Abstract
E-Learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. The utilisation of electronic content to provide education and development is referred to as e-learning. While the broadening internet has a plethora of e-learning tools, knowledge acquisition is not just an aspect that adds to an individual's enrichment. Assessment and evaluation are crucial parts of every learning system. Due to more complex assessments and quicker inspection, multiple choice questions are becoming extremely prevalent in current evaluations. However, establishing a diversified pool of MCQs relevant to a certain subject matter presents a hurdle. Manually creating high-quality MCQ exams is a time-consuming and arduous procedure that demands skill. As a result, research has concentrated on the automated construction of well-structured MCQ-based tests. This paper presents a paradigm using natural language processing based on semantic similarity and dynamic ontology. The proposed QG-SKI model uses LOD Cloud and Wiki Data to generate Ontologies dynamically and Knowledge reservoir is performed. The dataset is analysed using TF-IDF algorithm and the Semantic Similarity and Semantic Dissimilarity are computed using Shannon’s entropy, Jaccard Similarity and Normalised Google Distance. These algorithms are executed for a multitude of degrees and levels to generate a similar of similar instances. The suggested model has a 98.15% accuracy and outperforms previous baseline models by dampening resilience.
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Dhanvardini, R., Deepak, G., Santhanavijayan, A. (2023). QG-SKI: Question Classification and MCQ Question Generation Using Sequential Knowledge Induction. In: Abraham, A., Hong, TP., Kotecha, K., Ma, K., Manghirmalani Mishra, P., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2022. Lecture Notes in Networks and Systems, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-27409-1_11
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