1995). Lack of self-efficacy has also been associated with the debilitating affect of high test-anxiety (Pajares, 2002).

Self-regulation also consists of students’ use of learning strategies, such as organizing and applying new information, self-monitoring one’s performance, seeking assistance, and managing time and student environments (Pintrich, Smith, Garcia & McKeachie, 1991; Zimmerman, 1989). Students’ use of self-regulated learning strategies depends not only on their knowledge of strategies but also on their academic goals and self-efficacy perceptions. Students with learning goals tend to use deep processing strategies that enhance their understanding of concepts (Pintrich & Garcia, 1994). Conversely, students with performance goals, tend to use strategies that promote only short-term and surface level processing, like memorizing and rehearsing (Weinstein, Husman & Dierking, 2000).

Much of the previous research on self-regulated learning has indicated that self-regulatory processes are linked with content domains, and individuals learn how to apply these skills in a given learning or applied context (Kiewra, 2002; Zimmerman, 1998). Determining specific self-regulation processes associated with successful learning in particular content domains is an important next step in this line of research.

Computer Literacy courses are offered by many colleges and universities and are often taken by students from various academic majors. For many students, this course is a requirement of their degree programs. For others, importance and applicability of content information are influential factors. Since these courses are very prevalent and the content a necessity to many students, it is therefore important to determine the relationship of motivation and learning strategies affecting learning and performance. This information may then be used to improve student success in future courses.

The purpose of this study was to determine the relationship among students’ reports about their goal orientation, self-efficacy and self-regulated strategy use and their academic performance in a Computer Literacy course as indicated by course grade. Also investigated were students’ reports about their most preferred and utilized study techniques and the techniques they used to monitor their learning in this course.

Methodology

Participants were students in a general studies Computer Literacy course at a large university in the southwest. Of the 291 participants, 193 were female and 98 male. The majority of participants were education (27%), communication (18%), or broadcasting (11%) majors. In total, 26 different academic majors were represented. Four percent were freshman, 27% sophomores, 47% juniors, 21% seniors, and 1% graduate students. Students ranged in age from 18 to 50 years, with an overall average age of 22.

The course was a multi-section course, consisting of a lecture class and lab. The lecture portion met in a large lecture hall twice a week for 50 minutes, while the lab section met in a PC computer lab once a week for a period of one hour and 50 minutes. Data were collected at the end of the fall semester. Participation was voluntary.

Materials

The participants completed a three-part survey. The first section included demographic questions as well as selected-response questions regarding the lowest grade they would be happy with in this course, how many hours a week they study, and their reasons for enrollment. The second section included 73 motivation and learning strategies questions adopted from the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich et.al., 1991). The motivation section of the MSLQ consists of six sub-scales and the learning strategy section consists of nine sub-scales. Students rate themselves on a 7-point Likert scale (1 = not true of me, to 7 = very true of me). The third section consisted of two selected-response and six open-ended questions focusing on student study habits.

Data Analysis

Using the method developed by Pintrich et.al. (1991), the MSLQ sub-scale scores for each participant were constructed by taking the mean of the items that make up that scale. Multiple regression analysis for two unordered sets of predictors was used to evaluate how well the use of specific motivation and learning strategies predicted course grade. Responses to open-ended questions were analyzed and categorized by discernable themes. Responses to the selected-response questions were compiled and summarized by frequency of occurrence.

MSLQ Results

In response to the sub-scale items on the motivation scale, participants rated extrinsic goal orientation and self-efficacy fairly high. The mean response score for the Extrinsic Goal Orientation sub-scale was 5.0 and Self-Efficacy for Learning and Performance was 5.3. Additionally, participants appear to not worry about course tests, as indicated by a mean response score of 3.8 on the Test Anxiety sub-scale.

There were four items on the Extrinsic Goal Orientation sub-scale, with three items focusing on importance of course grades and one item focusing on the approval of others. Mean response scores for each of the three items asking students to rate the importance of earning high course grades were fairly high, with each item mean score over 5.0.

There were eight items on the Self-efficacy for Learning and Performance sub-scale, with five items focusing on the students’ judgment about their ability to accomplish tasks for this course, and three items focusing on the students’ expectation for success in the course. Mean response scores for items focusing on the students’ beliefs about being able to accomplish course tasks were positive and ranged from 4.7 to 6.1. These items asked students to rate their beliefs in their ability to understand both basic and complex course material, and their confidence in performing well on course assignments and tests. Mean response scores for the items focusing on the students’ expectancy for success were also very positive and ranged from 5.1 to 5.6. These items asked students to rate their beliefs on being able to earn an excellent grade, and overall ability to do well in the course.

There were five items on the Test Anxiety sub-scale, with three items focusing on worry or negative thoughts during test taking and two items focusing on physiological arousal aspects of anxiety, such as upset feelings, and rapid heart beat. The mean response scores for items focusing on worry were approximately at the mid-point, ranging from 3.2 to 4.2. These mean scores seem to indicate that students were not worrying about the possibility of poor performance during test taking. The mean response scores for items focusing on physiological aspects of anxiety were 3.6 and 4.2. These mid-range mean scores indicated most students were not upset nor had uneasy feelings during test taking.

In response to sub-scale items on the learning strategy scales, participants rated elaboration fairly high and peer learning fairly low as indicated by the Elaboration and Peer Learning sub-scale mean scores. The mean scores for the Elaboration sub-scale was 4.21 and Peer Learning was 3.06.

There were six items on the Elaboration scale all focusing on study techniques that help students integrate and connect new information with prior knowledge. Mean response scores for these items ranged from a low mean score of 3.0 to a fairly high mean score of 5.0. The mean response score for the item asking students whether they write brief summaries of course readings had a low score of 3.0, indicating most students did not use this study technique. The remaining items on this sub-scale asked students if they try to connect the information learned in this course to prior knowledge or to other courses had higher scores of 4.2 to 5.0, indicating many students used these methodologies when studying.

There were three items on the Peer Learning scale focusing on whether students worked with classmates to complete assignments or enhance their understanding of course content. Mean response scores for all three items were fairly low, ranging from 3.3 to 3.8. These low scores seem to indicate students did not prefer to work with classmates in order to learn course material.

Relationships Between Strategies and Course Grade

The range of final course grades was from A through E. Final course grades resulted in the following distribution: A = 65 (22%), B = 120 (42%), C = 75 (26%), D = 24 (8%), and E = 7 (2%).

Two multiple regression analyses were conducted to predict final course grade from students’ self-reported motivation and learning strategies. One analysis included the six motivation strategies as predictors (intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy and test anxiety). The second analysis included the seven learning strategies as predictors (elaboration, organization, critical thinking, metacognition, environment regulation, effort regulation, and peer learning). The regression equations for both the motivation strategies and the learning strategies were significant. Of the motivation components, extrinsic goal and self-efficacy were positively related to course grade, while test anxiety was negatively related. Of the learning strategies, elaboration was positively related and peer learning was negatively related to course grade.

Student Responses to Study Habit Questions

Students were also asked to respond to two selected-response questions and six open-ended questions focusing on their study habits. Not all participants answered all of the questions, possibly due to time constraints or lack of interest in responding.

The first selected-response question asked students if they studied differently for this course than for their other courses. Of the 150 participants responding, 78, or 52% circled “Yes” and 72, or 48%, circled “No”.

The second question asked students who they thought has responsibility for their success in learning. Again, 150 students responded. The majority of students, 119, or 79% circled “I am” indicating personal responsibility, 12, or 8%, circled “My instructor”, and 19, or 13% wrote in that both they and the instructor are responsible for their learning.

The first open-ended item asked students to list two ways they studied for this course. Reading the text and notes was the most frequently listed study technique, with 106 responses or 47%, followed by applying information learned in lecture to the lab class, with 51 responses, or 23%. Studying with peers was listed only 9 times, or 4%.

The second open-ended question asked students to list two ways they studied for other courses. Again, the most frequently listed study technique mentioned by students was reading the text and notes, with 115 responses, or 56%. The next most frequently occurring response was outlining readings, listed 31 times, or 15%. Studying with peers was listed 21 times, accounting for 10% of the responses.

The third open-ended question asked students to describe how they check their understanding of the course material. Thirty-two students, or 30%, indicated that applying the lecture information by working on the computer helped them to determine their understanding of the material, 29 students, or 28%, stated they quizzed themselves, and 16, or 15%, stated they didn’t check their understanding.

The fourth open-ended question asked students what they considered their strength as a learner. In total, 108 students responded. Twenty-six participants, or 24% indicated their strength was their ability to memorize, 22 students, or 20%, stated that they were visual learners, and 19, students, or 18%, cited their ability to comprehend and understand.

The fifth open-ended question asked students what they considered to be their weakness as a learner. Of the 77 students who responded, 33 students, or 43% indicated procrastination, lack of motivation and laziness, and 28, or 36% of students indicated they had a low attention span.

The final open-ended question asked participants what they thought would help them to become a better learner. Of the 97 students that responded, 23, or 24%, indicated a study schedule would be helpful, 23 indicated they needed to be more disciplined, 17, or 18%, stated that they needed more real world applications, and 14 students, or 14%, needed more time in their daily lives to dedicate toward school.

Student Responses to General Course Questions

Participants were also asked to respond to a series of selected response questions regarding the lowest grade they would be happy with, and how many hours a week they study for this course. They were also asked to respond yes or no to a series of nine items aimed at discovering their reasons for taking this course.

All participants wanted to earn a grade higher than C. For each student, actual grade earned was compared to their lowest grade acceptable. In total, 156 students, or 54%, earned the grade they indicated would be the lowest grade acceptable, 104 students, or 36% earned a grade lower than that which was acceptable, and 30 students, or 10%, earned a grade higher than their lowest grade acceptable.

Participants were also asked how many hours a week they study for this course. In general, 206 students, or 71%, indicated they studied between one to three hours per week, and 37 students or 13% indicated that they studied four to six hours per week. Forty students, or 14%, responded that they did not study at all for this course.

The last question asked students about the reasons they had for taking this course. The responses indicated most students, 248 or 85%, thought this course would be helpful in other courses, and for 233 students, or 80%, this course was a requirement of their academic major. Many students, 211, or 73% felt the course would improve their academic skills and 205 students, or 70%, felt the course would improve their career prospects. One hundred ninety-one students, or 66%, took the course because the content seemed interesting.

Discussion

The results portray a complex combination of the motivation and learning strategies utilized by college students in a Computer Literacy course. Overall, the results appear to indicate that these students held both extrinsic and intrinsic goal orientations concurrently. For many students, earning a high grade was important, and many took the courses because the content seemed valuable and interesting. These students also reported they have both high self-efficacy and low test-anxiety, they utilized elaboration learning strategies and prefer to not study with classmates. Approximately half of the students earned the grade they indicated was the lowest grade acceptable to them, but about one-third earned a poorer grade than the lowest grade acceptable to them. The majority of students reported that they spent between one and three hours per week studying for this course, however, many indicated that more discipline and a study schedule would help them become better learners.

In terms of achievement goals, findings indicated extrinsic goal orientation was positively related to course grade. This finding is similar to results from a previous study focusing on college students’ goal orientations and use of self-regulation strategies in the classroom. In their study, Pintrich & Garcia (1994) found that having an extrinsic goal orientation, such as commitment to earning high grades, may actually help students focus not only on learning course material, but may also help maintain their self-efficacy.

In the current study, self-efficacy was also positively related to course grade. From this finding, it appears students had a combination of extrinsic goal orientation and high self-efficacy, which may have caused persistence in learning course material to achieve their desired academic goal.

