Annotated Bibliography 2

PREDICTORS OF COLLEGE STUDENT ACHIEVEMENT IN UNDERGRADUATE ASYNCHRONOUS WEB-BASED

COURSES

PAUL D . BELL

Ph.D., RHIA, CTR

This study examined the effects of self-regulated learning (SRL) and epistemological beliefs (EB) on individual learner levels of academic achievement in Web-based learning environments while holding constant the effect of computer self-efficacy, rea- son for taking an online course, prior college academic achievement, and parental level of education. The study con- stituents included 201 undergraduate students enrolled in a variety of asynchronous Web-based courses at a university in the southeastern United States. Data was collected via a Web-based questionnaire and subjected to the following analyses: separate exploratory factor analyses of the self-regulated learning and the epistemological beliefs question items, conelations between the independent variables and the dependent variable, and linear regression of final course grades with all the variables in the model. Analysis of the data revealed that three independent vari- ables (prior academic achievement (GPA), expectancy for learning, and an interaction term based on the cross product of these two variables were significant predictors in the model of learning achievement in asynchronous online courses. Discus- sion of the study's predictive model follows.

Increasingly, public institutions of high- er learning are adding asynchronous Web-based instruction to their undergrad- uate degree programs. Although online learning has been hailed as the next revo- lution in access to higher education, many undergraduate learners (late adolescent stu- dents between the ages of 18 and 25 years of age) who function well in traditional on-campus classrooms may not be ready for the demands of asynchronous Web- based learning (AWBL). This is because online learning requires more learner con- trol and self-direction than traditional classroom-based instruction. These demands are representative of higher lev- els of intellectual development that "may

well be unattainable during the late ado- lescent years".

There is little research from the asyn- chronous online learning literature that examines the relationship between learn- er control and self-monitoring and successful learning in AWBL environ- ments. However, recent research in educational psychology has identified two characteristics that appear to be related to academic success in learner-controlled environments such as online courses. These characteristics are self-regulation of learn- ing and epistemological beliefs about knowing and learning.

Self-regulated learning (SRL) is an ele- ment of social cognitive learning theory

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that states that learner behaviors and moti- structs are included in the same study, vations as well as aspects of the learning environment affect learner achievement. Some experts have argued that self-regu- lation of learning (SRL) has a positive * n » * 8-12

mfluence on academic success. Episte- mological beliefs (EB) are beliefs held by individuals about knowledge and leam-

13,14

mg. Researchers m this area contend that the more sophisticated students' beliefs are about knowledge and learning, the more successful they should be in think- ing and problem solving.

The majority of the research literature for each genre (SRL and EB) is composed of theoretical work that has made con- vincing arguments for why each construct should influence learner achievement. On the other hand, empirical studies that have been conducted in both traditional class- room and computer-based settings have yielded limited results concerning the effects of either SRL or EB on student achievement. These limited results may be because the majority of such research has examined each construct (SRL and EB) separately from the other. Yet, Elavell (1979) and Hofer (2001) argued that self- regulated individuals who actively self-monitor their leaming also tend to have sophisticated beliefs about knowledge and leaming.

One would expect, then, that combin- ing an individual's level of self-regulated leaming with his epistemological belief profile might be more effective in pre- dicting leamer performance than relying on either measure alone. Therefore, a better understanding about how subfactors relat- ed to SRL and EB affect learner achievement may be realized if both con-

Purpose of the Study There are relatively few studies that

have used predictive modeling in order to explain the effect of self-regulated leam- ing (SRL) and epistemological beliefs (EB) on leamer achievement in asynchronous Web-based environments. Most studies in this area have looked at either SRL or EB but not both in the same model. In addi- tion, these investigations have varied in the number and types of covariate factors included in the final models. For example, the asynchronous online leaming litera- ture indicates that other factors such as reason for taking an online course, self- efficacy for using computer technology, and prior academic achievement infiuence leaming achievement in online courses. Furthermore, the general leaming litera- ture has also suggested that parental level of education affects individual undergrad-

19.20

uate leaming achievement. The purpose of the current study, then,

was to examine the effects of SRL and EB on individual levels of achievement in an asynchronous Web-based leaming envi- ronment while controlling for the effects of the covariate factors listed above.

