Predicting Effective Course Conduction Strategy Using Datamining Techniques

Autor/inn/en	Parkavi, A.; Lakshmi, K.; Srinivasa, K. G.
Titel	Predicting Effective Course Conduction Strategy Using Datamining Techniques
Quelle	In: Educational Research and Reviews, 12 (2017) 24, S.1188-1198 (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1990-3839
Schlagwörter	Data Analysis; Educational Research; Goodness of Fit; Decision Making; Mathematical Models; Prediction; Outcomes of Education; Regression (Statistics); Teaching Methods; Instructional Effectiveness; Academic Achievement; Accuracy; Educational Improvement; Comparative Analysis; Computer Software; Programming Languages + Suchen Sie Ihr Suchwort? Auswertung; Bildungsforschung; Pädagogische Forschung; Decision-making; Entscheidungsfindung; Mathematical model; Mathematisches Modell; Vorhersage; Lernleistung; Schulerfolg; Regression; Regressionsanalyse; Teaching method; Lehrmethode; Unterrichtsmethode; Unterrichtserfolg; Schulleistung; Teaching improvement; Unterrichtsentwicklung
Abstract	Data analysis techniques can be used to analyze the pattern of data in different fields. Based on the analysis' results, it is recommended that suggestions be provided to decision making authorities. The data mining techniques can be used in educational domain to improve the outcome of the educational sectors. The authors carried out this research study by devising a mathematical model and tool to determine effective course conduction strategy, for teaching the students regression analysis and goodness of fit test. This helps the faculty to take necessary remedial actions to improve performance in courses by selecting effective course conduction based on the students' performance. The accuracy of prediction in this research is more as it is measured by using Rsquare value which is near to one. (As Provided).
Anmerkungen	Academic Journals. e-mail: err@academic.journals.org; e-mail: service@academicjournals.org; Web site: http://academicjournals.org/journal/ERR
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01