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Toward Large-scale Learning Design : Categorizing Course Designs in Service of Supporting Learning Outcomes. / Davis, Daniel; Seaton, Daniel; Hauff, Claudia; Houben, Geert-Jan.

L@S 2018: Proceedings of the Fifth Annual ACM Conference on Learning at Scale. New York, USA : Association for Computing Machinery (ACM), 2018. p. 1-10 4.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Davis, D, Seaton, D, Hauff, C & Houben, G-J 2018, Toward Large-scale Learning Design: Categorizing Course Designs in Service of Supporting Learning Outcomes. in L@S 2018: Proceedings of the Fifth Annual ACM Conference on Learning at Scale., 4, Association for Computing Machinery (ACM), New York, USA, pp. 1-10, L@S '18 The Fifth Annual ACM Conference on Learning at Scale, London, United Kingdom, 26/06/18. https://doi.org/10.1145/3231644.3231663

APA

Davis, D., Seaton, D., Hauff, C., & Houben, G-J. (2018). Toward Large-scale Learning Design: Categorizing Course Designs in Service of Supporting Learning Outcomes. In L@S 2018: Proceedings of the Fifth Annual ACM Conference on Learning at Scale (pp. 1-10). [4] New York, USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3231644.3231663

Vancouver

Davis D, Seaton D, Hauff C, Houben G-J. Toward Large-scale Learning Design: Categorizing Course Designs in Service of Supporting Learning Outcomes. In L@S 2018: Proceedings of the Fifth Annual ACM Conference on Learning at Scale. New York, USA: Association for Computing Machinery (ACM). 2018. p. 1-10. 4 https://doi.org/10.1145/3231644.3231663

Author

Davis, Daniel ; Seaton, Daniel ; Hauff, Claudia ; Houben, Geert-Jan. / Toward Large-scale Learning Design : Categorizing Course Designs in Service of Supporting Learning Outcomes. L@S 2018: Proceedings of the Fifth Annual ACM Conference on Learning at Scale. New York, USA : Association for Computing Machinery (ACM), 2018. pp. 1-10

BibTeX

@inproceedings{8f38bb9e996f450e8a5df692a1ced74b,
title = "Toward Large-scale Learning Design: Categorizing Course Designs in Service of Supporting Learning Outcomes",
abstract = "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.",
author = "Daniel Davis and Daniel Seaton and Claudia Hauff and Geert-Jan Houben",
note = "Green 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.",
year = "2018",
doi = "10.1145/3231644.3231663",
language = "English",
isbn = "978-1-4503-5886-6",
pages = "1--10",
booktitle = "L@S 2018",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Toward Large-scale Learning Design

T2 - Categorizing Course Designs in Service of Supporting Learning Outcomes

AU - Davis, Daniel

AU - Seaton, Daniel

AU - Hauff, Claudia

AU - Houben, Geert-Jan

N1 - Green 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.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.1145/3231644.3231663

DO - 10.1145/3231644.3231663

M3 - Conference contribution

SN - 978-1-4503-5886-6

SP - 1

EP - 10

BT - L@S 2018

PB - Association for Computing Machinery (ACM)

CY - New York, USA

ER -

ID: 46721899