Factors related to success and satisfaction in online learning.
We investigated factors that may relate to positive outcomes in a web-based introductory nutrition course: age, gender, prior nutrition knowledge, nutrition attitude, attitude toward technology, satisfaction with the instructor, and satisfaction with peer interaction. Fifty-four students completed pre-post surveys of knowledge, attitudes, and course satisfaction. When adjusted for multiple variables, satisfaction with the instructor and prior nutrition knowledge related to achievement in nutrition knowledge from pre- to posttest. Age, satisfaction with the instructor, and prior nutrition knowledge predicted course grade. Satisfaction with the instructor and satisfaction with peer interaction related to self-reported learning. Satisfaction with peer interaction, satisfaction with the instructor, and attitude toward technology predicted course satisfaction. Satisfaction with the instructor and age related to a student's willingness to improve nutrition behavior. Satisfaction with the instructor predicted a student's perceived increase in computer competence. Findings provide evidence that student interaction with instructors and peers has important implications for success and satisfaction in online learning.
Since the mid 1990s, online web-based instruction has continued to expand as a tool for increased student access to higher education while providing opportunities for active learning and interactive communication among participants (Cravener, 1999). From 1995 to 1998, the percentage of higher education institutions offering asynchronous Internet-based instruction has roughly tripled, from 22 to 60 percent, while the number of those using interactive video and one-way prerecorded video for distance education remained relatively static (USDE, 1999). Furthermore, many institutions plan to begin, or increase, offerings of web-based courses (USDE, 1999). Although computer-mediated distance education is widely popular and encourages collaborative learning among students, not all participants are successful (Cravener, 1999; Diekelmann, 2000). Check sheets for learners (Palloff & Pratt, 1999) and guides for instructors have been published (Harasim, Hiltz, Teles, & Turoff, 1996; Cornell & Martin, 1997; Reeves & Reeves, 1997; Gibbs, 1998; Bischoff, 2000; Collison, Elbaum, Haavind, & Tinker, 2000; Draves, 2000; Hanna, Glowacki-Dudka, & Conceicao-Runlee, 2000; Salmon, 2000) and may be helpful toward increasing student success. However, more rigorous investigations of outcomes in online distance education are needed (Merisotis & Phipps, 1999).
Much of the current research in online web-based instruction compares this method of delivery to the effectiveness of learning in the face-to-face environment (Cravener, 1999; Navarro & Shoemaker, 1999; Rosenlund, Damask-Bembenek, Hugie, & Matsumura, 1999; Yucha & Princen, 2000; Ostiguy & Haffer, 2001; Miller, Cohen & Beffa-Negrini, 2001). Diaz and Cartinal (1999) found online distance students to have more independent learning styles than learners in traditional settings, but they did not measure factors that predict student outcomes such as grades or course satisfaction. To date, there is a scarcity of published research related to student factors that predict success when studying online (Merisotis & Phipps, 1999). An investigation of a telecourse reported that successful students are older and female, have positive attitudes toward the course and instructor, completed higher numbers of college credits, perceived the course to have a difficulty no higher than expected, and have high grade point averages (GPAs) (Bink, Biner, Huffman, Geer, & Dean, 1995). However, we cannot assume that research on televised courses can be applied to web-based instruction. As technology evolves, it is important to re-examine factors affecting student success when learning at a distance. Videoconferencing, email only, or CD-ROM courses (Bink, Biner, Huffman, Geer, & Dean, 1995; Cookson, 1989; Miller, 1998; Roblyer, 1998; Edwards, Hugo, Cragg, & Peterson, 1999) may not be completely appropriate to compare with the asynchronous interactive courses available today. One must carefully consider, along with the course design, the technical design of the online course; student attitudes, perseverance and coping skills when working online; and the qualities of effective online instructors. Other factors to consider are the amount of interaction the students have with other participants (McIsaac, Blocher, Mahes, & Vrasidas, 1999), and the gender (Blocher, 1997) and age (Lively, 1997) of the student.
