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Sustainable Education Technologies in Big Data and Artificial Intelligence Era

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

Deadline for manuscript submissions: closed (7 July 2023) | Viewed by 36516

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Special Issue Editors

Prof. Dr. Haoran Xie

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Guest Editor

Department of Computing and Decision Sciences, Lingnan University, 8 Castle Peak Road, Tuen Mun, New Territories 999077, Hong Kong, China
Interests: AI in education; affective computing; digital humanities; educational data mining
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gary Cheng

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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, Collections and Topics in MDPI journals

Prof. Dr. Gwo-Jen Hwang

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Guest Editor

Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan
Interests: mobile learning; digital game-based learning; flipped learning; artificial intelligence in education
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Morris JONG Siu-yung

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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, Collections and Topics 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

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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

artificial intelligence in education
educational big data
educational models
sustainable education
teaching and learning innovations

Published Papers (14 papers)

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Research

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18 pages, 934 KiB

Open AccessArticle

Enhancing Sustainable Design Thinking Education Efficiency: A Comparative Study of Synchronous Online and Offline Classes

by Joungmin Kim and Sun Joo Ryu

Sustainability 2023, 15(18), 13293; https://doi.org/10.3390/su151813293 - 05 Sep 2023

Cited by 1 | Viewed by 939

Abstract

As online education advances, there is a growing interest in conducting various online courses. However, design thinking education, which heavily relies on active interactions and discussions among team members, has predominantly taken place in offline environments. This raises the question of whether online design thinking education can be equally as effective as offline education. To address this, our study conducted comparative research between offline and synchronous online design thinking classes to investigate how these different environments contribute to developing design thinking mindsets. The acquisition levels of seven design thinking mindsets—ambiguity, curiosity, empathy, experimental spirit, integrative thinking, open mind, and teamwork—were used to measure the efficiency of the design thinking classes. The research involved a 15-week project-based course provided to 126 engineering students at a university, examining the differences in design thinking mindsets before and after the completion of the process. The study’s findings demonstrated that synchronous online classes favorably influence the cultivation of design thinking mindsets, exhibiting efficiency comparable to that observed in traditional offline courses. Specifically, synchronous online classes were found to be more effective in cultivating empathy, integrative thinking, and open mind, while experimental spirit showed more significant development in offline courses. These findings contribute to a better understanding of the potential of synchronous online design thinking education and contribute the development of sustainable and effective online learning environments. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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16 pages, 2407 KiB

Open AccessArticle

Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude

by Ali Çetinkaya, Ömer Kaan Baykan and Havva Kırgız

Sustainability 2023, 15(17), 12917; https://doi.org/10.3390/su151712917 - 27 Aug 2023

Viewed by 1169

Abstract

With the increasing prevalence and significance of computer programming, a crucial challenge that lies ahead of teachers and parents is to identify students adept at computer programming and direct them to relevant programming fields. As most studies on students’ coding abilities focus on elementary, high school, and university students in developed countries, we aimed to determine the coding abilities of middle school students in Turkey. We first administered a three-part spatial test to 600 secondary school students, of whom 400 completed the survey and the 20-level Classic Maze course on Code.org. We then employed four machine learning (ML) algorithms, namely, support vector machine (SVM), decision tree, k-nearest neighbor, and quadratic discriminant to classify the coding abilities of these students using spatial test and Code.org platform data. SVM yielded the most accurate results and can thus be considered a suitable ML technique to determine the coding abilities of participants. This article promotes quality education and coding skills for workforce development and sustainable industrialization, aligned with the United Nations Sustainable Development Goals. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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25 pages, 2522 KiB

Open AccessArticle

Teacher Evaluation in Primary and Secondary Schools: A Systematic Review of SSCI Journal Publications from 2012 to 2022

by Xiangdong Wei, Man-Kong Chow, Lisha Huang, Xinyi Huang and Gary Cheng

Sustainability 2023, 15(9), 7280; https://doi.org/10.3390/su15097280 - 27 Apr 2023

Cited by 2 | Viewed by 2033

Abstract

This study revealed the current situation and developments in teacher evaluation in primary and secondary schools by reviewing 54 articles published in the recent decade (i.e., from January 2012 to October 2022). The coding scheme was developed based on the three components of effective teacher evaluation systems: “what”, “how”, and “who”. Specifically, we investigated the frameworks used for teacher evaluation, methods of evaluation, and participants in teacher evaluation. Based on our results, most studies evaluated teachers from the dimension of Instructional Support. Evaluation through video recording became popular due to technological advancement. Further, an increasing number of schools invited external experts to conduct teacher evaluations to ensure fairness. We also identified several crucial factors for teacher development: effective use of teaching resources and technology, high-quality feedback and communication, emotional support, classroom organization, and professional responsibilities. Due to COVID-19, many schools adopted distance learning, prompting the need to develop technological skills for teachers. Through the in-depth analysis of the current situation and development trends in the various dimensions of teacher evaluation in primary and secondary education, future research directions and issues were discussed and explored in this review. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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14 pages, 2846 KiB

