Special Issue "Sustainable Education Technologies in Big Data and Artificial Intelligence Era"

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

Deadline for manuscript submissions: 7 July 2022.

Special Issue Editors

Prof. Dr. Haoran Xie
E-Mail Website
Guest Editor
Department of Computing and Decision Sciences, Lingnan University, 8 Castle Peak Road, Tuen Mun, New Territories, Hong Kong SAR
Interests: AI in education; affective computing; digital humanities; educational data mining
Special Issues and Collections in MDPI journals
Prof. Dr. Gary Cheng
E-Mail Website
Guest Editor
Department of Mathematics and Information Technology, The Education University of Hong Kong, 10 Lo Ping Road, Tai Po, New Territories, Hong Kong 999077, China
Interests: artificial intelligence in education; information technology supported L2 learning; ePortfolio-mediated learning; computer programming
Special Issues and Collections in MDPI journals
Prof. Dr. Gwo-Jen Hwang
E-Mail Website
Guest Editor
Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
Interests: mobile learning; ubiquitous learning; digital game-based learning; artificial intelligence in education
Special Issues and Collections in MDPI journals
Prof. Dr. Morris JONG Siu-yung
E-Mail Website
Guest Editor
Department of Curriculum and Instruction & Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China
Interests: educational gamification; game-based learning; VR+AR in education; STEM/AI education
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of artificial intelligence (AI) and big data in recent years, a huge number of applications have been employed in educational contexts. Specifically, adaptive/personalized learning has been facilitated by recommendation models based on deep neural networks; affective learning is further explored based on emotion detection techniques according to bio-signal data sources like eye-tracking and EEG signals; classroom management or instant feedback is supported by face recognition techniques. Meanwhile, the employment of big data from mobile devices and learning logs enables AI models to have an in-depth understanding of learning behaviors and patterns. The existing educational models are transformed by these emerging techniques. It is critical for academic communities to address a research issue: how to sustain the innovative educational models that employ AI and big data techniques. There are many alternative solutions: establishing communities of practice for AI and big data innovations; proposing easily employed educational models based on AI and big data; developing novel teacher training framework for introducing AI and big data skills, and so on. Therefore, this Special Issue aims to provide some potential directions and solutions for this research issue.

Prof. Dr. Haoran Xie
Prof. Dr. Gary Cheng
Prof. Dr. Gwo-Jen Hwang
Prof. Dr. Morris JONG Siu-yung
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 papers will be 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 1900 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
  • educational big data
  • educational models
  • sustainable education
  • teaching and learning innovations

Published Papers (5 papers)

