Understanding Student Continuance Intention in MOOC-based Blended Courses:A Learner Profile Approach

Session Information

ERG SES H 01, Challenges in Education

Paper Session

Time:
2016-08-23
11:00-12:30
Room:
OB-E1.17 (ALE 1)
Chair:
Ninetta Santoro

Contribution

Introduction
Picciano (2014) argues that the 4th wave of online education and blended learning has arrived, which is characterized by leveraging externally developed MOOC content together with a variety of new tools to create more personalized and effective blended learning experiences for learners. In comparison to the familiar concept of blended learning, MOOC-based blended learning it requires learners to be more self-regulated to succeed in MOOC-based learning environments (Milligan, et al. 2013). Motivation is one of key aspects that underlies self-regulated learning (Kuo et al., 2013). It has been proved to be a key factor that influences student learning and achievement in traditional and online learning environments alike.

Motivation is considered a multidimensional construct (Vallerand, 1997). A useful conceptual framework that helps us gain a complete understanding of motivation is to deal with it from intrinsic motivation, extrinsic motivation and amotivation (Deci & Ryan, 1985; Vallerand, et al. 1989). Intrinsic motivation can be further decomposed into intrinsic motivation to know, to accomplish things and experience stimulation (Vallerand et al., 1989). Intrinsic motivation is to know is closely related to goal orientation, a construct of specific importance to learning. Goal orientation describes the reasons for doing a task. Generally, there are two types of goals: mastery goal and performance goal (Rawsthorne & Elliot, 1999). Intrinsic motivation to know is similar to mastery goal. Mastery goal concerns learning or mastering the task. Research indicates that mastery goals are positively related to adaptive cognitive and self-regulatory strategy use as well as actual achievement in college classroom.

Extrinsic motivation describes doing an activity for outcomes not directly derived from the activity (Ryan & Deci, 2000). In examining learner motivation in a technology-enhanced learning context, we need to take into account the influence of the technology adopted. Curiosity in new features of e-learning system can foster motivation to learn (Egan & Gibb, 1997). In this study, extrinsic goal is conceptualized as advantages related to the MOOCs-based blended learning as a new learning mode.

In order to fully understand human behavior, Dec and Ryan (1985) proposed an amotivation construct to be added to the intrinsic and extrinsic dichotomy. Amotivation describes the state when people have no intention to act (Ryan & Deci, 2000).

Some studies tried to understand motivation and behavior of MOOC learners. For example, Wang and Baker(2015) explored the motivation difference between MOOC completers and non-completers. They found that both MOOC completers and non-completers initially started out with strong mastery goals and completers showed more interest in the course content and non-completers were more attracted by MOOCs as a type of learning experience.

Research objective and questions
The current study took a person-centered approach to explore motivation and learning strategy use of a sample of university students in MOOC-based blended courses. Using cluster analysis on a combination of goal orientations and general metacognitive self-regulation, we tried to see what kind of learner SRL profiles existed within this student sample and how each cluster group differed in their goal orientation and use of metacognitive self-regulation. Then learner profiles were further explored to explain student online grades and continuance intention. The specific research questions:
(1). What kind of learner profiles can be identified among students in MOOC-based blended courses?
(2). How do learners with different profiles differ in their online scores and intention to participate in MOOC-based blended course in the future?

Method

Research context and participants Participants of this study consisted of undergraduate students enrolled in two MOOC-based hybrid elective courses at a north-western Chinese university. The two mathematics-related MOOCs were developed by Chinese university and hosted on a local Chinese online learning platform. Students could choose to sign up for maximum two MOOCs out of five MOOCs approved by the university. They learned the MOOCs at their own pace and came to class for group discussions and webinars with the facilitation of the on-campus instructors. During the webinars students could interact with the MOOC instructor. The elective MOOC-based blended courses were six weeks long including three group discussion sessions and three webinars. The two MOOCs were taught by the same virtual instructor but facilitated by different on-campus instructors. With the permission of and assistance from the on-campus instructors, an online survey link was distributed by them to their students. Items were translated into Chinese. Of the 194 students enrolled in the two MOOC-based blended courses, 138 completed the online survey. After data screening for missing value and outliers, 110 questionnaires were retained. There were more male students (N=94) than female students (N=14). Profile approach A recent trend in understanding learner motivation and self-regulated learning is the use of profile approach (Nelson, et al. 2015). In contrast to variable-centered approach, a profile approach focuses on individuals in a study.It allows researchers to understand motivational variables and learning strategy variables together (Nelson, et al. 2015). Two motivational belief variables, mastery goal and extrinsic goal, as well as metacognitive self-regulation from were used as profile variables to construct learner profiles in MOOC-based blended learning courses. Instrumentation The survey included questions on demographics, three SRL subcomponents, student continuance intention. Demographic questions covered gender and prior online learning experience. Items adapted from prior research were used to measure the subcomponents of SRL. All the survey items used a 5-point Likert-type scale (1= Not at all agree, 5=strongly agree) The goal orientation construct included mastery goal and extrinsic goal: a) a three-item mastery goal orientation subscale adapted from mastery approach goals of Achievement Goal Questionnaire-Revised (AGQ-R) by Elliot and Murayama (2008) ; and b) three-item extrinsic goals adapted from Wang and Baker (2015) to reflect advantages of MOOC-based blended learning. Metacognitive self-regulation (N=6) were assessed with items adapted from Artino and Stephens (2009). Continuance intention to enroll in future MOOC-based blended courses was assessed with a single item.

