Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system
| Autor: | Aranyak Maity, Pijush Kanti Dutta Pramanik, Prasenjit Choudhury, Saurabh Pal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Correctness
General Computer Science Computer science media_common.quotation_subject E-learning (theory) Word ordering error 02 engineering and technology Soft cosine similarity Interactive system Semantics computer.software_genre E-learning User experience design Tri-gram 0202 electrical engineering electronic engineering information engineering Quality (business) Sequential pattern media_common Syntax (programming languages) business.industry N-gram 05 social sciences 050301 education QA75.5-76.95 Language model Natural Language and Speech Human-Computer Interaction Computational Linguistics n-gram Algorithms and Analysis of Algorithms Computer Education Electronic computers. Computer science 020201 artificial intelligence & image processing Artificial intelligence business 0503 education computer Natural language processing Java |
| Zdroj: | PeerJ Computer Science PeerJ Computer Science, Vol 7, p e532 (2021) |
| ISSN: | 2376-5992 |
| Popis: | In an interactive online learning system (OLS), it is crucial for the learners to form the questions correctly in order to be provided or recommended appropriate learning materials. The incorrect question formation may lead the OLS to be confused, resulting in providing or recommending inappropriate study materials, which, in turn, affects the learning quality and experience and learner satisfaction. In this paper, we propose a novel method to assess the correctness of the learner's question in terms of syntax and semantics. Assessing the learner’s query precisely will improve the performance of the recommendation. A tri-gram language model is built, and trained and tested on corpora of 2,533 and 634 questions on Java, respectively, collected from books, blogs, websites, and university exam papers. The proposed method has exhibited 92% accuracy in identifying a question as correct or incorrect. Furthermore, in case the learner's input question is not correct, we propose an additional framework to guide the learner leading to a correct question that closely matches her intended question. For recommending correct questions, soft cosine based similarity is used. The proposed framework is tested on a group of learners' real-time questions and observed to accomplish 85% accuracy. |
| Databáze: | OpenAIRE |
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