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Mathematics
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Artificial Intelligence in Education
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Artificial Intelligence in Education

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 73569

Special Issue Editors

Prof. Dr. Heui Seok Lim

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Human-inspired AI Computing Research Center, Korea University, Seoul 13557, Korea
Interests: AI; educational data mining; NLP; learning science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kyu Han Koh

E-Mail Website
Guest Editor
Department of Computer Science, California State University Stanislaus, Turlock, CA, USA
Interests: Computational Thinking Education; Visual Data Analytics; Data-Driven Assessment; End User Programming

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) technology has recently attracted attention for its potential in improving various fields, including education. In brief, AI has been shown to be capable of improving quality of education by enhancing conventional learning and teaching approaches. More specifically, by automating some basic activities such as grading, it can provide teachers with more time to interact with their students, or focus on professional development tasks. By adjusting learning based on an individual student’s particular needs, ability, or interest, it can enable students at different levels to work together in one classroom, while teachers offer support and help when needed. AI in education can provide effective intelligent tutoring understanding students’ capability, emotion, and level of achievement. Furthermore, AI-enhanced programs can generate useful feedback, hints, and recommendations for both students and teachers, assisting them in their learning and teaching experience.

Considering the crucial roles of AI in education, and the fact that its application to education has become more significant due to the spread of COVID-19, this Special Issue focuses on the involvement of AI in education.

This Special Issue will accept high-quality papers containing original research results and survey articles of excellent merit in (but not limited to) the following fields:

KEYWORDS

  • Artificial intelligence in education
  • Natural language processing in education
  • Education data mining and learning analytics
  • Educational recommender system
  • Affective computing in education
  • Neural symbolic inference in education
  • Artificial neural networks, machine learning, and statistical and optimization methods in education
  • Evaluation of artificial intelligence, adaptive, or personalized educational systems
  • Adaptivity and personalization in education
  • Intelligent tutoring systems, virtual reality, and dialog system in education

We look forward to receiving your submission of original research and review articles revolving around the application of AI to education.

Prof. Dr. Heui Seok Lim
Prof. Dr. Kyu Han Koh
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial intelligence in education
  • Natural language processing in education
  • Education data mining and learning analytics
  • Educational recommender system
  • Affective computing in education
  • Neural symbolic inference in education
  • Artificial neural networks, machine learning, and statistical and optimization methods in education
  • Evaluation of artificial intelligence, adaptive, or personalized educational systems
  • Adaptivity and personalization in education
  • Intelligent tutoring systems, virtual reality, and dialog system in education

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (9 papers)

