Improving Learning Analytics--Combining Observational and Self-Report Data on Student Learning

Autor/inn/en	Ellis, Robert A.; Han, Feifei; Pardo, Abelardo
Titel	Improving Learning Analytics--Combining Observational and Self-Report Data on Student Learning
Quelle	In: Educational Technology & Society, 20 (2017) 3, S.158-169 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Academic Achievement; Data Analysis; Outcomes of Education; Observation; Prediction; Educational Improvement; Educational Technology; Cognitive Style; Higher Education; Research Universities; Measurement Techniques; Foreign Countries; Learning Strategies; Questionnaires; Factor Analysis; Engineering Education; Student Attitudes; Management Systems; Grades (Scholastic); Correlation; Multivariate Analysis; Undergraduate Students; Qualitative Research; Multiple Regression Analysis; Statistical Analysis; Australia; Study Process Questionnaire + Suchen Sie Ihr Suchwort? Schulleistung; Auswertung; Lernleistung; Schulerfolg; Beobachtung; Vorhersage; Teaching improvement; Unterrichtsentwicklung; Unterrichtsmedien; Cognitive styles; Kognitiver Stil; Hochschulbildung; Hochschulsystem; Hochschulwesen; Forschungseinrichtung; Messtechnik; Ausland; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Fragebogen; Faktorenanalyse; Ingenieurausbildung; Schülerverhalten; Notenspiegel; Korrelation; Multivariate Analyse; Qualitative Forschung; Statistische Analyse; Australien
Abstract	The field of education technology is embracing a use of learning analytics to improve student experiences of learning. Along with exponential growth in this area is an increasing concern of the interpretability of the analytics from the student experience and what they can tell us about learning. This study offers a way to address some of the concerns of collecting and interpreting learning analytics to improve student learning by combining observational and self-report data. The results present two models for predicting student academic performance which suggest that a combination of both observational and self-report data explains a significantly higher variation in student outcomes. The results offer a way into discussing the quality of interpretations of learning analytics and their usefulness for helping to improve the student experience of learning and also suggest a pathway for future research into this area. (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Available from: National Sun Yat-sen University. Department of Information Management, 70, Lien-Hai Rd, Kaohsiung, 80424, Taiwan. Web site: http://www.ifets.info
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01