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Generative Artificial Intelligence (AI), especially machine learning models that autonomously generate human-like content, has recently attracted significant attention in the education sector. This paper explores the potential of generative AI, including tools like ChatGPT, to shift from traditional outcome-oriented educational practices to a more balanced approach that values both the learning process and its outcomes. Traditionally, education has emphasized achieving predefined results, but the advent of generative AI tools, which enable students to easily produce tangible results, calls for a reevaluation of these practices. This shift suggests a need for a broader focus that encompasses the entire learning process leading to the final product, thereby promoting an educational practice that equally emphasizes both the journey and the destination of learning. Recognizing that the implementation of such practices, facilitated by generative AI, still requires exploration, this paper proposes a solution that integrates the experiential learning cycle and learning portfolio. This approach is designed to demonstrate the realization of process-outcome-balanced educational practices through the use of a pedagogical AI agent.

Understanding the obstacles and causes students faced while learning with new technologies is the key to inform effective instructional designs. To achieve this aim, this study conducted a qualitative video analysis on language learners’ observable behaviors when they took part in learning activities supported by the technology of robots and IoT-based tangible objects. Insightful findings and instructional implications emerge from the attempt to explore learners’ learning process in terms of the obstacles learners encountered and the causes of the obstacles. Based on the findings and implications, eight instructional guidelines are proposed for teachers/instructional designers to design effective language learning activities with robots and IoT-based tangible objects. This study contributes to the literature on enhancing learning and teaching by integrating educational robots and IoT-based tangible objects in the field of robot assisted language learning (RALL).

The purpose of this study is to explore the existing problems associated with using generative AI in education and to propose a potential solution for addressing those issues through the design of pedagogical AI agents. The existing problems are examined from two different perspectives: those of teachers and students. The proposed solutions for designing pedagogical AI agents are systematically presented, including main concepts, design considerations, functions, procedures, and structure/templates. An example of how to apply the proposed solution in designing a pedagogical AI agent is provided, illustrating its application in teaching order words (or sequencing words). Finally, the paper concludes with a discussion of potential topics for further research.

Abstract Traditional physical education mainly relies on the instructor’s verbal explanations and physical demonstrations. However, learners might be confused about whether their movements and positions are correct. Moreover, a typical badminton class has approximately 50 students, creating a huge teaching load for an instructor. To reduce the instructor’s workload and improve learners’ badminton performance, a multiple feedback WISER model was designed for badminton classes. The model provides visual feedback, information feedback, and verbal guidance to learners. In this study, a quasi-experiment was designed and participants were divided into experimental and control groups. The experimental group adopted the multiple feedback WISER model while the control group applied the conventional method. The teaching experiment lasted for 8 weeks with 46 participants in the experimental group and 50 participants in the control group, respectively. To measure the learning performance, a movement detection system using wearable technology was utilized. The results indicate that the experimental group, which used the multiple feedback WISER model, outperformed the control group, which used traditional teaching methods, on badminton clear and smash skill learning (Clear: p

Generative artificial intelligence (GAI) applications, such as ChatGPT (Chat Generative Pre-trained Transformer) and Midjourney, have recently attracted much attention from researchers and school teachers. While many people are eager to learn more about GAI applications, some scholars are concerned about the potential misuse of them. It is predicted that the use of GAI applications will increase rapidly in the coming years. Therefore, it is important to consider the challenges and research issues through some concrete application examples of using GAI for education. In this position paper, the authors aim to address these issues from the perspectives of academic research and educational objectives. Along with defining GAI, several illustrative examples of using GAI applications in educational settings are provided. Moreover, potential research issues of GAI-based learning, including research design, relevant learning strategies, research focus, and measuring tools, are discussed. ET&S journal is especially welcoming research on unlocking the potential of GAI for education to realize the two notions of “Knowing [why] is the essential element for learners to have in-depth understanding” and “It is all about prompts: Get rid of the ‘search’ mindset and use ‘programming prompt’ instead.”

