Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content
Information Discovery and Delivery
ISSN: 2398-6247
Article publication date: 19 May 2020
Issue publication date: 10 October 2020
Abstract
Purpose
For studies in educational data mining or learning Analytics, the prediction of student’s performance or early warning is one of the most popular research topics. However, research gaps indicate a paucity of research using machine learning and deep learning (DL) models in predictive analytics that include both behaviors and text analysis.
Design/methodology/approach
This study combined behavioral data and discussion board content to construct early warning models with machine learning and DL algorithms. In total, 680 course sections, 12,869 students and 14,951,368 logs were collected from a K-12 virtual school in the USA. Three rounds of experiments were conducted to demonstrate the effectiveness of the proposed approach.
Findings
The DL model performed better than machine learning models and was able to capture 51% of at-risk students in the eighth week with 86.8% overall accuracy. The combination of behavioral and textual data further improved the model’s performance in both recall and accuracy rates. The total word count is a more general indicator than the textual content feature. Successful students showed more words in analytic, and at-risk students showed more words in authentic when text was imported into a linguistic function word analysis tool. The balanced threshold was 0.315, which can capture up to 59% of at-risk students.
Originality/value
The results of this exploratory study indicate that the use of student behaviors and text in a DL approach may improve the predictive power of identifying at-risk learners early enough in the learning process to allow for interventions that can change the course of their trajectory.
Keywords
Acknowledgements
Funding: This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
Declarations of interest: The researchers were supported in this study with a fellowship from the Michigan Virtual Learning Research Institute.
Citation
Hung, J.-L., Rice, K., Kepka, J. and Yang, J. (2020), "Improving predictive power through deep learning analysis of K-12 online student behaviors and discussion board content", Information Discovery and Delivery, Vol. 48 No. 4, pp. 199-212. https://doi.org/10.1108/IDD-02-2020-0019
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited