Editor’s Note: Art and science may be two ways to achieve the same result. When we achieve success in a particular teaching - learning experience, we try to determine the reasons so we can replicate success.
If we achieve the result intuitively we call it art; if we identify and manage significant elements, we call it science. Research is an attempt to identify, relate, apply, and measure the impact of significant elements. This study researches reasons for a significantly lower dropout rate for online compared to print based independent learning among high school students.
Distance Education: The Impact of Goal Orientation, Motivational Beliefs and Strategies,
and Course Satisfaction
The primary purpose of this study was to investigate course satisfaction, goal orientations, and motivational beliefs and strategies of students enrolled in print and online independent study courses. Completion rates for online high school courses have been approximately twice that for print high school courses for several of the last fiscal years. One of the first steps in understanding why was to develop student characteristic profiles for those enrolled.
A total of 160 university and high school students enrolled in print and online versions of the same independent study courses were surveyed. Factor and discriminant analyses were conducted to evaluate the survey data collected and develop prediction equations for those enrolling in online versus print-based courses. The analysis was somewhat successful in predicting group membership for university and high school course enrollments based on three factor scores. The discriminant function analysis at the high school level approached significant differences between groups for the third factor, which consisted of several self-handicapping items. Many institutions are beginning to offer entire degree or high school diploma programs online; the implications of examining these topics extend worldwide due to these trends.
Keywords: Distance education, goal orientation, motivation, self-efficacy, course satisfaction, retention, mastery goals, performance goals, academic self-handicapping
This study is about a segment of distance education known as independent study. The Center for Distance and Independent Study (CDIS) at the University of Missouri offers a choice of two delivery methods, print-based or online, for many of its courses. While delivery methods are different, courses with the same title include the same course content. Despite the similarity in content, a substantial difference in student retention rates at CDIS has occurred, depending on the delivery method chosen to complete the course. This study focuses on a comparison of student characteristics such as course satisfaction, goal orientations, and motivational beliefs and strategies of students enrolled in high school and university print-based and online courses at CDIS in an effort to understand which student characteristics, if any, facilitate success given the delivery method chosen.
Considerable research exists concerning how classroom environments influence student learning, goals, and academic motivational outcomes (Ames, 1992). Little research exists concerning the goals and motivational attributes of students enrolled in distance education courses. Phipps and Merisotis (1999) found that certain gaps existed in the distance education research base they reviewed from the 1990s, with one gap consisting of the failure to take into account differences among students.
An interesting trend at CDIS concerning online and print-based enrollments is the difference in course completion rates. For example, completion rates for online high school courses were approximately twice that for print high school courses during several of the last fiscal years. Since retention is a factor of great interest to those in the independent study field, this trend is of some import, and research should be conducted to help understand the reasons, since the literature is lacking in this area.
The purpose of the study was to investigate which variables differed on students enrolled in print-based and online independent study courses. The goal of the study was to use goal orientation, motivational beliefs and strategies, and student satisfaction variables to make prediction equations of who is likely to enroll in, and complete or withdraw from, online versus print-based independent study courses. In addition to providing data that could aid in instructional design and potentially improve learning outcomes, demographic characteristics of students enrolled in online and print-based versions of the same course were also determined; these areas are currently unresearched in the literature.
One set of research questions addressed in this study was whether high school and university online versus print-based course enrollment could be predicted by goal orientation, motivational beliefs and strategies, and course satisfaction. It was predicted that students with mastery goal orientations were enrolling in either print or online versions, that students with performance goals were enrolling in online versions, and that students with avoidance goals were enrolling in print versions. It was also predicted that students enrolled in online courses were more satisfied with their courses, as evidenced by their survey responses. Another set of research questions addressed which characteristics—online versus print-based course enrollment, course satisfaction, goal orientation, and motivational beliefs and strategies—most likely predicted course withdrawals or completions.
Distance education delivery systems are categorized by the type of communication used between the students and the instructor, other students, or the distance provider, and the various technological options used when communicating. Willis (2000) suggested that educators focus on instructional outcomes, student needs, content requirements, and instructional constraints before a delivery system is selected, resulting in a mix of media designed to meet student needs in an instructionally effective manner. Understanding is limited concerning how the distance student, the learning task, and a particular delivery system interact (Phipps & Merisotis, 1999).
