The Community for Technology Leaders
Advanced Learning Technologies, IEEE International Conference on (2012)
Rome, Italy Italy
July 4, 2012 to July 6, 2012
ISBN: 978-1-4673-1642-2
pp: 420-421
Social networks contain a multitude of messages that can be utilized to motivate learning. However, while some messages may increase a learner's motivation, other messages could undermine it. How can we tell which is which? Conceptual motivation models provide many answers, but how to translate these models into a concrete programmatic implementation (required by e-Learning systems) is often unclear. We approach the problem from a different angle, taking a data-driven approach by (1) assembling a corpus of over 100,000 messages, and (2) applying machine learning methods to this data to create a first-of-its-kind message motivation classifier. The constructed corpus and classifier provide for a new empirical way of studying text-based motivation, developing new models, and empirically evaluating such models on a large-scale.
Algebra, Feature extraction, Electronic learning, Accuracy, Least squares approximation, Adaptation models, machine learning, motivation, message, classifier

N. Rubens, T. Okamoto and D. Kaplan, "Message-Based Motivation Modeling," 2012 IEEE 12th International Conference on Advanced Learning Technologies (ICALT), Rome, 2012, pp. 420-421.
252 ms
(Ver 3.3 (11022016))