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This study reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI) algorithms. Results indicate that, from 1997 to 2009, AIoL research focused on the combination of intelligent tutoring systems and distance learning. In 2010-2014, AIoL research emphasized learner-oriented learning. In 2015-2019, learner-system interactions to facilitate personalized, adaptive, and collaborative learning became the main focus. "Intelligent tutoring systems" have played the most important role in AIoL, followed by "profiling and prediction," and "adaptive systems with personalization." Accordingly, the roles and research trends as well as several suggestions for future research in the field of AIoL are provided as a reference for researchers and policy makers.

With the rapid development of Artificial Intelligence, automatic writing evaluation (AWE) has received much attention from English Foreign Language (EFL) writing teachers. However, the obstacles and potential problems of integrating AWE in EFL writing instruction have yet to be explored. Scholars have indicated that the effectiveness of AWE in EFL writing instruction depends on the learners' depth of reflection. Hence, this study proposes a learning approach that integrates AWE and peer assessment (PA) based on the knowledge-building theory, with the expectation that learners will be able to strengthen their reflections on AWE feedback through PA, and thereby improve their EFL writing performance. To examine the effectiveness of the proposed approach, a quasi-experiment was conducted in a university EFL writing class. One of the classes (33 students) was the experimental group using the PA-AWE approach, and the other class (31 students) was a control group that studied using the conventional AWE approach (C-AWE approach). Findings revealed that the PA-AWE group outperformed the C-AWE group regarding EFL writing performance, learning motivation, critical thinking, and reduced EFL writing anxiety. In addition, a thematic inductive qualitative analysis of the interview data indicated each approach's benefits and learning conceptions.

At present, with the rapid development of the internet and the gradual promotion of online collaborative learning, the social regulation of learning is receiving increasing attention, which involves socially shared metacognition, one facet of social metacognition. To date, social regulation of learning or socially shared metacognition have been widely studied using qualitative approaches. Although a variety of scales have been developed to measure metacognition in traditional individual learning, little work has been done to develop a scale to measure social metacognition in collaborative learning contexts. This study originally developed a social metacognition inventory consisting of 24 indicators by referring to the literature for assessing beliefs of other persons (BOP), awareness of other persons' thinking (AOPT), judgment of other persons' emotions (JOPE), co-regulation of each other's thinking (CREOT), and evaluation of other persons' thinking (EOPT). After EFA using 218 undergraduates' questionnaires of social metacognition in collaborative argumentation on a social psychological issue from a Sino-Foreign Cooperative Educational Institution, 17 indicators showed good factorability and reliability. After CFA using another 300 questionnaires on social metacognition in collaborative argumentation about the pandemic received from undergraduates who come from 52 countries in the International College of Education, among the 17 indicators derived from the first sample's EFA, three indicators had high correlation with others. Finally, based on the reviews of three experts, these three indicators were deleted. The remaining 14 indicators formed good construct validity with acceptable convergent and discriminant validities. In addition, the multi-group invariance test demonstrated that the structural model of the Social Metacognition Inventory has better configural invariance, which indicates that it can be generalized to other online collaborative argumentation contexts. The Social Metacognition Inventory can be used to quantify social metacognition in online collaborative argumentation when administering a large-scale experiment.

This study reviewed the mobile technology-supported music education (MTSME) studies published in several academic databases, namely Scopus, WOS, ERIC, and RILM, from 2008-2019. Based on the technology-based learning model, the application domains, research issues, sample groups, research methods, adopted devices, and learning strategies were examined. In addition, visual categorization analysis was conducted to further analyze the keywords adopted in the studies. The results show that the number of MTSME studies increased in the time period. It was also found that a majority of studies mainly focused on learner perceptions (e.g. learning reception, learning attitude, and learning motivation). Tablet computers were the most frequently adopted mobile devices. In terms of learning strategies, guided learning, project-based learning, and inquiry-based learning were extensively used in the MTSME studies. In addition, the results of the keyword analysis showed three thematic clusters of MTSME studies, namely "mobile technology-supported teaching management and learning design," "developing appropriate music educational resources for different educational levels," and "mobile technology facilitated learning motivation in music education." According to the research results, several suggestions for future research are proposed.

