Analysis of Student Behaviour in "Habitable" Worlds Using Continuous Representation Visualization

Autor/inn/en	Pardos, Zachary A.; Horodyskyj, Lev
Titel	Analysis of Student Behaviour in "Habitable" Worlds Using Continuous Representation Visualization
Quelle	In: Journal of Learning Analytics, 6 (2019) 1, S.1-15 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Pardos, Zachary A.) ORCID (Horodyskyj, Lev)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Visualization; Online Courses; Course Descriptions; Data Analysis; Behavior Patterns; Models; Teaching Methods; Instructional Improvement; Student Behavior; Recordkeeping; Graphs; Case Studies; Introductory Courses; Science Instruction; Academic Failure; Academic Achievement; Computer Mediated Communication; Group Discussion; Undergraduate Students; Arizona + Suchen Sie Ihr Suchwort? Visualisation; Visualisierung; Online course; Online-Kurs; Kursstrukturplan; Auswertung; Analogiemodell; Teaching method; Lehrmethode; Unterrichtsmethode; Unterrichtsqualität; Student behaviour; Schülerverhalten; Leistungsnachweis; Grafische Darstellung; Case study; Fallstudie; Case Study; Einführungskurs; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Schulleistung; Computerkonferenz; Gruppendiskussion
Abstract	We introduce a novel approach to visualizing temporal clickstream behaviour in the context of a degree-satisfying online course, "Habitable Worlds," offered through Arizona State University. The current practice for visualizing behaviour within a digital learning environment is to generate plots based on hand-engineered or coded features using domain knowledge. While this approach has been effective in relating behaviour to known phenomena, features crafted from domain knowledge are not likely well suited to making unfamiliar phenomena salient and thus can preclude discovery. We introduce a methodology for organically surfacing behavioural regularities from clickstream data, conducting an expert in-the-loop hyperparameter search, and identifying anticipated as well as newly discovered patterns of behaviour. While these visualization techniques have been used before in the broader machine-learning community to better understand neural networks and relationships between word vectors, we apply them to online behavioural learner data and go a step further, exploring the impact of the parameters of the model on producing tangible, non-trivial observations of behaviour that suggest pedagogical improvement to the course designers and instructors. The methodology introduced in this paper led to an improved understanding of passing and non-passing student behaviour in the course and is applicable to other datasets of clickstream activity where investigators and stakeholders wish to organically surface principal patterns of behaviour. (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