Self-efficacy beliefs also influence the amount of stress and anxiety individuals experience as they engage in a task and the level of accomplishment they realize. Students reported high self-efficacy beliefs, therefore, it is not surprising they also indicated they had low-test anxiety. Individuals with a strong sense of competence approach difficult tasks as challenges to be mastered rather than dangers to be avoided (Pajares, 1997).

Student selection of learning strategies used to accomplish a task is also dependent on both goal orientation and self-efficacy. In the current study, the learning strategy of elaboration was positively related to course grade. Results from research by Pintrich and Garcia (1994) found that students with either intrinsic or extrinsic goal orientations both reported substantial use of cognitive and self-regulated learning strategies, such as elaboration and organization. It appears a high level of concern for grades may actually lead to better cognitive engagement.

Interestingly, results of the current study also indicated students’ valued information they were learning. Though intrinsic goal orientation was not significantly related to course grade, student responses to items focusing on their reasons for taking the course indicated a majority of students enrolled because the course material was interesting. Students who enroll in courses because the content is interesting or enjoyable are intrinsically motivated (Ryan & Deci, 2000). Based on these findings it appears students in this course had a combination of intrinsic and extrinsic goal orientations.

Another characteristic of extrinsically motivated students is their desire to demonstrate ability, or hide their perceived lack of ability. The fear of appearing incompetent can cause students to use behaviors they feel might protect their sense of self-worth (Newman, 2002). Results of the current study indicated peer learning was negatively related to course grade.

Also investigated were the reported study techniques utilized by students, and what they felt would help them become more successful in their learning. Interestingly, approximately half the students indicated they study the same way for this course as they do their other courses, and half stated they study differently. Results from previous research have indicated that the use of various learning strategies may be conditional and contextualized. Students, therefore, need to understand situations when certain learning strategies may be more or less effective (Kiewra, 2002). When encountering a learning situation for the first time, students may not know how to think within that discipline. Pintrich (1995) suggests that in order for students to become successful self-regulated learners, teachers should help them become aware of how to think, learn and reason within the particular discipline. Perhaps this would be beneficial for students in Computer Literacy courses.

In order for students to be more successful in this course, learning techniques may need to be improved. The majority of students indicated they felt responsible for their success in learning, however, only half of them earned the grade that was the lowest acceptable, with one-third of students earning a poorer grade. Students also indicated they believed they could be more successful if they had a study schedule and more discipline. It may be beneficial, therefore, to provide students with appropriate strategies for learning course material and assisting them in establishing suitable study schedules.

Conclusion

This study investigated the use of motivational and learning strategies among students in a Computer Literacy course, and the relationship between their use of these strategies and their performance in the course, as indicated by course grade. Also examined were students’ study habits, desired course grade, and reasons for enrollment.  Students rated motivational strategies related to extrinsic goal orientation and self-efficacy quite highly. They also rated themselves as not very anxious about tests.  In terms of learning strategies, these students rated elaboration strategies highest and peer learning strategies lowest. 

Though it appears that these students were extrinsically motivated, their responses to open-ended questions indicated that they were also intrinsically motivated. Their reasons for taking the course included not just that it was required, but that they were interested in the content, and that the course would help them in other courses, as well as improve their academic and career skills.

While some of the students’ strategies seemed to help them earn their desired grade, many earned a grade that was lower. Over two-thirds of the students only studied one to three hours a week for the course, with 14% indicated they didn’t study at all.  Students’ own suggestions to remedy these results included aiding them in setting up study schedules. Other methods for assisting students in courses like these might include providing students practice with the study strategies, such as elaboration, which are most related to their success. Further investigations of students’ self-regulation and learning strategies can be expected to help college students such as these in achieving success in their college courses.
References

Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261-271.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Kiewra, K. (2002). How classroom teachers can help students learn and teach them how to learn. Theory Into Practice, 41(2), 71-80.

Meece, J., Blumenfeld, P. & Hoyle, R. (1988). Students’ goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80(4), 514-523.

Newman, R. (2002). How self-regulated learners cope with academic difficulty: The role of adaptive help seeking. Theory Into Practice, 41(2), 132-138.

Pajares, F. (1997). Current directions in self-efficacy research. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in motivation and achievement (pp. 99-141). Greenwich, CT: JAI Press.

Pajares, F. (2002). Gender and perceived self-efficacy in self-regulated learning. Theory Into Practice, 41(2), 116-125.

Pintrich, P. R. (1995). Understanding self-regulated learning. In P. Pintrich (Ed.), Understanding Self-regulated Learning (pp. 3-12). San Francisco: Jossey-Bass Publishers.

Pintrich, P. & Garcia, T. (1994). Self-regulated learning in college students: Knowledge, strategies, and motivation. In P. R. Pintrich, D. R. Brown, C. E. Weinstein (Eds.), Students motivation, cognition, and learning: Essays in honor of Wilbert J. McKeachie, (pp.113-133). Hillsdale, N.J.: Lawrence Earlbaum Associates, Inc.

Pintrich, P. R., Smith, D. A., Garcia, T. & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). (Tech. Rep. No. 91-B-004). The Regents of The University of Michigan.

Ryan, R. M. & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54-67.

Urdan, T. C., (1997). Achievement goal theory. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in motivation and achievement (pp. 99-141). Greenwich, CT: JAI Press.

Weinstein, C., Husman, J. & Dierking, D. (2000). Self-regulation interventions with a focus on learning strategies. In M. Boekaerts, P.R. Pintrich & M. Ziedner (Eds.), Handbook of self-regulated learning (pp. 727-747). San Diego, CA: Academic Press.

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81, (3), 329-339.

Zimmerman, B. J. (1998). Academic studying and the development of personal skill: A self-regulatory perspective. Educational Psychologist, 33(2/3), 73-86.

[/et_pb_text][/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_sidebar _builder_version=”4.5.6″ _module_preset=”default”][/et_pb_sidebar][/et_pb_column][/et_pb_row][/et_pb_section]

[et_pb_section fb_built=”1″ admin_label=”Courses Hero” _builder_version=”3.22″ use_background_color_gradient=”on” background_color_gradient_start=”#474ab6″ background_color_gradient_end=”#9271f6″ background_image=”http://chan.higher-ed.org/wp-content/uploads/2020/08/coding-background-texture.jpg” background_blend=”overlay” custom_padding=”144px|0px|146px|0px||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”2%” locked=”off”][et_pb_row _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” custom_width_px=”1280px”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.6.0″ header_font=”|on|||” header_font_size=”42px” header_line_height=”1.3em” background_size=”initial” background_position=”top_left” background_repeat=”repeat” text_orientation=”center” background_layout=”dark” max_width=”530px” module_alignment=”center” custom_padding=”|||” header_font_size_tablet=”” header_font_size_phone=”” header_font_size_last_edited=”on|desktop” locked=”off”]

The Relationship of Math Anxiety and Gender

[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Courses” _builder_version=”3.22″ custom_padding=”0px|0px|100px|0px”][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.5.6″ _module_preset=”default” custom_padding=”46px|||||”][et_pb_column type=”3_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_text _builder_version=”4.6.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]

Martha Tapia, Berry College

George E. Marsh II, The University of Alabama

Martha Tapia is assistant professor of mathematics education at Berry College where she teaches mathematics and mathematics education courses. Her research agenda includes attitudes toward mathematics, technology in education, and emotional intelligence.

George E. Marsh II is a professor of instructional technology in the Institute of Interactive Technology at the University of Alabama.  He teaches research and technology courses.  His research agenda includes attitudes toward mathematics, technology in education, and distance education.

Abstract

The effects of mathematics anxiety and gender on attitudes toward mathematics were examined using the Attitudes Toward Mathematics Inventory (ATMI). A sample of 134 students enrolled in mathematics classes in a state university was asked to complete the ATMI. Data were analyzed using a multivariate factorial model. In this sample, the results showed that gender had no effect on attitudes toward mathematics, and gender and math anxiety had no influence on attitudes toward mathematics. There was an overall significant effect of math anxiety on self-confidence, enjoyment and motivation with large effect size. Students with no math anxiety scored significantly higher in enjoyment than students with high math anxiety. Students with little or no math anxiety scored significantly higher than students with some or high math anxiety in measures of self-confidence and motivation. Students with some math anxiety scored significantly higher in motivation than those with high math anxiety.

Introduction

It is indisputable that males have higher achievement in mathematics and higher levels of enrollment in mathematics courses (Hanna, 2003), but whether these results are caused by socialization factors or innate differences has been a matter of dispute. Gender differences in mathematics have long been explained as deficits, particularly inferior spatial visualization among girls (Collins & Kimura, 1997). Some presume this to be a sex-linked characteristic of females. Justification for this point of view is often based on deficits found in boys, such as higher levels of reading disabilities and attention deficit disorders, as well as the superiority of males on spatial tests (Nass, 1993; Nordvik & Amponsah, 1998). As a result, innate differences have long been used to explain the performance gap between the sexes. A report by the American Association of University Women (1992) blames achievement differences on differential treatment of girls in the classroom, curricula that either ignores or stereotypes women, and gender bias that undermines girls’ self-esteem.

Boys and girls have similar mathematics and science proficiency scores on tests at the age of 9, but a gap begins to appear at around age 13, or at least this has been the pattern from 1973 to 1994 on the National Assessment of Educational Progress (NAEP). However, in 1994 there was no measurable difference in the math proficiency of 13-year-old boys and girls (Campbell, Reese, O’Sullivan, & Dossey, 1996). If there was a problem in spatial visualization or other innate sexual-biological traits explaining math differences, they suddenly cleared up about a decade ago.

According to the Third International Math and Science Study (TIMSS) results, among participating countries, girls and boys had similar average mathematics achievement scores (U.S. National Research Center, 1996). However, on the NAEP, 17-year-old females have consistently scored lower, on average, than 17-year-old males, and in 1994, they were 5 scale-score points lower than males (Campbell et al., 1996). Even more interesting, average mathematics scores among 17-year-girls turned down between 1973 and 1982, but increased in 1994 to a level similar to the age cohort in 1973 (Campbell et al., 1996).

Students who do well in mathematics have more positive attitudes about the subject, thus they are likely to take more courses and may perform better. Attitudinal research has been limited and instruments have been lacking, but generally the questions asked of students show mixed findings about 13-year-old boys and girls. Linn and Hyde (1989) reported that attitudes are more negative for girls earlier than age 13, but the Longitudinal Study of American Youth found no differences for 7th-grade students (Miller, Kimmel, Hoffer, & Nelson, 1999).

Students’ attitudes are clearly important, but little is known about the factors that intervene to create significant differences. It is clear that career aspirations of boys and girls are quite different beginning around age 13, which must be explained by social and cultural factors, because girls at that age have virtually identical abilities in mathematics. Boys are twice as likely to say they want to become scientists or engineers, but girls express a preference for professional, business, or managerial occupations (U.S. Department of Education, 1990).

Enrollment patterns of college undergraduates show that few students anticipate a career in science, mathematics or engineering, and very few major in mathematics, in fact, less than 1 percent of undergraduates (Haycock & Steen, 2002). The Conference Board of the Mathematical Sciences (Lutzer & Maxwell, 2000) showed that bachelor degrees granted in mathematics fell 19 percent between 1990 and 2000, although undergraduate enrollment rose 9 percent. Attitudinal research among college students has not been thoroughly investigated. This study was an effort to determine if there are gender differences in college, a level where there has been little research compared to that at the K-12 level.

Method

Subjects

The subjects were 134 undergraduate students enrolled in mathematics classes at a state university in the southeast. Seventy-one subjects were male and 58 were female. Five participants did not report their gender. Approximately 80% of the sample was Caucasian and about 20% African-American. The ages of the sample ranged from 17 to 34. Ten participants did not report their ages. All subjects were volunteers and all students in the classes agreed to participate.