Student characteristics play a major role in the achievement and satisfaction levels of the distance learner (Phipps & Merisotis, 1999). Of the 1.6 million distance education enrollments at post-secondary institutions in 1997–98, 1.36 million were in college-level credit-granting courses, primarily at the undergraduate level (National Center for Education Statistics, 1999). Most students enrolled in distance education courses are characterized as being over 25, employed, with previous college experience (“Who is Learning,” 2001). However, these statistics do not take into account the different student populations that may comprise a particular institution’s distance education students. For example, at least two-thirds of the enrollments at CDIS are from high school students, and demographics are very different from those cited (Center for Distance and Independent Study [CDIS], 2000).
Despite a large body of research literature examining achievement outcomes in distance education, researchers have not fully investigated affective outcomes such as student satisfaction (DeBourgh, 1999). The attitudes and satisfaction of distance education students are generally characterized as positive (Phipps & Merisotis, 1999). However, in a review of written research published in the 1990s, Phipps and Merisotis (1999) believed the overall quality of the original research was questionable because of reasons such as the use of nonrandom subjects and unreported instrument reliability and validity, which rendered many of the findings inconclusive.
Environment should be considered when studying the learner’s ability to exercise autonomy (Gibson, 1990). Independent study is one of the forms of distance education that grants the greatest autonomy due to the involvement of self-pacing, self-discipline, individualized interaction, and self-directed learning (Gibson, 1990). Proponents cite several advantages of independent study such as the student’s flexibility in completing course work without attending class, decreasing costs for both students and institutions (CDIS, 2000; Khan, 1997). Another advantage is that most independent study courses are asynchronous allowing the student to work on the course at his or her convenience (CDIS, 2000).
Independent study also has been criticized. Withdrawal rates may be higher than those for campus-based instruction. Moore and Kearsley (1996) estimated that in the past, 30–50% of students who started a distance education course did not complete it and that more recent withdrawal rates probably still lie at the lower end of that range. Michael Lambert, the executive director of the Distance Education and Training Council (DETC), suggests that retention rates may be lower because not everyone has the discipline and motivation to complete an independent study course on their own (Thomas, 2001). Factors such as the length of time it takes to complete an external degree and differences in services may also account for lower retention (Thomas, 2001). Another disadvantage is that the student may feel isolated or disconnected; the most-cited shortcoming is little or no face-to-face interaction between the instructor and students or between students (Thomas, 2001). However, using strategies and technologies that encourage cooperative and independent work as well as interaction can help eliminate this limitation (Hill, 1997).
Mayer (2002) defined motivation as “an internal state that initiates and maintains goal-directed behavior” (p. 238) that is construed as personal, directed, and activating. Motivation plays a factor in a student’s successful completion of independent study course work; intent to finish the course, consistent submission of work, and completion of other distance education courses influences student success and motivation (Moore & Kearsley, 1996). Other factors that potentially influence student motivation are course design, the type and amount of interaction provided and available, and the role of the offering institution or site facilitator (Cornell & Martin, 1997).
In recent years, goal orientation theory has been used in an attempt to understand the psychological processes which accompany motivational patterns, particularly the role of mastery and performance goals (Kaplan & Midgley, 1997). Goal orientation theory focuses on the goals perceived for achievement rather than the level of motivation (Middleton & Midgley, 1997). Mastery goals have been related to higher levels of self-efficacy, or perception of competence, interest, achievement, and other outcomes (Pintrich, 2000; Midgley et al., 1997). Students with mastery goal orientations believe that effort leads to a positive outcome; their self-efficacy is based upon this belief (Ames, 1992). Students with mastery goals are willing to learn and are focused on developing new skills and understanding and mastering content (Ames, 1992). Research indicates that students with mastery goals use strategic thinking strategies such as self-monitoring and deep processing (Ames, 1992).
Students with performance goals focus on self-worth and ability, judging ability by how well they perform in relation to others or how easily they achieve success with minimal effort (Ames, 1992). Public recognition of performance is important to students with performance goals (Ames, 1992). Students with performance goal orientations may refrain from challenging tasks, use superficial or short-term learning strategies such as rehearsal or memorization, and base beliefs in personal academic ability on their successes and failures (Ames, 1992). Performance goals have been less adaptive in terms of future motivation because students focus on competitive goals such as doing better than others or the avoidance of looking incompetent (Pintrich, 2000).
