Sentiment-enhanced learning model for online language learning system

Li Li ()
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Li Li: Shanghai University of Political Science and Law

Electronic Commerce Research, 2018, vol. 18, issue 1, No 3, 23-64

Abstract: Abstract With the rapid development of the Internet, online learning is gradually taking the place of part of the offline learning. At the same time, a wide range of methods have been adopted to improve the e-learning system, such as semantic technologies, sentiment computing, forgetting curve. There are still some defects in the existing e-learning models when used in the online language learning. (1) The resource organization module is inefficient when facing a large amount of various language learning resources. (2) The sentiment module is too complex to apply to those simple online learning systems. To solve the above-mentioned problems, we propose a sentiment-enhanced learning model for the online language learning system, which consists of a knowledge-flow-based learning resource organization module, a time-decayed user profile module, and a simple and effective sentiment analysis module. With the support of these modules, the online learning system can organize the language learning resources, model the learners’ characters, and intervene in the language learning process effectively. Finally, the experimental results show that the proposed method provides more effective means for bettering the efficiency of online language learning.

Keywords: Sentiment computing; Knowledge flow; Ebbinghaus forgetting curve; User profile; Online language learning; E-learning (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10660-017-9284-5

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