Open AccessArticle

Artificial Intelligence-Empowered Art Education: A Cycle-Consistency Network-Based Model for Creating the Fusion Works of Tibetan Painting Styles

by Yijing Chen, Luqing Wang, Xingquan Liu and Hongjun Wang

Sustainability 2023, 15(8), 6692; https://doi.org/10.3390/su15086692 - 15 Apr 2023

Cited by 2 | Viewed by 1443

Abstract

The integration of Tibetan Thangka and other ethnic painting styles is an important topic of Chinese ethnic art. Its purpose is to explore, supplement, and continue Chinese traditional culture. Restricted by Buddhism and the economy, the traditional Thangka presents the problem of a single style, and drawing a Thangka is time-consuming and labor-intensive. In response to these problems, we propose a Tibetan painting style fusion (TPSF) model based on neural networks that can automatically and quickly integrate the painting styles of the two ethnicities. First, we set up Thangka and Chinese painting datasets as experimental data. Second, we use the training data to train the generator and the discriminator. Then, the TPSF model maps the style of the input image to the target image to fuse the two ethnicities painting styles of Tibetan and Chinese. Finally, to demonstrate the advancement of the proposed method, we add four comparison models to our experiments. At the same time, the Frechet Inception Distance (FID) metric and the questionnaire method were used to evaluate the quality and visual appeal of the generated images, respectively. The experimental results show that the fusion images have excellent quality and great visual appeal. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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16 pages, 3844 KiB

Open AccessArticle

Learning Performance Prediction and Alert Method in Hybrid Learning

by Huijuan Zhuang, Jing Dong, Su Mu and Haiming Liu

Sustainability 2022, 14(22), 14685; https://doi.org/10.3390/su142214685 - 08 Nov 2022

Cited by 3 | Viewed by 1465

Abstract

In online learning, students’ learning data such as time and logs are commonly used to predict the student’s learning performance. In a hybrid context, learning activities occur both online and offline. Thus, how to integrate online and offline learning data effectively for an accurate learning performance prediction becomes very challenging. This paper proposes a “prediction and alert” model for students’ learning performance in a hybrid learning context. The model is developed and evaluated through analyzing the 16-week (one semester) attributes of English learning data of 50 students in the eighth grade. Six significant variables were determined as learning performance attributes, namely, qualified rate, excellent rate, scores, number of practice sessions, practice time, and completion. The proposed model was put into actual practice through four months of application and modification, in which a sample of 50 middle school students participated. The model shows the feasibility and effectiveness of data analysis for hybrid learning. It can support students’ continuous online and offline learning more effectively. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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18 pages, 3299 KiB

Open AccessArticle

Research Status, Hotspots, and Evolutionary Trends of Intelligent Education from the Perspective of Knowledge Graph

by Dingpu Shi, Jincheng Zhou, Dan Wang and Xiaopeng Wu

Sustainability 2022, 14(17), 10934; https://doi.org/10.3390/su141710934 - 01 Sep 2022

Cited by 13 | Viewed by 2706

Abstract

Intelligent education research has become a research hotspot in recent years. The Citespace software that operates a graph visualization function was used to clarify the current situation, hot spots, and evolutionary trends of intelligent education research development; the authors, institutions, and countries engaged in intelligent education research, as well as the basic knowledge structure, main keywords, citation clustering, dual-map overlay of journals and citation emergence of intelligent education research. The results show that the annual number of publications in the field has shown an upward trend since 2010, with strong communication among research institutions and countries, but weak communication among researchers. Among them, the United States is the center of the global collaborative network of intelligent education research. The basic knowledge structure of intelligence education research is mainly composed of Classroom Management, Evaluation Index, 5G Network, and Big Data Analytics. The dual-map overlay analysis of journals shows that the core areas of intelligence education are increasing, and the analysis of keywords and cited literature shows that Intelligence Tutoring System, AI system, Students and Education, Model, and System are high-frequency words with high-intensity burstness. In addition, research on intelligent education is characterized by multi-country, multi-field, and multi-disciplinary integration, and the adoption of Big Data, Distance Education Technology and Artificial Intelligence Technology to provide scientific support for teaching and learning will become the key research content in this field in the future. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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22 pages, 2997 KiB

Open AccessArticle

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

by Wala Bagunaid, Naveen Chilamkurti and Prakash Veeraraghavan

Sustainability 2022, 14(17), 10551; https://doi.org/10.3390/su141710551 - 24 Aug 2022

Cited by 17 | Viewed by 4120

Abstract

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. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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20 pages, 936 KiB

Open AccessArticle

Teachers’ Perceptions of Teaching Sustainable Artificial Intelligence: A Design Frame Perspective

by Xiao-Fan Lin, Lu Chen, Kan Kan Chan, Shiqing Peng, Xifan Chen, Siqi Xie, Jiachun Liu and Qintai Hu

