@inproceedings{5fb432bf5dd0491c94acc3070d637ea3,
title = "A probabilistic approach for discovering difficult course topics using clickstream data",
abstract = "One of the main factors affecting the success and effectiveness of Massive Open Online Courses is the ability of the instructor to acquire and incorporate student feedback in a timely manner, and preferably before assigning grades to student assessments. This research uses raw clickstream data from video watching sessions of the Coursera MOOC: {"}Text Retrieval and Search Engines{"}1 to discover which topics are difficult for the students. We introduce a measure for topic difficulty based on these clickstream events, and rank the topics according to this measure. The validity of our ranking is evaluated by comparing it with the ranking of topics based on student votes and find that our method agrees with the ranking based on student votes with > 63% accuracy.",
keywords = "Probabilistic clustering, Student feedback, Topic difficulty",
author = "Assma Boughoula and Chase Geigle and Zhai, {Cheng Xiang}",
note = "Funding Information: We thank Dr. Maryalice Wu for granting us access to the MOOC data. This material is based upon work supported by the NSF under Grant Number DGE-1144245 (GRFP) and Grant Number 1629161. Publisher Copyright: {\textcopyright} 2017 ACM.; 4th Annual ACM Conference on Learning at Scale, L@S 2017 ; Conference date: 20-04-2017 Through 21-04-2017",
year = "2017",
month = apr,
day = "12",
doi = "10.1145/3051457.3054010",
language = "English (US)",
series = "L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale",
publisher = "Association for Computing Machinery",
pages = "303--306",
booktitle = "L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale",
address = "United States",
}