Thumbnail
Access Restriction
Subscribed

Author McLaughlin, Elizabeth A. ♦ Stamper, John C. ♦ Koedinger, Kenneth R.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF ♦ HTM / HTML
Language English
Abstract Advanced educational technologies are developing rapidly and online MOOC courses are becoming more prevalent, creating an enthusiasm for the seemingly limitless data-driven possibilities to affect advances in learning and enhance the learning experience. For these possibilities to unfold, the expertise and collaboration of many specialists will be necessary to improve data collection, to foster the development of better predictive models, and to assure models are interpretable and actionable. The big data collected from MOOCs needs to be bigger, not in its height (number of students) but in its width more meta-data and information on learners' cognitive and self-regulatory states needs to be collected in addition to correctness and completion rates. This more detailed articulation will help open up the black box approach to machine learning models where prediction is the primary goal. Instead, a data-driven learner model approach uses fine grain data that is conceived and developed from cognitive principles to build explanatory models with practical implications to improve student learning.
Description Affiliation: Carnegie Mellon (Koedinger, Kenneth R.) || Carnegie Mellon University (McLaughlin, Elizabeth A.) || Carnegie Mellon University and LearnLab's DataShop (Stamper, John C.)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-06-29
Publisher Place New York
Journal Ubiquity (UBIQ)
Volume Number 2014
Issue Number May
Page Count 13
Starting Page 1
Ending Page 13


Open content in new tab

   Open content in new tab
Source: ACM Digital Library