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Research

Jump to: Review

13 pages, 245 KiB  
Article
A Comparison of Two-Stage Least Squares (TSLS) and Ordinary Least Squares (OLS) in Estimating the Structural Relationship between After-School Exercise and Academic Performance
by Kyulee Shin, Sukkyung You and Mihye Kim
Mathematics 2021, 9(23), 3105; https://doi.org/10.3390/math9233105 - 2 Dec 2021
Cited by 4 | Viewed by 2871
Abstract
The current study examines the structural relationship between the academic performance exam scores of Korean middle school students and their after-school exercise hours. Although prior literature theoretically or experimentally predicts that these variables are positively associated, this association is difficult to empirically verify [...] Read more.
The current study examines the structural relationship between the academic performance exam scores of Korean middle school students and their after-school exercise hours. Although prior literature theoretically or experimentally predicts that these variables are positively associated, this association is difficult to empirically verify without controlling for mutual effects with other variables, or unless a full model is estimated by specifying the whole structure of all variables affecting the two variables in question. Unlike previous studies, this study estimates the structural relationship using two-stage least squares method, which does not require experimental observations collected for our particular purpose or estimating the full model. From this estimation, we empirically affirm that there is a positive structural relationship between students’ after-school exercise hours and their academic performance exam scores, whereas the ordinary least squares method consistently estimates a negative relationship. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
27 pages, 3483 KiB  
Article
Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities
by Diego Opazo, Sebastián Moreno, Eduardo Álvarez-Miranda and Jordi Pereira
Mathematics 2021, 9(20), 2599; https://doi.org/10.3390/math9202599 - 15 Oct 2021
Cited by 21 | Viewed by 4911
Abstract
Student dropout, defined as the abandonment of a high education program before obtaining the degree without reincorporation, is a problem that affects every higher education institution in the world. This study uses machine learning models over two Chilean universities to predict first-year engineering [...] Read more.
Student dropout, defined as the abandonment of a high education program before obtaining the degree without reincorporation, is a problem that affects every higher education institution in the world. This study uses machine learning models over two Chilean universities to predict first-year engineering student dropout over enrolled students, and to analyze the variables that affect the probability of dropout. The results show that instead of combining the datasets into a single dataset, it is better to apply a model per university. Moreover, among the eight machine learning models tested over the datasets, gradient-boosting decision trees reports the best model. Further analyses of the interpretative models show that a higher score in almost any entrance university test decreases the probability of dropout, the most important variable being the mathematical test. One exception is the language test, where a higher score increases the probability of dropout. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
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15 pages, 845 KiB  
Article
Portability of Predictive Academic Performance Models: An Empirical Sensitivity Analysis
by Jose Luis Arroyo-Barrigüete, Susana Carabias-López, Tomas Curto-González and Adolfo Hernández
Mathematics 2021, 9(8), 870; https://doi.org/10.3390/math9080870 - 15 Apr 2021
Cited by 5 | Viewed by 2071
Abstract
The portability of predictive models of academic performance has been widely studied in the field of learning platforms, but there are few studies in which the results of previous evaluations are used as factors. The aim of this work was to analyze portability [...] Read more.
The portability of predictive models of academic performance has been widely studied in the field of learning platforms, but there are few studies in which the results of previous evaluations are used as factors. The aim of this work was to analyze portability precisely in this context, where preceding performance is used as a key predictor. Through a study designed to control the main confounding factors, the results of 170 students evaluated over two academic years were analyzed, developing various predictive models for a base group (BG) of 39 students. After the four best models were selected, they were validated using different statistical techniques. Finally, these models were applied to the remaining groups, controlling the number of different factors with respect to the BG. The results show that the models’ performance varies consistently with what was expected: as they move away from the BG (fewer common characteristics), the specificity of the four models tends to decrease. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
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17 pages, 403 KiB  
Article
Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout
by Róbert Csalódi and János Abonyi
Mathematics 2021, 9(5), 463; https://doi.org/10.3390/math9050463 - 24 Feb 2021
Cited by 11 | Viewed by 2657
Abstract
A data-driven method to identify frequent sets of course failures that students should avoid in order to minimize the likelihood of their dropping out from their university training is proposed. The overall probability distribution of the dropout is determined by survival analysis. This [...] Read more.
A data-driven method to identify frequent sets of course failures that students should avoid in order to minimize the likelihood of their dropping out from their university training is proposed. The overall probability distribution of the dropout is determined by survival analysis. This result can only describe the mean dropout rate of the undergraduates. However, due to the failure of different courses, the chances of dropout can be highly varied, so the traditional survival model should be extended with event analysis. The study paths of students are represented as events in relation to the lack of completing the required subjects for every semester. Frequent patterns of backlogs are discovered by the mining of frequent sets of these events. The prediction of dropout is personalised by classifying the success of the transitions between the semesters. Based on the explored frequent item sets and classifiers, association rules are formed providing the estimates of the success of the continuation of the studies in the form of confidence metrics. The results can be used to identify critical study paths and courses. Furthermore, based on the patterns of individual uncompleted subjects, it is suitable to predict the chance of continuation in every semester. The analysis of the critical study paths can be used to design personalised actions minimizing the risk of dropout, or to redesign the curriculum aiming the reduction in the dropout rate. The applicability of the method is demonstrated based on the analysis of the progress of chemical engineering students at the University of Pannonia in Hungary. The method is suitable for the examination of more general problems assuming the occurrence of a set of events whose combinations may trigger a set of critical events. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
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20 pages, 5052 KiB  
Article
Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement
by Pieter Vanneste, José Oramas, Thomas Verelst, Tinne Tuytelaars, Annelies Raes, Fien Depaepe and Wim Van den Noortgate
Mathematics 2021, 9(3), 287; https://doi.org/10.3390/math9030287 - 1 Feb 2021
Cited by 38 | Viewed by 5878
Abstract