Research Question The research question was as follows:

What is the predictive ability of self-reg- ulated leaming; epistemological beliefs; reason for taking an online course; com- puter self-efficacy; prior academic achievement (GPA); and parental level of education on final grade in asynchronous undergraduate online college courses?

Predictors of College.../ 525

Participants The site of the present study was a coed-

ucational public university situated in the southeastern region of the United States. According to registrar records, approxi- mately 2,700 students were enrolled in Web-based undergraduate courses at the university. About a quarter of this group, 629 students, was selected via a random numbers procedure tO; receive a recruit- ment e-mail. Finally, 201 individuals from this group completed the study question- naire. Students ranged in age from 18 to 50 with a mean age of 22.4 S.D. 6.14. Sur- vey respondents were 77 percent female (n = 155) and 23 percent male (n = 46) and comprised a diverse ethnic sample with 74 percent Caucasian, 16.5 percent African American, and 5 percent Native Ameri- can. The remaining 5 percent of the sample self-reported as either Asian American/Pacific Islander, mixed race, or Hispanic. Of the students sampled, 46 per- cent (n = 93) had no prior experience taking online courses, while 54 percent (n = 108) had taken at least one online course pre- viously.

Materials Data was collected via a Web-based

self-report inventory during the spring 2005 academic semester. Survey questions were taken from two different instruments as a means of collecting data relative to the variables of self-regulated learning (SRL) and epistemological beliefs (EB).

A review of the theoretical research in SRL showed that individuals must display certain fundamental attributes in order to be successful self-regulators of their leam- ing. These include: (a) being intrinsically

motivated to reach goals, (b) expecting that one's efforts to leam will result in positive outcomes, (c) expecting to succeed in one's leaming, (d) being confident in one's abil- ity to perform and complete an academic task, (e) monitoring one's progress toward goal completion, (f) controlhng one's effort and attention, and (g) managing time and place resources for leaming and studying. Self-regulated leaming theory argues that these conditions must be present before students can successfully employ cogni- tive strategies in their leaming. Moreover, according to Pintrich, Smith, Garcia and McKeachie (1991), the Motivational Strategies for Learning Questionnaire (MSLQ) scales "are designed to be mod- ular and can be used to fit the needs of researchers." Therefore, 24 Likert-scaled question items were taken from the MSLQ and used to assess participant ratings on the self regulated-leaming subfactors target- ed by the current study. AU 32 Likert-scaled question items from the Epistemological Beliefs Inventory (EBI) were used to assess participant ratings on epistemological belief subfactors targeted by the current study. This instrument was developed by Schraw, Bendixen, and Dunkle.

The survey instrument also included questions related to the covariates as fol- lows: (a) two Likert-scaled question items were included that referenced the study participants' self-efficacy for computer usage, (b) a short answer question item was included that referenced the study par- ticipant's reasons for taking the online course, (c) a multiple item short answer question asked the study participant to indi- cate parent's highest level of education achieved and (d) each participant's grade

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point average (GPA) was collected from university registration records. Last, final course grades fell on a scale from 0-100 and were collected from course instructors at the end of the exam period. Permission to gather this information was obtained from each study participant.

Methods and Designs A cross-sectional predictive study was

used in order to examine the effect of the following factors on learning achievement in asynchronous online undergraduate courses: (a) subfactors of self-regulated learning, (b) subfactors of epistemologi- cal beliefs, (c) self-efficacy for computer technology, (d) reason for taking a Web- based course, (e) prior college academic achievement, and (f) parental level of edu- cation.

Three separate steps were followed in order to analyze the predictive ability of the SRL and EB subfactors on academic achievement: Despite published claims of validity and reliability for the original instruments," ' the first step was to run separate factor analyses of the self-regu- lated learning and epistemological beliefs question items in order to establish their factor structure in the current study. Fac- tor intemal reliability coefficients obtained for the self-regulated learning and episte- mological beliefs subfactors were then compared with those obtained for the orig- inal instruments. Second, a correlation matrix of the independent variables (the SRL and EBI subfactors as well as the study covariates) and the dependent vari- able was generated. An analysis of the matrix determined which of the indepen- dent variables were correlated with the

dependent variable and which were corre- lated with each other. Third, a multiple regression analysis of the predictor vari- ables in the proposed model with the dependent variable (final course grade as a measure of learning achievement) was performed.