Sustainability 2022, 14(13), 7811; https://doi.org/10.3390/su14137811 - 27 Jun 2022

Cited by 3 | Viewed by 4661

Abstract

Teaching artificial intelligence (AI) is an emerging challenge in global school education. There are considerable barriers to overcome, including the existing practices of technology education and teachers’ knowledge of AI. Research evidence shows that studying teachers’ experiences can be beneficial in informing how appropriate design in teaching sustainable AI should evolve. Design frames characterize teachers’ design reasoning and can substantially influence their AI lesson design considerations. This study examined 18 experienced teachers’ perceptions of teaching AI and identified effective designs to support AI instruction. Data collection methods involved semi-structured interviews, action study, classroom observation, and post-lesson discussions with the purpose of analyzing the teachers’ perceptions of teaching AI. Grounded theory was employed to detail how teachers understand the pedagogical challenges of teaching AI and the emerging pedagogical solutions from their perspectives. Results reveal that effective AI instructional design should encompass five important components: (1) obstacles to and facilitators of participation in teaching AI, (2) interactive design thinking processes, (3) teachers’ knowledge of teaching AI, (4) orienteering AI knowledge for social good, and (5) the holistic understanding of teaching AI. The implications for future teacher AI professional development activities are proposed. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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19 pages, 3099 KiB

Open AccessArticle

For Learning Analytics to Be Sustainable under GDPR—Consequences and Way Forward

by Thashmee Karunaratne

Sustainability 2021, 13(20), 11524; https://doi.org/10.3390/su132011524 - 18 Oct 2021

Cited by 3 | Viewed by 2059

Abstract

Personalized learning is one of the main focuses in 21st-century education, and Learning Analytics (LA) has been recognized as a supportive tool for enhancing personalization. Meanwhile, the General Data Protection Regulations (GDPR), which concern the protection of personal data, came into effect in 2018. However, contemporary research lacks the essential knowledge of how and in which ways the presence of GDPR influence LA research and practices. Hence, this study intends to examine the requirements for sustaining LA under the light of GDPR. According to the study outcomes, the legal obligations for LA could be simplified to data anonymization with consequences of limitations to personalized interventions, one of the powers of LA. Explicit consent from the data subjects (students) prior to any data processing is mandatory under GDPR. The consent agreements must include the purpose, types of data, and how, when and where the data is processed. Moreover, transparency of the complete process of storing, retrieving, and analysing data as well as how the results are used should be explicitly documented in LA applications. The need for academic institutions to have specific regulations for supporting LA is emphasized. Regulations for sharing data with third parties is left as a further extension of this study. Full article

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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16 pages, 1317 KiB

Open AccessArticle

Prototype Development of a Cross-Institutional Credit Transfer Information System for Community College Transfer Students

by Kin Cheung, Bin Li, Peter Benz, Ka Ming Chow, Jeremy Tzi Dong Ng, Wilson Yeung Yuk Kwok, Hilda Tsang, Dicky Nok Hang Leung, Janus Ka Yee Lui, Yee Na Li, Eunice So and Alice Leung

Sustainability 2021, 13(16), 9398; https://doi.org/10.3390/su13169398 - 21 Aug 2021

Cited by 5 | Viewed by 2629

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 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

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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18 pages, 2937 KiB

Open AccessArticle

Examining Online Discourse Using the Knowledge Connection Analyzer Framework and Collaborative Tools in Knowledge Building

by Yuqin Yang, Jan van Aalst and Carol Chan

Sustainability 2021, 13(14), 8045; https://doi.org/10.3390/su13148045 - 19 Jul 2021

Cited by 4 | Viewed by 1970

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 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

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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26 pages, 1261 KiB

Open AccessArticle

Effect of Flipped Teaching on Cognitive Load Level with Mobile Devices: The Case of a Graphic Design Course

by Yi-Chieh Chen, Kuo-Kuang Fan and Kwo-Ting Fang

Sustainability 2021, 13(13), 7092; https://doi.org/10.3390/su13137092 - 24 Jun 2021

Cited by 9 | Viewed by 3192

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. 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

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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17 pages, 609 KiB

Open AccessArticle

Vocabulary Learning Based on Learner-Generated Pictorial Annotations: Using Big Data as Learning Resources

by Di Zou and Haoran Xie

Sustainability 2021, 13(11), 5767; https://doi.org/10.3390/su13115767 - 21 May 2021

Cited by 6 | Viewed by 2138

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 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

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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Review

Jump to: Research

21 pages, 33665 KiB

Open AccessReview

A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics

by Xieling Chen, Di Zou, Haoran Xie and Gary Cheng

Sustainability 2021, 13(9), 4859; https://doi.org/10.3390/su13094859 - 26 Apr 2021

Cited by 4 | Viewed by 3823

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 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

(This article belongs to the Special Issue Sustainable Education Technologies in Big Data and Artificial Intelligence Era)

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