For professional development programs, students' professional identity and learning perceptions and earning performances have been recognized as important educational objectives. Scholars have indicated that engaging students in social interactions facilitates their deep and diverse thinking, and hence to promoting their learning outcomes. However, in traditional instruction, students generally learn in an individual learning mode and hesitate to interact with peers and teachers. To address this problem, a mobile team-based competition approach to situate students in social interactions was proposed. To evaluate the effectiveness of the proposed approach, an experiment was conducted in a college curriculum design course. 88 students in a teacher education program participated in the experiment, including 42 in the experimental group learning with the proposed approach, and 46 in the control group learning with the conventional mobile individual competition approach. The results showed that the proposed approach outperformed the mobile individual competition approach in terms of learning attitude, collaborative learning tendency and professional identity. It is found that the students generally perceived the proposed learning approach to be an effective way to encourage knowledge sharing, promote collaboration awareness and improve learning attitudes and motivation.

Concept maps have been verified as an effective learning tool in technology-supported learning environments, in particular, in K-12 education. To enable educational technology researchers and school teachers to thoroughly know the research foci and applications of concept maps in technology-supported learning environments for K-12 education (CM-K12), the present study conducted a systematic review of studies published in seven SSCI (Social Science Citation Index) journals of educational technology by referring to the technology-based learning model. The review results showed that concept maps mostly played the role of a personal Mindtool and a collaborative Mindtool, and were mainly applied in elementary schools. The majority of elementary school teachers employed concept maps as a collaborative Mindtool (for a learning group), a personal Mindtool (for individual students), and a learning design tool. As for secondary school teachers, concept maps were mostly adopted as an assessment and evaluation tool, personal Mindtools, and collaborative Mindtools. In terms of application issues, the most adopted medium was desk-top computers, and the most adopted application domains were science, engineering, and language. The research focus explored the most in CM-K12 research was cognition, followed by affect and learning behavior. Based on the findings, several recommendations are proposed.

Mobile learning strategies have been employed for social learning activities, including library- and museum-supported learning. Previous studies have reviewed the literature from the technological aspect. However, a retrospective study from the perspective of bibliometric and network structure has not yet been provided. The aim of this study was therefore to systematically review journal papers on library-supported mobile learning (LibML). A coding framework including library types, mobile learning strategies, and research issues was adopted based on the literature and was used to screen and categorize the research papers. A co-citation network analysis was then adopted to analyze and visualize the structural relationships among the papers. A total of 53 eligible articles with 1370 citations in follow-up studies were collected from the Scopus database. The results showed that two main research streams of LibML were identified from the overall network structure, including library- and museum-supported mobile learning. In terms of the mobile learning strategy, library-supported research mainly focused on self-directed learning, whereas museum-supported research emphasized inquiry-based learning. In terms of research issues, most library-supported research focused on patrons' affective engagement, whereas museum-supported research emphasized learning performance. This study provides a citation-based approach to reveal the research trends and mainstream LibML research. The main contribution of combining co-citation and social network analysis is to provide a visualized network diagram of LibML research. Limitations of the methodological approach are noted. Discussion and future directions from the follow-up study are provided.

Artificial intelligence (AI) in higher education has proven to be a useful learning technology; it can help learners achieve positive learning outcomes in the learning environment and can also enable teachers to better understand learners' learning status and further improve their teaching strategies. This study reviewed the top 50 AI in higher education studies in the Web of Science database from the perspective of highly cited papers and based on a technology-based learning model. The results show that predictions of learners' learning status (including dropout and retention, student models, and academic achievement) are most frequently discussed in the AI in higher education studies; AI technology is most commonly applied in engineering (including computer courses); AI technologies most often play the role of profiling and prediction in higher education, followed by intelligent tutoring systems and assessment and evaluation. In terms of research issues, the most frequently discussed issues are learning behaviour, accuracy, sensitivity and precision, cognition and affect. Learners' higher order thinking skills, collaboration or communication, self-efficacy or confidence and skills are less frequently discussed in AI in higher education studies. Accordingly, we propose potential research issues and practitioner notes for AI in higher education as a reference for researchers, educators and decision-makers.