Results While students from all four class lev-

els participated in this study, juniors and seniors accounted for about two-thirds (64.7 percent) of the sample. Final course grades ranged from 0 - 1 0 0 (M = 86.36, SD = 13.31) with 55.7 percent earning a grade of 90 or above. GPA of the sample population ranged from 1.00-4.00 (M = 3.00, SD = 0.63). It is possible that previous experience with learning online could have had an impact on the study's results. Therefore, an independent samples t test was used to determine whether there was a significant difference in learning achievement (mean final course grade) between those students who had never taken an online course before and those students who had already taken at least one online course. This analysis revealed that there was no significant difference between the two groups (t (199) = 1.4; p = 0.17).

Separate exploratory factor analyses of the self-regulated learning and epistemo- logical beliefs survey items yielded the following factor structures: three SRL sub- factors—expectancy for learning, intrinsic goal orientation, and resource regulation; four epistemological beliefs subfactors— innate ability, quick learning, simple knowledge, and omniscient authority. These subfactors paralleled those yielded by the original instruments and their reli-

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ability estimates compared favorably with their counterparts in the original instru- ments.

Survey participants fell into three cat- egories according to their reason for taking an online course during the spring 2005 semester. Of the respondents, 47.8 percent (n = 96) stated that learning online was more convenient for them than taking a traditional face-to-face course, while 33.8 percent (n = 68) reported that they had no option. "No option" meant that at the time the student registered, either the course was only offered online or there were no face-to-face course sections available. A smaller number of students, 18.4 percent (n = 37) gave a reason related to their curiosity or interest in learning via the elec- tronic medium.

Student responses to two survey ques- tions about self-efficacy for the use of computer technology were added togeth- er and the sum represented the student's overall self-report score for computer self- efficacy (M = 6.38, SD = 1.18). Prior academic college achievement was mea- sured using the current semester GPA. The mean GPA for the sample was 3.01 and the SD 0.63.

The research literature suggests that the key discriminator for parental level of edu- cation is based on either having taken or

" • i fnot havmg taken college courses. Therefore, based on this criterion, 81.6% (n=164) of the study participants indicat- ed that their parent's level of education included at least some college while 18.4% (n=37) indicated that their parent's high- est level of education was high school.

In the current study, GPA and expectan- cy for learning were found to be

moderately correlated (r = .3) This r value as well as literature-based evidence for

, • • • , • 5,7,23-26

their positive correlation drove the decision to create an interaction term, con- sisting of the cross product of the variable that measured prior college academic achievement (GPA) and the variable that measured individual expectancy for learn- ing. This new variable was included in the

27

predictive model. Coefficients >.l were considered

indicative of a correlation between a par- ticular predictor variable and the dependent variable. Therefore, based on this criteri- on, the following bivariate correlations revealed five predictor variables signifi- cantly related to learning achievement: (a) interaction of GPA and expectancy (r = .52), (b) prior college achievement as measured by GPA (r = .40), (c) expectan- cy (r = .39), (d) effort regulation (r = .32), and (e) quick learning ( r = -.16). All of these correlations were significant at least at p < .05, and all were in the predicted directions.

Using multiple regression, final course grades were regressed on the linear com- bination of all the variables in the model. These twelve variables included (a) prior academic achievement, (b) computer self- efficacy, (c) intrinsic goal orientation, (d) resource management, (e) expectancy, (f) quick learning, (g) innate ability, (h) omni- scient authority, (i) simple knowledge, (j) reason for taking an online course, (k) the interaction between prior academic achievement (GPA) and expectancy, and (1) parental level of education.

The linear combination of the inde- p e n d e n t v a r i a b l e s s i g n i f i c a n t l y predicted final course grade in asyn-

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chronous undergraduate online cours- es (adj. R ' = .36, p<.001). Three of the eleven independent variables were significant (P<.0001) pre- dictors of undergraduate learning achievement in asynchronous online cours- es; these predictors were prior college leaming achievement (GPA), expectancy for leaming, and the interaction of prior college learning achievement with expectancy for leaming.