Students with mastery goals are likely to maintain achievement, while students with performance goals may settle into a pattern of failure avoidance, a perspective known as normative goal theory (Ames, 1992). However, in some situations, performance goals can result in superior achievement and performance, with mastery goals connected to more intrinsic preoccupation with the task (Pintrich, 2000; Harackiewicz, Barron, Carter, Lehto, & Elliot, 1997; Harackiewicz, Barron, Tauer, Carter, & Elliot, 2000).
In this revised goal theory perspective, performance goals are divided into two categories, approach and avoidance performance goals. Students with approach performance goals are “oriented to doing better than others and to demonstrate their ability and competence” (Pintrich, 2000, p. 544). Students with avoidance performance orientations avoid tasks in order to avoid looking incompetent or stupid (Pintrich, 2000). Students may have both mastery and performance goals, possibly simultaneously (Pintrich, 2000). High levels of both mastery and approach performance goals are the most adaptive pattern of multiple goals, compared to normative goal theory which hypothesizes that the most adaptive pattern is a high mastery level accompanied by a low performance level (Pintrich, 2000).
Pintrich (2000) compared multiple goals of junior high students in math classrooms and included measures such as mastery and performance goals, self-efficacy, and motivational strategies such as self-handicapping in his research. Academic self-handicapping “refers to strategies, such as procrastinating and fooling around, that are used by students so that if subsequent performance is low, these circumstances, rather than lack of ability, will be seen as the cause” (Midgley et al., 1997, p. 10). Pintrich (2000) predicted several negative consequences would occur over time, given general research findings for motivational beliefs at the junior high level. Results for self-efficacy and other adaptive outcomes were as predicted, decreasing over time, as was the increase in maladaptive outcomes such as in self-handicapping (Pintrich, 2000).
Current research has not connected independent study with goal orientation theory, as in this study. Course structures with interesting, challenging tasks and evaluation processes that focus on effort rather than grades, that are supportive of autonomy and self-directed learning with an emphasis on independent thought and content mastery, facilitate learning and are more likely to lead to a mastery goal orientation (Ames, 1992). Such attributes of independent study as autonomy and self-paced, self-directed learning are features of course structures that may facilitate mastery goal orientations (Ames, 1992). Since most students progress through independent study course work on their own, it is less likely for judgments of ability differences or social comparisons to occur, also fostering mastery goal orientations. In terms of current research, performance goal orientation may be less important. However, students may choose a particular goal orientation based on past experiences (Ames, 1992), and some students enroll in groups, which may foster social comparison and perceptions of differences in ability. Independent study courses also include predefined assignments and examinations that determine course grades, a performance-oriented evaluation structure (Ames, 1992).
For those reasons, both mastery and performance goal orientations were of interest to this study. Goal orientation, course satisfaction, and motivational beliefs and strategies are crucial for course completion when students are enrolled on their own in a distance education course. Therefore, these measures were chosen as the measures of interest for this study.
The target population consisted of fiscal year 2001–02 CDIS enrollments in courses with both print and online versions, for a total of 61 courses. At the time of data collection, high school and university enrollments in those courses totaled 5,036 and 712, respectively, divided nearly equally between print and online enrollments.
No published measure was found which measures the constructs of interest in relation to independent study. Therefore, a survey instrument was developed which measures goal orientation, motivational beliefs and strategies, and student satisfaction with independent study courses. Several course satisfaction questions were taken from a survey developed by CDIS used in a previous study. Most of the measures of personal goal orientation and motivational beliefs and strategies were adapted from the Patterns of Adaptive Learning Survey (PALS) (Midgley et al., 1997), which measures similar constructs for middle school and junior high students; permission was received from the authors (Midgley et al., 1997) to include those items on the survey, rewritten to be more appropriate for the older distance education audience. The PALS survey scales (Midgley et al., 1997) chosen that measure personal achievement goal orientations included three scales focused on mastery, performance approach, and performance avoidance goal orientations (α = .75 to .86). The PALS survey scales (Midgley et al., 1997) chosen to measure motivational beliefs and strategies focused on academic self-efficacy (a = .77) and self-handicapping (a = .84), respectively. In other research, these measures demonstrated good construct validity and internal consistency (Kaplan & Midgley, 1997; Midgley, Kaplan, Middleton, & Maehr, 1998).
