Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-05-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-532.pdf |
| _version_ | 1819047227061960704 |
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| author | Saurabh Pal Pijush Kanti Dutta Pramanik Aranyak Maity Prasenjit Choudhury |
| author_facet | Saurabh Pal Pijush Kanti Dutta Pramanik Aranyak Maity Prasenjit Choudhury |
| author_sort | Saurabh Pal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T10:57:00Z |
| format | Article |
| id | doaj.art-f8a3b41882a74d899b709fec0977de73 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-21T10:57:00Z |
| publishDate | 2021-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-f8a3b41882a74d899b709fec0977de732022-12-21T19:06:29ZengPeerJ Inc.PeerJ Computer Science2376-59922021-05-017e53210.7717/peerj-cs.532Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning systemSaurabh Pal0Pijush Kanti Dutta Pramanik1Aranyak Maity2Prasenjit Choudhury3Department of Computer Science & Engineering, National Institute of Technology, Durgapur, West Bengal, IndiaDepartment of Computer Science & Engineering, National Institute of Technology, Durgapur, West Bengal, IndiaSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USADepartment of Computer Science & Engineering, National Institute of Technology, Durgapur, West Bengal, IndiaIn 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.https://peerj.com/articles/cs-532.pdfN-gramTri-gramLanguage modelWord ordering errorSequential patternInteractive system |
| spellingShingle | Saurabh Pal Pijush Kanti Dutta Pramanik Aranyak Maity Prasenjit Choudhury Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system PeerJ Computer Science N-gram Tri-gram Language model Word ordering error Sequential pattern Interactive system |
| title | Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system |
| title_full | Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system |
| title_fullStr | Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system |
| title_full_unstemmed | Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system |
| title_short | Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system |
| title_sort | learner question s correctness assessment and a guided correction method enhancing the user experience in an interactive online learning system |
| topic | N-gram Tri-gram Language model Word ordering error Sequential pattern Interactive system |
| url | https://peerj.com/articles/cs-532.pdf |
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