Information literacy is a core research topic in the field of library and information science. The developmental context of this field can be examined through a long-term retrospective analysis of relevant literature. This study explored the research trends and potential research issues in the top 100 most frequently cited articles on information literacy in higher education published from 2011 to 2020. In addition to a systematic review, this study employed bibliometric methods, including co-citation network analysis, to identify four main research streams in the field of information literacy in higher education: (a) the relationships among students' information literacy beliefs, competencies, attitudes and behaviour; (b) teachers', librarians' and students' perspectives on information literacy; (c) the relationship between students' information literacy and epistemic beliefs; and (d) the web search behaviour of digital natives. Accordingly, potential directions for future research and practitioner notes related to information literacy in higher education are proposed herein as a reference for researchers, teachers and policymakers.

Due to the rapid changes in global economic environments, enterprises have to continually enhance their business competitiveness. To improve business, planning educational training has been regarded as a channel to educate outstanding employees. Recently, most of the companies in the securities industry in Taiwan have adopted web-based educational training as a form of employee training. However, low e-learning acceptance on the part of employees is the essential obstacle when enterprises attempt to promote web-based continuing learning. Previous studies have shown that hardiness and Internet self-efficacy may be important factors that influence whether an individual will continue with web-based learning when facing pressure. Securities practitioners are required to deal with high pressure and persist in enhancing their professional knowledge in their working environment; therefore, continuing learning is crucial to maintaining the quality of professional service. The present study recruited securities practitioners as the research participants, and examined the effects of hardiness and Internet self-efficacy on their attitudes towards web-based learning when they were participating in web-based learning. The findings revealed that securities practitioners' hardiness and Internet self-efficacy both had direct positive effects on their attitudes towards web-based continuing learning. Meanwhile, their Internet self-efficacy had a mediating effect on the relationships between hardiness and attitudes towards web-based continuing learning.

The main purpose of this study was to examine the critical factors influencing university teachers' use of a mobile technology-enhanced teaching (MTT) platform during the new coronavirus (COVID-19) epidemic. An integrated model with multiple factors drawing from the theoretical models and learning theories was proposed in this study to examine university teachers' intentions to use an MTT platform. The multiple factors included the individual factor (e.g., growth mindset, help seeking, and self-efficacy), the social factor (e.g., social norms), and the technological acceptance factor (e.g., perceived usefulness and perceived ease of use). The survey method was used to collect data on university teachers' perceptions of the MTT platform use, and a two-step structural equation modeling approach was used for the data analysis. Based on the path analysis of a total of 214 valid responses, the results identified that growth mindset, help seeking, and self-efficacy from the individual factor, as well as perceived usefulness from the technology acceptance factor were the significant determinants of university teachers' intentions to adopt the MTT. The contributions of this study are twofold. First, the proposed model was derived from multiple literature sources, providing a sound theoretical foundation to understand MTT platform use from an academic angle. Second, university teachers' viewpoints are a unique observation of their actual platform use, providing practical insights into the improvement and maintenance of MTT-related platforms for all educators. The findings are especially valuable during the post-COVID-19 era.