 

Materials

The Attitudes Toward Mathematics Inventory (ATMI) consists of 40 items designed to measure students’ attitudes toward mathematics (Tapia, 1996). The items were constructed using a Likert-format scale of five alternatives for the responses with anchors of 1: strongly disagree, 2: disagree, 3: neutral, 4: agree, and 5: strongly agree. Eleven items of this instrument were reversed items. These items were given appropriate value for the data analyses. The score was the sum of the ratings.

A Student’s Demographic Questionnaire was also used. This questionnaire consisted of four questions. The purpose of these questions was to identify gender, age, ethnic background, and level of math anxiety. Level of math anxiety consisted of four levels (none, little, some, high).

Exploratory factor analysis of the ATMI using a sample of high school students resulted in four factors identified as self-confidence, value, enjoyment, and motivation. Self-confidence consisted of 15 items. The value scale consisted of 10 items. The enjoyment scale consisted of 10 items. The motivation scale consisted of five items. Alpha coefficients for the scores of these scales were found to be .95, .89, .89, and .88 respectively (Tapia, 1996).

 

Procedure

The ATMI was administered to participants during their mathematics classes. Directions were provided in written form and students recorded their responses on computer scannable answer sheets.

Results

Tapia (1996) found a four-factor solution from an exploratory factor analysis with maximum likelihood method of extraction and a varimax, orthogonal, rotation. The names for the factors reported were self-confidence, value of mathematics, enjoyment of mathematics, and motivation. Based on that factor analysis, the 40 items were classified into four categories each of which was represented by a factor. A composite score for each category was calculated by adding up all the numbers of the scaled responses to the items belonging to that category. Cronbach alpha coefficients were calculated for the scores of the scales and were found to be .96 for self-confidence, .93 for value, .88 for enjoyment, and .87 for motivation.

 

The data were analyzed by using multivariate factorial model with the four factors as dependent variables: (1) self-confidence, (2) value, (3) enjoyment, and (4) motivation and two independent variables: (1) gender and (2) level of math anxiety. Multivariate analysis of variance (MANOVA) was performed by using SPSS.

 

Data were analyzed testing for interaction effect and main effect at the .05 level. Data analysis indicated that the two-way interaction effect of the two variables Gender*MathAnxiety on the four dependent variables self-confidence, value, enjoyment, and motivation was insignificant with small effect size (Wilks’ Lambda F = 1.117, p < .35, eta squared = .04). Hence, it was concluded that there was not enough evidence to indicate a two-way multivariate interaction. The results also showed that the main effect of gender was insignificant with small effect size (Wilks’ Lambda F= 1.018, p < .40, eta squared = .03), but the main effect of mathematics anxiety was significant with large effect size (Wilks’ Lambda F = 7.237, p < .00, eta squared = .19). So it was concluded that there was enough evidence to say that there was an effect of the variable level of math anxiety on the four dependent variables self-confidence, value, enjoyment, and motivation. Therefore, follow ups were conducted.

Tests of between-subject effects showed that the effect of math anxiety to three of the four dependent variables was significant with large effect size. There was enough evidence to say that there was an effect of math anxiety on the variables self-confidence (F(3,121) = 31.158, p < .00, eta squared = .44), enjoyment (F(3,121) = 9.614, p < .00, eta squared = .19), and motivation (F(3,121) = 13.179, p < .00, eta squared = .25).

Estimated marginal means in self-confidence were 62.96 (SD = 2.12) for students with no math anxiety, 57.64 (SD = 1.68) for students with little math anxiety, 48.89 (SD = 1.71) for students with some math anxiety, and 36.42 (SD = 2.16) for students with high math anxiety. Pairwise comparisons showed students with no or little math anxiety scored significantly higher in self-confidence than students with high math anxiety.

In enjoyment estimated marginal means were 36.78 (SD = 1.49) for students with no math anxiety, 34.37 (SD = 1.19) for students with little math anxiety, 31.74 (SD = 1.20) for students with some math anxiety, and 26.08 (SD = 1.52) for students with high math anxiety. Pairwise comparisons showed students with no math anxiety scoring significantly higher in enjoyment than students with high math anxiety.

Estimated marginal means in motivation were 17.06 (SD = 0.79) for students with no math anxiety, 16.14 (SD = 0.63) for students with little math anxiety, 13.65 (SD = 0.64) for students with some math anxiety, and 10.88 (SD = 0.80) for students with high math anxiety. In motivation pairwise comparisons showed students with no or little math anxiety scoring significantly higher in motivation than students with some or high math anxiety and students with some math anxiety scoring significantly higher than students with high math anxiety.

Discussion

With the multiple analysis of variance, one interaction and two main effects were found: (a) Gender did not have an effect on attitudes toward mathematics; (b) Different levels of math anxiety by gender classification had no effect on attitudes, (c) Levels of math anxiety had an effect on attitudes toward math, independent of gender, and (d) The level of math anxiety had an effect on measures of self-confidence, enjoyment, and motivation.

Students with no math anxiety scored significantly higher in enjoyment than students with high math anxiety. Self-confidence was significant, with students having little or no math anxiety scoring significantly higher than students with some or high math anxiety. Motivation was also significant and had an inverse relationship: students having little or no math anxiety scored significantly higher than students with some or high math anxiety, and students with some math anxiety scored significantly higher than students with high math anxiety.

Jordan and Nettles (1999), who analyzed data from the National Educational Longitudinal Study of 1988 (NELS), reported that girls had lower scores than boys on math in the12th grade, which is a pattern that exists in many other countries (Hanna, Kundiger, & Larouche, 1990). Numerous studies have shown that male achievement in math is higher (Entwistle, Alexander, & Olson, 1994; Gamoran, 1992; Callahan & Clements, 1984; Dossey, Mulis, Lindquist, & Chambers, 1988). Due to the fact that gender differences do not appear until around puberty, and they appear in several countries, the differences have often been attributed to innate biological differences, social factors, and anxiety among females (Callahan & Clements, 1984; Dossey et al., 1988). The logic has been that, because females take the same courses, learn under the same conditions, but have lower scores, must be intervening factors to explain the difference. One of the first studies about math anxiety was by Richardson and Suinn (1972), whose work drew attention to the problem. Since then, the literature has included results of studies about math anxiety and its effect on math achievement (Stent, 1977; Betz, 1978; Hembree, 1990). Research has shown that females, as a group, do not enjoy math and often see it as having little relationship to their lives or their futures (Fennema & Sherman, 1978). Females display more math anxiety than males in secondary school and college (Woodard, 2004).

While girls at various ages may have cultural or social pressures that help shape their attitudes about mathematics as a subject of study or an element in a future career, results with this sample of college-age students showed that the main effect of gender was insignificant. From these results, we conclude that feeling good about mathematics is not related to gender among this group of college students, but rather it is likely to be something related to individual, personal experiences. While the literature has reported a high relationship between math anxiety and gender, in this sample of students it is clear that math anxiety is unrelated to gender.

References

American Association of University Women. (1992). How schools shortchange girls: A study of major findings on girls and education. Washington, DC: AAUW Educational Foundation, The Wellesley College Center for Research on Women.

Betz, N. E. (1978). Prevalence, distribution, and correlates of math anxiety in college students. Journal of Counseling Psychology, 25, 441-448.

Callahan, L. G., & Clements, D. H. (1984). Sex differences in rote-counting ability on entry to first grade: Some observations. Journal of Research in Mathematics Education, 15, 378-382.

Campbell, J. R., Reese, C. M., O’Sullivan, C. Y., & Dossey, J. A. (1996). NAEP 1994 trends in academic progress: Achievement of U.S. students in science, 1969 to 1994, mathematics, 1973 to 1994, reading 1971 to 1994, and writing, 1984 to 1994. Washington, DC: National Center for Education Statistics.

Collins, D. W., & Kimura, D. (1997). A large sex difference on a two-dimensional mental rotation task. Behavioral Neuroscience, 111(4), 845-849.

Dossey, J. A., Mulis, I. V. S., Lindquist, M. M., & Chambers, D. L. (1988). The mathematics report card: Are we measuring up? Trends and achievement based on the 1986 National Assessment. Princeton: Educational Testing Service.

Entwistle, D. R., Alexander, K. L., & Olson, L. S. (1994). The gender gap in math: Its possible origins in neighborhood effects. American Sociological Review, 59, 822-838.

Fennema, E., & Sherman, J. (1978). Sex related differences in mathematics achievement and related factors: A further study. Journal for Research in Mathematics Education, 9, 189-203.

Gamoran, A. (1992). The variable effects of high school tracking. American Sociological Review, 57(6), 812-828.

Hanna, G. (2003). Reaching gender equity in mathematics education. The Educational Forum, 67(3), 204-214.

Hanna, G., Kundiger, E., & Larouche, C. (1990). Mathematical achievement of grade 12 girls in fifteen countries. In L. Burton (Ed.), Gender and mathematics: An international perspective. London: Cassell Educational Ltd. Pp. 87-97.

Haycock, K. & Steen, L. A. (2002) . Add it up: Mathematics education in the U.S. does not compute. Thinking K-16, 6, 1.

Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal for Research in Mathematics Education, 21, 33-46.

Jordan, W. J., & Nettles, S. M. (1999). How students invest their time out of school: Effects on school engagement, perceptions of life chances, and achievement (Report No. 29). Washington, D.C.: Center for Research on the Education of Students Placed At Risk.

Linn, M. & Hyde, J. (1989). Gender, mathematics, and science. Educational Researcher 18, 17-19, 22-27.

Lutzer, D. J. & Maxwell, J. W. (2000). Statistical abstract of undergraduate programs in the mathematical sciences in the United States. Washington, D.C.: Conference Board of Mathematical Sciences.

Miller, J. D., Kimmel, L., Hoffer, T. B., & Nelson, C. (1999). Longitudinal study of American youth: User’s manual. Chicago: International Center for the Advancement of Scientific Literacy, Chicago Academy of Sciences.

Nass, R. D. (1993). Sex differences in learning abilities and disabilities. Annals of Dyslexia, 43, 61-78.

Nordvik, H. & Amponsah, B. (1998). Gender differences in spatial abilities and spatial ability            among university students in an egalitarian educational system. Sex Roles: A Journal of

            Research, June, 1998. Online: http://www.findarticles.com/cf_dls/m2294/n11-                     

            12_v38/21109782/p1/article.jhtml

Richardson, F. C. & Suinn, R. M. (1972). The mathematics anxiety rating scale: Psychometric data. Journal of Counseling Psychology, 19, 551-554.

Stent, A. (1977). Can math anxiety be conquered? Change, 9, 40-43.

Tapia, M. (1996). The attitudes toward mathematics instrument. Paper presented at the annual meeting of the Mid-South Educational Research Association, Tuscaloosa, AL (ERIC Reproduction Service No. ED 404165).

Woodward, T. (2004). The effects of math anxiety on Post-Secondary developmental students as related to achievement, gender, and age. Inquiry, 9(1), Spring 2004. Online: http://www.vccaedu.org/inquiry/inquiry-spring2004/i-91-woodard.html

U.S. Department of Education, Office of Educational Research and Improvement, National Center for Education Statistics. (1990). A profile of the American eighth-grader: NELS:88 Student descriptive summary, Washington, D.C.: Government Printing Office.

U.S. National Research Center. (1996).Third international math and science study (Report No. 7). East Lansing, MI: Michigan State University.

