“To Watch or Not to Watch”: Exploring Student Engagement with Online Learning Material

Online learning is everywhere these days, with students engaging with both synchronous and asynchronous learning activities. But how do online student engagement patterns impact their learning? Using a quantitative analysis framework, Li and Tsai examine how different patterns of engagement with online resources correlate with learning performance. They also examine how trends in students’ motivation towards learning may play a role in online engagement.

Reference: 

Li, L. Y., & Tsai, C. C. (2017). Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286-297. https://doi.org/10.1016/j.compedu.2017.07.007

What is this research about?

Learning management systems like Avenue to Learn are used throughout post-secondary education these days. With a shift to online and blended learning, providing ways for students to effectively engage with online learning materials has become even more important.

Everyone is different when it comes to how they learn and the steps they take to study. In their 2017 article, Li and Tsai had questions about how students’ engagement behaviours with online learning material like class slides and recorded lectures would influence learning outcomes. They also wondered how students’ motivations about learning would be connected to their engagement patterns. To answer these questions, they tracked student engagement patterns in a third-year computer science undergraduate course.

What did the researchers do?

The researchers took a quantitative approach to learn more about student engagement with online learning material. They tracked the engagement activities of 59 computer science students in a blended learning course. Blended learning is when a course has both in-person and online learning components. The researchers measured engagement by noting which online resources the students used through the course’s learning management system. They also measured the amount of time spent on a particular resource by tracking the time between opening and closing of resources through the learning management system. Types of resources included class slides, recorded lectures, assignments, and discussion boards.

Student motivation was measured using the Motivated Strategies for Learning Questionnaire, a tool previously used in the literature. Learning performance was measured by student performance on homework assignments and the final exam. The researchers analyzed students’ engagement patterns and their correlation with students’ motivation and learning performance.

What did the researchers find?

Three main clusters of online engagement behaviour were identified:

  • “Consistent use” students who used all learning resources to a great extent;
  • “Slide intensive use” students who used lectures slides to a great extent, but tended not to engage with other resources as often; and
  • “Less use” students who were infrequent in their usage of any of the resources.

Roughly a third of the class fell into each category. Statistically significant differences in student motivation and performance were found between the three groups.

On homework assignments, “consistent use” students had significantly higher scores than the two other groups, although “slide intensive use” students did score significantly better than “less use” students. Both “consistent use” and “slide intensive use” students scored similarly on the final exam, which was higher than “less use” students.

Three types or drivers of motivation were identified that differentiated the groups from each other.

  • Intrinsic Goal Orientation: Internal motivation such as enjoying a challenge, curiosity, or genuine love for the subject. Intrinsic motivation values the process of doing a task, rather than simply being a means to an end.
  • Task Value: The motivation derived from the student’s perception that a learning task is interesting, important, or useful to their studies and future work.
  • Self-efficacy: The student’s judgment of their own ability to complete a task and confidence in their own skills.

“Consistent use” students had higher intrinsic goal orientation and self-efficacy than both other student groups. This is consistent with past literature suggesting that if students enjoy the material and are confident in their ability to complete their assignment, they’ll perform better than peers who do not. The authors suggest that since “consistent use” students engaged with the resources the most, their confidence may have been a consequence of their increased engagement. Alternatively, students who felt more confident about their abilities may have felt more comfortable engaging with the resources. The causality of student confidence or comfort engaging with materials was not examined by the researchers.

“Less use” students had the lowest task value score out of the three groups, suggesting that they viewed assignments in the course as less interesting, important, or useful than their peers. As they had a poorer impression of the assignments, this may explain why they spent less time preparing for them through online engagement.

How can you use this research?

Motivation and students’ engagement patterns with online resources are somewhat of a “chicken and egg” problem: Did a lack of motivation decrease engagement? Or does a lack of engagement decrease motivation? Although changing intrinsic motivation is difficult, instructors can focus on trying to encourage students’ self-efficacy and explicitly promote the task value of assessments.

This research also suggests that providing structure and support for course assignments may increase students’ confidence in their own abilities. One instructional strategy would be to have iterative assignments that build on each other, allowing students to build up their confidence with each task.

In terms of task value, it may not be obvious to students why a particular assignment is important or will be useful to them down the road. Engaging them in an explicit discussion about how what they are learning might apply in practice may increase students’ understanding of its usefulness.

Authors:

Liang-Yi Li, PhD, is an Assistant Professor in the Program of Learning Sciences, National Taiwan Normal University (Taipei, Taiwan).

Chin-Chung Tsai, PhD, is the Chair Professor and Head of the Program of Learning Sciences, National Taiwan Normal University (Taipei, Taiwan). See http://www.cctsai.net/eng/curriculum_vita.html

Reference:

Li, L. Y., & Tsai, C. C. (2017). Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286-297. https://doi.org/10.1016/j.compedu.2017.07.007

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