Owing to the rapid advancements in artificial intelligence (AI) technologies, there has been increasing concern about how to promote the use of AI technologies in school settings to enhance students' learning performance. Teachers' intention to adopt AI tools in their classes plays a crucial role in this regard. Therefore, it is important to explore factors affecting teachers' intention to incorporate AI technologies or applications into course designs in higher education. In this study, a structural equation modeling approach was employed to investigate teachers' continuance intention to teach with AI. In the proposed model, 10 hypotheses regarding anxiety (AN), self-efficacy (SE), attitude towards AI (ATU), perceived ease of use (PEU) and perceived usefulness (PU) were tested, and this study explored how these factors worked together to influence teachers' continuance intention. A total of 311 teachers in higher education participated in the study. Based on the SEM analytical results and the research model, the five endogenous constructs of PU, PEU, SN, and ATU explained 70.4% of the changes in BI. In this model, SN and PEU were the determining factors of BI. The total effect of ATU was 0.793, followed by SE, with a total effect of 0.554. As a result, the intentions of teachers to learn to use AI-based applications in their teaching can be predicted by ATU, SE, PEU, PU and AN. Among them, teachers' SE positively influenced teachers' PEU and ATU towards adopting AI-based applications, and also influenced PU through PEU. In addition, the relationship between teachers' SE and AN was negatively correlated, which indicated that enhancing teachers' SE could reduce their AN towards using AI-based applications in their teaching. Accordingly, implications and suggestions for researchers and school teachers are provided.

The present study employed the draw-a-picture technique and epistemic network analysis (ENA) to reveal university students' viewpoints on ChatGPT-supported learning, as well as the conceptions, roles, and educational objectives of ChatGPT-supported learning among university students with different learning attitudes. The results showed that university students' conceptions of ChatGPT-supported learning were diverse and interrelated. Most students agreed that ChatGPT could facilitate learning anytime and anywhere, and held positive emotions and attitudes. In the conceptions of ChatGPT-supported learning, there were significant differences in the four categories of locations, learning content, learning activities, and objects between the low learning attitude (LLA) and the high learning attitude (HLA) groups. Besides, the HLA group preferred to use ChatGPT for specific learning content, and showed higher levels of engagement in discussions and consultations. The LLA group was inclined to use ChatGPT at home or in places with traditional classroom equipment for completing reports or assignments. Most students in the HLA group regarded ChatGPT as a tutor, while those in the LLA group viewed it as a tool. Based on the above findings, this study puts forward some suggestions for how to implement ChatGPT-supported learning in higher education as a reference for educators, researchers and training developers. [ABSTRACT FROM AUTHOR]

Teachers' instructional design skills influence their teaching practices and student learning performances. However, researchers have found that the traditional one-to-many model of preservice teacher education prevents preservice teachers from receiving timely and individualized feedback, making it difficult to fill in theoretical knowledge gaps to achieve better instructional design. In order to address this issue, a chatbot-based 5E learning approach developed on the constructivist 5E (engage, explore, explain, elaborate, and evaluate) learning model was proposed in this study.To investigate the effects of this learning approach, we conducted a quasi-experimental study at a university (31 in the experimental group and 37 in the control group). The chatbot-based 5E learning approach was applied by one class as the experimental group, while the control group applied the conventional 5E learning approach. The results showed that students in the experimental group reported more significant increases in learning performance, learning attitudes, and extrinsic motivation. In addition, learners were generally satisfied with the information quality supplied by the chatbot. The interview results showed that the approach could help students create a clarified learning framework and thus improve their learning performance. [ABSTRACT FROM AUTHOR]

The game-based learning approach to developing students' computational thinking (CT) current has received attention from researchers. However, the compatibility between games and instruction is often insufficient to accommodate the entertaining and educational nature of the curriculum entirely, and the benefits of game-based learning could be compromised. Therefore, this study proposed a role-playing-based micro-game strategy for developing students' CT in a Python programming course, and designed a quasi-experiment to verify the effectiveness of the role-playing-based micro-game strategy. A total of 133 eighth-grade students from schools in eastern China were randomly divided into experimental and control groups. The experimental group used a role-playing-based micro-game strategy, and the control group used a traditional instructional strategy. The results showed that the experimental group scored significantly higher than the control group on the CT test, especially in the dimensions of computational concepts and computational practices. The learning engagement of the experimental group was significantly higher than that of the control group, and the learning motivation of the experimental group was significantly improved before and after the experiment. In addition, qualitative analysis of the interview data indicated that micro-game strategy on role-playing could promote classroom learning and meet the needs of the course knowledge system. [ABSTRACT FROM AUTHOR]

