Analyzing Student Strategies in Blended Courses Using Clickstream Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).

Autor/inn/en	Akpinar, Nil-Jana; Ramdas, Aaditya; Acar, Umut
Titel	Analyzing Student Strategies in Blended Courses Using Clickstream Data [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020).
Quelle	(2020), (12 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Learning Strategies; Blended Learning; Learning Analytics; Student Behavior; Behavior Patterns; Undergraduate Students; Computer Science Education; Natural Language Processing; Interaction; Study Habits; Sequential Approach; Predictor Variables; Attendance; Homework; Tests; Pennsylvania (Pittsburgh) + Suchen Sie Ihr Suchwort? Learning methode; Learning techniques; Lernmethode; Lernstrategie; Student behaviour; Schülerverhalten; Computer science lessons; Informatikunterricht; Natürliche Sprache; Interaktion; Study behavior; Study behaviour; Studienverhalten; Schrittfolge; Prädiktor; Anwesenheit; Hausaufgabe; Examination; Prüfung; Examen
Abstract	Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern mining and models borrowed from Natural Language Processing (NLP) to understand student interactions and extract frequent strategies from a blended college course. Finegrained clickstream data is collected through Diderot, a noncommercial educational support system that spans a wide range of functionalities. We find that interaction patterns differ considerably based on the assessment type students are preparing for, and many of the extracted features can be used for reliable performance prediction. Our results suggest that the proposed hybrid NLP methods can provide valuable insights even in the low-data setting of blended courses given enough data granularity. [For the full proceedings, see ED607784.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01