Discussion In this study, the best predictors of leam-

ing achievement in undergraduate asynchronous online courses were prior college academic achievement, expectan- cy for leaming, and the interaction term based on the cross product of prior acad- emic achievement and expectancy. In addition to being the most important inde- pendent variables in the model, these three variables also correlated most strongly with the dependent variable compared to other independent variables in the model.

The study's results yielded a parsimo- nious solution to the original study research question and indicated that although there were multiple factors that were bivariate- ly correlated with leaming, only one of the original self-regulated leaming subfactors and none of the epistemological beliefs subfactors was a predictor of learning achievement in asynchronous online under- graduate courses. For example, although quick leaming was weakly correlated with the dependent variable, final course grade (-.16), it was more highly correlated with expectancy (r = -.34). Likewise, even though effort regulation was fairly corre- lated with final grade (r = .32), it was more

highly correlated with the other self-reg- ulated learning subfactor, expectancy (r = .50). Therefore, it appears that quick learning and effort regulation probably shared variance in common with expectan- cy and, as a result, were weaker predictors of final grade than was the expectancy for control of leaming subfactor. As a result, expectancy acted as an "umbrella" term that represented the other correlates of the dependent variable in the predictive model of leaming achievement in asynchronous online undergraduate courses.

Although previous research has demon- strated that self efficacy for computer use, reason for taking an online course, and parental level of education are associated with academic success in web based leam- ing, these 2 factors did not contribute to the current study's predictive model of leam- ing achievement in asynchronous web based undergraduate courses. Plausible explanations for why this might have occurred follow below.

Computer self efficacy Most of the participants in the present

study were traditional undergraduates (mean age was 22.4) who, unlike the under- graduate cohorts of earlier research studies, grew up with computers and Intemet tools since an early age. There was very little variation in the computer technology self- efficacy scores for this cohort as most students self-rated high on their computer self efficacy. This is not surprising as they are representative of a generation of stu- dents who have grown up using computer and Intemet technology. Self-efficacy is a function of one's experience. Therefore the more experience one has with com-

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puter use, the more self efficacious she should feel regarding computer technolo- gy. Moreover, use of computer technology such as the Internet and e-mail has explod- ed within the past five years such that the current undergraduates have arrived at col- lege computer literate and fluent in the use of Internet tools. Therefore, it is likely that there was much less variation among the present cohort of undergraduates compared to those in earlier studies hecause the cur- rent students were learning to use computers and the Internet in middle school and high school while the study cohorts in previous research studies were probably first learning to use the technol-

. . . . , , 2 S J O

ogy while m college

Reason for taking an online course The lack of a statistically significant

association between reason for taking an online course and final course grade in the current research study may have occurred because of a general lack of variation in the final grades earned by the individuals in this study. For example, as noted earlier, the sample of 201 students had a mean final grade of 86.63 and SD =13.31. It is important to consider that almost 57% of the students in this sample earned a numer- ical final course grade that was equal to or greater than 90. If there had been a greater number of students in the study sample as well as a wider variability in final course grade scores, then perhaps reason for tak- ing a course online might have been a predictor of learning achievement among undergraduates taking asynchronous online courses.

Although not statistically associated with final grade, reason for taking an online

course was associated with expectancy for learning. There was a significant differ- ence in the mean score of expectancy for control of learning based on individual learner's category of reason for taking an online course. Analysis of a one way ANOVA between groups design revealed a significant effect for reason for taking an online course on individual expectan- cy for learning, F(2, 198) = 4.07, p = .02. Furthermore, a Tukey's studentized range test demonstrated that individuals who stat- ed that the reason they decided to study online was in order to satisfy their curios- ity or interest in learning via the electronic medium had statistically significant high- er expectancy for learning scores than either those who stated that learning online was more convenient for them than taking a traditional face-to-face course or those who reported that they had no option. This finding may explain why in previous research studies students who actively chose to study online tended to achieve greater academic success as online learn- ers. This is because such individuals probably also had higher expectancy for their learning and as a result were more likely to realize greater learning achieve- ment than other students. As stated earlier, expectancy for learning appeared to serve as an umbrella term for other self regulat- ed learning and epistemological belief factors in the predictive model. Expectan- cy may have also acted to represent reason for taking an online course in the predic- tive model of learning achievement in asynchronous undergraduate courses.

