Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Semi-supervised techniques for mining learning outcomes and prerequisites

Labutov, I and Huang, Y and Brusilovsky, P and He, D (2017) Semi-supervised techniques for mining learning outcomes and prerequisites. In: UNSPECIFIED.

Available under License : See the attached license file.

Download (3MB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


Educational content of today no longer only resides in textbooks and classrooms; more and more learning material is found in a free, accessible form on the Internet. Our long-standing vision is to transform this web of educational content into an adaptive, web-scale "textbook", that can guide its readers to most relevant "pages" according to their learning goal and current knowledge. In this paper, we address one core, long-standing problem towards this goal: identifying outcome and prerequisite concepts within a piece of educational content (e.g., a tutorial). Specifically, we propose a novel approach that leverages textbooks as a source of distant supervision, but learns a model that can generalize to arbitrary documents (such as those on the web). As such, our model can take advantage of any existing textbook, without requiring expert annotation. At the task of predicting outcome and prerequisite concepts, we demonstrate improvements over a number of baselines on six textbooks, especially in the regime of little to no ground-truth labels available. Finally, we demonstrate the utility of a model learned using our approach at the task of identifying prerequisite documents for adaptive content recommendation - an important step towards our vision of the "web as a textbook".


Social Networking:
Share |


Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Labutov, I
Huang, Y
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
He, Ddah44@pitt.eduDAH440000-0002-4645-8696
Date: 13 August 2017
Date Type: Publication
Journal or Publication Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume: Part F
Page Range: 907 - 915
Event Type: Conference
DOI or Unique Handle: 10.1145/3097983.3098187
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
ISBN: 9781450348874
Date Deposited: 31 Jul 2018 16:30
Last Modified: 03 May 2023 10:55


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item