Visualizing student calibration by developing tag-enhanced open learner models: towards self-regulated learning

Angra, Suneer

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https://hdl.handle.net/2142/113097 Copy

Description

Title

Visualizing student calibration by developing tag-enhanced open learner models: towards self-regulated learning

Author(s)

Angra, Suneer

Issue Date

2021-07-22

Director of Research (if dissertation) or Advisor (if thesis)

• Herman, Geoffrey Lindsay
• Amos, Jennifer

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Self-Regulated learning
• Open learner models
• Student calibration
• Digital tagging

Abstract

In near future we might be facing increase in class diversity, increase in class size, and a shift to online platforms. Moreover, current COVID crisis highlighted inequities in access to education as it moved online. In such scenarios students’ ability to self-regulate their learning and instructors’ ability to dynamically adapt their teaching become important. But current assessment techniques struggle to facilitate adaptive teaching or to promote self-directed learning. Knowledge monitoring or accurate self-evaluation is critical for self-regulation of learning. Towards this, we explore how asking students to self-evaluate and visualizing their calibration effects their calibration accuracy, performance, and self-regulated learning. Calibration describes the relationship between the learner’s performance and the perception of their performance on a task. We leverage Open Learner Models to create and share such visualizations. Also, introducing descriptive digital tags into assessment material is a useful method to effectively organize and analyze both group and individual student performance information. Furthermore, tagged assessment and instructional materials allow for categorical and chronological grouping of student data, thus allowing for creating visualizations which can help students better understand and monitor their own learning. This extracted information can also be used by the instructors to verify and predict their intended learning objectives. We added digital tags to create tag-organized assessments for an undergraduate bioengineering course. The assessments also prompted students to self-evaluate after each question. When students were weekly presented tag-enhanced OLMs, one visualizing their calibration and other their relative performance to class, there was an improvement in calibration accuracy moving from unit 1 to unit 2 but it was not sustained in unit 3. Also, there was a significant increase in self-regulatory learning skills of students with high variation in performance as compared to students with consistent performance.

Graduation Semester

2021-08

Type of Resource

Thesis

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http://hdl.handle.net/2142/113097

Copyright and License Information

Copyright 2021 Suneer Angra

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