The survey was comprised of 54 items consisting of 15 demographic, motivation, and goal-setting items; one overall course satisfaction grade; and 38 Likert items depicted on a 5-point continuum (1=strongly agree, 5=strongly disagree, N/A=does not apply). Of the 38 Likert items, 12 measured student satisfaction and 26 were achievement related. Lower scores on 11 student satisfaction items indicated greater course satisfaction; a higher score on the other student satisfaction item indicated greater course satisfaction. Of the 26 achievement-related items, five each measured mastery goal orientation and academic self-efficacy, and four each measured performance-approach goal orientation, performance-avoidance goal orientation, academic self-handicapping, and intent or schedule to complete lessons. These constructs were included in the self-developed survey instrument because they have a direct bearing on the research questions proposed in this study.
Fiscal year 2001–02 enrollments from 61 high school and university courses served as a database of potential participants. The data was divided into four participant pools from which samples were drawn; high school and university print and online course enrollments were assessed separately so that predictor variables most accurately portrayed the populations they represented.
Stratified samples of 50 university students and 200 high school students were drawn from each set of university and high school print and online participant pools, for a total of 500 participants. The 100 university potential participants consisted of 42 males and 58 females, ranging in age from 18 to 53 with a mean age of 28.37 (SD = 8.719). Most were Missouri residents. Ethnicity information is self-reported and was only available for 72 of those students; 88% were Caucasian.
The 400 high school potential participants consisted of 198 males and 202 females, ranging in age from 13 to 42 with a mean age of 17.89 (SD = 2.306). Most were residents of the Midwest or Pennsylvania. Ethnicity information was available for 179 students; 91% were Caucasian.
A response rate of 32% was obtained, consisting of 38 university and 122 high school students. The university participants consisted of 12 males and 26 females, ranging in age from 18 to 51 with a mean age of 28.47 (SD = 9.150). Most were Missouri residents and 84% were Caucasian. Seventeen were enrolled in print courses and 21 were enrolled in online courses.
The 122 high school participants consisted of 40 males and 82 females, ranging in age from 13 to 42 with a mean age of 16.97 (SD = 2.712). Most were residents of Missouri or charter school students from Pennsylvania, and 90% were Caucasian. Sixty-eight were enrolled in print courses and 54 were enrolled in online courses.
Students enrolled in online courses were asked how often they printed lesson commentaries from their online study guide; 81% of those enrolled in the university online courses and 72% of those enrolled in high school online courses indicated that they printed the lessons out for most or all lessons. This further emphasizes the similarity between the print and online course structures.
This study used a quasi-experimental design on one variable. The dependent or criterion variable consisted of instructional delivery method, (e.g., print-based or online) for two of the research questions, and retention (e.g., withdrew or completed) for the other two. Factor scores were created from the 38 Likert items on the survey. The factor scores were used as predictor variables in the prediction equations derived through discriminant function analysis. High school and university course enrollments were assessed separately for reasons mentioned previously.
Factor analysis was used for scale development to see which survey items were most closely associated with each other. Discriminant function analysis was used to eliminate variables if unrelated to group distinctions, to classify enrollments into groups, and to test theory by observing whether enrollments were classified as predicted. Group membership, or enrollments in print-based and online courses, was predicted based on a linear combination of the independent predictor variables. Student retention (e.g., withdrawals and completions) was also assessed based on the independent and dependent variables.
The null hypotheses were that there were no significant differences between means for the independent variables on the group variable, course delivery method or retention. A prediction was made earlier that goal orientation differed on the group variable. If the discriminant analyses produced results, MANOVA was used to provide confirmatory evidence or evidence of validity to ensure comparable findings.
Before analyzing the data, it was screened for missing data, distributions, and outliers. Answers to Likert items written in a reverse fashion to avoid test bias were recoded. One question was a six-part item requesting information about use of the CDIS Web site; after running a frequency distribution, three of those items were omitted. Several students did not have a computer or Internet access and were unable to answer those items; also, Web site usage was not a primary research interest. Inter item correlations and scale reliability were calculated. Coefficient alpha for the Likert items was .82 before those items were deleted and .80 afterward. Likert items were checked for outliers, and statistics for five high school enrollments exceeded the critical values, so those outliers were discarded from further analysis, reducing the high school sample size to 117, consisting of 65 print-based enrollments and 52 online enrollments.