[/et_pb_text][/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_sidebar _builder_version=”4.5.6″ _module_preset=”default”][/et_pb_sidebar][/et_pb_column][/et_pb_row][/et_pb_section]

[et_pb_section fb_built=”1″ admin_label=”Courses Hero” _builder_version=”3.22″ use_background_color_gradient=”on” background_color_gradient_start=”#474ab6″ background_color_gradient_end=”#9271f6″ background_image=”http://chan.higher-ed.org/wp-content/uploads/2020/08/coding-background-texture.jpg” background_blend=”overlay” custom_padding=”144px|0px|146px|0px||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”2%” locked=”off”][et_pb_row _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” custom_width_px=”1280px”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.6.0″ header_font=”|on|||” header_font_size=”42px” header_line_height=”1.3em” background_size=”initial” background_position=”top_left” background_repeat=”repeat” text_orientation=”center” background_layout=”dark” max_width=”530px” module_alignment=”center” custom_padding=”|||” header_font_size_tablet=”” header_font_size_phone=”” header_font_size_last_edited=”on|desktop” locked=”off”]

Relevance of Service-Learning in College Courses

[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Courses” _builder_version=”3.22″ custom_padding=”0px|0px|100px|0px”][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.5.6″ _module_preset=”default” custom_padding=”46px|||||”][et_pb_column type=”3_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_text _builder_version=”4.6.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]

Sally Cahill Tannenbaum, California State University, Fresno

Richard D. Berrett, California State University, Fresno

Sally Cahill Tannenbaum, Ed.D, is an Assistant Professor in the Department of Communication, and Richard D. Berrett, Ph.D., is Professor in the Child, Family, and Consumer Science Department at California State University, Fresno.

Abstract

This study analyzed student perceptions of the academic and social relevance of service-learning pedagogy, and how teacher adherence to best practices in service-learning may influence those perceptions. A total of 566 students in 19 classes that incorporated service-learning at a large university participated in the study. Survey results indicated that participation in service-learning courses impacted student perceptions of course content relevance. Analysis also suggested that faculty understanding and sophistication regarding service-learning impacted student perceptions and that faculty training and adherence to best practices is essential.

Introduction

Hundreds of colleges and universities across the country have implemented service-learning courses seeking to improve student learning and social behavior skills. Indeed a significant body of research on service-learning indicates that service-learning is an effective tool for teaching academic course content as well as improving social behavior skills. Studies also suggest that effective service-learning pedagogy requires the adherence to best practices (Gibson, Kostecki, & Lucas, 2001; Katula & Threnhauser, 1999).Faculty training also appears to be an important ingredient in maximizing the impact of service-learning in college courses.

Impact on Academic Performance

A number of research studies demonstrate that service-learning improves students’ ability to learn academic content and complete course goals. Vogelgesang and Alexander (2000) at the Higher Education Research Institute at UCLA conducted a longitudinal study of 22,000 students attending a cross-section of national colleges and universities. The study found that students participating in service-learning experienced positive outcomes in three academic areas: critical thinking, writing skills, and college grade-point average. Using the same student database, Astin, Vogelgesang, Ikeda, and Yee (2000) found that more than 80% of the students reported that service-learning participation made them more interested in course material. The study corroborated other findings on service-learning’s impact on academic achievement (Akujobi & Simmons, 1997; Astin & Sax, 1998; Batchelder & Root, 1994; Eyler & Giles, 1999; Kendrick, 1996; Keyton, 2001; Melchior, 1999; Motoff & Roehlin, 1994; O’Hara, 2001; Osborne, Hammerich, & Hensley, 1998; Strage, 2000). 

Impact on Social Behavior Skills

An even larger body of research suggests that service-learning improves students’ social skills. Osborne et al. (1998) found that students demonstrated positive changes in social competency, perceived ability to work with diverse others, self-certainty, and improved self-esteem after participating in service-learning. Myers-Lipton (1996) concluded that students who participated in service-learning perceived themselves more positively in self-worth and social competency and were more prepared to work with diverse populations than students who did not participate in service-learning. Melchior and Bailis (2002) reviewed the findings of three major national service-learning initiatives and found that students who had participated in service-learning consistently felt more confident in their ability to identify issues, work with others, organize and take action, and build a commitment to civic participation. Similar results were found in studies by Astin (1996), Kendrick (1996), Payne (2000), Rockquemore and Schaffer (2000), and Yate and Youniss (1996).

Characteristics of Effective Service-Learning Programs

While no definitive list of best practices for service-learning exists, a review of the literature shows that certain key practices are consistently needed for an effective service-learning program. These best practices include: (1) service that is connected to the curriculum; (2) service involving a specific action;  (3) student reflection at the end of the service; (4) ongoing reflection throughout the course; (5) student’s choice in selecting the service; (6) student training in the service area; (7) student involvement for a minimum of 10 hours; (8) faculty training in the use of service-learning; (9) ongoing communication between the faculty member and community service-learning partner; (10) assessment to determine if program outcomes were achieved; and (11) recognition of student contributions. (Astin, Vogelgesang, Ikeda, & Yee, 2000; Cumbo & Vadeboncoeur, 1999; Gibson, Kostecki & Lucas, 2001; Giles & Eyler, 1994; Honnet & Poulen, 1989; Learn and Serve America, 2002; Shumer, 1967; Stukas, Clary, & Snyder, 1999; Vernon & Ward, 1999; Whitfield, 1999; Yates & Youniss, 1996).

Methodology

Participants

Data for this study came from a survey that was administered to students in 19 service-learning classes taught by 10 faculty members at a large Western regional university. A total of 566 students completed the survey. The demographic profile of the sample was diverse. Racial backgrounds were identified as 3.4% African American; 10.8% Asian American; 48.7% Caucasian; 30.5% Latino/Mexican American, and 6.6% Native American, International Student, or Other. Women made up 72.8% and males made up 27.2% of the sample.  Age groups also varied, with 14.4% aged 17-19; 35.2% aged 20-22; 25% aged 23-25; 9.3% aged 26-28, and 16% aged 29 years or older. Class levels were 10.8% freshman; 7.2% sophomore; 28.7% juniors; 46.2% seniors and 7.1% graduate students.

The nineteen courses were labeled A through S for comparison purposes and are listed below.

A: upper division puppetry drama class

B: upper division children’s theatre drama class

C: lower division introduction to the university class

D and J: upper division classes focusing on family communication

E, O, P, and S: upper division classes on children and families in crisis

F, G, and I: lower division small group communication classes

H: upper division class in multicultural perspectives on children and families

K: upper division business class

L and M: upper division sociology classes

N: graduate health science class

Q: upper division social science parenting class

R: upper division gerontology class.

Procedure

Faculty members teaching courses that included a service-learning component were asked to participate in the study. Interviews were conducted with individual faculty members prior to administering the survey. Each instructor was asked to self-report on how service-learning was incorporated in his or her course design. Surveys were administered to the students in each class section toward the end of the fall and spring semesters. A follow-up interview with each faculty member was conducted. Survey results were shared and discussed.

Instrumentation

Students were asked to respond to 15 statements relating to their service-learning experience using a five-point Likert scale. Responses ranged from strongly agree to strongly disagree. This instrument was designed by one of the authors by compiling common elements from prior service-learning studies (Driscoll et al., 1998; Furco, 2000; Shumer, Duttweiler, & Furco, 2000). Responses to each statement were tabulated and aggregated for the entire sample (N  =  566) by class section (k =.19) and six demographic factors (age, gender, ethnicity, class level, number of enrolled units, and number of hours of outside employment). For each of the 15 statements in the questionnaire, an asymptotic Kruskal-Wallis H-test was conducted to determine if the differences among the groups could be considered statistically significant or might be attributed to chance variation. [The Kruskal-Wallis H test is a non-parametric alternative to the One-Way ANOVA, and is typically used when dealing with ranked data. When the number of groups is more than three (k > 3) and the size of each group is larger than five (n[i] > 5) asymptotic test procedures are used to compute the test statistic (Mundry & Fisher, 1998)].

Survey Results

Overall survey results indicate that the majority of students surveyed appeared to find that the service-learning assignments increased the academic relevance and understanding of course content. Responding to the statement “the service-learning assignment helped me to see how the content of this course can be applied in everyday life,” 73.8% of the students agreed or strongly agreed. In response to the statement concerning whether or not the service-learning helped students “better understand the lectures and readings in the course,” a slight majority, 53.4%, agreed or strongly agreed.

The service-learning assignments also appeared to increase social awareness for most students. In response to the statement “the service-learning assignment expanded my understanding of people in general,” 79.8% agreed or strongly agreed. An even higher percentage, 81.8%, agreed or strongly agreed with the statement “the service-learning assignment showed me how I can become more involved in my community.” In response to the statement “the service-learning assignment enabled me to learn more about diversity,” 73.2% of the students surveyed agreed or strongly agreed and, in response to the statement “the service-learning assignment helped me become more aware of the needs in my community,” 79.8% of the students agreed or strongly agreed.

Students also appeared to perceive service-learning as a useful pedagogical methodology. Over half of the students, 56.1%, agreed or strongly agreed with the statement that “service-learning should be used in more classes.”  A high percentage of students, 71.3%, agreed or strongly agreed that “the service-learning assignment had positively impacted [their] self-esteem”.

Based on information reported during instructor interviews, the service-learning best practices mentioned by each instructor for each course were recorded. While the original study was not designed to evaluate whether or not faculty adherence to best practices nor whether faculty training impacted student perceptions, a post comparison analysis was done.  Students in courses where faculty adhered to a greater number of best practices and had training in service-learning pedagogy tended to positively correlate with more favorable perceptions of the service-learning experience.

Discussion

Survey results indicated that participation in service-learning did positively impact students’ perceptions of academic relevance and understanding of content in these courses.  Students not only appeared to gain a clearer understanding of class assignments, but also reported seeing a connection between subject matter and everyday life.  These findings, consistent with previous research in service-learning, provide encouragement to instructors who want their students to understand the practical importance of the concepts they are learning.

Student perceptions of the social relevance of course material were also impacted by participation in service-learning.  Students felt that they had learned more about diversity, expanded their understanding of people in general, become more aware of the needs of their community, had a clearer grasp of how they could become more involved in their community, and felt the experience had positively impacted their self-esteem.

The information obtained by comparing student surveys with instructor self-reports suggested that there was a correlation between student perceptions and faculty utilization of best practices in service-learning. Students were more likely to agree or strongly agree with survey statements in courses in which instructors reported incorporating a large number of best practices, Student perceptions were more positive when instructors spent class time introducing students to the service ethic, had frequent class discussions that meaningfully connected course content with the service being performed, provided numerous opportunities for student reflection about the service-learning experience, required that students complete at least ten hours of service, and communicated regularly with the community service agencies.  Instructors in courses in which instructors reported incorporating fewer best practices in service-learning, students were less likely to agree or strongly agree with survey statements. These observations appear to reinforce the viewpoints of Katula and Threnhauser that service-learning is most effective when instructors contextualize and  “facilitate student comprehension of the intellectual basis and meaning of such experiences. (2001, p. 252).”  The authors agree that best practices can and should be utilized by all service-learning practitioners if students are to have an optimum learning experience.

One of the most interesting observations to come out of this study was that there also appeared to be a correlation between student perceptions and faculty service-learning training. Students were more likely to agree or strongly agree with survey statements in courses that had instructors trained in effective service-learning pedagogy or instructors who were being mentored by individuals who had been trained. Knowledgeable service-learning practitioners were more likely to incorporate best practices such as having class requirements that included an orientation to the service setting and the nature of service-learning, provide an opportunity for the community based organization to formally assess student work, and formally recognize student contributions. Instructors in the courses in which students were less likely to agree or strongly agree with survey statements had received little or no formal training. These observations are also consistent with Katula and Threnhauser who argue that it is critical that “faculty are properly trained in the Principles of Best Practice (2001, p. 252).”   While a number of colleges and universities are providing faculty with instruction in the use of service-learning, the authors suggest that formal training be universal.  They also believe that a program that provides mentoring and ongoing assessment will provide maximum benefits to both students and instructors participating in service-learning. 

