Interactive Learning Environments

Synchronous online courses (SOC), enabling real-time interaction, have become increasingly prevalent in higher education, particularly during emergency remote teaching (ERT). This surge in online education raised questions about how student engagement, a crucial factor for the quality of higher education practices, was achieved during ERT. To address these questions and offer insights into effective practices for fostering student engagement in SOC, this study explores the experiences of undergraduate students and instructors during ERT. While a substantial body of literature addresses student engagement, there is a gap in studies that comprehensively synthesize effective practices to engage undergraduate students in SOC across various departments. Therefore, the present study seeks to bridge this gap. Insights were gathered through semi-structured one-on-one interviews and a focus group with ten instructors and fifteen students across diverse departments and universities. The study findings unveil effective strategies and practices to promote student engagement in SOC. These strategies closely align with the elements of teaching, social, and cognitive presence within the Community of Inquiry (CoI) framework, emphasizing interaction-based pedagogical practices and the role of emotional communication in SOC. The study not only provides valuable strategies for enhancing student engagement during crises but also highlights their relevance in normal times.

This study explores the landscape of serious games for sustainable environmental education, focusing on game types, theoretical foundations, and pedagogical approaches to provide valuable insights for educators, researchers, and policymakers. Using an umbrella literature review methodology, it examines empirical studies published between 2006 and 2023 on serious games in this field. The findings reveal a growing interest in serious games for sustainability, highlighting advancements in gaming technologies. It also identifies various game types, theoretical perspectives, and pedagogical strategies used to promote sustainability education. The study offers a comprehensive overview of the role of serious games in fostering sustainable behaviors, synthesizing key empirical studies, frameworks, and practical applications. Its insights can help educators design and implement serious games that effectively integrate diverse pedagogical and theoretical approaches, enhancing learning outcomes and raising sustainability awareness. ARTICLE HISTORY

The concept of the Metaverse has increased in realizability with the rapid development of emerging technology, and this has led to the need for cognition of how to create a Metaverse world in educational settings. However, there is still little discussion in the existing research on teachers’ understanding of Metaverse literacy and their need for learning. In order to expand the understanding of this issue, in this study we developed a Metaverse literacy scale to assess teachers’ understanding of Metaverse literacy and their need for learning about the Metaverse. Adapting the importance-performance quadrant analysis approach, we integrated four quadrants: Keep up with professional development, possible motivation for future professional development, low priority for professional development, and prospective professional development. Results indicated that the need for learning to use Metaverse technology in the educational field is low, as most of the questions fell into the “Prospective professional development” quadrant. The attributes located in this quadrant can threaten teaching practices designed to attract students to face the Metaverse age. The results imply that Metaverse-related technologies can be used by college teachers to develop their students’ Metaverse literacy and to promote teachers’ and students’ Metaverse literacy competence. KEYWORDS: College teacher, educational application, emerging technology, importance-performance analysis, Metaverse literacy, professional development

To address the limitations of general-purpose artificial intelligence (AI) tools, we developed a task-oriented AI chatbot based on the 5E (i.e. “engage”, “explore”, “explain”, “elaborate” and “evaluate”) model to scaffold students’ instructional design process. We examined the impact of integrating the 5E instructional model-informed AI chatbot on students’ learning performance and perceptions. The results indicated that the AI chatbot, when combined with human teacher scaffolding, significantly improved the students’ instructional design performance relative to receiving human teacher scaffolding only. The chatbot provided valuable suggestions on instructional design frameworks, class activities and teaching topics during the “explore” phase. In the “evaluate” phase, the chatbot offered immediate feedback on the students’ design plans and proposed alternative instructional frameworks regarding areas for improvement. However, the students expressed concerns about the chatbot’s evaluation quality, noting that it needed to be better aligned with the course assessment rubric. We recommend using AI chatbots for instructional design conceptualisation, although we emphasise the critical role of human teachers in evaluating final design work and providing timely support.

Generative AI for help students to development of essay writing skills and teachers to design course material.