Internal consistency reliability for each of the subscales was calculated. Cronbach’s alpha was .88 for the course satisfaction items, .75 for the mastery goal items, .55 for the performance-approach goal items, .82 for the performance-avoidance goal items, .61 for the academic self-efficacy items, .84 for the self-handicapping items, and .67 for the intent or schedule to complete items. Internal consistency for the reversed performance-approach goal items (α = .55) was low. Students reported a somewhat low level of performance-approach orientation for Q33 (mean = 2.62) and fairly high levels for Q34–Q36 (means of 3.59, 2.97, and 3.46, respectively). This resulted in Q33 correlating negatively with Q34–Q36. This result was not problematic because the items loaded on the same factor during the factor analysis.
Factor analysis was conducted on the Likert variables, and the initial analysis indicated five factors explained over 49% of the variance. Items were eliminated, the remaining three factors analyzed, and factor scores saved for use in the discriminant analysis. The first factor score included course satisfaction and self-efficacy items as well as two mastery goal orientation items. A mastery goal orientation is thought to lead to higher levels of self-efficacy, or perceptions of competence (Ames, 1992). Because such items were included, this factor could be considered one of course and self-satisfaction. The second factor score included both performance approach and avoidance items; it created a single measure for performance goal orientation. The third factor score was comprised of academic self-handicapping items.
High school data was assessed first in the discriminant analysis as shown in Table 1. The .057 significance value calculated for the self-handicapping factor score indicated high school print and online group differences approached significance on that factor. Means for the reversed self-handicapping items (Q46–Q49) ranged from 2.77 to 3.20 for students in print courses and 2.60 to 2.85 for students in online courses. This meant that of those high school students who responded to the survey, those in the print courses employed greater levels of self-handicapping strategies than students in the online courses.
High School Tests of Equality of Group Means
REGR course and self-satisfaction factor score
REGR performance goal orientation factor score
REGR self-handicapping factor score
Classification coefficients and constants were used to create a classification or prediction score for new enrollments, and the equations may be tested by placing new enrollments in the group for which its score is highest and checking that classification against actual data. The degree of success for the sample classification was calculated. The classification was fairly successful, with 64.1% being classified correctly.
University enrollments were assessed next. All records were valid, although the small n = 38 was a concern. The F values calculated were greater than the critical value, which indicated group differences were not significant. The classification was fairly successful, with 63.2% being classified correctly. MANOVA was used to validate, with similar results obtained. This suggested most differences between means were attributable to chance differences alone.
Since only two mastery items loaded on a factor, MANOVA was used to test the prediction that students with mastery orientations were enrolling in either print or online courses. Results were difficult to interpret since not all the mastery items loaded on a factor, but they may indicate that students with mastery goal orientations are enrolling in either print or online courses. Effect size was .053 and .009 for the high school and university groups, respectively, which indicated only 5.3 and .9 percent of the total variability in print or online enrollment could be attributed to factor scores. Observed power of the tests was .529 and .067 for the high school and university groups, respectively. While observed power for the university sample was low due to the small sample size, observed power for the high school group was fairly good. Since the factor scores accounted for little of the total variability, other variables probably play a significant role in differences on enrollment.
The research questions that used retention (e.g., withdrew or completed) could not be assessed properly due to the small number of withdrawals for this particular sample of students. Of the 155 students, only five withdrew from their course work (two high school and three university students).
The results of this study did not provide clear support for the original predictions. Only two of the five mastery items loaded on a factor, a combination of self-efficacy, course satisfaction, and mastery items. Because of the variety of items that comprised the course and self-satisfaction factor, it cannot be stated with certainty that students with mastery goal orientations were enrolling in either print or online versions as predicted. Results using individual mastery items, opposed to the combined course and self-satisfaction factor, were mixed but indicated overall that students with mastery goals were enrolling in either print or online courses, as predicted.
The results of the study did not provide support for the predictions that students with performance approach goals were enrolling in online versions, that students with avoidance goals were enrolling in print versions, or that students enrolled in online courses were more satisfied with their course. However, the course and self-satisfaction factor was comprised of a combination of items, and the performance approach and avoidance items all loaded onto one performance goal orientation factor and could not be differentiated. Means for several of the performance approach items indicated that students employed high levels of performance approach orientations.
The discriminant function analysis at the high school level approached significant differences between groups for the self-handicapping factor. The conclusion drawn was that for this sample of high school students, there was a possible difference between students enrolled in online and print-based courses for the self-handicapping factor, and the data indicated that students enrolled in the high school print courses employed greater levels of self-handicapping strategies than students enrolled in the high school online courses.
