In Fall 2013 we offered an open online Introduction to Recommender Systems through Coursera, while simultaneously offering a for-credit version of the course on-campus using the Coursera platform and a flipped classroom instruction model. As the goal of offering this course was to experiment with this type of instruction, we performed extensive evaluation including surveys of demographics, self-assessed skills, and learning intent; we also designed a knowledge-assessment tool specifically for the subject matter in this course, administering it before and after the course to measure learning. We also tracked students through the course, including separating out students enrolled for credit from those enrolled only for the free, open course. This article reports on our findings.
%0 Conference Paper
%1 citeulike:14139192
%A Konstan, Joseph A.
%A Walker, J. D.
%A Brooks, D. Christopher
%A Brown, Keith
%A Ekstrand, Michael D.
%B Proceedings of the First ACM Conference on Learning @ Scale Conference
%C New York, NY, USA
%D 2014
%I ACM
%K log-mining mooc recommender recsys2016
%P 61--70
%R 10.1145/2556325.2566244
%T Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC
%U http://dx.doi.org/10.1145/2556325.2566244
%X In Fall 2013 we offered an open online Introduction to Recommender Systems through Coursera, while simultaneously offering a for-credit version of the course on-campus using the Coursera platform and a flipped classroom instruction model. As the goal of offering this course was to experiment with this type of instruction, we performed extensive evaluation including surveys of demographics, self-assessed skills, and learning intent; we also designed a knowledge-assessment tool specifically for the subject matter in this course, administering it before and after the course to measure learning. We also tracked students through the course, including separating out students enrolled for credit from those enrolled only for the free, open course. This article reports on our findings.
%@ 978-1-4503-2669-8
@inproceedings{citeulike:14139192,
abstract = {{In Fall 2013 we offered an open online Introduction to Recommender Systems through Coursera, while simultaneously offering a for-credit version of the course on-campus using the Coursera platform and a flipped classroom instruction model. As the goal of offering this course was to experiment with this type of instruction, we performed extensive evaluation including surveys of demographics, self-assessed skills, and learning intent; we also designed a knowledge-assessment tool specifically for the subject matter in this course, administering it before and after the course to measure learning. We also tracked students through the course, including separating out students enrolled for credit from those enrolled only for the free, open course. This article reports on our findings.}},
added-at = {2018-03-19T12:24:51.000+0100},
address = {New York, NY, USA},
author = {Konstan, Joseph A. and Walker, J. D. and Brooks, D. Christopher and Brown, Keith and Ekstrand, Michael D.},
biburl = {https://www.bibsonomy.org/bibtex/2cbd67fc265b0c604c327441c46424dee/aho},
booktitle = {Proceedings of the First ACM Conference on Learning @ Scale Conference},
citeulike-article-id = {14139192},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=2566244},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/2556325.2566244},
doi = {10.1145/2556325.2566244},
interhash = {e681e232f6c3114a0ac60849d475681a},
intrahash = {cbd67fc265b0c604c327441c46424dee},
isbn = {978-1-4503-2669-8},
keywords = {log-mining mooc recommender recsys2016},
location = {Atlanta, Georgia, USA},
pages = {61--70},
posted-at = {2016-09-17 22:26:43},
priority = {2},
publisher = {ACM},
series = {L@S '14},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC}},
url = {http://dx.doi.org/10.1145/2556325.2566244},
year = 2014
}