Conclusion

Student responses in this study substantiate previous research that found service-learning improved the academic and social relevance of course content for students. Perhaps even more important, the study suggests that if service-learning is going to have optimum impact on students, it must occur in classrooms where teachers are trained and successfully adhere to best practices in service-learning.

A number of the instructors who participated in this study have subsequently received service-learning training and ongoing mentoring. Replicating the survey in the same courses with instructors who have been trained would provide insight as to whether or not faculty training changed student perceptions in those courses. Follow-up interviews with instructors would also provide information as to whether or not course design, implementation, and faculty satisfaction has changed subsequent to faculty training.

References

Akujobi, C., & Simmons, R. (1997). An assessment of elementary school service-learning teaching methods: Using service-learning goals. NSEE Quarterly, 23(2), 19-28.

Astin, A. (1996). The role of service in higher education. About Campus, 1(1), 14-19.

Astin, A., and Sax, L. (1998). How undergraduates are affected by service participation. Journal of College Student Development, 39(3), 251-263.

Astin, A., Vogelgesang, L., Ikeda, E., & Yee, J. (2000). How service learning affects students. Executive Summary, Higher Education Research Institute, University of California, Los Angeles.

Batchelder, T. & Root, S. (1994). Effects of an undergraduate program to integrate academic learning and service: Cognitive, prosocial cognitive and identity outcomes. Journal of Adolescence, 17, 341-356.

Cumbo K., & Vadeboncoeur, J. (1999). What are students learning?: Assessing cognitive outcomes in K-12 service-learning. Michigan Journal of Community Service Learning, 6, 84-96.

Cyrs, T. E. (1997). Competence in teaching at a distance. New Directions for Teaching and Learning, 71, 15-18.

Driscoll, A., Gelmon, S., Holland, B., Kerrigan, S., Spring, A., Grosvold, K. et al. (1998). Assessing the impact of service learning:  A workbook of strategies and methods. 2nd edition. Portland: Portland State University, Center for Academic Excellence.

Eyler, J., & Giles, D. (1999). Where’s the learning in service learning?  San Francisco: Jossey-Bass.

Furco, A. (2000). Self-assessment rubric for the institutionalization of service-learning in higher education. Berkeley, CA:  University of California at Berkeley.

Gibson, M. K., Kostecki, M, & Lucas, M. K. (2001). Instituting principles of best practices for service-learning in the communication curriculum, Southern Communication Journal, 66(3), 187-200.

Giles, D. E., & Eyler, J. (1994). The impact of a college community service laboratory on students’ personal, social, and cognitive outcomes, Journal of Adolescence, 17(4) 327-339.

Honnet, E. P., & Poulen, S. J. (1989). Principles of good practice for combining service and learning, a Wingspread Special Report, Racine, WI: The Johnson Foundation, Inc.

Katula, R. A., & Threnhauser, E.. (1999). Experiential education in the undergraduate curriculum, Communication Education, 48(3), 238-255.

Kendrick, J. R., Jr. (1996). Outcomes of service-learning in an introduction to sociology course. Michigan Journal of Community Service Learning, 3, 72-81.

Keyton, J., (2001). Integrating service-learning in the research methods course. Southern Communication Journal, 66(3), 201-210.

Learn and Serve America (2002). Key elements of service learning, Washington, D. C.: Corporation for National and Community Service.

Melchoir, A. (1999). Summary report: National evaluation of learn and serve America. Waltham, MA: Center for Human Resources, Brandeis University.

Melchior, A., & Bailis, L. (2002). Impact of service-learning on civic attitudes and behaviors of middle and high school youth. In A. Furco & S.H. Billig (Eds.), Service-learning the essence of the pedagogy (pp. 201-222). Greenwich, CO: Information Age Publishing.

Motoff, J., & Roehling, P. (1994). Learning while serving in a psychology internship. Michigan Journal of Community Service Learning, 1, 70-76.

Mundry, R., & Fischer, J. (1998). Use of statistical programs for nonparametric tests of small samples often leads to incorrect P values: Examples from Animal Behavior. Animal Behavior, 56, 256-259.

Myers-Lipton, S. (1996). Effect of a comprehensive service program on college students’  level of modern racism. Michigan Journal of Community Service Learning, 3, 44-54.

O’Hara, L. S. (2001). Service-learning: Students’ transformative journey from communication student to civic-minded professional. Southern Communication Journal, 66(3), 251-266.

Osborne, R., Hammerich, S., & Hensley, C. (1998). Student effects of service-learning: Tracking change across a semester. Michigan Journal of Community Service Learning, 5, 5-13.

Payne, C. (2000). Changes in involvement preferences as measured by the community service involvement preference inventory, Michigan Journal of Community Service Learning, 7, 41-45.

Rockquemore, K. A., & Schaffer, R. H. (2000). Toward a theory of engagement: A cognitive mapping of service-learning experiences. Michigan Journal of Community Service Learning, 7, 14-24.

Shumer, R. (1997). What research tells us about designing service learning programs.  NASSP Bulletin, 81, 18-24.

Shumer, R., Duttweiler, P., & Furco, A. (2000). Shumer’s self-assessment for service-learning. St. Paul, MN: Center for Experiential and Service-Learning, Department of Work, Community, and Family Education, College of Education and Human Development, University of Minnesota.

Strage, A. A. (2000). Service-learning: Enhancing student learning outcomes in a college-level lecture course, Michigan Journal of Community Service Learning, 7, 5-13.

Stukas, A. A., Clary, E. G., & Snyder, M. (1999). Service learning: who benefits and why, Social Policy Report: Society for Research in Child Development, 13(4), p. 3-20.

Vernon, A., & Ward, K. (1999). Campus and community partnerships:  Assessing  impacts and strengthening connections, Michigan Journal of Community Service Learning, 6, 30-37.

Vogelgesang, L. J., & Alexander, W. (2000). Comparing the effects of community service and service-learning, Michigan Journal of Community Service Learning, 7, p. 25-34.

Whitfield, T. S. (1999). Connecting service- and classroom-based learning: The use of problem-based, Michigan Journal of Community Service Learning, 6, 106-111.

Yates, M., & Youniss, J. (1996). Perspective on community service in adolescence. Social Development, 5, 85-111.

[/et_pb_text][/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_sidebar _builder_version=”4.5.6″ _module_preset=”default”][/et_pb_sidebar][/et_pb_column][/et_pb_row][/et_pb_section]

[et_pb_section fb_built=”1″ admin_label=”Courses Hero” _builder_version=”3.22″ use_background_color_gradient=”on” background_color_gradient_start=”#474ab6″ background_color_gradient_end=”#9271f6″ background_image=”http://chan.higher-ed.org/wp-content/uploads/2020/08/coding-background-texture.jpg” background_blend=”overlay” custom_padding=”144px|0px|146px|0px||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”2%” locked=”off”][et_pb_row _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” custom_width_px=”1280px”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.6.0″ header_font=”|on|||” header_font_size=”42px” header_line_height=”1.3em” background_size=”initial” background_position=”top_left” background_repeat=”repeat” text_orientation=”center” background_layout=”dark” max_width=”530px” module_alignment=”center” custom_padding=”|||” hover_enabled=”0″ header_font_size_tablet=”” header_font_size_phone=”” header_font_size_last_edited=”on|desktop” locked=”off” sticky_enabled=”0″]

Promoting Health Information Literacy
Collaborative Opportunities for Teaching and Academic Librarian Faculty

[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Courses” _builder_version=”3.22″ custom_padding=”0px|0px|100px|0px”][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.5.6″ _module_preset=”default” custom_padding=”46px|||||”][et_pb_column type=”3_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_text _builder_version=”4.6.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]

Chiehwen Ed Hsu, Ph.D., Lynn F. Johnson, MSIS and Ann N. Brooks, MLS, MBA

 

Department of Health Management and Policy, School of Public Health

and Gibson D. Lewis (GDL) Health Science Library

University of North Texas Health Science Center

3500 Camp Bowie Blvd

Fort Worth, Texas 76107

Tel:  817-735-5134

                                                       Fax:  817-735-0446       

Keywords and Phrases:

Health Information Literacy, Public Health Informatics

Please address correspondence to:

1. Ed Hsu, Ph.D

Assistant Professor of Health Management and Policy

University of North Texas HSC, School of Public Health
3500 Camp Bowie Blvd. ME1-740.

Fort Worth, TX 76107

Tel:  (817) 735-5134

Fax:  (817) 735-0446

Email:  chhsu@hsc.unt.edu

1. Ed Hsu is assistant professor of health management and policy, University of North Texas School of Public Health, where he coordinates the Master of Public Health in Health Informatics. He received PhD, MS and MPH from the University of Texas at Houston.

Lynn F. Johnson is the special projects librarian with the GDL Library and an instructor in the Department of Education. She serves as Project Director for the OSTMED® database project. She received the MS in Information Science in Medical Informatics from the UNT.

Ann N. Brooks is assistant professor with the department of education and associate director of public services for the GDL Health Science Library. She earned the Master of Library Science from University of Pittsburgh and MBA from Texas Christian University.

Abstract:

The recent development of public health informatics as an interdisciplinary field, and the dissemination of this body of knowledge, have brought forth new opportunities for collaboration between the faculty of health sciences and academic library. This paper explores the potential areas for collaboration, describes empirical collaborative projects between these two parties in enhancing the information literacy of public health discipline in a major health science center, and discusses the lessons learned, including the opportunities and challenges associated with the collaboration.

 

Background

1.1 Information literacy and health literacy

According to the Association of College and Research Libraries, information literacy is a set of competencies that enables individuals to recognize needs for information, and have the ability to locate, evaluate, and use the needed information effectively.[1]  General information literacy is an important attribute in achieving lifelong learning, because it contributes to informed decisions based on critical reasoning and thinking. On the health spectrum and as a subset of information literacy, health literacy relates to the degree to which people can obtain, understand and process basic health information and services, and then act on appropriate health decisions. It is one of the crucial, enabling capabilities that could contribute to the realization of the goals of Healthy People 2010 as stipulated by the Center of Disease Control and Prevention (CDC).[2]  By comparison, both definitions of literacy address the acquisition of information when needed, assessment of information with scientific facts and expert advice as the knowledge base, and utilization of the results of the combined actions to execute knowledge-based strategies leading to informed decisions, such as the choice of a healthy lifestyle.

1.2 Public Health Informatics

 

Closely related to health information sciences, the National Library of Medicine (NLM) defines public health informatics as the systematic application of information and computer sciences to public health practice, research, and learning.[3]  The literature suggests that effective dissemination of public health literacy play an important role in the decision of the general public in seeking preventive health measures and healthcare services. For seeking preventive health, one study found that patients who had inadequate reading skills did not know that mammography was associated with diagnosing breast cancer.  Conversely, women with adequate literacy skills who read on at least a 9th grade level appeared to be adequately informed about mammography.[4]  Another study found that increased access to self-care books, telephone advice nurses, and Internet-based health information was associated with decreases in reported pediatric healthcare utilization.[5]

 

In terms of literacy and health care, research results of two separate hospital studies have suggested that the literacy skills of patients with diabetes, hypertension, and asthma were the strongest correlates of knowledge about their illness and disease management skills, even after statistical adjustments were made for conventional sociodemographic co-variables.[6][7]^,  The results of the studies also suggest that the interventions (of enhanced health literacy) were associated with a decreased reliance on health professionals for information.  These findings suggest that improved literacy is a critical component in improving public well being, and in the context of public health, improved literacy is crucial in the pursuit of preventive health and appropriate management of diseases.

Public health informatics is a discipline that applies information technology to public health science.[8]  By this definition, the faculty of the School of Public Health (SPH) conducts teaching, research and community services widely involving public health informatics. These may include using relational databases to store the results of survey questions, presenting epidemiological data using the Geographic Information Systems (GIS), analyzing various potential socio-biological risk determinants of health disparities, and recommending scarce health resources allocation based on computer-assisted analysis.

