This paper applies theory and methodology from the learning design literature to large-scale learning environments through quantitative modeling of the structure and design of Massive Open Online Courses. For two institutions of higher education, we automate the task of encoding pedagogy and learning design principles for 177 courses (which accounted for for nearly 4 million enrollments). Course materials from these MOOCs are parsed and abstracted into sequences of components, such as videos and problems. Our key contributions are (i) describing the parsing and abstraction of courses for quantitative analyses, (ii) the automated categorization of similar course designs, and (iii) the identification of key structural components that show relationships between categories and learning design principles. We employ two methods to categorize similar course designs---one aimed at clustering courses using transition probabilities and another using trajectory mining. We then proceed with an exploratory analysis of relationships between our categorization and learning outcomes.
|Title of host publication||L@S 2018|
|Subtitle of host publication||Proceedings of the Fifth Annual ACM Conference on Learning at Scale|
|Place of Publication||New York, USA|
|Publisher||Association for Computing Machinery (ACM)|
|Number of pages||10|
|Publication status||Published - 2018|
|Event||L@S '18 The Fifth Annual ACM Conference on Learning at Scale - London, United Kingdom|
Duration: 26 Jun 2018 → 28 Jun 2018
Conference number: 5
|Conference||L@S '18 The Fifth Annual ACM Conference on Learning at Scale|
|Period||26/06/18 → 28/06/18|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.