Modeling and Evaluating Tutors' Function Using Data Mining and Fuzzy Logic Techniques

Autor/inn/en	Bendjebar, Safia; Lafifi, Yacine; Seridi, Hamid
Titel	Modeling and Evaluating Tutors' Function Using Data Mining and Fuzzy Logic Techniques
Quelle	In: International Journal of Web-Based Learning and Teaching Technologies, 11 (2016) 2, S.39-60 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1548-1093
DOI	10.4018/IJWLTT.2016040103
Schlagwörter	Electronic Learning; Teaching Methods; Classification; Student Needs; Profiles; Models; Higher Education; Foreign Countries; Data Collection; Data Analysis; Distance Education; Computer Mediated Communication; Tutors; Artificial Intelligence; Knowledge Level; Teacher Behavior; Individual Characteristics; Mathematical Formulas; Questionnaires; Algeria + Suchen Sie Ihr Suchwort? Teaching method; Lehrmethode; Unterrichtsmethode; Classification system; Klassifikation; Klassifikationssystem; Charakterisierung; Profilanalyse; Analogiemodell; Hochschulbildung; Hochschulsystem; Hochschulwesen; Ausland; Data capture; Datensammlung; Auswertung; Distance study; Distance learning; Fernunterricht; Computerkonferenz; Förderlehrer; Lehrender; Tutor; Künstliche Intelligenz; Wissensbasis; Teacher behaviour; Lehrerverhalten; Personality characteristic; Personality traits; Persönlichkeitsmerkmal; Mathematische Formel; Fragebogen; Algerien
Abstract	In e-learning systems, the tutors play many roles and carry out several tasks that differ from one system to another. The activity of tutoring is influenced by many factors. One factor among them is the assignment of the appropriate profile to the tutor. For this reason, the authors propose a new approach for modeling and evaluating the function of the tutors. This technique facilitates the classification among tutors for adapting tutoring to student's problems. The component of the proposed tutor model is a set of profiles which are responsible for representing the necessary information about each tutor. A fuzzy logic technique is used in order to define tutor's tutoring profile. Furthermore, the K nearest neighbor algorithm is used to offer much information for each new tutor based on the models of other similar tutors. This new approach has been tested by tutors from an Algerian University. The first results were very encouraging and sufficient. They indicate that the use of fuzzy logic technique is very useful and estimate the adaptation of the tutoring process according to tutors' skills. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01