Learning analytics (LA) in educational games is considered an emerging practice due to its potential of enhancing the learning process. Growing research on formative assessment has shed light on the ways in which students' meaningful and in-situ learning experiences can be supported through educational games. To understand learners' playful experiences during gameplay, researchers have applied LA, which focuses on understanding students' in-game behaviour trajectories and personal learning needs during play. However, there is a lack of studies exploring how further research on LA in educational games can be conducted. Only a few analyses have discussed how LA has been designed, integrated, and implemented in educational games. Accordingly, this systematic literature review examined how LA in educational games has evolved. The study findings suggest that: (1) there is an increasing need to consider factors such as student modelling, iterative game design and personalisation when designing and implementing LA through educational games; and (2) the use of LA creates several challenges from technical, data management and ethical perspectives. In addition to outlining these findings, this article offers important notes for practitioners, and discusses the implications of the study’s results.

Learning Analytics (LA) approaches have proved to be able to enhance learning process and learning performance. However, little is known about applying these approaches for second language acquisition using educational games. Therefore, this study applied LA approaches to design a smart collaborative educational game, to enhance primary school children learning English vocabularies. Specifically, the game provided dashboards to the teachers about their students in a real-time manner. A pilot experiment was conducted in a public primary school where the students’ data from experimental and control groups, namely learning and motivation test scores, interview and observation, were collected and analyzed. The obtained results showed that the experimental group (who used the smart game with LA) had significantly higher motivation and performance for learning English vocabularies than the control group (who used the smart game without LA). The findings of this study can help researchers and practitioners incorporate LA in their educational games to help students enhance language acquisition.

The design, implementation, and outcome of educational robots in special education have not been sufficiently examined in a systematic way. In particular, learner-based and contextual factors, as well as the essential roles played by various stakeholders have not been addressed when robots are used as a learning tool in special education. Therefore, a systematic review using Activity Theory was conducted to analyze 30 studies in robot-assisted special education. Content analysis of the studies reported relevant information with respect to each activity component ─ (a) subject (learners with disabilities), (b) technology (robots supported by instructional design), (c) object (target skills or behaviors), (d) rules (implementation procedure and performance measures), (e) community (learners with disabilities, special education professionals, and parents), (f) division of labor (among learners, professionals and parents), and (g) outcome (performance of target skills or behaviors). Furthermore, the study identified existing gaps from the robot-assisted special education studies (e.g., lack of parental engagement), challenges (e.g., difficulty with standardizing performance measures due to heterogeneity of learner profiles), and contradictions (e.g., opposing views among experts on the role of robots in social interactions). Finally, recommendations were made under each activity component. The study concluded that both general and domain-specific guidelines should be created for each disability category proposed in this review to assist practitioners who wish to use robots to assist special education.

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi-directional long short-term memory model for emotion detection yielded the highest predictive performance, with an impressive F-measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low-level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed.

Abstract Training efficiency is important for functional fitness training because it assures the trainees receive appropriate training to meet their needs of skill formation. With the support of technologies, the functional fitness training process can be adapted to suit each elder’s physical conditions. This study proposes a dual adaptive strategy for adjusting exercise intensity levels based on the objective assessments and subjective feedback to balance body and mind conditions. An adaptive exercising system was also developed by using gesture recognition and perceived exertion to adjust exercise intensity levels and help the elders receive more appropriate training. The results show that the dual adaptive strategy proven to be able to provide better balance (body and mind) and improve training efficiency for elders’ functional fitness training at their own paces while gaining a satisfied training experience.

While researchers have highlighted the importance of considering learner's personality as a personalization parameter in computer based learning, very few studies have really addressed this parameter in educational games. Therefore, this paper presents an educational game which models learner's personality, specifically introvert/extrovert dimension, to serve as a personalized game learning environment. Fifty one learners were randomly assigned to a control and an experimental group which learned with a non-personalized and a personalized version of the game respectively. The experimental results show that the personalized educational game did not have a significant impact on learners' motivation, while it did significantly decrease learners' cognitive load. Besides, the learners who learned with the personalized version of the game revealed a significant higher degree of perceived usefulness and intention to use the game than those who learned with the non-personalized version of the game. The findings of this study can be used by educators and game designers to adopt and develop personalized game learning environments based on learner's personality.

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.

