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Sustainable Intelligent Education Programs
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Sustainable Intelligent Education Programs

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Education and Approaches".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 12623

Special Issue Editors

Prof. Dr. Ting-Chia Hsu

E-Mail Website
Guest Editor
Department of Technology Application and Human Resource Development, National Taiwan Normal University, Taipei 106, Taiwan
Interests: computer-assisted learning; mobile learning; personalized learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cheng-Hsuan Li

E-Mail Website
Guest Editor
Graduate Institute of Education Information and Measurement, National Taichung University of Education, Taichung 406, Taiwan
Interests: pattern recognition; data mining; multivariate analysis; hyperspectral image analysis; collabrative problem solving; nonparametric cognitive diagnosis

Special Issue Information

Dear Colleagues,

Today, AI is bringing rapid change in every aspect of our lives and, according to “AI in Education: Change at the Speed of Learning” of the UNESCO Institute for Information Technologies in Education (Duggan and Corporation, 2020), the application of AI in education helps to advance the Sustainable Development Goals (SDG), in particular SDG 4 which enshrines the need to “Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”. AI in education can highly impact four key stakeholders, namely students, educators, school leaders, and curriculum designers, through machine learning techniques such as supervised learning, unsupervised learning, and reinforcement learning. Based on previous perspectives, this Special Issue aims to use or develop machine learning algorithms for the four key stakeholders. We want to invite your submissions to this Special Issue on “Sustainable Intelligent Education Programs” to share your most recent research, developments, and implementations such as learning behaviors, personalized learning paths, dropout alarms, curriculum design suggestions, rural and urban differentials, and so on.

According to SDG 5 which concerns gender equality and women's empowerment, it is expected that there will be more studies concerning the learning situation and performance of men and women with sustainable intelligent education programs or systems. Therefore, this Special Issue is also interested in research exploring women’s and men’s learning effectiveness in terms of AI-related educational technologies in different learning stages including primary, secondary, higher and life-long adult education or workshops for older adults. Moreover, this Special Issue welcomes research probing into women and men who use AI programs or systems to enhance their learning in one discipline or across-disciplines, including formal and informal learning. To fulfill life-long learning, scholars who are developing sustainable intelligent education programs or sustainable AI-learning tools for cross-age learners are encouraged to submit research articles to this Special Issue, including those studies evaluating the learning performance or attitudes when people learn with the developed programs.

Prof. Dr. Ting-Chia Hsu
Prof. Cheng-Hsuan Li
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. Sustainability 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 2400 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

  • AI in education
  • instructional tools or programs for people to learn AI application
  • technology-assisted learning
  • intelligent tutoring system
  • teaching material recommendation system
  • learning platform log file analysis
  • learning analysis technology
  • affective AI in education

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Published Papers (4 papers)

