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Understanding and Supporting Academic Attribution in Online Learning Using Clickstream Data

  • Author(s): Li, Qiujie
  • Advisor(s): Baker, Rachel
  • Warschauer, Mark
  • et al.
Abstract

Colleges offer online courses as a cost-effective way to enhance the accessibility of higher education. However, one major concern about online learning has been the lack of student engagement. Attribution theory proposes that students will encounter great difficulties maintaining their motivation and engagement when experiencing failures in a course and that the causal factors, such as insufficient effort versus low ability, that students use to interpret their failures may influence how much subsequent effort students will expend and what learning strategies they will use in the course. Some studies have applied attribution theory to online learning, but very few of them have considered the unique challenges that may prevent online students from adopting more adaptive attributions when experiencing poor performance. There is also a lack of studies that take advantage of the rich and nuanced clickstream data collected in online learning environments to understand the role of attribution and to support online students in the process of seeking reasons for their poor performance and making productive adjustments.

To address these gaps, this dissertation uses clickstream data collected from online learning platforms to explore the behavioral consequences of attributions and to develop and evaluate the effect of an attribution intervention. In Chapter 1, I attempted to identify valid clickstream measures of self-regulated learning behaviors, which can then be used to examine the behavioral consequences of attribution. In Chapter 2, I used these clickstream measures to investigate the relationships between students’ attribution, expectancy, and changes in students’ subsequent behavior and performance in online learning. Lastly, in Chapter 3, I used clickstream data to build an informational intervention that aimed to correct students’ attributional biases and encourage students to attribute their performance to effort. I expect that my dissertation will lead to a deeper understanding of the role of attribution processes and provide guidance on the design of automatic and light touch attribution interventions in online learning. Moreover, my dissertation provides a novel example of and useful tools for using the clickstream data available in online learning environments to examine and support motivational processes, which is less feasible in in-person educational settings.

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