The analysis was somewhat successful in predicting group membership for the university and high school course enrollments based on the three factor scores. Had a sufficient number of students within each category responded to the survey, hold-out samples could have been used to validate the discriminant function. The majority of students enrolled in the online courses indicated they printed the lessons out for most or all lessons, emphasizing the similarity between the print and online course structures; however, access to course components and additional course resources remains very different for the two delivery methods, as well as lesson submission for many students. Retention trends at CDIS have been substantially better for high school students enrolled in online courses. If printing lessons meant the student was treating an online course as a print course, retention differences would not be so significant.
Some findings in this study provided preliminary evidence that a connection between independent study and goal orientation may exist. The potential positive findings for the prediction involving mastery items lend support to the theory that the autonomy and self-paced, self-regulated aspects inherent in independent study course structures facilitate a mastery goal orientation, no matter the course delivery method. Independent study course structure exemplifies those that foster mastery goal orientations (Ames, 1992). Since means for several of the performance approach items indicated that students employed high levels of performance approach orientations, the performance-oriented evaluation structure of independent study courses and students’ past experiences may factor into the particular goal orientation chosen (Ames, 1992). These findings are consistent with Ames’ theories.
Another potential positive finding concerned the possible difference between high school students on the self-handicapping factor, with those enrolled in print courses employing greater levels of self-handicapping strategies than those enrolled in online courses. This was somewhat expected, given the course completion trends for the high school population. In addition, it is likely that the interactivity and novelty of the online courses increase the interest level of those enrolled, keeping those students on track academically in their course work instead of procrastinating. This finding and the high school online course completion rate give support to the trend of providing course work online and the benefits derived.
A problem in this study was one of power. One way to increase the power of the test would be to increase the sample size. The number of university students who responded was not sufficient. Since only 32% of the potential participants responded to the survey, the sample may not be representative of the true population. Those who responded may be different from those who did not; it is not known whether these results would generalize to other samples. However, the total number of those who responded was approximately equal when subdivided into the high school and university print and online groups. Demographic characteristics such as ethnicity and the geographic location of those who responded also mirrored those for the original pool of 500 potential participants. Course grade distributions were assessed for the university and high school samples and they were fairly normally distributed. Repeating the study with a different pool of participants at a later date may increase the generalizability of the findings.
The number of withdrawals in the sample was surprisingly low. Instead of surveying students during their course enrollment, it might be best to wait until students have completed their course work and revise the sampling technique to ensure more equal numbers for withdrawals and completions. Future testing should be done to validate the prediction equations derived using new survey samples. New data and random samples would cross-validate the discriminant function derived.
While the difference in retention for online and print course enrollments continues at the high school level, the retention trend for the 2002–03 fiscal year for university enrollments resulted in just as many print courses completed as online. This could be due to an increase in online enrollments resulting in a more equal balance between the two, or perhaps a leveling off of interest in new technology. At the high school level, increased interest, motivation, and participation may be occurring in relation to the online courses because the students are doing something different, as mentioned in relation to the self-handicapping factor.
The purpose of this study was to investigate the possibility of a link between independent study and motivational measures such as goal orientation. Since the study did not result in conclusive support for the original predictions, it is suggested that the potential links between independent study and goal orientation and the other measures be studied in greater depth using larger samples with slight modifications to the sampling method used. Future research may refine the research questions and investigate the possibility of those links more thoroughly, providing results that help with independent study student advisement and instructional design. Retention trends will continue to be assessed; if the university trend continues, potential participants should be taken from the high school population only. Additional outcome measures may need to be included in the model, although it is possible that using larger sample sizes will result in clearing up some of the problems previously mentioned based on the small number of students who responded.
While there is substantial research concerning the components of effective instructional design, there has not yet been much exploration of student characteristics that would contribute to an overall picture of the factors that must be taken into consideration before instruction is designed and delivery methods chosen. Studies that focus on student characteristics may help in developing more successful students (Diaz, 2000). While it can be quicker to develop online courses and it can be less costly for students to access online course materials, those should not be primary reasons online curriculum is being developed. Many institutions are beginning to offer entire degree or high school diploma programs online. The implications of examining these topics extend worldwide due to these trends.
Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of American Journal of Distance Educational Psychology, 84(3), 261–271.
Center for Distance and Independent Study. (2000). University of Missouri Center for Distance and Independent Study 2000–2001 university bulletin, 101(4).