Does inventing representations in open-ended computational environments contradict the goal of acquiring canonical scientific knowledge? We analyze the computational models of electric conductors that N = 35 eight-grade students built with a NetLogo-based microworld. Students used the microworld to sketch structures that represent models of electric conductors and then observed the resulting electric current. We use data from classroom discourse, computer log files and questionnaires, to identify different ways through which students used the platform, and how their engagement in construction, explains the variance in their learning gains. We found differences in the number and types of models that students constructed in the microworld: while most students built the models that were intended by the instructors, some added unconventional doodling models, that deviated from the intended structures. We found that students who built more models made larger gains in conceptual knowledge about electric current. However, we also found that students who constructed more unconventional models, produced lower learning gains on items that assessed the particle-level mechanism of electrons in conductors. ARTICLE HISTORY

Chatbot research has received growing attention due to the rapid diversification of chatbot technology, as demonstrated by the emergence of large language models (LLMs) and their integration with automatic speech recognition. However, among various chatbot types, speech-recognition chatbots have received limited attention in relevant research reviews, despite their increasing potential for language learning. To fill this gap, 32 empirical studies on speech-recognition chatbots for language learning were reviewed. The following information was reviewed for each study: basic publication information, research focus, location of chatbot use, methodology, group design format, participant information, intervention duration, target language, device type adopted, and chatbot role. An upward trend in research quantity starting in 2020 was identified, which accelerated exponentially in 2022. College students were more likely than other groups to be involved in research, and English as a second or foreign language was the most common target language. Most studies focused on participants' perceptions of chatbots and the degree to which using chatbots helped them develop their speaking or listening proficiency. Methodologically, single-chatbot design using mixed methods was the most common design format, and most studies were conducted for more than one month in laboratory or classroom settings. Conventional mobile devices, such as smartphones, tablet PCs, and smart speakers without a screen, were the most frequently adopted device types. The chatbots' most common role was as conversational partner. A detailed discussion of these results and their implications for future research on speech-recognition chatbots, particularly regarding the use of LLM-powered chatbots, is provided.

Providing greater access to computer science (CS) education for K-12 students in the United States (U.S.) has increased interest in integrating CS concepts within authentic science, technology, engineering, and mathematics (STEM) contexts. Physical computing is one method that has demonstrated promising results in other countries (e.g. England) and has been receiving growing attention in the U.S., yet there remains limited research on physical computing within the U.S. Therefore, this study utilized a modified version of the Computing Attitude Questionnaire (Yadav et al., 2014) to examine changes in 71 middle school students’ attitudes toward computing after participating in a four-week physical computing unit. Students reported significant gains in all five computing attitude constructs (definition, comfort, interest, classroom applications, and career/future use). Further analyses revealed male students had significantly greater gains than females in the career/future use construct, and there were no significant differences when controlling for completion of prior engineering design coursework (PEDC). Additionally, while the majority (77%) of students indicated they preferred physical computing over screen-based experiences for future computing lessons, analyses found gender and PEDC were not significant predictors of students’ preference for learning computing concepts. This study provides implications for improving computer science instruction within authentic STEM contexts.

This study examines the potentials and challenges of integrating conversational AI (Artificial Intelligence) tools in higher education. Specifically, the study delves into how ChatGPT is reshaping teaching, learning, research, and student evaluation while addressing ethical concerns such as academic integrity. We employ a qualitative research design based on semi-structured interviews with 12 highly accomplished academicians from North America, Asia, and Europe’s higher education institutions. Our analysis reveals ChatGPT’s potential to uplift educational productivity, learning outcomes, creativity, and idea generation but raises apprehensions about academic honesty, authenticity, and over-reliance. While ChatGPT can enhance educational practices such as brainstorming, analysis, and skills acquisition, its unchecked use may impede learning, critical thinking, and research quality. The study advocates for the responsible AI use in academia, underscoring educators and policymakers to establish policies guiding conversational AI ethical applications. Thus, training faculty and executing institutional policies are essential for effective and ethical integration of ChatGPT and other AI tools in higher education. Finally, this research contributes to ongoing research on AI in education by providing valuable insights that can harness potential of these transformative tools while upholding academic integrity and standards.

Recent advances in artificial intelligence (AI) give chatbots, e.g. ChatGPT, more human-like interaction and conversational capability. AI chatbots are becoming more popular for supporting language learning. Most current review research disregards the significance of teachers' roles in chatbot-assisted language learning. Self-determination theory (SDT) explains the roles by suggesting how teachers satisfy student needs and how chatbots thwart those needs. Therefore, this systemic review aims to: (i) suggest the roles that teachers play in student English learning with AI chatbot; (ii) discuss how those roles satisfy SDT needs of the students; and (iii) discuss the challenges in this learning. This review selected 23 articles published throughout the last ten years (2014-2023). The findings offer (i) four empirical and theoretical contributions: technological pedagogical content knowledge (TPACK) for chatbots, needs satisfaction from teachers, needs thwarting from chatbots, and SDT-based research designs for chatbots; (ii) two practical suggestions: understanding technological knowledge of chatbots and SDT needs support for chatbots; and (iii) six research directions: experimental studies, student language proficiency, more teacher roles, revisiting TPACK, future development of chatbots, and large language model teacher-like chatbots. Overall, the findings enhance our knowledge of TPACK and teacher digital and AI competences for chatbot-assisted language learning. ARTICLE HISTORY

