This study presents an immersive learning system integrating virtual reality (VR) and an expert system to enhance students' understanding of living technology processing machines. By simulating real machine operations and providing personalized guidance through Kelly's Repertory Grid, the system supports safe and effective skill acquisition. Experimental results show that the system significantly benefits students with medium to high prior knowledge, while students with limited background may require additional scaffolding. The findings highlight the potential of adaptive VR learning tools in improving technical education.

This study investigated the impact of self-regulated learning (SRL) motivation and strategies on academic performance in a programming course using learning analytics techniques. Data were collected from 250 students, including their coding behaviors, SRL motivations, and strategies. K-means clustering was applied to categorize students into two distinct groups based on SRLmotivation, SRLstrategy, code_copy, code_execution, code_speed, code_paste, and code_length, with the silhouette coefficient confirming the optimal number of clusters. Subsequent analysis revealed significant differences in the academic performance of the two clusters. Structural Equation Modeling (SEM) was employed to examine the mediating role of SRL motivation between SRL strategies and academic performance. The model demonstrated a good fit (AGFI = 0.927, CFI = 0.964, RMSEA = 0.067), confirming the reliability of the proposed relationships. Bootstrap tests further validated the mediating effect of SRL motivation. Our findings indicate that SRL strategies positively influence academic performance both directly and through SRL motivation, establishing a partial mediation effect. This research contributes to the understanding of SRL in online learning environments, highlighting the importance of motivation and strategic learning behaviors for enhancing student outcomes. The implications for designing effective educational interventions and fostering self-regulated learning practices are discussed.

This study examines the effects of combining design thinking with game- based learning (GBL) in an educational environment focused on improving learning achievement, computational thinking skills, and artificial intelligence literacy. using a one-group pretest-posttest design, this study engaged sixth-grade students to measure the impact of these instructional strategies on their learning achievement. Statistical analyses revealed that students' academic performance improved significantly, with a large effect size (Cohen's d = 0.90); computational thinking skills improved significantly, with a small effect size (Cohen's d = 020); and artificial intelligence literacy improved significantly, with a small effect size (Cohen's d = 0.35). These results highlight the of combining design thinking with GBL to not only engage students, but also to significantly improve a wide range of educational outcomes in order to prepare them to meet the needs of the digital future. This study highlights the of combining design thinking with interactive learning models to optimize student learning and development in current educational environments.

This research attempts to cultivate the AI literacy of junior high sci-ml students and aims to evaluate the learning effectiveness of integrating game-based learning with problem-based learning to improve their A1 literacy. using a self- developed board game on Naive Bayes Classifier and Generative AI applications as teaching material, students engage in a problem-based learning approach through group The results of the research show a significant improvement in students' AI literacy, particularly in their understanding of AI, and a significant increase in their behavioral intentions and engagement. The results confirm the effectiveness of this pedagogical approach for promoting AI literacy at the high level.

This study examines the progressive behavior patterns of learners in online asynchronous discussions during different phases of self-regulated learning, using the Bookroll platform. Learners engaged in self-regulated activities, such as previewing videos at home and participating in online discussions, over a three-month period. The activity logs were divided into three phases, with roles of speakers and respondents coded for analysis. The cyclic behavior analysis revealed significant correlations and differences in discussion behaviors. In the first two phases, responders played a key role in guiding discussions, providing analyses, and asking questions, which promoted critical thinking. In the third phase, positive reinforcement and additional explanations helped redirect discussions when topics deviated. The study highlights the importance of questions and positive reinforcement from instructors to motivate learners, maintain discussion depth, and enhance engagement, thereby promoting effective learning.

This panel discussion explores the theory-informed utilization of smart technologies in language learning, focusing on contextualized, authentic, and communicative approaches. Integrating advanced smart technologies such as Artificial Intelligence, Augmented Reality, Virtual Reality, learning analytics, and robotics into language education offers new avenues for enhancing learner engagement and outcomes. Panelists will present and discuss studies involving various pedagogical strategies that align with contemporary language learning theories. Case studies and research findings will illustrate effective smart technology integration in language classrooms and beyond, addressing practical considerations, challenges, and strategies for leveraging these tools to support second language acquisition and other language learning contexts. The panel aims to provide a comprehensive understanding of how to harness smart technologies to create engaging, effective, and theory-aligned language learning experiences, contributing to the academic discourse on the transformative potential of these tools in fostering meaningful language education.

This study aims to investigate the impact of peer assessment and educational board games on computational thinking (CT) and artificial intelligence (AI) courses using a two-factor experimental design. Combining educational technology, we designed an AI speech recognition course suitable for first-year students with no prior knowledge of CT or AI. The goal is to develop their foundational understanding and application of CT and AI. By employing educational board games, the study seeks to enhance learning enjoyment, increase students’ self-efficacy in CT, and reduce their learning anxiety. Additionally, through the strategy of peer assessment, the study aims to provide benefits to students in both evaluating and being evaluated, thereby improving their learning outcomes and engagement.

The purpose of this study was to determine whether educational board games have a significant impact on learning motivation by developing the information media literacy educational board game Hi-Life. It also examined the differences in learning motivation between the competitive group and the cooperative learning group through a randomized experimental design. A total of 26 students from a national university in northern Taiwan participated in the study, 14 students in the control group for cooperative game-based learning and 12 students in the experimental group for competitive game-based learning. Through the analysis of the Mann-Whitney U test, the results showed that the dimension of Relevance was significant in both the experimental and control groups. Although the rest of the dimensions are not significant, the overall data showed that the average values of the three dimensions of Relevance, Confidence and Satisfaction were all higher than 3.5. The students demonstrated a favorable learning motivation in the ARCS model after playing the educational board game.

