Editor’s Note: This study determines a significant correlation between three predictors of success in online learning. A large study population and exemplary design provide convincing support for the findings and conclusions.

Can Factors Related to Self-Regulated Learning
Predict Learning Achievement in Undergraduate Asynchronous Web-based Courses?

Paul D. Bell, Duane Akroyd

Abstract

This study examined the effect of self-regulated learning (SRL) on individual learner levels of academic achievement in Web-based learning environments while holding constant the effect of computer self-efficacy, reason for taking an online course, and prior college academic achievement. The study constituents 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: exploratory factor analyses of the self-regulated learning question items, correlations 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 variables (GPA, Expectancy, and GPA_Exp) were significant predictors in the model of learning achievement in asynchronous online courses. Discussion of the study’s predictive model follows.

Keywords: self-regulated learning, asynchronous Web-based learning, online learning, expectancy for learning, regression analysis, learning achievement.

Introduction

Increasingly, public institutions of higher learning are adding asynchronous Web-based instruction to their undergraduate degree programs. Although online learning has been hailed as the next revolution in access to higher education, many undergraduate learners (late adolescent students 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 control and self-direction than traditional classroom-based instruction. These demands are representative of higher levels of intellectual development that “may well be unattainable during the late adolescent years”.¹

There is little research from the asynchronous online learning literature that examines the relationship between learner control and self-monitoring and successful learning in AWBL environments. However, recent research in educational psychology has identified self-regulation of learning as a key characteristic that appears to be related to academic success in learner-controlled environments such as online courses. ^2-4

Self-regulated learning (SRL) is an element of social cognitive learning theory that states that learner behaviors and motivations as well as aspects of the learning environment affect learner achievement.^5-7 Some experts have argued that self-regulation of learning (SRL) has a positive influence on academic success.^8-12

The majority of the SRL research literature is composed of theoretical work that has made convincing arguments for why SRL should influence learner achievement. On the other hand, empirical studies een conducted in both traditional classroom and computer-based settings have yielded limited results concerning the effects of SRL on student achievement.

Purpose of the Study

There are relatively few studies that have used predictive modeling in order to explain the effect of self-regulated learning (SRL) on learner achievement in asynchronous Web-based environments. In addition, these investigations have varied in the number and types of covariate factors included in the final models. For example, the asynchronous online learning literature indicates that other factors such as reason for taking an online course, self-efficacy for using computer technology, and prior academic achievement influence learning achievement in online courses.

The purpose of the current study, then, was to examine the effect of SRL on individual levels of achievement in an asynchronous Web-based learning environment 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-regulated learning, reason for taking an online course, computer self-efficacy, and prior academic achievement (GPA) on final grade in asynchronous undergraduate online college courses?

Participants

The site of the present study was a coeducational public university situated in the southeastern region of the United States. According to registrar records, approximately 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 recruitment e-mail. Finally, 201 individuals from this group completed the study questionnaire. Students ranged in age from 18 to 50 with a mean age of 22.4 S.D. 6.14. Survey 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 American. The remaining 5 percent of the sample self-reported as either Asian American/Pacific Islander, mixed race, or Hispanic. Of the students sampled, 46 percent (n = 93) had no prior experience taking online courses, while 54 percent (n = 108) had taken at least one online course previously.

Data collection occurred during the spring 2005 academic semester.

Materials

Data was collected via a Web-based self-report inventory consisting of question items designed to assess participant ratings on the variables that were the focus of current study.

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 learning. These include: (a) being intrinsically motivated to reach goals, (b) expecting that one’s efforts to learn will result in positive outcomes, (c) expecting to succeed in one’s learning, (d) being confident in one’s ability to perform and complete an academic task, (e) monitoring one’s progress toward goal completion, (f) controlling one’s effort and attention, and (g) managing time and place resources for learning and studying. Self-regulated learning theory argues that these conditions must be present before students can successfully employ cognitive strategies in their learning. Moreover, according to Pintrich, Smith, Garcia and McKeachie (1991),¹³ the Motivational Strategies for Learning Questionnaire (MSLQ) scales “are designed to be modular and can be used to fit the needs of researchers.” Therefore, 24 Likert-scaled question items were taken from the MSLQ to assess participant ratings on self regulated-learning subfactors targeted by the current study.

