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

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-le...

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Vydáno v:Sustainability Ročník 14; číslo 17; s. 10551
Hlavní autoři: Bagunaid, Wala, Chilamkurti, Naveen, Veeraraghavan, Prakash
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2022
Témata:
Academic achievement
Algorithms
Artificial intelligence
Big Data
Clustering
Collaboration
COVID-19
Data mining
Deep learning
Education
Educational aspects
Educational tests and measurements
Examinations
Homework
Learning management systems
Machine learning
Methods
Neural networks
Online education
Online instruction
Recommender systems
Students
Sustainability
Technology application
Australia
ISSN:2071-1050, 2071-1050
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Shrnutí: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.
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content type line 14
ISSN:2071-1050
2071-1050
DOI:10.3390/su141710551