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.
In Revolution in Higher Education, Georgia Tech Professor Richard DeMillo contrasts the rapid innovations made in online learning by a small band of interested, outside-of-academia adventurers with the slow progress made within the ivory tower. DeMillo follows innovators with now-familiar names and projects like Coursera and Udacity through their startup phases, describing motives and methods, with an insider's view at their effects on college learning. Then, after a thorough lesson in the history of the academy, DeMillo argues that adherence to academic tradition will not save higher education. In fact, he says, only flexibility and speed can help colleges and universities meet the challenges of today and the inevitable disintegration of higher education as we know it of tomorrow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.