Curricular Design Analysis: A Data-Driven Perspective

Autor/inn/en	Méndez, Gonzalo; Ochoa, Xavier; Chiluiza, Katherine; de Wever, Bram
Titel	Curricular Design Analysis: A Data-Driven Perspective
Quelle	In: Journal of Learning Analytics, 1 (2014) 3, S.84-119 (36 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Data Analysis; Data Collection; Educational Research; Curriculum Design; Curriculum Development; Case Studies; Undergraduate Students; Computer Science Education; Foreign Countries; Educational Indicators; Difficulty Level; Scaling; Grade Point Average; Academic Achievement; Curriculum Evaluation; Evaluation Methods; Dropouts; Academic Failure; Probability; Enrollment Trends; Ecuador + Suchen Sie Ihr Suchwort? Auswertung; Data capture; Datensammlung; Bildungsforschung; Pädagogische Forschung; Lehrplangestaltung; Curriculum; Development; Curriculumentwicklung; Lehrplan; Entwicklung; Case study; Fallstudie; Case Study; Computer science lessons; Informatikunterricht; Ausland; Educational indicato; Bildungsindikator; Schwierigkeitsgrad; Scale construction; Skalenkonstruktion; Schulleistung; Evaluation; Curriculumevaluation; Rahmenplan; Evaluierung; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of learning analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain better insight into the inner workings of their programs in order to tune or correct them. This work presents a set of simple techniques that, if applied to readily available historical academic data, could provide such insights. The techniques described are real course difficulty estimation, course impact on the overall academic performance of students, curriculum coherence, dropout paths, and load/performance graph. The usefulness of these techniques is validated through their application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum redesign. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01