For formally structured graduate courses, the training modules of health informatics are usually prepared for graduate students in a classroom setting. The training modules are not specifically designed to transform discipline-specific attributes of knowledge to general competencies, nor to deliver the terminology-ridden scientific research to an audience of general literacy level.  Therefore, it is desirable to strengthen the collaboration between full-time public health faculty and library faculty, in order to disseminate technically-oriented training modules to a wider audience.

1.3 Collaboration: bridging the great divides

By convention, classroom faculty members have not been interacting with academic library faculty at an extensive level until recently. Articles have been written about the “tension” between academic librarians and classroom faculty. Carpenter [9] contends that an “enmity” exists between classroom faculty and academic library faculty.  Kotter [10]  claims that the tension and the lack of interaction are the “great divides” to be bridged. In seeking potential causes of such tension, Ren [11] observes that the phenomenon arises from classroom faculty perceiving library faculty as inexperienced in conducting research and teaching and not as “academic equals” at work. Owusu-Ansah [12] attributes the “latent tension” in the relationship to the observation that teaching faculty “would have little to do with the library and have little respect for the academic librarians”. 

Notwithstanding these contestable arguments, collaboration could expand the synergistic opportunities that would further the mission of academic enterprise. In his proposal to “bridge the great divide”, Kotter [10] suggests that the improved association between faculty and library faculty would enhance librarians’ ability to promote and support research among classroom faculty, while allowing the librarians to actively participate in the enterprise of scholarship. Farber perceives that the true benefits of collaboration are the mutually reinforced and shared visions between classroom faculty and librarians.[13]   The classroom faculty objectives are to help students attain a better understanding of the course subject matter.  The library faculty objectives are to enhance the students’ ability to find and evaluate information which in turn enhances the students’ understanding of the subject matter and contributes to their life long learning skills.

The expanded opportunities – brought forth by both the promotion of health informatics knowledge and the dissemination of the literacy programs, warrant strengthened collaboration between the faculty of health sciences and academic librarians. The following sections seek to identify potential areas for collaboration and calls for action. The remainder of this paper presents recent cases on how public health informatics literacy are implemented in practice, specifically with reference to the acquistion and assessment of knowledge components. It then describes the potential areas for collaborative work and presents a recent intitiative in a Health Science Center (HSC) that underscores this collaboration.  The last section discusses how such initiatives may expand the collaboration between the faculty of health sciences and academic librarians.

2. Public Health Informatics Literacy In Action: Two Recent Initiatives

This section examines two essential competencies of information literacy, namely the acquisition and assessment of health information.  It describes how these competencies are addressed in the public health informatics discipline, and explores the opportunities for interdisciplinary collaboration.

2.1 Web Portals of Health Information

According to Ferguson, the increased adoption of Internet technology affords at least four major sources of health information that take advantage of the information highway.[14]  These include commercial services, on-line mailing lists, Internet newsgroups (also known as USENET newsgroups) and the World Wide Web. The expanded channels for distributing health information have introduced new challenges and opportunities.  A recent study sought to characterize health-related portal websites has found that while many Internet users are surfing the Web, they are likely to encounter advertisements that are usually promoting products that are unsupported by scientific research, such as those of “weight-loss supplements”.[15]  Therefore, a major challenge confronting consumers in the acquisition of health information, is herhaps how to distinguish accountable health information from mis-information (or info-mercials).

 

One case illustrating opportunities to enhance health information acquisition and assessment on the information highway is the recent proliferation of public health “portal websites” or “toolboxes”.  At the federal level, there are several initiatives of health literacy portal websites pertinent to general consumer health or thematic health interests.  Among these productions, CDC Wonder, healthfinder.gov and health.gov are examples of current general health portals to the websites of a number of multi-agency health initiatives and activities, including those of the U.S. Department of Health and Human Services and other federal agencies. Other websites have been produced for specific public health interests.  For example, in response to the recently heightened alert of potential terrorist activities, CDC [16] produced the “bioterrorism” portal website to provide information associated with frequently asked questions on bioterrorism agents and preparedness, organizations that are dedicated to readiness training advisories, alert and bulletin, and on late-breaking news. Similarly, the University of North Carolina produced a “toolbox” website as a repository of public health data management instruments.[17][i]

The National Library of Medicine (NLM) and medical librarians have also recently focused attention on the public’s need for health information. As mentioned previously, portal websites have been developed that provide reliable health information from the government.  In addition, NLM provides free access to MEDLINE plus® that includes MEDLINE and quality, up-to-date drug information, encyclopedias, dictionaries, directories and clinical trials.  To emphasize consumer health information, NLM provides consumer health and public health information grants through its National Networks of Libraries of Medicine (NN/LM), a network of 4,500 health science libraries.  NLM’s online training programs prepare medical librarians to use its products and services and in turn the medical librarians train others – public health professionals, hospital medical staff, residents and interns, and public librarians.

Portal websites/toolboxes offer convenient access to reliable health information at the users’ fingertips, as they provide “one-stop-shopping” convenience for accessing and acquiring accountable health information with ease.  These websites allow quick updates on reliable and time-sensitive health information. These strengths are particularly crucial in time of urgency, and may therefore reduce the public’s anxiety in the events of uncertainty.

2.2 Community health monitoring systems

Community health monitoring systems demonstrate another initiative in promoting health information literacy.  Community Health Monitoring System (CHMS) are a continued set of performance measurement activities that involve the selection and use of quantitative measures of health program capacities, processes, and outcomes to inform the public or a designated public agency about critical aspects of a program.[18]  Performance monitoring has evolved over the past 20 years and has been termed in the literature as:  Community Health Monitoring Systems (CHMS), Community Health Information Networks (CHINs), Community Health Information Management Systems, Community Health Information Systems (CHIS), Community Health Report Cards, Community Care Networks, and Health Information Networks,[19] among other derivative appellations with a similar focus on community health.

In the United States, concerted interests are matched by rigorous efforts to develop health information systems for monitoring purposes at both the national and local levels.  According to Furukawa, there were about 500 Community Health Information Networks that closely monitored health across the nation in 1996.[20]  A survey conducted by the UCLA Center for Healthier Children indicated that nationwide at least 115 Community Health Report Cards were profiling community health in 1999,[21] including the Community Health Status Indicators that are provided by CDC.[22]  To make the community health profile more accessible to the general public, some CHMS also present health outcomes in a Web-based or GIS-enabled format (examples include Community Health Information Systems of Houston[23] and MICA in Missouri[24]).  CHMS emphasize various determinants of health ranging from environmental factors, income, and race to motor vehicle crash prevalence.[25]  The CHMS intend to provide accountable health information, including health indicators and outcome measures to quantify community health performance and to promote public awareness.  The systems help the public to access community health profiles, provide a knowledge-base for community health initiatives, and seek to narrow health disparities in the nation.

2.3 Areas of collaboration for health librarians and faculty

The collaboration between public health faculty and academic library faculty seems both logical and intuitive. Both focus on public interest, and seek to fulfill teaching and service roles to enhance health literacy delivery and utilization.  The relationship between both parties may be improved by joint involvement in promoting health informatics literacy in at least three levels of professional interactions: curriculum development, instructional design, and classroom instruction. In delivery of public health informatics literacy, faculty members are qualified content-providers in their respective subject disciplines, so they can focus on the information assessment.  Librarians can contribute professional assistance in instructional design, such as polishing course modules to be more content-and-setting specific, and clarifying the vocabulary and concepts for the general public to digest and utilize.

Thus, the aforementioned two public health informatics initiatives underline the need and opportunity for a collaborative effort:  in acquiring accountable health information and the production of portal websites/toolboxes, faculty members may serve as content-providers; while many academic librarians are comparatively well-versed with web-authoring technology, they may provide assistance on instructional design, such as web development and maintenance. In the case of producing CHMS, faculty members may be responsible for assessing health data and conducting analysis, while academic librarians may assist in teaching the outcomes databases, querying data or results in response to users’ request, prioritizing and presenting information in a content-specific and culturally-appropriate manner to the general public.  In terms of utilization, librarians also may facilitate the process of information delivery. The goal of a public service librarian is to identify pertinent information based on the specific consumer requests and supply the most relevant materials regardless of format, as well as current bibliography of additional items for the consumers’ judgment.

3. Collaborative projects in action in a health science center

The following outlines two collaborative projects between SPH faculty and academic library faculty in a major Health Science Center (HSC).  The collaboration intends to make health information more accessible to the general public and to strengthen existing graduate programs.

3.1 Center for Health Informatics and MPH in Health Informatics

In 2001, the HSC President requested that the HSC plans an initiative to create a Center for Health Informatics. This collaborative project involved a team of health information practitioners, including faculty in the Library and SPH faculty members.  The purpose of the Center is to support teaching, interdisciplinary research, and community services of the academic enterprise.  The Center seeks to improve student education by articulating and efficiently fulfilling their information needs, and to augment current instruction efforts at HSC by offering separate courses in information seeking and informatics.  The Center seeks innovative methods of information integration and provision, and serves as a focal point for collaboration between the Library, Graduate School of Biomedical Sciences, Medical School and SPH. The Center serves as a community outreach center for other organizations in the Dallas-Ft. Worth Metroplex area, and as a center for health informatics research and outreach in a multistate region. 

In addition to the Center, an MPH program with a concentration in health informatics was established in SPH.  The new MPH program joins thirteen other health informatics programs in this country. This program enrolled its first cohort of students in fall 2002.  Major teaching and research areas include public health data analysis and interpretation, the GIS and spatial analysis in public health, and the design and evaluation of hospital information systems. Central to the program is the adoption of the courses jointly offered by the SPH and the Library.

3.2 Information Access for public health professionals

In the summer of 2002, the Library, SPH, Office of Professional and Continuing Education (PACE), and local health department responded to a National Library of Medicine (NLM) RFP (“Information Access for Public Health Professionals”) with a proposal to improve public health information literacy.  The project proposal seeks to assess the needs of public health professionals who would be best served by NLM and CDC products, to enhance the accessibility of health information through training development and delivery, and to produce a public health website portal.

• Needs Assessment

In this project, a statistically significant sample of public health department directors in target Public Health Regions will be surveyed to assess users’ familiarity and accessibility of NLM, CDC and other public health databases. The survey will 1) establish what information resources are of interest, and 2) if users are interested in a free or low cost training program targeted at public health officials focusing on accessing reliable and authoritative health information and research.  The survey will serve to publicize the development of the Public Health Informatics Training Program and to build enthusiasm among relevant parties.  The survey will be jointly developed by PACE, SPH, and will be conducted by the local health department.

• Training Program Development and Delivery

A training program will be developed based on the results of the needs assessment.  Different educational formats that incorporate adult learning principles will be included in the curriculum. The Library, PACE, and local health department will jointly develop the program. Once developed, the training modules will be offered to health departments throughout north Texas.  The four hour session will be conducted at departments with training centers and incorporated into PACE activities.  A suitable education facility will be sought for departments without training centers, and three sites will be chosen.  Training activities will be accredited to award continuing education credit for certified health educators, registered sanitarians, physicians and nurses by PACE.

• Web-based Portal

A web portal will be developed to provide access to all databases and resources discussed in the Training Program.  The web portal will allow registration into an optional email list and monitored bulletin board.  Those participating in the email list will receive updates and notices about the website and will be emailed continuing education vignettes that will require visiting the website to claim credit.  Officials from the local health department, SPH faculty members and others will create training emails/alerts.  The first will cover NLM and CDC resources.  The monitored bulletin board will allow registered professionals the ability to post questions related to public health and to receive replies from pre-screened and authorized health officials.  The local health department will monitor the bulletin board and the web portal will be maintained beyond the project period by PACE.

