Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/44107
Title: Comparative study on heterogeneous profiling sources for second language learners
Authors: Zou, D
Xie, H
Wang, FL
Wong, TL
Poon, CK
Ho, WS
Keywords: Evaluation
Language learning
Learner profile
Profile accuracy
Issue Date: 2015
Publisher: Springer
Source: Communications in computer and information science, 2015, v. 559, p. 209-218 How to cite?
Journal: Communications in computer and information science 
Abstract: Stimulated by the arrival of the big data era, various and heterogeneous data sources such as data in social networks, mobile devices and sensor data for users have emerged, mirroring characteristics and preferences of data owners. These data sources are often used to construct user profiles so as to facilitate personalized services like recommendations or personalized data access. In the context of second language learning, learner data involve learning logs, standard test results, and individual learning preferences and styles. Given its attribute of reflecting the characteristics of learners, such data can be exploited to build the learner profiles. However, these data sources possibly include noises or bias, and hence influence the reliability of the correspondingly constructed learner profiles. Consequently, the inaccurate profiles may result in ineffective learning tasks that are generated by e-Learning systems. To tackle this issue, it is significant and critical to evaluate the accuracy of learner profiles. In a response to this call, we propose a novel metric named "profile mean square error" to examine the accuracy of learner profiles founded upon diverse sources. We also demonstrate how to construct various learner profiles though applying different data sources such as learning logs, standard test results, and personal learning preferences in e-Learning systems and pedagogical activities. Moreover, we conduct an experimental study among some second language learners, the results of which illustrate that the most accurate profiles are generated from multiple data sources if they are integrated in a rational way.
Description: 2nd International Conference on Technology in Education: Technology-Mediated Proactive Learning, ICTE 2015, 2-4 July 2015
URI: http://hdl.handle.net/10397/44107
ISBN: 978-3662489772
ISSN: 1865-0929
DOI: 10.1007/978-3-662-48978-9_20
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