Theoretical or conceptual frameworks have been recommended to form the basis of research about online distance education (Merisotis & Phipps, 1999). Dunkin & Biddle (1974) proposed a model to guide the study of teaching and learning that involved four major variable types: presage, context, process, and product. Presage Variables are those that influence teachers and teaching behaviors. Context Variables relate to learner characteristics such as their personality traits and learning styles. Process Variables describe the interaction between instructor and student behaviors during the learning. Product Variables encompass knowledge and skills attained or attitudes changed as a result of teaching and learning. For distance education environments, one might expand Dunkin & Biddle's (1974) model to include other variables such as the technology used, instructional design (how the content is delivered to students), and interaction among peers in the course (McIsaac, Blocher, Mahes & Vrasidas, 1999). The following figure presents an expansion of Dunkin & Biddle's (1974) model, used as the conceptual framework for our research. See <http://rapidintellect.com/AEQweb/fal02.htm>
The purpose of this paper was to investigate certain Context Variables (age, gender, prior nutrition knowledge, attitude toward nutrition, attitude toward technology) and Process Variables (participant's self-reported effort, amount of time spent learning, satisfaction with instructor behaviors, and satisfaction with the participant-participant interaction) on success in an online introductory nutrition course. Success was measured based on the following Product Variables: achievement in nutrition knowledge from pretest to posttest, course grade, students' self-rated knowledge gain, course satisfaction, desire to change nutrition behavior, and post-course self-ratings of computer competence. With only one instructor and course platform, an investigation to compare Presage Variables (various instructor qualities and different modes of technology or instructional design) was beyond the scope of this research.
This was an exploratory study using a pre-post research design to determine factors that predict success in an online introductory nutrition course. The course was designed following the Seven Principles for Good Practice in Undergraduate Education from the American Association for Higher Education: encourage contact between students and faculty, develop reciprocity and cooperation among students, use active learning techniques, give prompt feedback, emphasize time on task, communicate high expectations, and respect diverse talents and ways of learning (Chickering & Gamson, 1987). Students completed pretests during in-person trainings given at the start of the course. Using a workbook as a guide, students completed weekly assignments on their own, returning to campus three times to take two mid-term examinations and a cumulative final. Course activities required students read from a textbook, visit websites, or conduct application-based activities at home. Students then posted reflections on the reading, research, or application by communicating with peers in small groups using an asynchronous threaded discussion platform (WebBoard, http://webboard.oreilly.com). Communication with the instructor and technical support personnel occurred primarily through e-mail and in the WebBoard. Additionally, the course included interactive self-administered online quizzes taken throughout the semester and a collaborative small-group project at the end of the course. During the final examination, students completed the posttest, which included measures of knowledge gain, attitude toward the course, behavior change, effort and time expended, and course satisfaction. Collected data were used to develop multiple regression equations to predict course outcomes.
Subjects were a convenience sample of students enrolled in an online introductory course in nutrition at a large university in the northeast. The course was offered for three semesters from the spring of 1999 to the fall of 2000 with a total of 54 students. Pre-surveys were missing for three students who completed the course in the fall of 2000 and therefore these subjects were not included in the multiple regression analysis.
Pretest The pretest allowed students to supply demographic data, specifically Context Variables of Age and Gender. Pretest Knowledge was determined using a 28-item multiple-choice test of nutrition knowledge questions administered at the start of the course. The nutrition knowledge instrument was tested for reliability using the Kuder-Richardson method of rational equivalence and had an average score of .68. The pretest also asked students to report their attitudes toward nutrition and technology prior to the instructional treatment. For Prior Attitude-Nutrition, five questions rated the importance of diet in 1) the prevention of chronic disease, 2) eating a variety of foods, 3) consuming adequate fiber, 4) avoiding too much fat, and 5) avoiding too much cholesterol. The range of responses was on a 5-point Likert-type scale, with 1 being "strongly disagree" and 5 being "strongly agree." A mean score on these five questions was computed for each student. A scale of student Prior Attitude-Technology was developed by combining responses to three statements using 5-point Likert scales: 1) the importance of using Internet technology, 2) the usefulness of the World Wide Web as a source of information, and 3) if the student found computers to be intimidating. Responses were transformed if needed and a mean score was computed.