• Training Program Promotion/Publicity

The Public Health Informatics Training Program will allow information access for public health professionals.  It will be promoted through 1) the initial Needs Assessment Survey interaction, 2) exhibits and educational sessions at Public Health Meetings, 3) listing on the PACE website and calendar, 4) letters of invitation to Public Health Department Directors, 5) the elective Track in selected CE activities by PACE, 6) existing Health Alert Networks, 7) Department of Health “Resources and Information Digest”, and 8) health educators email lists.

 

4. Lessons learned: opportunities and challenges

The above examples demonstrate recent initiatives in promoting public health informatics, and illustrate how faculty and academic library faculty may work together to enhance information acquisition and assessment.  After completing the collaborative projects, we identified additional areas for collaboration and potential challenges in practice. One potential area includes adding literacy programs such as introductory informatics courses for first year students of health sciences.  Another includes the need for establishing a Writing Center to prepare students in academic communication and to assist local health authorities to prepare for grant writing. The functions may be efficiently carried out with a faculty-librarian synergistic collaboration. Challenges are primarily the motivation for faculty involvement. In designing collaborative activities, consideration needs to be given to the tenure and promotion criteria (i.e., teaching, research or community services) of faculty members so that faculty participation as an institutional commitment can be assured, and sufficient release time be requested and dedicated to participation.

In summary, the enhancement of public health informatics literacy is an endeavor of substantial magnitude. Since health literacy has been shown to be associated with population health outcomes, it therefore warrants strengthened collaborative effort between academic librarians and classroom faculty to address the unmet needs.  The collaborative projects are mutually-rewarding, and hold promise to take the health information literacy and well-being of the general public to the next level.

 

References

 

[1] Information Literacy Competency Standards for Higher Education (2000). Chicago: IL: Association of   

      College and Research Libraries.

[2] Healthy people 2010. http://www.health.gov/healthypeople Retrieved: September 13, 2002.

[3] Public health informatics. Current Bibliographies in Medicine 2001-2. National Library of Medicine.  

      http://www.nlm.nih.gov/pubs/cbm/phi2001.html Accessed: February 5, 2004

[4] Davis TC, Michielutte, R., Askov, EV et al. (1996) Practical assessment of adult literacy in health care.

      Health Education and Behavior, 25(5), 613-624.

[5] Wagner TH, Greenlick MR (2001). When parents are given greater access to health information, does it

      affect pediatric utilization? Medical Care. 39:848-55.

[6] Williams, M.V., Baker, D. W., Honig, E.G., Lee, T.M., & Nowlan, A. (1998) Inadequate literacy is a  

      barrier to asthma knowledge and self-care. Chest, 114, 1008-1015.

[7] Williams, M.V., Baker, D. W., Parker, R. M., Nurss, J.R. (1998). Relationship of functional health

      literacy to patient’s knowledge of their chronic disease: A study of patients with hypertension or

      diabetes. Archives of Internal Medicine, 158, 166-172.

[8] Yasnoff, WA (2001). The promise of public health informatics. Journal of Public Health Management

      and Practice: JPHMP, 7 (6). p iv-iv.

[9] Carpenter KE (1997). The librarian-Scholar. The Journal of Academic Librarianship. 24(3). p 398-401.

[10] Kotter WR. (1999). Bridging the great divide: improving relations between librarians and classroom 

       faculty. The Journal of Academic Librarianship 25(4). p 294-303.

[11] Ren WH (2000). Attending to the relational aspects of faculty citation search. The Journal of

        Academic Librarianship, 26(2). p 119-123.

[12] Owusu-Ansah EK (2001). The academic library in the enterprise of colleges and universities: toward a

        new paradigm. The Journal of Academic Librarianship.27(4). p 282-94.

[13] Farber E (1999). Faculty –librarian cooperation: a personal retrospective. Reference Services Review,

        27(3).  pp 229-234.

[14] Ferguson T and Madara ET (1996). Health Online: How to Find Health Information, Support Groups,

        and Self-Help Communities in Cyberspace. Perseus Publishing.

[15] Slater MD and Zimmerman, DE (2002). Characteristics of health-related web sites identified by

        common internet portals. JAMA, 288 (3):pp 316-317.

[16] Public health preparedness and response. Centers for Disease Control and Prevention.

        http://www.bt.cdc.gov Retrieved February 5, 2004.

[17] Data Skills Online. University of North Carolina Chapel Hill – SPH. http://www.sph.unc.edu/toolbox

        Retrieved February 5, 2004

[18] Perrin EB, Jane S. Durch Skillman SM eds. (1999). Health performance measurement in the public

    sector . Principles and policies for implementing an information network. National Research Council.

       National Academy Press. pp. 19.

[19] Kralovec JO and Kennedy R (1994). A new vision of health care delivery. Community Health

        Information Networks. American Hospital Association. pp 3-21.

[20] Stipe SE (1996), “Health Information Networks: A connection to an efficient future.” Best’s Review in

        life and health. 96(10) pp. 28-31.

[21] Fielding JE, Sutherland CE, and Halfon N (1999). “Community Health Report Cards – Results of a

        national survey.” Am J Prev Med 17(1) pp 79.

[22] Community Health Status Indicators. http://www.communityhealth.hrsa.gov Retrieved February 5, 2004.

[23] Community Health Information Systems. http://www.slehc.org Retrieved February 5, 2004

[24] Missouri Information for Community Assessment. Missouri Department of Health.

        http://www.dhss.state.mo.us/MICA/nojava.html Retreived: February 5, 2004.

[25]  Public Health Foundation (1998). Inventory of public/private health information initiatives.

        http://www.phf.org/data-infra.htm. Retreived: February 5, 2004.

[/et_pb_text][/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_sidebar _builder_version=”4.5.6″ _module_preset=”default”][/et_pb_sidebar][/et_pb_column][/et_pb_row][/et_pb_section]

[et_pb_section fb_built=”1″ admin_label=”Courses Hero” _builder_version=”3.22″ use_background_color_gradient=”on” background_color_gradient_start=”#474ab6″ background_color_gradient_end=”#9271f6″ background_image=”http://chan.higher-ed.org/wp-content/uploads/2020/08/coding-background-texture.jpg” background_blend=”overlay” custom_padding=”144px|0px|146px|0px||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”2%” locked=”off”][et_pb_row _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” custom_width_px=”1280px”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.6.0″ header_font=”|on|||” header_font_size=”42px” header_line_height=”1.3em” background_size=”initial” background_position=”top_left” background_repeat=”repeat” text_orientation=”center” background_layout=”dark” max_width=”530px” module_alignment=”center” custom_padding=”|||” hover_enabled=”0″ header_font_size_tablet=”” header_font_size_phone=”” header_font_size_last_edited=”on|desktop” locked=”off” sticky_enabled=”0″]

Submit a manuscript – Phd students can position themselves as emerging scholars

[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Courses” _builder_version=”3.22″ custom_padding=”0px|0px|100px|0px”][et_pb_row column_structure=”3_4,1_4″ _builder_version=”4.5.6″ _module_preset=”default” custom_padding=”46px|||||”][et_pb_column type=”3_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_text _builder_version=”4.6.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]

Call for Manuscripts Academic Exchange Quarterly, ISSN 1096-1453, independent double-blind-peer-reviewed print journal, welcomes research, commentary, and other manuscripts that contribute to the effective instruction and learning regardless of level or subject. See School Index. for articles published by your colleagues. In addition to faculty, our primary authorship group, we accept submissions co-authored by graduate students and professors. We encourage doctoral students to position themselves as emerging scholars. Early submission is encouraged; it facilitates journal’s double-blind-peer-reviewed process and offers — longer time for revision — eligibility to have article’s abstract and/or full text posted on journal’s web portal main page — opportunity to be considered for Editors’ Choice and/or Readers’ Choice — opportunity to be considered for inclusion in RIG upcoming books Sound Instruction Series.

Procedure, Requirements, Deadline & Editorial Policy

Try six simple

submission steps

See Flowchart

for an overview of the submission process

Submission Procedure

Electronic (E-mail) Submission Send e-mail attachment, MS-Word-doc-file type, to academicexchange@yahoo.com Tables etc… must be JPEG or GIF file type and in a separate attachment. Due to virus and security concerns, we do not accept zipped or compressed files or over 75 KB in size. Follow sample manuscript layout. E-mail will be our way to communicate with you.

or

Conventional (Paper) Submission Submit 3 copies to: Academic Exchange Quarterly, P.O. Box 131 Stuyvesant Falls, NY 12174 USA Put on first page: date, author’s name, address, title, and 2-3 line academic bio. Pay optional “pm-fee” Switch to ELECTRONIC submission and pay nothing. Submit final version in two formats: one paper copy and one electronic. Postal mail will be our way to communicate with you.

Please read this entire page carefully and observe requirements: (1) you will avoid Optional Redactory Fee and (2) you will get a rewarding and pleasaant experience.

1. To ensure rapid quality blind peer review, remember that manuscript submission is a step-by-step process.
   
2. Submission Procedure # 1-12
3. Format and Style Requirements # 13-18
4. Submission Deadline
5. Editorial Policy # 20-29
6. Checklist for submitting # 30-31
7. Submission disqualification or penalty fee Optional Redactory Fee (For examples, read entry # 2, 7, 11, 19)
8. Identify your submission with two keywords from this KEYWORD LIST
9. All papers are considered with the understanding that they have not been accepted or published elsewhere.
10. However, we will consider papers presented at a conference, symposium, or workshop – when copyright remains vested with the author. Such papers and papers resulting from research conducted under a grant or fellowship (financial or professional support) may incur a redactory fee.
11. 1. Note, we will consider only one-submission-per-author(s)-per-issue.

