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Nabizadeh, Amir Hossein; Goncalves, Daniel; Gama, Sandra; Jorge, Joaquim – IEEE Transactions on Learning Technologies, 2022
The main challenge in higher education is student retention. While many methods have been proposed to overcome this challenge, early and continuous feedback can be very effective. In this article, we propose a method for predicting student final grades in a course using only their performance data in the current semester. It assists students in…
Descriptors: College Students, Prediction, Grades (Scholastic), Game Based Learning
Barata, Gabriel; Gama, Sandra; Jorge, Joaquim; Gonçalves, Daniel – IEEE Transactions on Learning Technologies, 2016
State of the art research shows that gamified learning can be used to engage students and help them perform better. However, most studies use a one-size-fits-all approach to gamification, where individual differences and needs are ignored. In a previous study, we identified four types of students attending a gamified college course, characterized…
Descriptors: Prediction, Performance, Profiles, Games
Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta – IEEE Transactions on Learning Technologies, 2017
Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are first grouped into clusters. Then, an…
Descriptors: Student Behavior, Predictive Validity, Predictor Variables, Predictive Measurement