Recommending Personalized Summaries of Teaching Materials


Today's teaching activities are aided by a range of technology equipment. Teachers can use formative assessment methods to test students' understanding during frontal lessons and modify the next teaching activities accordingly. Despite the fact that many educational materials are available in text format, manually searching through these massive collections of documents might take a long time. Learner-generated data (e.g., test results) can be used to recommend brief extracts of training documents based on the needs of the individual learner. The purpose of this study is to present a novel methodology for recommending summaries of potentially huge training papers. Summary recommendations are tailored to the needs of students based on the results of comprehension exams administered at the conclusion of frontal courses. Multiple-choice assessments are administered to pupils via a smartphone application. A set of topic-specific summaries of the teaching documents is also prepared in simultaneously. They are made up of the most important sentences about a particular subject. Summaries are personally advised to students based on the test results. We tested the proposed approach in a real-world setting, namely a B.S. university-level course. Its usability was proven by the results of the experimental examination.

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