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Research

24 pages, 5053 KiB  
Article
Exploring Artificial Intelligence in Smart Education: Real-Time Classroom Behavior Analysis with Embedded Devices
by Liujun Li, Chao Ping Chen, Lijun Wang, Kai Liang and Weiyue Bao
Sustainability 2023, 15(10), 7940; https://doi.org/10.3390/su15107940 - 12 May 2023
Cited by 6 | Viewed by 2425
Abstract
Modern education has undergone tremendous progress, and a large number of advanced devices and technologies have been introduced into the teaching process. We explore the application of artificial intelligence to education, using AI devices for classroom behavior analysis. Embedded systems are special-purpose computer [...] Read more.
Modern education has undergone tremendous progress, and a large number of advanced devices and technologies have been introduced into the teaching process. We explore the application of artificial intelligence to education, using AI devices for classroom behavior analysis. Embedded systems are special-purpose computer systems tailored to an application. Embedded system hardware for wearable devices is often characterized by low computing power and small storage, and it cannot run complex models. We apply lightweight models to embedded devices to achieve real-time emotion recognition. When teachers teach in the classroom, embedded portable devices can collect images in real-time and identify and count students’ emotions. Teachers can adjust teaching methods and obtain better teaching results through feedback on students’ learning status. Our optimized lightweight model PIDM runs on low-computing embedded devices with fast response times and reliable accuracy, which can be effectively used in the classroom. Compared with traditional post-class analysis, our method is real-time and gives teachers timely feedback during teaching. The experiments in the control group showed that after using smart devices, the classroom teaching effect increased by 9.44%. Intelligent embedded devices can help teachers keep abreast of students’ learning status and promote the improvement of classroom teaching quality. Full article
(This article belongs to the Special Issue Sustainable Intelligent Education Programs)
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14 pages, 1720 KiB  
Article
Developing a Learning Pathway System through Web-Based Mining Technology to Explore Students’ Learning Motivation and Performance
by Shu-Chen Cheng, Yu-Ping Cheng and Yueh-Min Huang
Sustainability 2023, 15(8), 6950; https://doi.org/10.3390/su15086950 - 20 Apr 2023
Cited by 2 | Viewed by 1389
Abstract
There are many resources on the Internet. Searching for articles or multimedia videos is usually interspersed with irrelevant information or advertisements, which may cause students to spend a lot of time judging whether the search results are suitable for learning materials. Therefore, this [...] Read more.
There are many resources on the Internet. Searching for articles or multimedia videos is usually interspersed with irrelevant information or advertisements, which may cause students to spend a lot of time judging whether the search results are suitable for learning materials. Therefore, this study developed a learning pathway system by analyzing the representative keywords and difficulty of Internet articles in an automated way and then explored the learning performance and motivation of students using this system. In addition, 67 students were recruited into this study for 18 weeks of experimental activities. In the experimental activities, students can use the learning pathway system to search for algorithm-related materials for reading, and they can also continue to use the system proposed in this study for self-learning after class. The results show that the students’ post-test scores are significantly higher than their pre-test scores, indicating that students can use the learning pathway system to improve their academic performance in algorithm courses. In addition, the intrinsic motivation of high-achieving students was improved, while the intrinsic and extrinsic motivation of low-achieving students were both improved. This means that the learning pathway system can provide suitable learning materials for students to learn, allowing students to achieve autonomous learning. Full article
(This article belongs to the Special Issue Sustainable Intelligent Education Programs)
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17 pages, 1054 KiB  
Article
A Nonparametric Weighted Cognitive Diagnosis Model and Its Application on Remedial Instruction in a Small-Class Situation
by Cheng-Hsuan Li, Yi-Jin Ju and Pei-Jyun Hsieh
Sustainability 2022, 14(10), 5773; https://doi.org/10.3390/su14105773 - 10 May 2022
Cited by 1 | Viewed by 1658
Abstract
CDMs can provide a discrete classification of mastery skills to diagnose relevant conceptions immediately for Education Sustainable Development. Due to the problem of parametric CDMs with only a few training sample sizes in small classroom teaching situations and the lack of a nonparametric [...] Read more.
CDMs can provide a discrete classification of mastery skills to diagnose relevant conceptions immediately for Education Sustainable Development. Due to the problem of parametric CDMs with only a few training sample sizes in small classroom teaching situations and the lack of a nonparametric model for classifying error patterns, two nonparametric weighted cognitive diagnosis models, NWSD and NWBD, for classifying mastery skills and knowledge bugs were proposed, respectively. In both, the variances of items with respect to the ideal responses were considered for computing the weighted Hamming distance, and the inverse distances between the observed and ideal responses were used as weights to obtain the probabilities of the mastering attributes of a student. Conversely, NWBD can classify students’ “bugs”, so teachers can provide suitable examples for precision assistance before teaching non-mastery skills. According to the experimental results on simulated and real datasets, the proposed methods outperform some standard methods in a small-class situation. The results also demonstrate that a remedial course with NWSD and NWBD is better than one with traditional group remedial teaching. Full article
(This article belongs to the Special Issue Sustainable Intelligent Education Programs)
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16 pages, 1514 KiB  
Article
Impact of Artificial Intelligence News Source Credibility Identification System on Effectiveness of Media Literacy Education
by Tosti H. C. Chiang, Chih-Shan Liao and Wei-Ching Wang
Sustainability 2022, 14(8), 4830; https://doi.org/10.3390/su14084830 - 18 Apr 2022
Cited by 6 | Viewed by 4718
Abstract
During presidential elections and showbusiness or social news events, society has begun to address the risk of fake news. The Sustainable Development Goals 4 for Global Education Agenda aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” [...] Read more.
During presidential elections and showbusiness or social news events, society has begun to address the risk of fake news. The Sustainable Development Goals 4 for Global Education Agenda aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” by 2030. As a result, various nations have deemed media literacy education a required competence in order for audiences to maintain a discerning attitude and to verify messages rather than automatically believing them. This study developed a highly efficient message discrimination method using new technology using artificial intelligence and big data information processing containing general news and content farm message data on approximately 938,000 articles. Deep neural network technology was used to create a news source credibility identification system. Media literacy was the core of the experimental course design. Two groups of participants used different methods to perform message discrimination. The results revealed that the system significantly expanded the participants’ knowledge of media literacy. The system positively affected the participants’ attitude, confidence, and motivation towards media literacy learning. This research provides a method of identifying fake news in order to ensure that audiences are not affected by fake messages, thereby helping to maintain a democratic society. Full article
(This article belongs to the Special Issue Sustainable Intelligent Education Programs)
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