Information literacy is an essential twenty-first-century skill for students. Most students face difficulty in discerning online information while searching, evaluating, and utilizing it effectively. Appropriate information evaluating framework, such as RADAR (relevance, authority, date, appearance, and reason for writing), has been recommended to enhance students' accuracy in identifying information. Research suggests that the effectiveness of the learning framework can be enhanced in the gamification context by using graphic organizers (e.g. concept maps) to help students organize and analyze information. Therefore, this study proposed a concept mapping-based information evaluating framework in the gamification context to teach information identification skills in an elementary school. A quasi-experimental study was conducted with the experimental group class (35 students) using the concept mapping-based information evaluating framework (CM-IEF), and the control group (35 students) using the conventional information evaluating framework (C-IEF). According to the study's findings, students in the experimental group had more significant increases in learning achievement, self-efficacy, and information discernment skills. However, there was no significant difference in learning motivation. Furthermore, the learning behavior of both groups was analyzed from the lag sequential analysis and interview. The study suggests that larger sample sizes and longer durations could be explored in the future. [ABSTRACT FROM AUTHOR]

Peer assessment has been regarded as an effective learning strategy in art education, such as in music, dance and art design. For music education, technology-supported peer assessment makes it easier for learners to reflect on their learning performance. However, the process of reflection needs solid and systematic theoretical knowledge. In terms of vocal music, which develops students' singing skills, it is difficult for learners to compare their own works with those of others because of a lack of sufficient theoretical knowledge. Therefore, a WSQ (watch-summary-question)-based mobile peer assessment approach was used to help learners reflect based on their theoretical knowledge with the support of WSQ learning sheets. To investigate the effectiveness of the approach, an experiment was carried out in a Chinese university. The experimental group (N = 22) learned with the WSQ approach, whereas the control group (N = 22) adopted a mobile peer assessment approach without WSQ. Vocal music skills, learning attitude and learning motivation were assessed. The experimental results indicate that the approach effectively enhanced the students' vocal music skills but did not improve their learning motivation or attitude. In addition, a higher correlation between teacher scoring and peer scoring was found for the experimental group.

Precision medicine has become an essential issue in the medical community as the quality of medical care is being emphasized nowadays. The technological data analysis and predictions made by Artificial Intelligence (AI) technologies have assisted medical staff in designing personalized medicine for patients, making AI technologies an important path to precision medicine. During the implementation of the new emerging technology, medical staff's learning intentions will have a great influence on its effectiveness. With reference to the Technology Acceptance Model, this study explored medical staff's attitudes, intentions, and relevant influencing factors in relation to AI application learning. A total of 285 valid questionnaires were collected. Five major factors, perceived usefulness (PU), perceived ease of use (PEU), subjective norms (SN), attitude towards AI use (ATU), and behavioral intention (BI), were used for analyzing the AI learning of medical staff in a hospital. Based on the SEM analytical results and the research model, the four endogenous constructs of PU, PEU, SN, and ATU explained 37.4% of the changes in BI. In this model, SN and PEU were the determining factors of BI. The total effects of SN and PEU were 0.448 and 0.408 respectively, followed by PU, with a total effect of 0.244. As a result, the intentions of medical staff to learn to use AI applications to support precision medicine can be predicted by SN, PEU, PU, and ATU. Among them, subjective norms considering the influences of both supervisors and peers, such as encouragement, communication, and sharing, may assist precision education in supporting the learning attitudes and behavior regarding precision medicine. The research results can provide recommendations for examining medical staff's intention to use AI applications.