Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system

• Published in: Kybernetes Vol. 51; no. 2; pp. 535 - 556
• Main Authors: B.R, Rajakumar, Yenduri, Gokul, Vyas, Sumit, D, Binu
• Format: Journal Article
• Language: English
• Published: London Emerald Publishing Limited 07.02.2022; Emerald Group Publishing Limited
• Subjects: Algorithms; Alignment; Cluster analysis; Clustering; Collaboration; Data mining; Distance learning; Documents; Error analysis; Evaluation; Instructional design; Internet; Knowledge; Learning; Logic programming; Matching; Modules; Online instruction; Ontology; Optimization; Pattern analysis; Semantic web; Semantics; Web Ontology Language-OWL; RR-OU model; Algorithms; Pre-processing; e-Learning; Clustering; Artificial intelligence; Ontology Construction
• DOI: 10.1108/K-11-2020-0746
• ISSN: 0368-492X

Purpose This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades. Findings On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.