VISUALIZING STUDENT CALIBRATION BY DEVELOPING TAG-ENHANCED OPEN LEARNER MODELS: TOWARDS SELF-REGULATED LEARNING
Suneer Angra, Drashti Sikligar, Colin Castleberry, Jennifer R. Amos · 2024
In the near future we might be facing an increase in class diversity, increase in class size, and shift to online platforms.Moreover, the 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-regulated learning.Accurate knowledge monitoring or self-evaluation is a critical part of self-regulated learning.Calibration describes the relationship between the learner's performance and the perception of their performance on a task.Towards this, we explore how asking students to self-evaluate and visualizing their calibration affects their calibration accuracy over time.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 for such visualizations.We added digital tags to create tag-organized assessments for an undergraduate bioengineering course.The assessments prompted students to self-evaluate after each question.When students were weekly presented with tag-enhanced OLMs, one visualizing their calibration and other their relative performance to class, there was an overall improvement in calibration accuracy moving from Exam 1 to Exam 3, both for low and high performers.