Posttest Process Variables (Effort, Time, Satisfaction-Instructor, and Satisfaction-Interaction) were measured by a posttest given along with the final examination. The score for Effort was derived from five questions posed to students using 5-point Likert-type scales and included 1) their desire to get a grade of B or better in the course, 2) the amount of time spent on the course in comparison to other college courses, 3) the amount of effort put into the course, 4) the amount of work they felt the course required, and 5) how often they read messages posted in the asynchronous discussion. Students noted how much Time (hours/week) the course required and rated their satisfaction with the participant-participant interaction (Satisfaction-Interaction) on a 5-point Likert scale. A scale developed from six core questions used by our university and administered to all students completing university courses measured satisfaction with the instructor (Satisfaction-Instructor). These questions reflected teaching constructs that are deemed important to student learning and achievement such as instructor skill and clarity, course structure, teacher availability and rapport with students, feedback to students, classroom interaction, and stimulation of student interest (Student Response to Instruction, 1997). Mean scores were computed for each student.
Product Variables were also measured at posttest. To measure student Achievement, the 28-item multiple-choice test of nutrition knowledge questions was administered again at the final examination and the difference was computed (posttest knowledge score minus pretest knowledge score). By asking students to report the amount of nutrition knowledge they felt they had gained through the course using a 5-point Likert scale from "none" to "a great deal," student Self-reported Learning was determined. A Course Satisfaction scale was obtained from student responses to nine questions that were structured to ascertain opinions of the course's online format (three questions), the level and pace of the course (three questions), satisfaction with technical support, if they would take another online course, and overall course evaluation. Student responses were averaged to acquire a mean Course Satisfaction score for each student. Willingness to Change Behavior was obtained by student response on a 5-point Likert-type scale, with 1 being "strongly disagree" and 5 being "strongly agree," to the statement "After taking this course, I have or will change my nutrition behavior." Finally, using a 5-point Likert-type scale, Increased Computer Competence ratings were obtained by responses to the question "To what extent have you become more competent with your computer skills due to taking this course?"
The researchers identified nine factors that were believed to be important toward predicting student success in the course. These independent variables were age, gender, pretest knowledge, prior attitudes toward nutrition and technology, time and effort spent in the course, satisfaction with the instructor, and satisfaction with the participant-participant interaction. Using SPSS (V.10), Pearson correlation coefficients were used to evaluate relationships among the independent and dependent variables. A regression equation was constructed for each of the dependent variables using the stepwise regression method of selecting significant independent variables also using SPSS (V.10). The dependent or outcome variables measured were achievement (difference in nutrition knowledge scores from pretest to posttest), course grade (percentage out of a possible 600 points), self-reported learning, course satisfaction, efficacy to change nutrition behavior, and improvement in computer competency.
Of the 54 students completing the course, 34 (63%) were female and 20 (37%) were male. Thirty (56%) students were of traditional age (between ages 18 and 24) and 24 (44%) participants were older than age 25. Seven (13%) students were Asian/Pacific Islanders, 2 were Hispanic (4%), 1 was Native American (2%), and 44 (81%) were white/Caucasian. All but one student reported at pretest that they had easy access to a computer during the course. Most reported that they were familiar with computers and had experience using the Internet. Ten students enrolled in Nutrition for Health Online (NHO) but did not complete the course, giving an 84% completion rate. Of those who dropped, most were female (70%) and older than age 25 (70%). Reasons for dropping the course varied and included health issues (n=1), loss of computer access (n=1), financial difficulties (n=1), personal/family concerns (n=2), and unknown (n=1). In addition, four students dropped early in the semester as the amount of time and work the course required was greater than anticipated.
Predictor (Context and Process) Variables
Table 1 shows the mean scores for the factors that were used to predict the outcomes of the course. The average age of the students enrolled was 27.4 [+ or -] 10.1, with a range of 18 to 53. Students scored an average of 12.3 [+ or -] 3.3 (44%) on the nutrition knowledge pretest. When asked about their attitudes toward nutrition, students were moderately positive with a scaled score of 3.9 [+ or -] 0.8. Their attitude toward technology and the Internet was slightly above neutral at 3.6 [+ or -] 0.5. Students reported that the course was moderately challenging, as based on average scores of 4.1 [+ or -] 0.5 on the effort scale. The amount of time spent on the course varied from student to student with an average of 6.2 [+ or -] 3.1 hours per week. Overall, students were satisfied with the instructor (4.3 [+ or -] 0.7). Satisfaction with participant-participant interaction was neutral, but varied widely (3.2 [+ or -] 1.3). See issue's website <http://rapidintellect.com/AEQweb/fal02.htm>
Outcome (Product) Variables
Following NHO, a paired t-test of students' pre- and posttest knowledge scores showed participant's nutrition knowledge improved significantly (p=.000) with an average score on the posttest of 18.9 [+ or -] 4.3 (see Table 1). The average course grade was 83%, or a letter grade of B. Students agreed that they learned about nutrition after taking the course, with an average score of 3.9 [+ or -] 1.1, and were moderately satisfied with the course (3.5 [+ or -] 0.9). Participants felt that they had or would change their nutrition behavior as a result of the course (3.7 [+ or -] 1.2), while overall they were neutral about having become more competent with their computer (3.0 [+ or -] 1.4).