With the rise of artificial intelligence (AI), cultivating the AI abilities of the new generation of learners has become crucial. However, currently, there is a lack of tools in education found to effectively enhance students' AI abilities. To discover effective ways to foster students' understanding of AI concepts and skills, this research developed an Artificial Intelligence Image Recognition Robot unit, which was conducted through a series of AI courses in conjunction with educational robots. There are total of 41 undergraduates participating the instructional experiment. The first half of the experiment focused on introducing AI concepts and implementing image recognition models, while the second half involved robot programming and a robot board game competition. At the beginning and the end of the nit, students completed the AI Learning Achievement Test, the Programming-Oriented Computational Thinking scale and the Robot Self-Efficacy Scale to assess differences before and after the unit. The research found that by taking the AI Image Recognition Robot unit, students' AI learning achievements, programming-oriented computational thinking, and robot self-efficacy were significantly enhanced. This suggests that students will be better equipped to evaluate and understand the effectiveness of algorithms in programming and problem solving in the future. It is recommended that robots be incorporated as educational tools when engaging in AI learning.

Having strong computational thinking (CT) skills is key for students studying computer science (CS) and STEM-related fields. However, relying too heavily on programming languages can hinder students' understanding and application of CT concepts. Therefore, to effectively promote the learning of CT, this study conducted a quasi-experimental design to integrate two different questioning strategies into the Online Robot City game for teaching purposes. The study aimed to explore the impact of using the 5W1H strategy and the context-based concept mapping strategy for information gathering and problem decomposition on students' achievement in CT and on their learning behavior patterns. The results are expected to respond to the importance of different learning approaches, in particular the context-based thinking method like the context-based concept mapping strategy proposed in this study, which may make relatively more contributions to CT learning achievement and self-efficacy.

Given the increasing international emphasis on the importance of identifying and critiquing media content and the proper use of information and communication software, it is imperative to enhance students’ media and information literacy education. In this study, an educational board game for media and information literacy was developed to cultivate the basic knowledge of media and information literacy among university school students in Taiwan. A quasi-experimental study was conducted to investigate the effects of the educational board game on students' learning achievement and flow state. Results showed that the educational board game increased students' learning achievement and flow state, but no difference was found between the collaborative-team gaming group and the competitive-multiplayer gaming group in terms of learning achievement and flow state.

This study attempted to carry out interdisciplinary learning activities which integrate computational thinking into language learning. The participants were 56 5th-grade Chinese as a Second Language (CSL) students in Singapore. The students were required to learn the ability to apply conditional complex sentences in Chinese language and learn the conditional structural logic through coding educational robots to interact with each other in Chinese. The results showed that the students improved significantly in the linguistic knowledge. Furthermore, the computer programming self-efficacy of the students has also promoted significantly in terms of logical thinking, control and debugging. The students had also shown a high level of learning engagement, low anxiety and a high intention in the robot coding activity. This study presented the empirical results of this cross-domain integration of educational robots and language learning, providing a reference and teaching practices for future teachers.

In this study, 3 Mobile-Assisted Language Learning interfaces are proposed considering the Cognitive Theory of Multimedia Learning, Cognitive Load Theory, and the Active/Reflective learning styles. A quasi-experimental design was adopted to compare the effects of 3 smartphone interfaces, namely partial English captions (EG1), full English captions (EG2), and partial English captions with Chinese subtitles (CG), on the cognitive load, learning motivation, and learning anxiety and enjoyment of 119 11th-grade students with different learning styles. It was found that EG2 had significantly higher learning motivation than EG1 and CG. On the other hand, significant interaction between the 3 smartphone interfaces and learning styles was found for the students’ cognitive loads. Further analysis showed that, in EG1, the active-style students’ cognitive load was significantly lower than that of the reflective-style students. In addition, it was found that the active-style students’ learning enjoyment was significantly higher than that of the reflective-style students, while no significant difference was found between the learning anxiety ratings of the students with different learning styles in the 3 groups. Therefore, it is suggested that the role of learning styles needs to be further considered when designing caption interfaces for smartphone applications in the future.

To assist young learners of Chinese Language in developing general orthographic awareness (i.e., the knowledge of Chinese character structures), a novel mobile game-based learning intervention was designed. In playing the “Chinese-PP” game in a 1:1 (one-deviceper-pupil) setting, each of the 31 participating 9-year-old pupils was assigned a Chinese character component in her smartphone. A pupil may make use of her own and peers’ character components to form a legitimate Chinese character, and invite the peers with matching components to join her group, resulting in rapid social negotiation. In this paper, the students’ collaborative learning processes in three game sessions are analyzed. The relationships between pupils with varied learning styles and their game behaviors and learning gains are unveiled. Through the intervention that stimulates active peer coaching and social learning, we discover that all the pupils (regardless of their learning styles) became active learning participants and achieved high learning gains towards the last game session.

This study develops a content-based computer-assisted language learning tool for training English listening through multimedia. The system automatically provides instant vocabulary translation and classification to users whenever they pause while watching videos. Experiments were conducted to compare the novel system with the traditional solution using a translation machine to support subtitle comprehension. The first goal of the novel system is to support listening comprehension, and the second goal is to confirm its usability. These two goals were successfully achieved in the study. Eye tracking was used to observe variations in gazing and reliance on subtitles. The results of vision analysis show that the degrees of reliance on subtitles remain unchanged.

In English courses, it is very important to assign proper articles to individual students for training their reading ability. This study proposes an innovative approach for developing reading material recommendation systems by eliciting domain knowledge from multiple experts. An experiment has been conducted to evaluate the performance of the approach; moreover, a comparison on the existing approaches is given to show the advantages of applying the innovative approach.