The survey instrument also included questions related to the covariates as follows: (a) two Likert-scaled question items were included that referenced the study participants’ self-efficacy for computer usage, and (b) a short answer question item was included that referenced the study participant’s reasons for taking the online course, and (c) each participant’s grade 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. Please see Appendix A for a list of the question items from the study survey.

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) self-efficacy for computer technology, (c) reason for taking a Web-based course, and (d) prior college academic achievement.

The following steps describe how the data was analyzed to reveal the predictive ability of the SRL subfactors on academic achievement.

1.  Despite published claims of validity and reliability for the original instrument,^13, the first step was to run a factor analysis of the self-regulated learning question items in order to establish their factor structure in the current study. Factor internal reliability coefficients obtained for the self-regulated learning subfactors were then compared with those obtained for the original instrument.

2. Next, a correlation matrix of the independent variables (the SRL subfactors as well as the study covariates) and the dependent variable was generated. An analysis of the matrix determined which of the independent variables were correlated with the dependent variable and which were correlated with each other.

3. Finally, a multiple regression analysis of the predictor variables 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 levels participated in this study, juniors and seniors accounted for about two-thirds (64.7 percent) of the sample. Final course grades ranged from 0–100 (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).

Exploratory factor analysis of the self-regulated learning survey items yielded the following factor structures: three subfactors—expectancy, intrinsic goal orientation, and resource regulation. These subfactors paralleled those yielded by the original instrument and their reliability estimates can be compared to their counterparts in the original instrument. Please refer to Table 1.

Table 1

Means, Standard Deviations, Intercorrelations, and Coefficient Alpha
Reliability Estimates

Note. N = 201. Reliability estimates appear on the diagonal. Estimates in parentheses are for the current sample after factor analysis, those in brackets are from original researchers’ instruments.
*p< .05, **p< .01 , ***p< .001

Survey participants fell into three categories 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 electronic medium. See Table 2.

Table 2

Final Grade Based on Reason for Taking the Course (N = 201)

Student responses to two survey questions about self-efficacy for the use of computer technology were added together 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 measured using the current semester GPA. The mean GPA for the sample was 3.01 and the SD 0.63. See Table 1.

Interaction term. In the current study, GPA and expectancy for learning were found to be moderately correlated (r = .3) (see Table 1). This r value as well as literature-based evidence for their positive correlation^5,7,14-17 drove the decision to create an interaction term, consisting of the cross product of the variable that measured prior college academic achievement (GPA) and the variable that measured individual expectancy for learning (ExpSE_sum). This new variable was labeled GPA_Exp and was included in the predictive model.¹⁸

Mean standard deviations, Pearson correlations, and coefficient alpha reliability estimates for the study’s independent variables appear in Table 1. Coefficients ≥.1 were considered indicative of a correlation between a particular predictor variable and the dependent variable. Therefore, based on this criterion, the following bivariate correlations revealed five predictor variables significantly related to learning achievement: (a) interaction of GPA and expectancy (r = .52), (b) prior college achievement as measured by GPA (r = .40), (c) expectancy (r = .39), and (d) effort regulation (r = .32. 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 combination of all the variables in the model. These seven variables included (a) prior academic achievement (GPA), (b) computer self-efficacy (Comp_SE), (c) intrinsic goal orientation (IGO_sum), (d) resource management (TPEffreg), (e) expectancy (ExpSE_sum), (f) reason for taking an online course (reason_ol), and (g) the interaction between GPA and expectancy (GPA_Exp). Table 3 depicts the prediction of final grade based on the full model.

Table 3

Prediction of Final Grade Based on the Full Model (N = 201)

Full Model: F 7, 193 = 17.12, p = .0001. adj R² = .361.

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