Predicting Student Actions in a Procedural Training Environment

Autor/inn/en	Riofrio-Luzcando, Diego; Ramirez, Jaime; Berrocal-Lobo, Marta
Titel	Predicting Student Actions in a Procedural Training Environment
Quelle	In: IEEE Transactions on Learning Technologies, 10 (2017), S.463-474 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Riofrio-Luzcando, Diego)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2017.2658569
Schlagwörter	Student Behavior; Predictive Validity; Predictor Variables; Predictive Measurement; Models; Student Records; Cluster Grouping; Tutoring; Feedback (Response); Intelligent Tutoring Systems; Computer Simulation; Biotechnology; Teaching Methods; Program Validation; Data Analysis; Computer System Design; Affective Measures; Foreign Countries; College Students; Spain + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; Prädiktor; Analogiemodell; Schülerakte; Eingruppierung; Förderkonzept; Nachhilfeunterricht; Intelligentes Tutorsystem; Computergrafik; Computersimulation; Biotechnologie; Teaching method; Lehrmethode; Unterrichtsmethode; Auswertung; Ausland; Collegestudent; Spanien
Abstract	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 extended automaton is created for each cluster based on the sequences of events found in the cluster logs. The main objective of this model is to predict the actions of new students for improving the tutoring feedback provided by an intelligent tutoring system. The proposed model has been validated using student logs collected in a 3D virtual laboratory for teaching biotechnology. As a result of this validation, we concluded that the model can provide reasonably good predictions and can support tutoring feedback that is better adapted to each student type. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01