Predicting students' final performance from participation in on-line discussion forums

By Cristóbal Romero, M. I. López, J. M. Luna, Sebastián Ventura. Comput. Educ., 2013

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Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, unknown): “their results are easily interpreted [87].”
Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory (2015, methodology): “Some representation formats, especially the ‘‘if-then’’ rules set, are generally considered to be more easily interpretable than others (Freitas et al., 2010; Huysmans et al., 2011; Romero et al., 2013).”
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, background): “…data recorded by LMSs and other online learning environments (Agudo ‐ Peregrina, Iglesias ‐ Pradas, Conde ‐ González, & Hernández ‐ García, 2014; Romero, López, Luna, & Ventura, 2013; Romero, Ventura, & García, 2008; Zafra, Romero, & Ventura, 2011); and III. combinations of data sources…”
A Systematic Review on Educational Data Mining (2017, background): “Clustering in e-learning 13 [40] To determine the selection of instances and attributes gathered that affect the accuracy and comprehensibility of prediction.”
Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks (2017, background): “[2], [17], [18] and what their final grades will be [19], [20], typically using their past assessment scores.”