Correlation of Predictor Variables
Table 2 shows Pearson correlation coefficients for the predictor variables. Age had a positive correlation with time and effort spent on the course and also on satisfaction with the instructor. Although there was a correlation between higher scores on the pretest and being female, no other correlation between gender and predictor variables was noted. Prior attitude toward nutrition correlated with pretest nutrition knowledge and prior attitude toward technology. Other significant positive correlations included time spent on the course with self-reported effort, and satisfaction with the instructor with satisfaction with the participant-participant interaction. See issue's website <http://rapidintellect.com/AEQweb/fal02.htm>
Correlation of Outcome Variables
Achievement, as measured by change in nutrition knowledge score from pre- to posttest, did not correlate with course satisfaction or self-ratings of increased computer competence. Otherwise all other outcome variables correlated positively with one another (see Table 3). Issue's website <http://rapidintellect.com/AEQweb/fal02.htm>
Significant multiple regression models were found for each of the outcomes (Table 4). Satisfaction with the instructor positively predicted achievement in nutrition knowledge from pretest to posttest, while pretest nutrition knowledge was a negative predictor of achievement. Fifty-percent of a student's grade in the course was predicted by age, satisfaction with the instructor, and pretest knowledge. Self-reported learning was strongly predicted (63%) by satisfaction with the instructor and satisfaction with the participant-participant interaction. A student's satisfaction with the course was strongly predicted (73%) by satisfaction with the participant-participant interaction, satisfaction with the instructor, and prior attitude toward technology. Willingness to change nutrition behavior was predicted by satisfaction with the instructor and the student's age (34%), while improved computer competence was predicted by satisfaction with the instructor (29%). See <http://rapidintellect.com/AEQweb/fal02.htm>
The strongest predictors of successful outcomes in our online nutrition course were satisfaction with the instructor, satisfaction with the participant-participant interaction, prior nutrition knowledge and attitude toward technology, and age. Participants' satisfaction with the instructor predicted all three measures of student success (knowledge achievement, course grade, and self-reported learning) as well as course satisfaction, willingness to change nutrition behavior, and improved computer competence. Our research demonstrated the importance of positive interaction between the learners and the instructor, as it can affect student success and satisfaction with the course. NHO mirrored some of the important aspects of traditional courses redesigned for online undergraduate nursing education: technical simplicity in navigation to groups, instructors and assignments; in-person orientation where students meet instructors; student support; and all student questions answered in a timely fashion (Rosenlund, Damask-Bembenek, Hugie, & Matsumura, 1999). Student evaluations from their research found that students felt safe asking questions and were at ease with instructors (Rosenlund, Damask-Bembenek, Hugie, & Matsumura, 1999). In contrast, after completing a web-based module (Yucha & Princen, 2000) or general education courses (Ostiguy & Haffer, 2001) students were not as satisfied with the amount of interaction with their instructors as when the course was delivered face-to-face. After discovering that insufficient feedback from the professor was a common complaint in another Internet-based course for nursing students in Korea, the authors recommended training and support for faculty interested in distance education (Soon, Sook, Jung, & Im, 2000).
In face-to-face learning, students receive immediate feedback through the instructor's verbal responses, facial expressions, and gestures. When students are at a distance and working asynchronously, feedback becomes an important issue for a student's sense of social presence and course satisfaction. One behavior of the NHO instructor was to give students personal feedback on their work, usually within 24-hours. The instructor also participated in some of the discussions in the WebBoard, as recommended by McIsaac's group (1999). In a telecourse offered to 106 continuing education students at a distance, promptness of material delivery significantly correlated with final course grades (Bink, Biner, Huffman, Geer, & Dean, 1995). McIsaac, Blocher, Mahes, & Vrasidas (1999) found that if online students did not receive timely feedback, they felt isolated.