Abstract While most studies focus on individual training of oral communication in English as a Foreign Language (EFL), this study took a social constructivist perspective in examining the effect of an interpersonal process, that is, peer feedback, on the development of oral communication performance and communication strategy use. A mobile application was developed to facilitate learners’ engagement in discussion tasks, replay their conversation as well as carry out peer feedback. To examine the effect of mobile-assisted peer feedback, a quasi-experimental was conducted at a vocational university in Taiwan for five weeks. Forty EFL participants were assigned to an experimental group (n = 20), who received peer feedback on their oral communication performance and strategy use, and a control group (n = 20), who received no peer feedback on their oral communication ability and strategy use. The results show that the use of peer feedback enhanced students’ oral communication performance but did not improve their communication strategy use. The findings suggest that with the support of mobile-based peer feedback, students were able to monitor their oral production and provide/receive corrective feedback that enhanced their overall communication performance. It was also found that the feedback provided by the students was not concrete enough to address the use of the target communication strategies. However, the peer feedback data show that students tended to provide varying communication strategies that were not taught in the study but were considered beneficial to their overall oral communication performance. Possible explanations for the findings and future directions are discussed in this paper.

Abstract Blockchain is the core technology used to create the cryptocurrencies, like bitcoin. As part of the fourth industrial revolution since the invention of steam engine, electricity, and information technology, blockchain technology has been applied in many areas such as finance, judiciary, and commerce. The current paper focused on its potential educational applications and explored how blockchain technology can be used to solve some education problems. This article first introduced the features and advantages of blockchain technology following by exploring some of the current blockchain applications for education. Some innovative applications of using blockchain technology were proposed, and the benefits and challenges of using blockchain technology for education were also discussed.

Abstract Many Indigenous learners worldwide find the learning materials presented in current educational settings culturally irrelevant. A lack of relevant learning materials within formal education settings subscribing to Western Euro-centric curriculum standards is a key factor. Learning materials provided through modelling, practice, and apprenticeship, are desired. With the literature identifying the lack of a formal curriculum design process, a framework is presented for standardized creation of learning objects for Indigenous learners. To determine the efficacy of the framework, two case study examples and prototype tools were presented to Indigenous knowledge expert interviewees to identify strengths, weaknesses and additional benefits to the proposed framework. Incorporating learners in the design process from the beginning was highlighted as being a very positive approach. The benefits of this research include that it provides an interface, in the form of a prototype, to allow educators to create relevant and authentic learning for Indigenous learners. Additional work is needed on simplifying the process of knowledge creation for educators and on ensuring that any knowledge used maintains cultural and contextual meaning to the communities in which it was generated.

Background: This study presents an intelligent table tennis e-training system based on a neural network (NN) model that recognizes data from sensors built into an armband device, with the component values (performances scores) estimated through principal component analysis (PCA). Methods: Six expert male table tennis players on the National Youth Team (mean age 17.8 ± 1.2 years) and seven novice male players (mean age 20.5 ± 1.5 years) with less than 1 year of experience were recruited into the study. Three-axis peak forearm angular velocity, acceleration, and eight-channel integrated electromyographic data were used to classify both player level and stroke phase. Data were preprocessed through PCA extraction from forehand loop signals. The model was trained using 160 datasets from five experts and five novices and validated using 48 new datasets from one expert and two novices. Results: The overall model’s recognition accuracy was 89.84%, and its prediction accuracies for testing and new data were 93.75% and 85.42%, respectively. Principal components corresponding to the skills “explosive force of the forearm” and “wrist muscle control” were extracted, and their factor scores were standardized (0–100) to score the skills of the players. Assessment results indicated that expert scores generally fell between 60 and 100, whereas novice scores were less than 70. Conclusion: The developed system can provide useful information to quantify expert-novice differences in fore-hand loop skills.

With the advancement of information and communication technologies, technology-supported peer assessment has been increasingly adopted in education recently. This study systematically reviewed 134 technology-supported peer assessment studies published between 2006 and 2017 using a developed analysis framework based on activity theory. The results found that most peer assessment activities were implemented in social science and higher education in the past 12 years. Acting assignments such as performance, oral presentations, or speaking were the least common type of assignments assessed across the studies reviewed. In addition, most studies conducted peer assessment anonymously and assessors and assessees were randomly assigned. However, most studies implemented only one round of peer assessment and did not provide rewards for assessors. Across studies, it was more often the case that students received unstructured feedback from their peers than structured feedback. Noticeably, collaborative peer assessment did not receive enough attention in the past 12 years. Regarding the peer assessment tools, there were more studies that adopted general learning management systems for peer assessment than studies that used dedicated peer assessment tools. However, most tools used within these studies only provide basic functionalities without scaffolding. Furthermore, the results of cross analysis reveal that there are significant relationships between learning domains and anonymity as well as learning domains and assessment durations. Significant relationships also exist between assignment types and learning domains as well as assignment types and assessment durations.

