Visualising alignment to support students\u2019 judgment of confidence in open learner models

Lamiya Al-Shanfari,Mahvish Nazir,Chris Baber,Carrie Demmans Epp

doi:10.1007/s11257-019-09253-4

Lamiya Al-Shanfari, Mahvish Nazir + Show 2 more

Open Access

https://doi.org/10.1007/s11257-019-09253-4

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Abstract

Knowledge monitoring is a component of metacognition which can help students regulate their own learning. In adaptive learning software, the system’s model of the student can be presented as an open learner model (OLM) which is intended to enable monitoring processes. We explore how presenting alignment, between students’ self-assessed confidence and the system’s model of the student, supports knowledge monitoring. When students can see their confidence and their performance (either combined within one skill meter or expanded as two separate skill meters), their knowledge monitoring and performance improves, particularly for low-achieving students. These results indicate the importance of communicating the alignment between the system’s evaluation of student performance and student confidence in the correctness of their answers as a means to support metacognitive skills.

Highlights

Knowledge monitoring is a required component of metacognitive skills
A post hoc, paired sample t test was conducted to explore these changes in student confidence for each condition classified by low- and high-achieving students
This shows that students from the control condition had a large change in their tendency to be overconfident in Set 1 to being slightly underconfident in Set 2

Summary

Introduction

Knowledge monitoring (the ability to distinguish between what one knows or does not know) is a required component of metacognitive skills. The main reasons for opening the learner model are to promote metacognitive behaviour, support students with self-regulated learning, and promote reflection (Bull and Kay 2013): the goal being to help students with knowledge monitoring by having them use open learner models (OLMs). Skill meters, which resemble bar charts, are the most commonly employed form of OLM visualisation (Demmans Epp and Bull 2015; Mitrovic and Martin 2007; Weber and Brusilovsky 2001). They tend to be the most used visualisation when students can choose how their model will be visualised (Bull et al 2013, 2014a, b)

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