A review of studies on the outcomes of distance education found that faculty act as "content experts, learning process design experts, process implementation managers, motivators, mentors, and interpreters" (Merisotis & Phipps, 1999). Our results support their observation that contact with the instructor during online learning is more important to course quality than interaction with the technology. Online instructors find that replying to students' messages and online communications can enhance their teaching effectiveness (McIsaac, Blocher, Mahes, & Vrasidas, 1999). In computer-mediated environments, as compared to face-to-face classes, instructors appear to be more concerned with fostering student participation, more often initiate communication with students, and perceive that interaction with students is of higher quality (McIsaac, Blocher, Mahes, & Vrasidas, 1999). In addition, we found that satisfaction with the instructor can help students adopt positive nutrition behaviors and feel more competent with computer technology. In addition to interaction with the instructor, participant interaction has been observed to be a key ingredient in distance education programs, although few research studies on interaction have been published. Our research provides evidence that satisfaction with peer interaction relates to course satisfaction and perceived knowledge gain, but not grades. Taking an online course can make students feel isolated or be a positive experience if a community of learners forms. Cooperative learning and group competition has been shown to increase student motivation in traditional learning situations (Stipek, 1993). When designing online course tasks, choosing technology, and providing feedback, maximum interaction among the participants should be taken into consideration (Reeves & Reeves, 1997; Harasim, Hiltz, Teles, & Turoff, 1996; Gibbs, 1998). Some researchers recommend that students be required to participate in online discussions in small groups (Yucha & Princen, 2000) and we required participation in our course. The obligation to participate in online discussions necessitates that students complete assignments at about the same time and sacrifices some of the self-paced benefits of online learning. However, based on the outcomes of this study, we feel that it is more important that interaction be maximized.
Research investigating the content of online students' messages has demonstrated that student interactions have a purpose and thus occur while students are completing tasks or meeting a need. The five categories of students' online communication goals were to find or share information on course content, seek help on technology, submit assignments, participate in discussions, and socialize (McIsaac, Blocher, Mahes, & Vrasidas, 1999). From our study we conclude that instructors should encourage these types of communications and require collaborative learning assignments to promote interaction and aid student success and course satisfaction. However, the volume of student communications in online courses is greater than in a traditional classroom (Harasim, Hiltz, Teles, & Turoff, 1996). In another of our online courses for practicing teachers (Cohen, Beffa-Negrini, Sternheim, Volpe, Laus, & Sternheim, 1997), we found that if there were too many messages in the asynchronous discussion, students felt overwhelmed. To avoid this problem, NHO enrollees were placed in teams of five to seven, thus reducing the number of messages to be attended to by each student. We found that being part of a study team fosters students' efforts to support one another as they work through course content or technical problems.
Another factor, pretest score on nutrition knowledge, was a negative predictor of achievement as measured by change from pre- to posttest, but positively predicted grades and satisfaction with the course. Students entering the course with more nutrition knowledge would naturally score higher on the pretest and could not show as much change at posttest due to a ceiling effect. Web-based instruction can fall on a continuum from being purely instructivist (learners are passive recipients of instruction) or largely constructivist (learners actively construct knowledge based on past knowledge and experience) (Reeves & Reeves, 1997). NHO was designed with a strong constructivist approach, with a focus on active learning. The course was challenging and fulfilled general education science credits. Students who entered the course with more knowledge of nutrition could construct new knowledge with greater efficiency and enjoyment, thus higher course grades and satisfaction might be expected. For 603 women enrolled in college chemistry classes, prior knowledge also was a significant predictor of course achievement, along with motivation and use of learning strategies (Yu, 1999). Prior attitude toward technology added only 6% to the prediction of course satisfaction in NHO. Our results concur with Merisotis & Phipps (1999) who concluded that in online learning "technology is not nearly as important as other factors such as learning tasks, learner characteristics, student motivation, and the instructor." Ross (1996) found that as long as assistance was provided, a lack of prior technical skills had little effect on participation in online classes.