Existing research has established immense importance in contextual learning for second language development. However, to provide real-life context in the learning of a foreign language is challenging. To help create such a learning environment, our previous research developed an innovative system called the R&T System, utilizing robots (R) and tangible objects (T) with Internet of Things (IoT sensors). As the R&T system is still new, there is no sufficient knowledge about what learning obstacles learners would encounter in such a context-rich environment and how best to support them. To explore these issues, in the current study, we designed an English vocabulary game to engage learners to interact with the R&T System. Adopting an action research methodology, we first conducted a pilot study with 12, fourth grade students to identify learning obstacles in a game setting. They were randomly assigned to two groups, one with learning support and the other without learning support. The experiment lasted for four weeks. Both pre-test and post-test on English vocabulary were used to measure the effectiveness of these learning support mechanisms. From the video recordings of the participants' engagement with the R&T System, the results reveal that the learning support mechanisms had positive impact on the participants' learning performance and significantly reduced the occurrences of learning obstacles. This research could serve as a useful reference for future research when designing learning support for contextual vocabulary learning supported by a robotic system.

Gamification has gained an increasing attention from researchers and practitioners in various domains including education as it can increase learners' engagement and motivation. However, little is known about how educational gamification experiences can be influenced by learners' characteristics. Therefore, this study provides a systematic meta-review of empirical studies related to learners' characteristics and educational gamification experiences. The obtained results from the meta-analysis of forty related articles are: (a) learners' psychological and behavioral outcomes were affected by learners' characteristics in educational gamification systems; (b) quantitative methods, using questionnaires, are the most used method to measure the effect of learners' characteristics on their learning outcomes in educational gamification systems, and this needs to be changed to the new potential of using educational big data and learning analytics approaches; (c) personality traits is the most investigated characteristic followed by player types, but there is a need to further investigate other important factors of learners' characteristics, such as working memory capacity and age; and, (d) a set of game design guidelines that should be taken into consideration while designing educational gamification catering individual difference were proposed.

While previous studies have focused on the outcomes of using dubbing apps to foster learner's development of English-speaking ability, this study, grounded in cognitive apprenticeship (CA), is an investigation of the learning processes, which included modeling, coaching, scaffolding, articulation, reflection, and exploration, involved in the use of a dubbing app, Mofunshow. Participants included 26 college students at a science and technology university in Taiwan. Data included GEPT speaking pre-and post-test scores, initial and final dubbing videos, learning logs, and reflective essays. The results showed that after engaging in video dubbing the students improved their English-speaking ability, in terms of accuracy and fluency. Coaching and modeling were ranked as the two most useful processes which supported students as they improved their English-speaking ability through repeatedly listening, echoing, and imitating. Implications and limitations of the study are discussed.

Research has shown that language learning with the form of human body could promote learner performance on the basis of theory of embodied cognition. Total physical response (TPR) has long been used to enhance vocabulary learning. However, TPR has its limitation that teachers are unable to attend to all individual students when the class size is beyond manageable. Thus, to enhance English vocabulary learning, this study proposes an integration of motion-sensing technology and theory of embodied cognition into the total physical response (TPR) approach, called Embodiment-based TPR approach. To test the effectiveness of the proposed approach, a total of 50 fifth-grade elementary students participated in this study. Experimental group adopted Embodiment-based TRP learning approach, while control group took conventional TPR learning approach. Cognitive performance and acceptance feedback for the proposed approach were collected in the experiment. Results showed that both the post-test and the delay test concerning English vocabulary learning performance between the two groups had no significant difference. However, the result of learning retention showed a significant regression for the control group while the experimental group’s learning retention retained, which implies the Embodiment-based TPR approach could bring better learning retention than the conventional TPR approach. In addition, experimental group showed a highly positive level of acceptance toward the proposed learning approach.