12. We seek to acknowledge receipt of submissions, Electronic or Paper, within five business days. You will receive manuscript submission number. Use it in all correspondence with this journal.
13. This journal uses several email address, each reserved for a specific function/activity.
14. Send one correspondence to one email address in a timeframe specified by this journal.
15. AEQ routine replies are formulated by a computer program instead of a live person.
16. Read also entry #11 Please read… We discourage any…
18. In general, allow 1-5 months for review and evaluation process, four distinct stages. The timeline depends on author’s submission date in relation to publication deadline; plus on reviewers’ interest/inclination to read it. Authors can monitor the four stages, by article submission number and title, at track-your-submission. Early submission will allow you more time for revision. We welcome your questions… Read also entry # 10.
19. Journal’s double-blind-peer-reviewed policy assures that the reviewers do not know the name of the author and vice versa. Authors: to preserve anonymity, before you send your submission, make sure that your name/identity appears three times only (first line, academic affiliation, academic bio) as noted in the sample. Reviewers come from AEQ Editorial Staff and may select any manuscript by entry number. Occasionally, AEQ may invite a subject specialist outside journal’s staff. See Manuscript Reviewing Guidelines.
20. When reviews are completed, you will be notified whether your manuscript was accepted as is; accepted with corrections; accepted with revision; rejected. See journal’s acceptance rate. Usually, authors receive all-reviews-in-one: 2-3 reviews are combined into one file/attachment. In selected submissions, for predetermined redactory fee (see #5 Other), we will provide assistance in revision – involving text-based changes and/or needing several readings and/or when guest subject specialist is needed to verify complex conflicting data, interpretations… You are eligible for 33% off any redactory fee when your library has an annual subscription to AEQ paper version or will get one annual subscription in the next ten days.
21. In order to receive reviews, you must sign and return by postal mail or e-mail the following Manuscript Authentication & Copyright Agreement. Do not send with your submission. Wait for submission number and then send it promptly. Letter “e” next to “ab” in submission entry at track-your-submission confirms compliance with copyright agreement. Failure to comply may result in disqualification or penalty fee.
22. As you compose your final copy, remember revising and editing is the most important stage in this submission process. Please follow reviewers’ Rating Table and notes on how to improve the content and methodological accuracy, or to make text-based changes. Your final copy will be read and verified against the reviewers’ copy. If there is a conflict between reviewers, use designated review as your guideline.
23. Reviewers have invested time and effort in your manuscript and so should you.
24. Careless or sloppy revision will disqualify your submission from further consideration.
25. We do not encourage/offer any direct dialogue between authors and editors
    while revision is in progress – in a double-blind-review it is difficult to coordinate any dialogue…
26. Your final copy must be free of reviewers notes and have all parts: title, author(s) and academic affiliation, author’s biography, abstract, text, references… see lay-out sample. We need 2-3 weeks to offer decision: rejected or accepted for publication. Read also Editorial Policy # 20-29
27. Please read this entire page carefully and observe requirements. This journal will not provide technical or remedial help concerning article submission or revision. This page offers detailed submission guidelines. All reviews are self-explanatory. We discourage any lengthy-frequent-repetitive contacts with this journal. For the complexity of AEQ review process see Flowchart
28. Journal’s ten year publishing experience suggests 6-8 e-mail/postal contacts
    between the author and AEQ as the norm
    1) submission 4) reviews 7) extra contact
    2) submission clarification 5) reviews clarification 8) extra contact
    3) copyright 6) final copy
29. Exceeding eight contacts may disqualify your submission from further consideration as it drives up journal’s administrative cost above the forty-five dollars average per one submission.
30. Keep in mind that every month Academic Exchange Quarterly receives over one thousand web, email, postal and phone submission inquiries. Number of contacts for each month is listed to the right of month’s name in Track-your-submission Entry in grey like this: 1398 queries in July 2006
     Jul06…1398-3526…2006-t30-21st…2005-t44-10th…2004-t29-09th…2003-t29-16th…
31. Hence, in order to work efficiently, we had to establish “eight contacts limit” per one submission.
32. This journal does not offer page proofs, offprints, hard copy of galleys or author’s complimentary copy of the issue. Instead, see your library for a free electronic copy or check whether you qualify for 1/2 price author’s copy. If you plan to purchase a paper copy, you may want to order ASAP when you are assured of paper’s publication. Usually, Academic Exchange Quarterly is sold out the month it is printed.
    

    Academic Exchange Quarterly print edition has readers in 50 U.S. states and 68 countries 3230+ authors as of October 2007, residing in 760+ colleges and universities, located in 44 countries AEQ print edition = wide global recognition

    
    Format and Style Requirements
    Because of the interdisciplinary nature of Academic Exchange, no specific reference style and format is required. Authors are free to use whatever style they see appropriate for their work: APA, CBE, MLA, or Chicago. For a quick review see The Ohio State University Libraries Citation Style Guides. Note, author needs to decide on which style of documentation to use and stick with the same style throughout the manuscript. The editors reserve the right to make changes, in accepted manuscripts, for clarity and space considerations.
33. Observe 2000-3000 word article limit (title, authors, academic bio, abstract, text, notes, references). Excess-Page fee is charged for exceeding the set limit.
34. make your title and abstract meaningful as they include important keywords. When researchers search for articles to cite, they search for keywords. If the keywords are missing from the title/abstract, the article will not “pop up” in a literature search. Avoid citing references in abstract.
36. when using bulleted lists, follow journal style – a hollow bullet.
38. use blank lines to separate paragraphs. Do not use automatic paragraph spacing.
40. do not use spaces or tabs to indent paragraphs, center text, or justify text.
42. Tracking changes must be turned off. All text should be left-aligned, unjustified so the right margins remain uneven. See sample manuscript layout.
    Avoid:
43. special formatting codes, AutoText, Rich Text Format, macros, and boilderplate or boilerplate text
    1. metadata, hidden text, and header/footer like footnotes at the end of each page; footnotes should be endnotes visible in normal layout
    2. Word or WordPerfect footnote functions, special margins or tabs or control characters
    3. hard carriage-returns for line breaks; instead use the automatic word-processing wraparound feature
    4. use of hyphen at the end of a line to divide words, including compound words
    5. tracking marks on the side “Printout Layout” under “View”
    6. protecting or securing your document to prevent editing, printing, etc…
44. double spacing (in electronic submission)
45. indention (instead use one line space between consecutive paragraphs)
46. author’s information at the end of the article
47. quotation marks if you are using a direct quotation which is longer than two sentences; the quote should be indented eight spaces and quotation marks omitted
48. listing any references not cited in the text
49. running head and pagination or period, colons, quotation marks, etc… after manuscript’s title or any subtitle
50. word underline and UPPERCASE (use of bold-face or italics is acceptable)
51. superscripts and subscript (instead use bracketed numbers); in dates and editions write, e.g. 5th or 3rd ed.
52. Tables, figures, charts etc… can be published either on journal’s webpage or in AEQ print edition, embedded in article’s text.
    

    Note: the FIRST OPTION separates tables and text; the SECOND OPTION keeps tables and text together. Please read carefully entries # a, b, c…

53. FIRST OPTION on journal’s webpage, black & white or color: see example, Spring 2005
    1. do not include tables, figures… in the body of the submitted text.
    2. in the text, indicate where each table/figure is by [Figure ONE] [Table ONE] etc…
    3. each table, figure… must be submitted as individual JPEG or GIF file.
    4. each table, figure… must be in a separate attachment, not more than 75 KB.
    5. each table, figure… should be able to stand alone: define all abbreviations;
       make headings descriptive and easily understood.
54. SECOND OPTION in AEQ print edition, black & white in article’s text
    1. do not include tables, figures… in the body of the submitted text.
    2. in the text, indicate where each table/figure is by [Figure ONE] [Table ONE] etc…
    3. each table, figure… must be submitted as individual Microsoft Word DOC format file
    4. image size 4.5 x 7.5 inches or less
    5. each table, figure… must be in a separate attachment, not more than 75 KB.
    6. each table, figure… should be able to stand alone: define all abbreviations;
       make headings descriptive and easily understood.
    7. NO photographs or illustrations. Line art consists of black-on-white illustrations
       containing no shades of gray or tonal variation.
55. to be accepted for review, you must comply with #2 Table-Figure-Chart-Appendix Fee.
    Send the above form when asked, after manuscript submission number is assigned.
56. This journal assumes that
57. any personal communication used by the author ( personal interviews, letters, memos, emails, messages from discussion groups and bulletin boards, telephone conversations etc… ) has interviewee’s knowledge/permission. It should be listed parenthetically within the text and not cited in the list of References. Example: (A. Smith, personal communication: telephone, May 17, 2003)
58. all participants are treated in accordance with the “Ethical Principles of Psychologists and Code of Conduct” (American Psychological Association, 1992).
59. Use English letters only as all other letters and diacritic characters are deleted automatically.
60. Symbols signifying a trademark (TM), a service mark (SM) or a registration with the U.S. Patent Office (R in a circle) are primarily for the use of the owner to indicate rights; use of the symbols is not required in journalistic publications. The same applies to the use of copyright symbol in text.
61. Mathematical, physical, chemical formulas can be put in the Appendix as a webpage. See entry #15 above. Preferred option is to rewrite mathematical notation.
62. In addition, spell out the words percent, degrees (temperature), feet, inches, and cents…
63. Whilst all care is taken, it is the author’s responsibility to ensure that any factual, stylistic and grammatical errors are corrected prior to publication. Your article mirrors your scholarship.Submission Deadline
64. Academic Exchange Quarterly publishes four issues a year: Spring, Summer, Fall, Winter.
    Each issue has three submission deadlines: early, regular, and short.
65. early – all accepted submissions will be published in a specified issue; there is an opportunity to be considered for
66. Editors’ Choice and/or Readers’ Choice
67. regular – all accepted submissions will be published in a specified issue; there is no Editors’ Choice and/or Monthly Exchange consideration
68. short – all accepted submissions will be published in a specified issue; there is no Editors’ Choice and/or Monthly Exchange consideration; requires Optional Redactory Fee. We may refuse to consider any submission under short deadline constraints.
69. 
    

    No.	Issue submission deadline > > >	early	regular	short	publication *	delivery **
    1.	Spring any time until the end of	October	November	December	March	April
    2.	Summer any time until the end of	January	February	March	June	July
    3.	Fall any time until the end of	April	May	June	September	October
    4.	Winter any time until the end of	July	August	September	December	January

    * Publication may be delayed by one month when reviews and/or revisions are late.
    For the exact publication date monitor specific issue Table of Contents SCHEDULED FOR PUBLICATION
    ** Delivery of Media Mail and foreign orders may take one month longer than specified.
    Editorial Policy

70. Academic Exchange Quarterly retains copyright of anything published in the print journal, uploaded to website and/or in any and all forms of media.
71. This quarterly does not guarantee that the information on our website, e-mail communication, or journal itself will be accurate, complete, continuously available, or error-free.
    All information is provided “as is.”
72. This journal is not responsible for lost, late, damaged postal mail or e-mail and any resulting publishing consequences.
73. Corrections & Clarifications, any errors of consequence and factual matter will be published in Errata. Please compose 3-5 line text and send to the editor ASAP.
74. This journal does not keep, store or archive submitted paper copies or disks.
    Submissions are not returned.
75. In a virtual organization environment, it is author’s responsibility to monitor manuscript submission process.
76. Remember that journal’s associates answering your e-mail or postal mail are not reviewers or editors. They can not pass any judgment or wave any submission requirements.
77. We do not reveal reasons for our publication decisions beyond the one stated in blind reviews.
    The Copy Editor’s decision is final.
78. We reserve the right to remove from publication any submission at any time for any reason.
79. We’d like to hear from you, please send us any feedback to:
    Rapid Intellect Group – AEQ P.O. Box 131 Stuyvesant Falls, NY 12174 USAChecklist for submitting
80. manuscript for review
81. read Submission Procedure # 1-12
83. prepare manuscript according to Format and Style Requirements # 13-18
85. adhere to Submission Deadline
87. notice that there are NO article processing charges or any other publication fees
89. observe that Optional Fees apply when you do not follow Format and Style Requirements
    and/or as outlined in entry # 6, 9, 13; you are not obligated to revise or pay any redactory fees.
91. often your department, school or university will cover the Optional Fees expense; we will give you
92. Paid-in-full invoice
93. final copy for publication
94. we assume that you are familiar with the above entry # 30 manuscript for review
95. familiarity with entries # 8, 9, 10, 11 will assure your submission to be a rewarding and pleasant experience
96. compliance with journal’s Procedure, Requirements, Deadline & Editorial Policy is a material condition for publishing your submission
97. failure to comply may result in a redactory fee or submission suspension and/or rejection
98. payment of a redactory fee (Optional Fees) or subscription to the journal etc…
    has no influence on the blind review process and does not guarantee publication.

Thanks for selecting Academic Exchange Quarterly for your professional needs.
See feedback from authors…
Got a question? See Guidelines & Contacts at a Glance

Title your e-mail or postal submission correspondence: Issue Editor, _____ (insert issue you wish to be published) e.g. Issue Editor, Summer 2007 Academic Exchange Quarterly P.O. Box 131 Stuyvesant Falls, NY 12174 USA Article submission e-mail: academicexchange@yahoo.com Help: in order to weed out “spam” from legitimate email type AEX in the subject field. Read more… No phone calls will be accepted.

[/et_pb_text][/et_pb_column][et_pb_column type=”1_4″ _builder_version=”4.5.6″ _module_preset=”default”][et_pb_sidebar _builder_version=”4.5.6″ _module_preset=”default”][/et_pb_sidebar][/et_pb_column][/et_pb_row][/et_pb_section]