Analyzing User Interaction to Design an Intelligent e-Learning Environment

Autor/in	Sharma, Richa
Titel	Analyzing User Interaction to Design an Intelligent e-Learning Environment
Quelle	In: International Journal on E-Learning, 10 (2011) 4, S.441-460 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1537-2456
Schlagwörter	Electronic Learning; Educational Environment; Instructional Design; Student Needs; Adjustment (to Environment); Mathematics; Achievement Gains; Interaction; Computer Mediated Communication; Data Analysis; Pattern Recognition; Classification; Educational Strategies; Peer Relationship; Teacher Student Relationship; Prediction; Academic Achievement; Scores; Bayesian Statistics + Suchen Sie Ihr Suchwort? Lernumgebung; Pädagogische Umwelt; Schulumwelt; Lesson concept; Lessonplan; Unterrichtsentwurf; Mathematik; Achievement gain; Leistungssteigerung; Interaktion; Computerkonferenz; Auswertung; Mustererkennung; Classification system; Klassifikation; Klassifikationssystem; Lehrstrategie; Peer-Beziehungen; Teacher student relationships; Lehrer-Schüler-Beziehung; Vorhersage; Schulleistung
Abstract	Building intelligent course designing systems adaptable to the learners' needs is one of the key goals of research in e-learning. This goal is all the more crucial as gaining knowledge in an e-learning environment depends solely on computer mediated interaction within the learner group and among the learners and instructors. The patterns generated out of discussions among e-learners may reveal significant information regarding their learning growth. This paper presents an algorithm to predict the knowledge gain of students by analyzing their online interaction pattern amongst each other. This knowledge gain is used to categorize the students as prospective "gainers" or "non-gainers" through Naive Bayes Classifier. The preferences of non-gainers in terms of instructor-oriented v/s peer-oriented interaction are subsequently obtained. The paper further suggests some of the remedial plans based on proven instructional strategies, to be adopted that may help learners strengthen their weak areas. The actual knowledge gain of learners is evaluated by performing paired t-test on the previous and post-test score pairs. (Contains 3 figures and 6 tables.) (As Provided).
Anmerkungen	Association for the Advancement of Computing in Education. P.O. Box 1545, Chesapeake, VA 23327-1545. Tel: 757-366-5606; Fax: 703-997-8760; e-mail: info@aace.org; Web site: http://www.aace.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10