Computer vision has shown great accomplishments in a wide variety of classification, segmentation and object recognition tasks, but tends to encounter more difficulties when tasks require more contextual assessment. Measuring the engagement of students is an example of such a complex task, as [...] Read more.
Computer vision has shown great accomplishments in a wide variety of classification, segmentation and object recognition tasks, but tends to encounter more difficulties when tasks require more contextual assessment. Measuring the engagement of students is an example of such a complex task, as it requires a strong interpretative component. This research describes a methodology to measure students’ engagement, taking both an individual (student-level) and a collective (classroom) approach. Results show that students’ individual behaviour, such as note-taking or hand-raising, is challenging to recognise, and does not correlate with students’ self-reported engagement. Interestingly, students’ collective behaviour can be quantified in a more generic way using measures for students’ symmetry, reaction times and eye-gaze intersections. Nonetheless, the evidence for a connection between these collective measures and engagement is rather weak. Although this study does not succeed in providing a proxy of students’ self-reported engagement, our approach sheds light on the needs for future research. More concretely, we suggest that not only the behavioural, but also the emotional and cognitive component of engagement should be captured. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
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15 pages, 2038 KiB  
Article
Development of Intelligent Information System for Digital Cultural Contents
by Yuna Hur and Jaechoon Jo
Mathematics 2021, 9(3), 238; https://doi.org/10.3390/math9030238 - 26 Jan 2021
Cited by 3 | Viewed by 2613
Abstract
A significant amount of digital cultural contents is shared online, but learners do not know where subject matter content is or how to find it. Therefore, there is a need for a service to improve educational quality by effectively providing relevant information in [...] Read more.
A significant amount of digital cultural contents is shared online, but learners do not know where subject matter content is or how to find it. Therefore, there is a need for a service to improve educational quality by effectively providing relevant information in response to searches for content that is useful to learners. This study developed and tested the usability and utility of an intelligent information system that effectively searches and visualizes digital cultural contents. The system collects data on digital cultural contents, automatically classifies them, and creates content triple data to automatically display the results with a 3D timeline, knowledge network map, and keyword relation network map through content search, triple search, and keyword search. We also conducted a survey and in-depth interviews to verify users’ satisfaction with respect to the use and utility of the system. For the experiment, we developed survey questions to measure user satisfaction and conducted in-depth interviews regarding the system’s utility with a total of 65 subjects. The results show that the response for satisfaction with regard to the use and utility was generally “satisfied”. In addition, the system stability was evaluated as “high”. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
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18 pages, 1646 KiB  
Article
An Extended Theory of Planned Behavior for the Modelling of Chinese Secondary School Students’ Intention to Learn Artificial Intelligence
by Ching Sing Chai, Xingwei Wang and Chang Xu
Mathematics 2020, 8(11), 2089; https://doi.org/10.3390/math8112089 - 23 Nov 2020
Cited by 77 | Viewed by 11247
Abstract
Artificial Intelligence (AI) is currently changing how people live and work. Its importance has prompted educators to begin teaching AI in secondary schools. This study examined how Chinese secondary school students’ intention to learn AI were associated with eight other relevant psychological factors. [...] Read more.
Artificial Intelligence (AI) is currently changing how people live and work. Its importance has prompted educators to begin teaching AI in secondary schools. This study examined how Chinese secondary school students’ intention to learn AI were associated with eight other relevant psychological factors. Five hundred and forty-five secondary school students who have completed at least one cycle of AI course were recruited to participate in this study. Based on the theory of planned behavior, the students’ AI literacy, subjective norms, and anxiety were identified as background factors. These background factors were hypothesized to influence the students’ attitudes towards AI, their perceived behavioral control, and their intention to learn AI. To provide more nuanced understanding, the students’ attitude towards AI was further delineated as constituted by their perception of the usefulness of AI, the potential of AI technology to promote social good, and their attitude towards using AI technology. Similarly, the perceived behavioral control was operationalized as students’ confidence towards learning AI knowledge and optimistic outlook of an AI infused world. Relationships between the factors were theoretically illustrated as a model that depicts how students’ intention to learn AI was constituted. Two research questions were then formulated. Confirmatory factor analysis was employed to validate that multi-factor survey, followed by structural equational modelling to ascertain the significant associations between the factors. The confirmatory factor analysis supports the construct validity of the questionnaire. Twenty-five out of the thirty-three hypotheses were supported through structural equation modelling. The model helps researchers and educators to understand the factors that shape students’ intention to learn AI. These factors should be considered for the design of AI curriculum. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
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19 pages, 2834 KiB  
Article
Automatic Judgement of Online Video Watching: I Know Whether or Not You Watched
by Eunseon Yi, Heuiseok Lim and Jaechoon Jo
Mathematics 2020, 8(10), 1827; https://doi.org/10.3390/math8101827 - 18 Oct 2020
Cited by 2 | Viewed by 3668
Abstract
Videos have long been viewed through the free choice of customers, but in some cases currently, watching them is absolutely required, for example, in institutions, companies, and education, even if the viewers prefer otherwise. In such cases, the video provider wants to determine [...] Read more.
Videos have long been viewed through the free choice of customers, but in some cases currently, watching them is absolutely required, for example, in institutions, companies, and education, even if the viewers prefer otherwise. In such cases, the video provider wants to determine whether the viewer has honestly been watching, but the current video viewing judging system has many loopholes; thus, it is hard to distinguish between honest viewers and false viewers. Time interval different answer popup quiz (TIDAPQ) was developed to judge honest watching. In this study, TIDAPQ randomly inserts specially developed popup quizzes in the video. Viewers must solve time interval pass (RESULT 1) and individually different correct answers (RESULT 2) while they watch. Then, using these two factors, TIDAPQ immediately performs a comprehensive judgement on whether the viewer honestly watched the video. To measure the performance of TIDAPQ, 100 experimental subjects were recruited to participate in the model verification experiment. The judgement performance on normal watching was 93.31%, and the judgement performance on unusual watching was 85.71%. We hope this study will be useful in many areas where watching judgements are needed. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
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Review

