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AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data

Author

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  • Wala Bagunaid

    (Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3086, Australia)

  • Naveen Chilamkurti

    (Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3086, Australia)

  • Prakash Veeraraghavan

    (Computer Science and Information Technology, La Trobe University, Bundoora, VIC 3086, Australia)

Abstract

Educational systems have advanced with the use of electronic learning (e-learning), which is a promising solution for long-distance learners. Students who engage in e-learning can access tests and exams online, making education more flexible and accessible. This work reports on the design of an e-learning system that makes recommendations to students to improve their learning. This artificial intelligence-based student assessment and recommendation (AISAR) system consists of score estimation, clustering, performance prediction, and recommendation. In addition, the importance of student authentication is recognised in situations in which students must authenticate themselves prior to using the e-learning system using their identity, password, and personal identification number. Individual scores are determined using a recurrent neural network (RNN) based on student engagement and examination scores. Then, a density-based spatial clustering algorithm (DBSCAN) using Mahalanobis distance clustering is implemented to group students based on their obtained score values. The constructed clusters are validated by estimating purity and entropy. Student performance is predicted using a threshold-based MapReduce (TMR) procedure from the score-based cluster. When predicting student performance, students are classified into two groups: average and poor, with the former being divided into below- and above-average students and the latter into poor and very poor students. This categorisation aims to provide useful recommendations for learning. A recommendation reinforcement learning algorithm, the rule-based state–action–reward–state–action (R-SARSA) algorithm, is incorporated for evaluation. Students were required to work on their subjects according to the provided recommendations. This e-learning recommendation system achieves better performance in terms of true-positives, false-positives, true-negatives, false-negatives, precision, recall, and accuracy.

Suggested Citation

  • Wala Bagunaid & Naveen Chilamkurti & Prakash Veeraraghavan, 2022. "AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10551-:d:896388
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    References listed on IDEAS

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    1. Miroslava Raspopovic & Aleksandar Jankulovic, 2017. "Performance measurement of e-learning using student satisfaction analysis," Information Systems Frontiers, Springer, vol. 19(4), pages 869-880, August.
    2. Xuesong Zhai & Xiaoyan Chu & Ching Sing Chai & Morris Siu Yung Jong & Andreja Istenic & Michael Spector & Jia-Bao Liu & Jing Yuan & Yan Li & Ning Cai, 2021. "A Review of Artificial Intelligence (AI) in Education from 2010 to 2020," Complexity, Hindawi, vol. 2021, pages 1-18, April.
    3. Jiabo Tan & Naeem Jan, 2022. "Information Analysis of Advanced Mathematics Education-Adaptive Algorithm Based on Big Data," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, March.
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    Cited by:

    1. Ali Çetinkaya & Ömer Kaan Baykan & Havva Kırgız, 2023. "Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
    2. Dunhong Yao & Xian Zhang & Yiwen Liu, 2022. "Teaching Reform in C Programming Course from the Perspective of Sustainable Development: Construction and 9-Year Practice of “Three Classrooms–Four Integrations–Five Combinations” Teaching Model," Sustainability, MDPI, vol. 14(22), pages 1-22, November.

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