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A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems

Item

Title
A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems
Abstract/Description
Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we describe a generalizable approach for more efficient yet rich sensemaking of temporal data during student use of intelligent tutoring systems. This multi-step approach involves using coarse-grain temporality — learning trajectories across knowledge components — to identify and further explore “focal” moments worthy of more fine-grain, context-rich analysis. We discuss the application of this approach to data collected from a classroom study in which students engaged in a Chemistry Virtual Lab tutoring system. We show that the application of this multi-step approach efficiently led to interpretable and actionable insights while making use of the richness of the available data. This method is generalizable to many types of datasets and can help handle large volumes of rich data at multiple levels of granularity. We argue that it can be a valuable approach to tackling some of the most prohibitive methodological challenges involved in temporal learning analytics
Author/creator
Liu, Ran
Stamper, John C.
Davenport, Jodi
Date
2018
In publication
Journal of Learning Analytics
Volume
5
Issue
1
Pages
41-54
Resource type
en Research/Scholarly Media
Resource status/form
en Published Text
Scholarship genre
en Empirical
Keywords
mixed methods
Language
en
Open access/full-text available
en Yes
Peer reviewed
en Yes
ISSN
1929-7750
DOI
10.18608/jla.2018.51.4
URL
Open Access Journal Webpage (Journal of Learning Analytics)
Citation
Liu, R., Stamper, J. C., & Davenport, J. (2018). A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems. Journal of Learning Analytics, 5(1), Article 1. https://doi.org/10.18608/jla.2018.51.4
Rights
Copyright (c) 2018 Journal of Learning Analytics
Item sets
Handbook Chapter 20 Citations

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