Attribution Theory and Online Learning

In my earlier posts, I discussed the ESPRI and the factors associated with predicting student success. My research is looking to examine how we can use the ESPRI to help improve student performance in online courses by addressing areas of weakness in a student’s “soft” skill set. To recap, the four areas covered in the ESPRI are technology self-efficacy, achievement beliefs, organizational skills, and academic risk-taking. In this post, I will discuss ways to intervene with the first two.

To begin, some definitions. Self-efficacy relates to the beliefs about successfully performing academic tasks, while self-concept is the knowledge and perceptions about one’s own academic achievement (Ferla et al., 2009). Attribution theory (Weiner, 1992) relates to the reasons that students attribute academic outcomes, which fall into three categories: locus of control (i.e., are the reasons internal or external to the learner?), stability (i.e., is the reason temporary or lasting?), and controllability (i.e., how it relates to learner persistence).

Simply put, the more the student believes that he or she is in control of the outcome, the more likely the student is to persist, maintain motivation, and change behaviors to improve learning.  Since a large population of online learners are taking courses for credit recovery, or are in an alternative setting due to being unsuccessful in a traditional school, changing mindsets is critical.

But how do you teach that? Can you teach that? Studies have been somewhat scarce and mixed. Walden and Ramey (1983) found that high-risk students who participated in a long-term intervention on internalizing control did see improvement in academic achievement. Robertson’s (2000) review of attribution retraining studies found mixed results, and based on the review recommended that attribution retraining interventions should include additional steps to ensure positive results. In other words, simply telling students to internalize attributions may lead to decreased motivation if they are still unsuccessful. Robertson suggested the inclusion of other learning strategies; thus, if the student is not successful, it could be viewed as a mistake related to the strategy rather than overall ability. Finally, Chodkiewicz and Boyle (2014) provided an overall critique of attribution studies as they relate to education, stating that much of the literature from the field of psychology is conducted in clinical settings, with little taking place in the classroom.

Regarding my current research with online learning, my efforts seem to be in line with Robertson’s recommendations, as we are looking to tackle multiple strategies and not simply student belief systems.


Chodkiewicz, A. R., & Boyle, C. (2014). Exploring the contribution of attribution retraining to student perceptions and the learning process. Educational Psychology in Practice, 30(1), 78-87.

Ferla, J., Valcke, M., & Cai, Y. (2009). Academic self-efficacy and academic self-concept: Reconsidering structural relationships. Learning and Individual Differences, 19(4), 499-505.

Robertson, J. S. (2000). Is Attribution Training a Worthwhile Classroom Intervention For K–12 Students with Learning Difficulties? Educational Psychology Review, 12(1), 111-134.

Walden, T. A., & Ramey, C. T. (1983). Locus of control and academic achievement: Results from a preschool intervention program. Journal of Educational Psychology, 75(3), 347-358.

Weiner, B. (1992). Human Motivation: Metaphors, Theories and Research. Newbury Park, CA: Sage Publications.

Program-level measurements

Continuing our exploration of measures of success in online or blended settings, an important item to add to course-specific factors (identified in my previous post) that reflects program-level success is program completion or graduation rate. Conceptions of student success will vary depending on educational level – K-12, community college, 4-year College, and post-graduate degree – as well as credentialing requirements, but completion of the courses required for graduation is a critical measure of program success.

Concerns about students’ persistence in online or blended programs surfaced shortly after institutions started offering these programs. Rovai (2003) explored research on this phenomenon and described a composite model to explain persistence and attrition in online courses and programs.   

A definition of retention that applies to online or blended programs comes from Boston, Ice and Gibson (2011): “the progressive reenrollment in college, whether continuous from one term to the next or temporarily interrupted and then resumed” (¶ 38). Students in online programs may not complete courses each term, based on lack of resources, changes in their profession, personal or professional commitments, etc., but should eventually complete their degree. 

A broader conception of online program success from Kuh et al. (2006) includes the following factors: “academic achievement, engagement in educationally purposeful activities, satisfaction, acquisition of desired knowledge, skills, and competencies, persistence, attainment of educational objectives, and post college performance” (p. 7). This definition reflects a more comprehensive view of student experiences and expectations in online or blended degree programs. 

A popular measurement of student satisfaction in higher education institutions are student course evaluations (SCE). An analysis of 2-years of SCE data comparing our students’ perceptions of graduate course quality resulted in NO statistically significant differences based on course format – hybrid vs online. 

