Shapes of Educational Data in an Online Calculus Course

Autor/in	Caprotti, Olga
Titel	Shapes of Educational Data in an Online Calculus Course
Quelle	In: Journal of Learning Analytics, 4 (2017) 2, S.76-90 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Online Courses; Calculus; Markov Processes; Graphs; Models; Educational Resources; Learning Activities; Mathematics Instruction; Study Habits; Teaching Methods; Mathematical Models; Management Systems; Student Centered Learning; Peer Teaching; Data Analysis; College Students; Tests; Grades (Scholastic); Recordkeeping; Florida + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Analysis; Differenzialrechnung; Infinitesimalrechnung; Integralrechnung; Markowscher Prozess; Grafische Darstellung; Analogiemodell; Bildungsmittel; Lernaktivität; Mathematics lessons; Mathematikunterricht; Study behavior; Study behaviour; Studienverhalten; Teaching method; Lehrmethode; Unterrichtsmethode; Mathematical model; Mathematisches Modell; Group work; Student-entered learning; Student-centred learning; Student centred learning; Schülerorientierter Unterricht; Schülerzentrierter Unterricht; Gruppenarbeit; Peer group teaching; Peer Group Teaching; Auswertung; Collegestudent; Examination; Prüfung; Examen; Notenspiegel; Leistungsnachweis
Abstract	This paper describes investigations in visualizing logpaths of students in an online calculus course held at Florida State University in 2014. The clickstreams making up the logpaths can be used to visualize student progress in the information space of a course as a graph. We consider the graded activities as nodes of the graph, while information extracted from the logpaths between the graded activities label the edges of the graph. We show that this graph is associated to a Markov Chain in which the states are the graded activities and the weight of the edge is proportional to the probability of that transition. When we visualize such a graph, it becomes apparent that most students follow the course sequentially, section after section. This model allows us to study how different groups of students employ the learning resources using sequence analysis on information buried in their clickstreams. (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