Please use this identifier to cite or link to this item:
Title: Using Machine Learning to Understand Students’ Learning Patterns in Simulations
Authors: Jang, Wonkyung
Francisco, Joshua
Ranganathan, Nethra
McCarroll, Kathleen Marley
Ryoo, Kihyun
Keywords: Scale
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Jang, W., Francisco, J., Ranganathan, N., McCarroll, K. M., & Ryoo, K. (2020). Using Machine Learning to Understand Students’ Learning Patterns in Simulations. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 5 (pp. 2593-2596). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: This study explores how unsupervised machine learning (ML) techniques can identify productive learning patterns as students conduct virtual experiments using a simulation. The log data from 52 pairs of eighth-grade students were analyzed using two ML techniques, Finite Mixture Model (FMM) and Sequential Pattern Mining (SPM). The results show four levels of learning patterns (i.e., Low Activity, Random Interaction, High Analysis, Tasked Exploration), each of which have unique, sequential actions. This study shows the potential value of unsupervised ML for understanding which types of interactions with simulations could facilitate students’ understanding of complex scientific phenomena.
Appears in Collections:ICLS 2020

Files in This Item:
File SizeFormat 
2593-2596.pdf280.92 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.