As part of our evaluation of a blended and online graduate degree in educational technology, we analyzed student success, by format, along with GPA and retention, where retention includes the number of students who withdrew after the term started AND those who received a failing grade in the course. For the 2011-2014 academic years, our graduate courses had retention rates of 98.8% in online courses and 97% in hybrid courses. We are still analyzing measures of student program success and will share that information in a future posting.

Other measurements of student success in online programs include interactions and a sense of community. Interactions are often cited in the research as linked with student satisfaction in online educational experiences and we will explore conceptions of interactions in future postings. Exter et al. (2009) report on a study that measured students’ program-level sense of community and its possible link with overall success. A challenge for those implementing and supporting online programs is how to measure students’ sense of community at the program level and actions that can be taken to improve this affective element of students’ experience.  

Knowing how many students complete an online program and graduate is an important indicator of success. Identifying factors contributed to students’ failure to graduate is equally critical, especially if our goal is to improve graduation rates. This becomes especially salient as the marketplace for online courses and programs grows to include non-traditional educational institutions and stakeholders select programs based on their individual criteria. 

Andy

References

Boston, W.E., Ice, P., & Gibson, A.M. (2011, spring). Comprehensive assessment of student retention in online learning environments. Online Journal of Distance Learning Administration, 14(1). Retrieved from: http://www.westga.edu/~distance/ojdla/spring141/index.php

Exter, M.E., Korkmaz, N., Harlin, N.M., & Bichelmeyer, B.A. (2009). Sense of community within a fully online program: Perspectives of graduate students. The Quarterly Review of Distance Education, 10(2), 177-194.

Kuh, G.D., Kinzie, J., Buckley, J.A., Bridges, B.K., & Hayek, J.C. (2006). What matters to student success: A review of the literature. Commissioned Report for the National Symposium on Postsecondary Student Success: Spearheading a Dialog on Student Success. Available online: http://nces.ed.gov/npec/pdf/kuh_team_report.pdf

Rovai, A.P. (2002). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6, 1-16.

Measuring student success in online or blended courses

Course-level measurements

Continuing our focus in this blog on student success in online and blended settings, we start with a clear and concise definition of what constitutes “success.” Deka & McMurry (2006) offer the following baseline definition: “Two common indices for measuring success are class grade and retention rates” (p. 2). Different educational institutions and stakeholders might have additional criteria they would include in this definition, but these seem appropriate as a starting point. 

We could consider use of GPA instead of letter grade, to make the data easier to manipulate, although this will vary based on institution and educational level. A passing grade (Pass or C/D) might equate with academic success, measured as an indicator of student learning, although receiving credit with a poor grade may ultimately impact student status in a program based on a reduction in overall GPA putting them in academic jeopardy. In our institution, for example, grades below a C require graduate students to retake the course and impact their overall GPA.  
The 2^nd indicator of success - retention rate - offers nuanced uses for how it is measured and what it reflects. Retention might include the number of students who originally enrolled in and completed an online or blended course, or it might exclude those who dropped the course before the term started. In either case, retention is typically measured using a ratio or % of students who successfully completed the course compared with those who did not. 

This is also referred to as persistence in the literature and should consider the number of students who fail the course along with those who do not complete it. Hart (2012), in her review of the literature on student persistence, provides a more nuanced interpretation of persistence, contrasting it with attrition – withdrawal from an online course - and identifying factors that might contribute to persistence in online programs.    

In the K-12 domain, Ronsisvalle and Watkins (2005) identified the following as measures of success in online courses and programs: academic performance (successful completion), retention (enrolling in future courses), academic achievement (performance and grade distribution), and stakeholder satisfaction (parent, student, teacher, etc.)" (p. 122). 

Ultimately, consideration of student success in online or blended courses should lead to questions about factors that influence failure or success. These might include those attributed to a student, to the instructor, and those outside the control of both – i.e., personal situations, parental support, institutional support, LMS, etc. See previous blog postings by Jason for more on identifying students who may struggle in online or blended courses or programs.

In my next post, I will explore program-level measurements of student success in online or blended education. 

Andy

References

Deka, T.S., & McMurry, P. (2006). Student success in face-to-face and distance telecasts environments: A matter of contact? The International Review of Research in Open and Distance Learning, 7(1), 1-15.

Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the literature. Journal of Interactive Online Learning, 11(1), 19-42. 

Ronsisvalle, R., & Watkins, R. (2005). Student success in online K-12 education. Quarterly Review of Distance Education, 6(2), 117-124, 184.