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Research

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Article
Prototype Development of a Cross-Institutional Credit Transfer Information System for Community College Transfer Students
Sustainability 2021, 13(16), 9398; https://doi.org/10.3390/su13169398 - 21 Aug 2021
Viewed by 313
Abstract
Credit transfer information systems in higher education are not well studied. This article demonstrates the prototype development of a cross-institutional credit transfer information system (CICIS) for community college transfer (i.e., vertical transfer) students in an Asian educational context. It exhibits credit transfer guidelines [...] Read more.
Credit transfer information systems in higher education are not well studied. This article demonstrates the prototype development of a cross-institutional credit transfer information system (CICIS) for community college transfer (i.e., vertical transfer) students in an Asian educational context. It exhibits credit transfer guidelines and past credit transfer records to enhance the transparency and sustainability of credit transfer information and to facilitate the transfer process of prospective community college transfer students. It also ensures the sustainability of credit transfer information and its application. The four-phase life cycle of the prototyping model was adopted to guide the study. In this paper, we report the first three phases of this development: (1) Users’ needs assessment and pre-prototyping groundwork, (2) prototype development, and (3) unforeseen circumstances and expert review. Challenges and difficulties throughout the whole process are documented and discussed. Based on this prototype development experience, a solid foundation of strategies for future engineering and enhancement of credit transfer information systems can be developed. Full article
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Article
Examining Online Discourse Using the Knowledge Connection Analyzer Framework and Collaborative Tools in Knowledge Building
Sustainability 2021, 13(14), 8045; https://doi.org/10.3390/su13148045 - 19 Jul 2021
Viewed by 392
Abstract
This study examines the problem of the fragmentation of asynchronous online discourse by using the Knowledge Connection Analyzer (KCA) framework and tools and explores how students could use the KCA data in classroom reflections to deepen their knowledge building (KB) inquiry. We applied [...] Read more.
This study examines the problem of the fragmentation of asynchronous online discourse by using the Knowledge Connection Analyzer (KCA) framework and tools and explores how students could use the KCA data in classroom reflections to deepen their knowledge building (KB) inquiry. We applied the KCA to nine Knowledge Forum® (KF) databases to examine the framework, identify issues with online discourse that may inform further development, and provide data on how the tools work. Our comparisons of the KCA data showed that the databases with more sophisticated teacher–researcher co-design had higher KCA indices than those with regular KF use, validating the framework. Analysis of KF discourse using the KCA helped identify several issues including limited collaboration among peers, underdeveloped practices of synthesizing and rising above of collective ideas, less analysis of conceptual development of discussion threads, and limited collaborative reflection on individual contribution and promising inquiry direction. These issues that open opportunities for further development cannot be identified by other present analytics tools. The exploratory use of the KCA in real classroom revealed that the KCA can support students’ productive reflective assessment and KB. This study discusses the implications for examining and scaffolding online discussions using the KCA assessment framework, with a focus on collective perspectives regarding community knowledge, synthesis, idea improvement, and contribution to community understanding. Full article
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Article
Effect of Flipped Teaching on Cognitive Load Level with Mobile Devices: The Case of a Graphic Design Course
Sustainability 2021, 13(13), 7092; https://doi.org/10.3390/su13137092 - 24 Jun 2021
Viewed by 367
Abstract
Due to the emergence of computer education, AI education, the Internet of Things, big data, and technological wisdom, it is easy for students to be distracted when engaged in traditional education. Flipped teaching is a teaching strategy frequently used in colleges and universities. [...] Read more.
Due to the emergence of computer education, AI education, the Internet of Things, big data, and technological wisdom, it is easy for students to be distracted when engaged in traditional education. Flipped teaching is a teaching strategy frequently used in colleges and universities. The focus of this research was conducted by a comparative analysis of the cognitive load between the experimental group and the control group through a quasi-experimental design for research with different learning methods and different classes. More specifically, flipped teaching was carried out with an experimental group, and traditional teaching a control group; they were observed at the same time, and 213 private university students participated in the experiment. The research proposes a practice of mixed teaching, carried out in a group communication behavior system, and enhancing the spirit of group interaction and learning through mobile devices. The core value of the research lies in (1) online learning, (2) group interaction, and (3) the learning load of the conceptual model. In addition, focus group interviews were used to provide feedback on participants’ cognition and emotions. The results indicate that there were differences in cognitive load between the two classes. Full article
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Article
Vocabulary Learning Based on Learner-Generated Pictorial Annotations: Using Big Data as Learning Resources
Sustainability 2021, 13(11), 5767; https://doi.org/10.3390/su13115767 - 21 May 2021
Cited by 1 | Viewed by 475
Abstract
This research discusses the potential of using big data for vocabulary learning from the perspective of learner-generated pictorial annotations. Pictorial annotations lead to effective vocabulary learning, the creation of which is however challenging and time-consuming. As user-generated annotations promote active learning, and in [...] Read more.
This research discusses the potential of using big data for vocabulary learning from the perspective of learner-generated pictorial annotations. Pictorial annotations lead to effective vocabulary learning, the creation of which is however challenging and time-consuming. As user-generated annotations promote active learning, and in the big data era, data sources in social media platforms are not only huge but also user-generated, the proposal of using social media data to establish a natural and semantic connection between pictorial annotations and words seems feasible. This research investigated learners’ perceptions of creating pictorial annotations using Google images and social media images, learners’ evaluation of the learner-generated pictorial annotations, and the effectiveness of Google pictorial annotations and social media pictorial annotations in promoting vocabulary learning. A total of 153 undergraduates participated in the research, some of whom created pictorial annotations using Google and social media data, some evaluated the annotations, and some learned the target words with the annotations. The results indicated positive attitudes towards using Google and social media data sets as resources for language enhancement, as well as significant effectiveness of learner-generated Google pictorial annotations and social media pictorial annotations in promoting both initial learning and retention of target words. Specifically, we found that (i) Google images were more appropriate and reliable for pictorial annotations creation, and therefore they achieved better outcomes when learning with the annotations created with Google images than images from social media, and (ii) the participants who created word lists that integrate pictorial annotations were likely to engage in active learning when they selected and organized the verbal and visual information of target words by themselves and actively integrated such information with their prior knowledge. Full article
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Review

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Review
A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics
Sustainability 2021, 13(9), 4859; https://doi.org/10.3390/su13094859 - 26 Apr 2021
Cited by 1 | Viewed by 498
Abstract
Computers in Human Behavior (CHB) is a well-established source with a wide range of audiences in the field of human interactions with computers and has been one of the most widely acknowledged and leading venues with significant scientific impact for more [...] Read more.
Computers in Human Behavior (CHB) is a well-established source with a wide range of audiences in the field of human interactions with computers and has been one of the most widely acknowledged and leading venues with significant scientific impact for more than 35 years. This review provides an overview of the status, trends, and particularly the thematic structure of the CHB by adopting bibliometrics and structural topic modeling on 5957 studies. Specifically, we analyzed the trend of publications, identified major institutions and countries/regions, detected scientific collaboration patterns, and uncovered important topics. Significant findings were presented. For example, the contribution of the USA and Open University of Netherlands was highlighted. Important research topics such as e-commerce, social interactions and behaviors, public opinion and social media, cyberbullying, online sexual issues, and game andgamification were identified. This review contributes to the CHB community by justifying the interest in human behavior issues concerning computer use and identifying future research lines on this topic. Full article
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