Cornell, R., & Martin, B. L. (1997). The role of motivation in Web-based instruction. In B. Khan (Ed.), Web-based instruction (pp. 93–100). Englewood Cliffs, NJ: Educational Technology Publications, Inc.
DeBourgh, G. A. (1999). Technology is the tool, teaching is the task: Student satisfaction in distance learning. Retrieved May 5, 2002, from ERIC database (ERIC Document Reproduction Service No. ED432226).
Diaz, D. P. (2000). Carving a new path for distance education research. The Technology Source. Retrieved May 5, 2002, from http://ts.mivu.org/default.asp?show=article&id=648
Gibson, C. C. (1990). Learners and learning: A discussion of selected research. In M. G. Moore (Ed.), Contemporary issues in American distance education (pp. 121–135). Oxford, United Kingdom: Pergamon Press.
Harackiewicz, J. M., Barron, K. E., Carter, S. M., Lehto, A. T., & Elliot, A. J. (1997). Predictors and consequences of achievement goals in the college classroom: Maintaining interest and making the grade. Journal of Personality and Social Psychology, 73(6), 1284–1295.
Harackiewicz, J. M., Barron, K. E., Tauer, J. M., Carter, S. M., & Elliot, A. J. (2000). Short-term and long-term consequences of achievement goals: Predicting interest and performance over time. Journal of Educational Psychology, 92(2), 316–330.
Hill, J. R. (1997). Distance learning environments via the World Wide Web. In B. Khan (Ed.), Web-based instruction, (pp. 75–80). Englewood Cliffs, NJ: Educational Technology Publications, Inc.
Kaplan, A. & Midgley, C. (1997). The effect of achievement goals: Does level of perceived academic competence make a difference? Contemporary Educational Psychology, 22, 415–435.
Khan, B. (1997). Web-based instruction (WBI): What is it and why is it? In B. Khan (Ed.), Web-based instruction (pp. 5–18). Englewood Cliffs, NJ: Educational Technology Publications, Inc.
Mayer, R. E. (2002). The promise of educational psychology, vol. II: Teaching for meaningful learning. Upper Saddle River, NJ: Pearson Education.
Middleton, M. J., & Midgley, C. (1997). Avoiding the demonstration of lack of ability: An underexplored aspect of goal theory. Journal of Educational Psychology, 89(4), 710–718.
Midgley, C., Kaplan, A., Middleton, M., & Maehr, M. L. (1998). The development and validation of scales assessing students’ achievement goal orientations. Contemporary Educational Psychology, 23, 113–131.
Midgley, C., Maehr, M., Hicks, L., Roeser, R., Urdan, T., Anderman, E., et al. (1997). Patterns of Adaptive Learning Survey (PALS). University of Michigan.
Moore, M. G., & Kearsley, G. (1996). Distance education: A systems view. Belmont, CA: Wadsworth.
National Center for Education Statistics. (1999). Distance education at postsecondary education institutions: 1997–98. (NCES No. 2000013). Washington, DC: U.S. Department of Education.
Phipps, R., & Merisotis, J. (1999). What’s the difference? A review of contemporary research on the effectiveness of distance learning in higher education. Retrieved May 5, 2002, from http://www.ihep.com/Pubs/PDF/Difference.pdf
Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92, 544–555.
Thomas, C. (2001). Distance learning goes the distance. Peterson’s: Getting you there. Retrieved May 5, 2002, from http://www.petersons.com/distancelearning/code/articles/distancelearngoes1.asp
Who is learning at a distance. (2001). Retrieved May 5, 2002, from http://www.petersons.com/distancelearning/code/articles/who.asp
Willis, B. (2000). Guide #1 distance education: An overview. Distance Education at a Glance. Retrieved May 5, 2002, from http://www.uidaho.edu/evo/dist1.htm.
About the Author
Terrie Nagel is a research assistant and student service coordinator at the University of Missouri, Center for Distance and Independent Study (CDIS). She is involved with student advisement, test assessment, student profiling, and curricular research. She was the recipient of the 2003 AACIS (American Association for Collegiate Independent Study) Student Services Award.
Terrie Nagel received her M.A. in educational psychology at the University of Missouri in 2003 and is currently a doctoral student in the same program. Her research interests are distance education, learning theory, assessment and measurement. Before coming to the University of Missouri she was a Statistical Analysis System programmer.
University of Missouri, 36 Clark Hall, Columbia, MO 65211
Phone: (800) 609-3727, option 4 or (573) 882-2564