2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT) (2014)
July 7, 2014 to July 10, 2014
Observing various learning goals from peers allows learners to specify new objectives and sub-goals to improve their personal experience. Setting goals for learning enhances motivation and performance. However an unrelated goal might lead to poor outcome. Hence learners have divergent objectives for a same learning experience. Latent Dirichlet Allocation (LDA) is a model considering documents as a mixture of topics. This study then proposed a recommendation model based on LDA, able to determine distinct categories of goals within a single dataset. Results focused on a dataset of 10 learning subjects and over 16,000 goal-based Twitter messages. It showed (1) different goal categories and (2) the correlation between the LDA parameter for the number of topics and the type of subject. Evaluations of goal attributes also showed an increase of goal specificity, commitment and self-confidence after observing different types of goals from peers.
Resource management, Twitter, Psychology, Media, Algebra, Estimation, Educational institutions
S. Louvigne, Y. Kato, N. Rubens and M. Ueno, "Goal-Based Messages Recommendation Utilizing Latent Dirichlet Allocation," 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT), Athens, Greece, 2014, pp. 464-468.