Early Prediction of Students' Final Grades in a Gamified Course

Autor/inn/en	Nabizadeh, Amir Hossein; Goncalves, Daniel; Gama, Sandra; Jorge, Joaquim
Titel	Early Prediction of Students' Final Grades in a Gamified Course
Quelle	In: IEEE Transactions on Learning Technologies, 15 (2022) 3, S.311-325 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Nabizadeh, Amir Hossein) ORCID (Goncalves, Daniel) ORCID (Gama, Sandra) ORCID (Jorge, Joaquim)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1109/TLT.2022.3170494
Schlagwörter	College Students; Prediction; Grades (Scholastic); Game Based Learning; Feedback (Response); Electronic Learning; Online Courses; Evaluation Methods; Accuracy; Multivariate Analysis; Cluster Grouping + Suchen Sie Ihr Suchwort? Collegestudent; Vorhersage; Notenspiegel; Online course; Online-Kurs; Multivariate Analyse; Eingruppierung
Abstract	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 analyzing how much effort they need to put into the course to obtain the desired grades while helping course instructors identify student types at the early stages of the course to provide better support for them. Our method, initially clusters students into several groups based on experience points (XP) that they obtain during a semester. Then, we estimate cluster size and balance clusters by generating and adding virtual students to the smaller clusters. Finally, we drop unimportant student attributes using a feature selection technique. We then predict their final grades via three different algorithms. We have compared the performance of our method with other approaches using data collected from a course for nine years, using data collected from 679 students. The results indicate that our method outperformed the others while achieving 78.02% average accuracy only four weeks after starting the course. It shows we can effectively predict final grades, which will potentially enhance students' learning outcomes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01