This paper examines issues around learning ‘content’ and its place in the new digital learning culture. We focus on the increasing demands of digital learners for content that is relevant and the challenges this poses if educators are to stay relevant to them. We say ‘relevance’ is best achieved when content is negotiated with learners in collaboration with instructors. We describe strategies in which technology enhanced teaching and learning solutions have enabled learners to negotiate and create digitised learning content that is educationally, culturally and socially relevant. We cite two case studies that exemplify this approach: a trial of negotiated content with primary school aged digital learners at Brisbane School of Distance Education (BSDE), Australia, and the content decision-making processes used for the development of e-learning courses for hearing health professionals and Auditory-Verbal Therapy at Hear and Say WorldWide Brisbane, Australia. We focus on the changing demands and skill sets of digital learners, their learning managers and subject matter experts, and the use of technology enhanced teaching and learning solutions as the negotiating tool in the development of digital content that is academically rigorous and also learner friendly.

In the past decade, the rapid development of computer and communication technologies brings many opportunities for developing innovative learning environments with rich resources. Technology enhanced learning shifted their focus from technology to support factual learning, memorization and the reinforcement of basic skills to stimulate students to engage in meaningful learning and situated learning. With the support of computer and communication technologies, students are able to develop higher-order skills, such as critical thinking and problem-solving skills individually or collaboratively. Technology enhanced learning has become an interdisciplinary issue that attracts researchers from various fields to work together.

Many online communities nowadays are emphasized more on peer interactions and information sharing among members; very few online communities are built with knowledge management in nature supported by knowledge management system (KMS). This study aims to present a community of practice on how to effectively adopt a knowledge management system (KMS) to neutralize a cyber collaborative learning community for a research lab in a higher education setting. A longitudinal case for 7 years was used to analyze the retention and extension of participants‟ community of practice experiences. Interviews were conducted for the comparison between experiences and theories. It was found that the transformations of tacit and explicit knowledge are in accordance with the framework of Nonaka‟s model of knowledge management from which we elicit the strategies and suggestions to the adoption and implementation of virtual collaborative research community supported by KMS.

TELearn 2009 has successfully attracted over 91 submissions, from 4 continents, 11 countries. Half of the submissions came from the outside of Taiwan, which indicates TELearn has become a truly international event. This year, TELearn accept 18 full papers, the acceptance rate of full papers is less than 20%. From the 18 full papers, 5 best papers from Japan, Taiwan, USA, Finland and South Africa were included in this special issue to contribute the understanding of social computing in e-learning and raise potential questions which will require reflection.

Due to the rapid advancements in mobile communication and wireless technologies, many researchers and educators have started to believe that these emerging technologies can be leveraged to support formal and informal learning opportunities. Mobile language learning can be effectively implemented by delivering learning content through mobile phones. Because the screen size of mobile phones is limited, the presentation of materials using different Learning Content Representation (LCR) types is an issue that needs to be explored. This study addresses the issue of content adaptation in mobile language learning environments. Two dimensions have been taken into consideration to identify a promising solution: instructional strategies (LCR types: written annotation and pictorial annotation), and learners’ cognitive models (verbal and visual short-term memory). Our findings show that providing learning content with pictorial annotation in a mobile language learning environment can help learners with lower verbal and higher visual ability because such learners find it easier to learn content presented in a visual rather than in a verbal form. Providing learning content with both written and pictorial annotation can also help learners with both high verbal and high visual abilities. According to the Cognitive Load Theory, providing too much information may produce a higher cognitive load and lead to irritation and a lack of concentration. Our findings also suggest that providing just the basic learning materials is more helpful to learners with low verbal and visual abilities.

Badminton is a very popular subject in Physical Education (PE). Many students enroll badminton courses in every semester which pose a tremendous teaching load to the instructors. The one-on-one guiding/feedback time provided by the instructor to each student is also greatly reduced. To overcome this challenge, some studies have tried to adopt pose recognition technique in teaching badminton. However, the lack of mobility and recognition accuracy problems hinder its applicability. To address this issue, a new pose recognition technique, OpenPose, was employed to develop a real-time pose recognition badminton teaching APP in this study. The APP was then installed on a mobile device to enhance badminton smash skill learning performance. The scores distribution of the experimental group shows majority of students achieved satisfactory performance, this implies the badminton teaching APP is helpful to all students no matter what their initial skill levels are.