Expected Outcomes

First, partial least squares (PLS) using SmartPLS 2.0was used to analyze the data and compute the reliability and validity of the goal orientation and metacognition constructs. The two goal orientation subscales and metacognitive self-regulation subscale exceeded the recommended threshold values recommended by by Fornell and Larcker (1981) for assessment of measurement model in terms of convergent validity, discriminant and reliability. Next, a hierarchical cluster analysis using Ward’s method was conducted using SPSS 19.0 on latent factor scores of scores of mastery goal, MOOC advantages and metacognitive self-regulation to determine subgroups within the sample. Based on the agglomeration coefficients and dendrogram, a four-cluster solution was deemed appropriate. Cluster 1 (N=24) was labelled MOOC motivators. Students in this group were extrinsically motivated by relative advantages of MOOC-based blended learning and they did not use a lot of metacognitive strategies. Cluster 2 (N=44) was labelled mastery goal students who were primarily driven by the goal to master the MOOC and used a lot of metacognitive. Cluster 3 (N=19) was labelled amotivators who generally lacked motivation to learn. Students in the last Cluster (N=23) group was the most adaptive learners who were appreciated the advantages of MOOC-based learning and determined to master the MOOC content. A series of ANOVAs indicated that each subscale significantly varied between clusters. Then, we examine if the four clusters differed significantly in their online scores and intention to continue participation in MOOC-based blended courses. One-way ANOVA indicated that the four clusters did not differ significantly in their online scores. As for course continuance intention, amotivators (Cluster 3) and most adaptive students (Cluster 4) differed significantly from each other and with the other groups. However, extrinsically (Cluster 1) and intrinsically (Cluster 2) motivated students did not differ much between each other.

References

Artino, A. R., & Stephens, J. M. (2009). Beyond grades in online learning: Adaptive profiles of academic self-regulation among naval academy undergraduates. Journal of Advanced Academics, 20(4), 568-601. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Egan, M., & Gibb, G. (1997). Student-centered instruction for the design of telecourses. In T. Cyrs (Ed.), Teaching and learning at a distance: What it takes to effectively design, deliver, and evaluate programs (pp. 33-39). San Francisco, CA: Jossey-Bass. Elliot, A. J., & Murayama, K. (2008). On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology, 100 (3), 613-628. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50. Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distributed Learning, 14(1), 16-39. Milligan, C., Margaryan, A., & Littlejohn, A. (2013). Patterns of engagement in massive open online courses. Journal of Online Learning with Technology, 9(2), 1-11. Nelson, K. G., Shell, D. F., Husman, J., Fishman, E. J., & Soh, L. K. (2015). Motivational and Self‐Regulated Learning Profiles of Students Taking a Foundational Engineering Course. Journal of Engineering Education, 104(1), 74-100. Picciano, A. G. (2014). A critical reflection of the current research in online and blended learning. Lifelong Learning in Europe (LLinE), 4. Rawsthorne, L. J., & Elliot, A. J. (1999). Achievement goals and intrinsic motivation: A meta-analytic review. Personality and Social Psychology Review, 3(4), 326-344. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 25(1), 54-67. Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 29, pp. 271–360). San Diego, CA: Academic Press. Vallerand, R. J., Blais, M. R., BriBre, N. M., & Pelletier, L. G. (1989). Construction et validation de 1'Echelle de Motivation en Education [Construction and validation of the Academic Motivation Scale]. Canadian Journal of Behavioral Sciences, 21, 323-349. Wang, Y., & Baker, R. (2015). Content or platform: Why do students complete MOOCs?.Journal of Online Learning and Teaching, 11(1), 191-218.

Author Information

Shihua Li (presenting / submitting)
Free Brussels University
Brussels
Free Brussels University, Belgium
Free Brussels University, Belgium
Free Brussels University, Belgium
Xidian University, China

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