Evaluating the effect of uncertainty visualisation in open learner models on students’ metacognitive skills

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

  • Lamiya Al-Shanfari
  • Carrie Demmans Epp
  • Chris Baber

Colleges, School and Institutes

External organisations

  • University of Pittsburgh

Abstract

Self-assessment is widely used in open learner models (OLMs) as a metacognitive process to enhance students’ self-regulated learning. Yet little research has investigated the impact of the visualisation when the OLM shows the conflict (i.e., uncertainty) between the system’s beliefs about student knowledge and students’ confidence in the correctness of their answers. We deployed such an OLM and studied its use. The impact of the uncertainty visualisation on student learning, confidence gains and actions was determined by comparing these measures across two treatment conditions and a control condition. Those who accessed the OLM performed significantly better on the post-test, and those in the treatment group who could see both sets of beliefs separately showed greater confidence gains and used the system more.

Bibliographic note

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10331). Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10331).

Details

Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings
Publication statusPublished - 23 Jun 2017
Event18th International Conference on Artificial Intelligence in Education, AIED 2017 - Wuhan, China
Duration: 28 Jun 20171 Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10331 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Artificial Intelligence in Education, AIED 2017
Country/TerritoryChina
CityWuhan
Period28/06/171/07/17

Keywords

  • Learning dashboards, Metacognitive skills, Open learner models, Self-confidence, Uncertainty, Visualisation