TY - JOUR
T1 - Improving Predictive Power Through Deep Learning Analysis of K-12 Online Student Behaviors and Discussion Board Content
AU - Hung, Jui-Long
AU - Rice, Kerry
AU - Kepka, Jennifer
AU - Yang, Juan
N1 - Publisher Copyright:
© 2020, Emerald Publishing Limited.
PY - 2020/10/10
Y1 - 2020/10/10
N2 - 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.
AB - 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.
KW - Deep learning
KW - Early warning
KW - Educational data mining
KW - Educational text mining
KW - Optimal threshold
KW - Performance prediction
UR - http://www.scopus.com/inward/record.url?scp=85085037697&partnerID=8YFLogxK
UR - https://scholarworks.boisestate.edu/edtech_facpubs/345
U2 - 10.1108/IDD-02-2020-0019
DO - 10.1108/IDD-02-2020-0019
M3 - Article
SN - 2398-6247
VL - 48
SP - 199
EP - 212
JO - Information Discovery and Delivery
JF - Information Discovery and Delivery
IS - 4
ER -