Augmented reality (AR) is a new emerging technology that can improve the learning experience while making learning interesting and keeping students’ attention. Schools and higher education have already implemented AR in the teaching and learning process to convey information more effectively. However, there is a lack of information about AR for skill training in TVET institutions. The purpose of this study is to determine the perceptions of TVET instructors regarding the use of AR technology for skill training in TVET institutions. This case study was conducted using a questionnaire with a 0.99 alpha-Cronbach value. The questionnaire is using 5 Likert-scale and open-ended questions as an instrument for this study. There were 37 respondents selected randomly from TVET institutions in the center region of Malaysia. The findings show that TVET instructors have a positive perception toward using AR in skill training delivery. Furthermore, this study also found there is a challenge to implement AR technology in certain aspects such as students’ acceptance, instructors’ expertise, technology barrier, and policy for TVET institutions. The implication of this study will contribute to the TVET area for training using AR.

The outbreak of the COVID-19 pandemic prompted schools and universities worldwide to switch to the online delivery of instructional activities. The use of fully online flipped learning correspondingly increased. This review identifies the challenges to this instructional approach and the elements required for effective course design and implementation across studies during the first two years of the pandemic (i.e. 2020-2021). The aim of this review is to draw on the experience gained through pandemic-led passive practices to establish a foundation for active instructional improvement. In the included articles (N = 70), new challenges (e.g. students' poor in-class participation, instructors' inability to observe students on-screen, and the lack of tangible hands-on practice) emerged when offering flipped courses remotely online. Using the identified course-design elements, this review proposes a RAISE design framework which can inform future practice in five aspects: Resources (e.g. providing instructional videos), Activities (e.g. stressing the application of knowledge and skills), Institutional facilitation (e.g. allocating budgets for educational technology), Support (e.g. employing online tools for student response and collaboration), and Evaluation (e.g. providing formative assessment and instructor feedback). This instructional approach can serve as an alternative to offering quality education independent of locations or during interruptions of campus operations.

In Massive Open Online Courses (MOOCs), online reviews serve as a basis for teachers to improve their courses. The disconfirmation effect of online reviews, i.e. the inconsistency between the level of attention paid to a course factor and the actual weight of that factor's influence on learner satisfaction, leads to erroneous judgments by teachers. Based on the two-factor theory of emotion, 4,070 courses and 165,705 online reviews are adopted as a corpus to identify the effect of learner sentiment on the disconfirmation effect. The empirical results show that there is a significant disconfirmation effect for negative reviews, but not for positive ones. A fine-grained analysis on negative sentiment finds that reviews containing more sadness and anger sentiments have a stronger disconfirmation effect. A comparison of course types reveals that the disconfirmation effect is stronger for instrument-based courses than that for knowledge-based and practice-based ones. In addition, negative word-of-mouth weakens the disconfirmation effect of sadness and anger reviews and enhances the disconfirmation effect of positive reviews. Further, learner's reputation weakens the disconfirmation effect of sadness reviews and enhances the disconfirmation effect of positive and anger reviews. ARTICLE HISTORY

This study aimed to gain insights into how real-time danmaku interaction (i.e. organized danmaku interaction (ODI) and unorganized danmaku interaction (UDI)) directly and indirectly relates to student engagement (SE) through community of inquiry (COI) presences in live video-streaming teaching. Survey data gathered from 386 Chinese high school students (10th to 12th grades) who participated in a live video-streaming course were analyzed. Partial least square structural equation modeling results indicated that real-time danmaku interaction has a positive predictive effect on SE, and COI presences have a positive effect on SE. Furthermore, the PROCESS plugin in SPSS was used to conduct mediation analysis. Results demonstrated that after controlling for gender and grade, the relationship between real-time danmaku interaction and SE was partially mediated by COI presences. In particular, COI presences have a significant positive mediating effect between ODI and SE. Social presence also has a significant positive mediating effect between UDI and SE. Meanwhile, cognitive presence has a significant negative mediating effect between UDI and SE. Findings highlight the value of real-time danmaku interaction for SE and the significant mediating role of COI presences. Finally, this research provides effective suggestions on how to enhance students’ SE in live video-streaming teaching.