It is interesting that the amount of effort put forth in the course and the amount of time spent did not predict course outcomes. Similarly, for students enrolled in face-to-face courses, Eppler & Harju (1997) found that the number of hours students spent studying each week was not related to their GPAs. However, in a televised distance education course, students who spent less time studying, more time on task, and completed assignments in a timely fashion more often earned a grade of A (Miller, 1998). Additional research is needed to investigate time management and study skills and their usefulness in the online environment. A final factor to consider is the online participant's age. In our course, older learners received better grades than younger learners, but did not score higher on pre- post achievement or self-reported knowledge gain. Older students were also more willing than younger students to change their nutrition behavior as a result of the course.
Enrollees in today's campuses are often described as "traditional" or "nontraditional." Traditional students are under the age of 25 and typically attend college directly following high school. Students over 25 are labeled nontraditional, though they are an increasing percentage of the student body. The nontraditional group contains those who need to return to school for career and family reasons (Gianakos, 1996) and with a higher percentage of women than the traditional group (Stage & McCafferty, 1992). Arbuckle & Gale (1996), who collected information from both traditional and nontraditional age freshmen during new-student orientation, propose that "human development does not occur on the same chronological time line for all individuals, and is thus not determined by age and stage order" (stage of development). Finding no significant differences in the developmental levels of traditional and nontraditional freshman, Arbuckle & Gale (1996) concluded many nontraditional age freshman students hold similar insecurities about their intellectual and life planning abilities as traditional students. Some characteristics of adults beginning or returning to college are their low self-confidence, high anxiety (Saunders & Bauer, 1998) and high attrition rates (Arbuckle & Gale, 1996, Honey & Botterill, 1999). In our study, we also found that a higher percentage of students who left the course were of nontraditional age (70%). Nevertheless, Internet and distance courses are appealing to nontraditional students, as their major concerns are the multiple demands of financial and family responsibilities (Saunders & Bauer, 1998). Although research has reported that older students are successful when enrolled in distance education (Marsh & Wells, 1996), few studies compare traditional and nontraditional students' achievement in courses offered in web-based formats. Our results did not concur with those of Bink, Biner, Huffman, Geer, & Dean (1995), who surveyed 106 undergraduate students enrolled in televised distance education courses at a large Midwestern university. They found GPA and year in college, not age, significantly correlated with final course grades.
According to Lively (1997), adult students tend to be more motivated, independent, and achievement-oriented than are traditional college students. In cooperative learning activities, like those used in NHO, older students can help younger students because their career and life experiences are valuable resources during group projects. Often, nontraditional students have learned project management skills in the workplace and thus can assist younger students to set goals and meet course deadlines (Lively, 1997). In addition, older learners appreciate the flexibility to be able to hand in assignments within specified limits without penalty. They also find that when instructors offer a choice of assignments during each segment of a course it helps them meet personal objectives (Lively, 1997). Flexible deadlines and assignments may partly explain the higher prediction of age on behavior change in NHO. In summary, we found that Process Variables, such as interaction of the learners with their instructors and peers, are important to the successful outcomes of internet-based instruction. Additional research to determine methods that optimize effective online interaction and participation in asynchronous communication is needed.
As online learning grows to become an important method of course delivery for higher education, faculty must not assume that all students are self-directed learners. Instead, they should be aware of the characteristics and course-behaviors of their students so they can work closely with those who are younger, while identifying those who lack prior knowledge and skills or who are not participating in the course discussions or communicating during small group assignments. By promoting and modeling positive, creative, and enjoyable human interaction in online classrooms, instructors can have positive impacts on online students' success and course satisfaction. Further research is needed to better understand the relationship of successful outcomes during web-based instruction to the Context Variables a student brings to each learning situation, such as learning style, motivational orientation, cognitive style, or personality construct.
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Patricia A. Beffa-Negrini, University of Massachusetts-Amherst Brian Miller, Ed.D, University of Delaware Nancy L. Cohen, University of Massachusetts-Amherst
Beffa-Negrini, Ph.D., R.D., is an Adjunct Associate Professor in the Department of Nutrition. Miller, Ed.D, is a faculty member in the Department of Hotel, Restaurant, and Institutional Management. Cohen, Ph.D., R.D., L.D.N., is a Professor and Head of the Department of Nutrition and serves as an Extension Faculty Member for the Nutrition Education Program.
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|Author:||Cohen, Nancy L.|
|Publication:||Academic Exchange Quarterly|
|Date:||Sep 22, 2002|
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