Student success prediction in MOOCs

Abstract

Predictive models of student success in Massive Open Online Courses (MOOCs) are a critical component of effective content personalization and adaptive interventions. In this article we review the state of the art in predictive models of student success in MOOCs and present a categorization of MOOC research according to the predictors (features), prediction (outcomes), and underlying theoretical model. We critically survey work across each category, providing data on the raw data source, feature engineering, statistical model, evaluation method, prediction architecture, and other aspects of these experiments. Such a review is particularly useful given the rapid expansion of predictive modeling research in MOOCs since the emergence of major MOOC platforms in 2012. This survey reveals several key methodological gaps, which include extensive filtering of experimental subpopulations, ineffective student model evaluation, and the use of experimental data which would be unavailable for real-world student success prediction and intervention, which is the ultimate goal of such models. Finally, we highlight opportunities for future research, which include temporal modeling, research bridging predictive and explanatory student models, work which contributes to learning theory, and evaluating long-term learner success in MOOCs.

Transfer Learning using Representation Learning in Massive Open Online Courses

“…transfer with the baselines (Label-Truth, Label-Truth-AE, Naive Transfer, In-Situ Learning, and Instance-Based Transfer), for the similar pairs of source and target 6.00.1x→6.00.1x (within offerings of one course) are shown in Table 4 and Figure 8a, and the dissimilar pairs 6.00.2x → 6.00.1x (across two courses) in Table 5 and Figure 8b. Note that the performance of In-Situ Learning and 8. It shows that Naive Transfer overfits to the source domain from week 5.…”

“…Feature identification is a critical precursor to prediction [8]. Some human selected and engineered features are page views, video interactions, forum posts, and content interactions.…”

MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data

“…A key area of research has been methods for feature engineering, or extracting structured information from raw data (i.e. clickstream server logs, natural language in discussion posts) [8].…”

Evaluating Predictive Models of Student Success: Closing the Methodological Gap

“…In the survey presented above and in other work [21], we have described the common practices of predictive modeling experiments in learning analytics. These include (a) a massive space of potential models due to many data sources, feature types, and algorithms used; (b) relatively small collections of datasets, for example, even the largest prior MOOC studies of which we are aware evaluate around 40 MOOCs (i.e., [47,17]) and (c) large individual datasets, which make repeated model-fitting undesirable, if not intractable.…”

“…We consider the following models in our experiment: (1) classical decision trees (CART) [7], (2) L2 (or "ridge") regularized logistic regression (L2LR); (3) gradient boosted tree (Adaboost) [12], used as a a stand-in for the widely used [21] random forest method 5 ; (4) support vector machine (SVM) with linear kernel; (5) naïve Bayes (NB). These represent five of the most commonly used modeling algorithm in predictive models of student success in MOOCs [21]. A summary of the models considered, and any special preprocessing, is shown in Table 4.…”