Jump to: Research

19 pages, 6022 KiB  
Review
Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review
by Gwo-Jen Hwang and Yun-Fang Tu
Mathematics 2021, 9(6), 584; https://doi.org/10.3390/math9060584 - 10 Mar 2021
Cited by 142 | Viewed by 33180
Abstract
Learning mathematics has been considered as a great challenge for many students. The advancement of computer technologies, in particular, artificial intelligence (AI), provides an opportunity to cope with this problem by diagnosing individual students’ learning problems and providing personalized supports to maximize their [...] Read more.
Learning mathematics has been considered as a great challenge for many students. The advancement of computer technologies, in particular, artificial intelligence (AI), provides an opportunity to cope with this problem by diagnosing individual students’ learning problems and providing personalized supports to maximize their learning performances in mathematics courses. However, there is a lack of reviews from diverse perspectives to help researchers, especially novices, gain a whole picture of the research of AI in mathematics education. To this end, this research aims to conduct a bibliometric mapping analysis and systematic review to explore the role and research trends of AI in mathematics education by searching for the relevant articles published in the quality journals indexed by the Social Sciences Citation Index (SSCI) from the Web of Science (WOS) database. Moreover, by referring to the technology-based learning model, several dimensions of AI in mathematics education research, such as the application domains, participants, research methods, adopted technologies, research issues and the roles of AI as well as the citation and co-citation relationships, are taken into account. Accordingly, the advancements of AI in mathematics education research are reported, and potential research topics for future research are recommended. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education)
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