Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

Abstract

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.

Mapping Learner's Query to Learning Objects using Topic Modeling and Machine Learning Techniques

“…Despite the availability of web search engines and in-portal searching tools, the common problem that inquiry-based learning confronts is retrieving precise information (in terms of LO) for user queries [10]. Since all search engines mostly rely on keyword-based metadata, if queries are ill-defined or missing a vital keyword, it fetches LOs that are superfluous and often fail to make any meaningful connection to the topic of a query [11].…”