AI in Education

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (25 February 2022) | Viewed by 20606

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Special Issue Editor

IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
Interests: human-computer interaction; machine learning; data science; education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is changing the world as we know it. Recent advances are enabling people, companies, and governments to envision and experiment with new methods of interacting with computers and modifying how virtual and physical processes are carried out. One of the fields in which this transformation is taking place is education. After years of witnessing the incorporation of technological innovations into learning/teaching processes, we can currently observe many new research works involving AI. Moreover, there has been increasing interest in this research area after the COVID-19 pandemic, driven towards fostering digital education.

Among recent research in this field, AI applications have been applied to enhance educational experiences, studies have considered the interaction between AI and humans while learning, analyses of educational data have been conducted, including machine learning techniques, and proposals have been presented for new paradigms mediated by intelligent agents.

This Special Issue, entitled "AI in Education", aims to reflect recent research in the field of AI and education. These works can present new advances in methods, applications, and procedures to enhance educational processes via artificial intelligence and its subfields (machine learning, neural networks, deep learning, cognitive computing, natural language processing, computer vision, etc.).

We expect submissions focused on both the theoretical aspects and applications of AI and its subfields in education. New ideas proposing novel approaches are also welcome.

Topics of interest include, but are not limited to, the following areas:

  • Human–computer interactions in aspects of AI in education;
  • New teaching/learning paradigms through the inclusion of AI in education;
  • Multimodal applications of AI in education;
  • New applications of AI, ML, DL, etc., in teaching/learning;
  • AI-based assessment of learning.

We hope that this Special Issue will allow researchers to reflect on the application of AI in education and propose new ways to enhance teaching/learning experiences.

Dr. Juan Cruz-Benito
Guest Editor

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Keywords

  • education
  • educational assessment
  • artificial intelligence
  • machine learning
  • natural language processing
  • deep learning
  • computer vision

Published Papers (5 papers)

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Research

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14 pages, 1143 KiB  
Article
Online University Students’ Perceptions on the Awareness of, Reasons for, and Solutions to Plagiarism in Higher Education: The Development of the AS&P Model to Combat Plagiarism
Appl. Sci. 2021, 11(24), 12055; https://doi.org/10.3390/app112412055 - 17 Dec 2021
Cited by 9 | Viewed by 7548
Abstract
Academic plagiarism has remained a major concern for higher education institutions, as it hampers not only the quality of the teaching-learning process and research, but also the overall educational institution. This issue appears to be even more serious in online and distance education [...] Read more.
Academic plagiarism has remained a major concern for higher education institutions, as it hampers not only the quality of the teaching-learning process and research, but also the overall educational institution. This issue appears to be even more serious in online and distance education institutions. As a result, a qualitative study was conducted on an online university in Pakistan to investigate the determinants of academic plagiarism and to find ways to address this issue. The students were given an open-ended questionnaire to reflect their opinions on the awareness and understanding of plagiarism, its determinants, and ways to address it. The findings revealed that most of the 267 online university students had a poor awareness and understanding of plagiarism. Major reasons for students’ plagiarism turned out to be a lack of a proactive approach to create awareness, an omission of citation conventions from course content, untrained teachers, a lack of strict penalties and their proper implementation, poor time management, a fear of failure, a lack of confidence, laziness, and a culture of plagiarism. The study proposes the Awareness, Support, and Prevention model (AS&P model) to address this issue in higher education institutions. Full article
(This article belongs to the Special Issue AI in Education)
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28 pages, 545 KiB  
Article
Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
Appl. Sci. 2021, 11(22), 10546; https://doi.org/10.3390/app112210546 - 09 Nov 2021
Cited by 1 | Viewed by 2024
Abstract
The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of [...] Read more.
The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of personalities we obtain yield significant (p<0.05) population correlation coefficients for traits against grade—0.846 for Extraversion and 0.319 for Conscientiousness. Furthermore, we demonstrate that a student’s e-behaviour and personality can be used with deep learning (LSTM) to predict and forecast whether a student is at risk of failing the year. Machine learning procedures followed in this report provide a methodology to timeously identify students who are likely to become at risk of poor academic performance. Using engineered online behaviour and personality features, we obtain a Cohen's Kappa Coefficient (κ) of students at risk of 0.51. Lastly, we show that we can design an intervention process using machine learning that supplements the existing performance analysis and intervention methods. The methodology presented in this article provides metrics that measure the factors that affect student performance and complement the existing performance evaluation and intervention systems in education. Full article
(This article belongs to the Special Issue AI in Education)
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28 pages, 1510 KiB  
Article
A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models
Appl. Sci. 2021, 11(19), 9296; https://doi.org/10.3390/app11199296 - 07 Oct 2021
Cited by 31 | Viewed by 2758
Abstract
Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due [...] Read more.
Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide. Full article
(This article belongs to the Special Issue AI in Education)
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Review

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22 pages, 3309 KiB  
Review
Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review
Appl. Sci. 2021, 11(21), 10007; https://doi.org/10.3390/app112110007 - 26 Oct 2021
Cited by 14 | Viewed by 4042
Abstract
Machine learning is emerging nowadays as an important tool for decision support in many areas of research. In the field of education, both educational organizations and students are the target beneficiaries. It facilitates the educational sector in predicting the student’s outcome at the [...] Read more.
Machine learning is emerging nowadays as an important tool for decision support in many areas of research. In the field of education, both educational organizations and students are the target beneficiaries. It facilitates the educational sector in predicting the student’s outcome at the end of their course and for the students in deciding to choose a suitable course for them based on their performances in previous exams and other behavioral features. In this study, a systematic literature review is performed to extract the algorithms and the features that have been used in the prediction studies. Based on the search criteria, 2700 articles were initially considered. Using specified inclusion and exclusion criteria, quality scores were provided, and up to 56 articles were filtered for further analysis. The utmost care was taken in studying the features utilized, database used, algorithms implemented, and the future directions as recommended by researchers. The features were classified as demographic, academic, and behavioral features, and finally, only 34 articles with these features were finalized, whose details of study are provided. Based on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability to predict the students’ performance based on specified features as categorized and can be used by students as well as academic institutions. A specific machine learning model identification for the purpose of student academic performance prediction would not be feasible, since each paper taken for review involves different datasets and does not include benchmark datasets. However, the application of the machine learning techniques in educational mining is still limited, and a greater number of studies should be carried out in order to obtain well-formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work. Full article
(This article belongs to the Special Issue AI in Education)
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Other

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11 pages, 2084 KiB  
Brief Report
Identifying Engineering Undergraduates’ Learning Style Profiles Using Machine Learning Techniques
Appl. Sci. 2021, 11(22), 10505; https://doi.org/10.3390/app112210505 - 09 Nov 2021
Cited by 4 | Viewed by 2625
Abstract
In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this research aims to generate a [...] Read more.
In a hybrid university learning environment, the rapid identification of students’ learning styles seems to be essential to achieve complementarity between conventional face-to-face pedagogical strategies and the application of new strategies using virtual technologies. In this context, this research aims to generate a predictive model to detect undergraduates’ learning style profiles quickly. The methodological design consists of applying a k-means clustering algorithm to identify the students’ learning style profiles and a decision tree C4.5 algorithm to predict the student’s membership to the previously identified groups. A cluster sample design was used with Chilean engineering students. The research result is a predictive model that, with few questions, detects students’ profiles with an accuracy of 82.93%; this prediction enables a rapid adjustment of teaching methods in a hybrid learning environment. Full article
(This article belongs to the Special Issue AI in Education)
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