Parental level of education Previous studies suggest that first gen-

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eration college students may be less inclined to self regulate their learning in learner controlled environments such as asynchronous web based instruction. For example, Williams & Hellman, (2004) sur- veyed undergraduate students enrolled in asynchronous online courses at a mid west- em institution regarding their level of self regulated learning. They found that first generation students reported significantly lower levels of SRL than their peers. The researchers concluded that the observed differences in self regulation between the two groups explained the observed differ- ences in success rates for online learning between first and second generation learn- ers. However, despite these previous findings, in the current study there was no significant difference between the mean final course grade earned by first genera- tion students and other students in the sample. This suggests that for the current study sample parental level of education did not explain variation in learning achievement. In fact, an analysis of a one way ANOVA between groups design revealed no significant effect for parental level of education on prior academic achievement (GPA), F ( l , 199) = 3.66, p=.O7.

Moreover, in the current study first gen- erational college students and students from families whose parents had previous col- lege experience had similar mean expectancy scores. An analysis of a one way ANOVA between groups design revealed no significant effect for parental level of education on expectancy for learn- ing, F (1,199) = 0.27, p = .60. Therefore, whether referring to the entire sample or to the sub-sample of first generational col-

lege students, variation in learning achieve- ment was best explained by the expectancy for learning variable rather than by parental level of education.

This study's findings suggest, then, that expectancy, or an expectation that one will experience positive outcomes in one's learning, is a central driving force for self- regulation. Moreover, Bandura and others have underscored the role played by individual self-efficacy in facilitating expectancy for learning. Therefore, an individual with strong expectancy for learn- ing possesses the "can do" attitude required to succeed in learning. Such an attitude is the product of positive reinforcement and explains the mutually positive or syner- gistic relationship not only between prior academic achievement and expectancy, but also between expectancy and other self- regulated learning and epistemological belief subfactors.

For example, it would appear that because expectancy was the only subfac- tor to make it into the predictive model, it acted as a global factor or "proxy" that rep- resented the other SRL subfactor (effort regulation) and the epistemological belief subfactor (quick learning) in the predic- tive model. This observation is reasonable because strong expectancy for learning depends on having other positive attitudes and behaviors consistent with success in learning. It is as though once an individ- ual expects positive outcomes for his learning and takes responsibility for his learning, he will do what it takes (such as regulate his effort accordingly and apply appropriate time and study management strategies) in order to be a successful learn- er. Furthermore, such an individual is

Predictors of College.../ 531

probably more likely than others to choose to leam in a learner controlled environ- ment such as an asynchronous web based course.

Thus, it is unlikely that a multiple regression equation that already contained expectancy would need other variables like quick learning, and effort regulation, in order to improve the accuracy of its pre- dictive power; any variance in final grade due to effort regulation and quick learn- ing had probably already been accounted for by expectancy. Furthermore, if one's reason for taking an online course is a func- tion of one's expectancy for control of learning, then this suggests that the expectancy variable is a more accurate pre- dictor of learning success in a learner controlled environment, such as the asyn- chronous medium, than is reason for taking an online course. The finding that parental level of education did not correlate with final course grade demonstrated that it was not a predictor of learning achievement in the current study. The fact that first gen- eration college students' mean scores for expectancy for learning were similar to those of their colleagues suggests that parental level of education was not a valid determinant of individual self regulated learning. They, further, suggest that expectancy for learning is an important predictor of learning achievement in asyn- chronous learning environments regardless of one's first generational college student status.

College has traditionally been a stage of education where individuals must assume greater responsibility for their learning compared to the primary and sec- ondary schooling experiences. Moreover,

today's college student is faced with an even greater need to be able to assume responsibility for his learning because more undergraduate courses and programs are being delivered via asynchronous online environments. This study's findings sug- gest that individuals with the greatest expectancy for learning, regardless of their prior academic achievement, were the most successful asynchronous online learners. Nevertheless, expectancy for learning appears to be a learner characteristic that is molded and shaped by previous acade- mic learning experiences. Therefore, in order to ensure academic learning success, it behooves responsible educators to ensure that students who enter college are armed with strong expectancy for controlling their learning.

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