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A Smart Testing Model Based on Mining Semantic Relations
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OA
AI
DOI：10.1109/ACCESS.2023.3260406.png)
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摘要
En 中文
Engaging personalization in the education process is considered one of the success factors for raising the educational process quality by altering the educational institutions' vision for gaining more flexibility while attaining the institution's objectives. It is a fact that the situation of the COVID-19 pandemic is one of the main reasons that forwarded attention to online learning as an obligatory path rather than being optional until the arisen situation of the COVID-19 pandemic. This situation has altered the educational institutions' perspective permanently. This research proposes an intelligent model which considers the personalized student characteristics in exploring the student learning styles variation, then considering this variation in building the student exam. Following this model ensures the compatibility of the conducted exam with the student's capabilities as well as the course Intended Learning Outcomes (ILOs) coverage. The balance in building the exam with covering the course objectives as well as the appropriateness with the student's personalized characteristics is the main objective of this research. The proposed model has been applied and proved its applicability in enhancing the students' exam results to 92.36% and raising the exam quality level.
Keyword:
Education
Text mining
Text analysis
Electronic learning
Distance learning
Semantics
Students' personalization
text mining
e-learning
similarity
term frequency
intended learning outcomes (IOLs)
learning styles
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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