Journal Article

Using a Learner-Topic Model for Mining Learner Interests in Open Learning Environments

Pengfei Wu, Shengquan Yu, Dan Wang

2018

Overview

The present study uses a text data mining approach to automatically discover learner interests in open learning environments. We propose a method to construct learner interests automatically from the combination of learner generated content and their dynamic interactions with other learning resources. We develop a learner-topic model to discover not only the learner's knowledge interests (interest in generating content), but also the learner's collection interests (interest in collecting content generated by others). Then we combine the extracted knowledge interests and collection interests to yield a set of interest words for each learner. Experiments using a dataset from the Learning Cell Knowledge Community demonstrate that this method is able to discover learners' interests effectively. In addition, we find that knowledge interests and collection interests are related and consistent in their subject matter. We further show that learner interest words discovered by the learner-topic model method include learner self-defined interest tags, but reflect a broader range of interests.

• Publisher: International Forum of Educational Technology ＆ Society,National Taiwan Normal University,International Forum of Educational Technology & Society
• Subject: Chinese / Collaborative learning / Collection / College Students / Data Analysis / Data Collection / Data mining / Discovery learning / Educational aspects / Educational environment / Educational Resources / Educational Technology / Foreign Countries / Information Technology / Learning / Learning modules / Learning strategies / Machine learning / Online learning / Open Education / Open learning / Special Issue Articles / Student Interests / Technology Uses in Education / Text analytics / Web 2.0 Technologies