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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).

Affective computing (AC) has been regarded as a relevant approach to identifying online learners' mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners' facial expression, to compute learners' affection. However, a single facial expression may represent different affections in various head poses. This study proposed a dual-source data approach to solve the problem. Facial expression and head pose are two typical data sources that can be captured from online learning videos. The current study collected a dual-source data set of facial expressions and head poses from an online learning class in a middle school. A deep learning neural network using AlexNet with an attention mechanism was developed to verify the syncretic effect on affective computing of the proposed dual-source fusion strategy. The results show that the dual-source fusion approach significantly outperforms the single-source approach based on the AC recognition accuracy between the two approaches (dual-source approach using Attention-AlexNet model 80.96%; single-source approach, facial expression 76.65% and head pose 64.34%). This study contributes to the theoretical construction of the dual-source data fusion approach, and the empirical validation of the effect of the Attention-AlexNet neural network approach on affective computing in online learning contexts.

Research on the use of augmented reality technology in museums is mostly limited to scientific knowledge. The use of wearable device technology learning materials to benefit students in the process of English learning has been somewhat explored, along with differences in students' learning styles and their influence on students' learning motivation, but there remains much to be done in this field. This study explored the learning effect of smart glasses integration into the English language dinosaur exhibition at the Museum of Natural Science and investigated their impact on motivation. The experimental group used smart glasses learning strategy to facilitate their learning experience, whereas the control group used a tablet learning strategy. The research results show that: 1) Learning effectiveness and motivation were significantly superior when using smart glasses learning strategy, as compared to using a tablet learning strategy when learning through the English language situated exploration game. 2) The interaction between different learning strategies and learning styles had a significant impact on learning effectiveness. In the smart glasses learning strategy group and the tablet learning strategy group, the learning effectiveness of the kinesthetic students was significantly higher than those of the auditory students. The learning effectiveness of the visual students using the tablet learning strategy was higher than auditory students. 3) The interaction between different learning strategies and learning styles had no significant effect on learning motivation. In terms of learning motivation, visual students were more affected than auditory and kinesthetic students.

Swing movements and muscle strength are essential for mastering badminton techniques. Traditionally, students learn badminton through their instructor's physical demonstration, verbal instructions, and small group activities. To enhance students' learning experience and assist badminton instructors more effectively, this study proposes an alternative teaching approach that utilizes a Badminton Teaching-Assisted System (BTAS) to teach swing movements. The system uses a gesture control armband to collect electromyography and gyroscope signals from professional players, which are then used to build a machine learning model based on the back propagation neural network. The model can be used as a scoring tool to assess the correctness of the student's swing movements and muscle strength. A total of 87 students participated in an eleven-week experiment focusing on two fundamental badminton skills: The backhand push and backhand lift. The results demonstrated a significant improvement in the correctness of the muscle strength during swing movements for the experimental group, compared to the control group. The BTAS can help to enhance the effectiveness of badminton instruction, thereby improving students' swing techniques. Future research could expand upon this study to develop more precise artificial intelligence-based sports assistance systems, which would facilitate personalized teaching and learning in physical education.

The field of language education has experienced a rise in using virtual reality (VR) to support interactive, contextualized, and collaborative language learning in recent years. The current study investigates the effects of auditory, visual, and textual input on speaking and writing in English for Tourism Purposes (ETP) through immersive, scenery-based virtual reality (SBVR) dyadic learning. Adopting a framework on ETP sociocultural, linguistic, and formulaic competence, the researchers designed relevant learning content using audios, images, and verbatim onscreen text on "EduVenture VR" to create an interactive, simulated tour. Thirty-eight English as a foreign language undergraduates participated in this single-group quasi-experiment. Various data sources included speaking and writing pre-, post- and delayed post-tests, a post-intervention survey, and reflections about the VR learning experience. The study reported significant speaking gains in language accuracy, destination promotion, knowledge retention, and replication of ETP language, as well as significant writing improvement in using sophisticated vocabulary, knowledge retention, and replication of ETP language. The study concludes that SBVR warrants positive impact on improving productive ETP skills. Possible factors, limitations, and pedagogical implications for using scenery-based VR in dyadic learning for ETP are discussed. Finally, the study suggests future research directions to combine technology-enhanced language learning theories with multimodal input and material design supported by immersive VR to enhance language production for specific purposes.

The ability of automatically modeling learners' personalities is an important step in building adaptive learning environments. Several studies showed that knowing the personality of each learner can make the learning interaction with the provided learning contents and activities within learning systems more effective. However, the traditional method of modeling personality is using self-reports, such as questionnaire, which is subjective and with several limitations. Therefore, this study presents a new unobtrusive method to model the learners' personalities in an intelligent Moodle (iMoodle) using Learning Analytic (LA) approach with Bayesian network. To evaluate the accuracy of the proposed approach, an experiment was conducted with one hundred thirty-nine learners in a public university. Results showed that recall, precision, F-measure and accuracy values are in acceptance range for three personality dimensions including extraversion, openness, and neuroticism. Moreover, the results showed that the LA approach has a fair agreement with the Big Five Inventory (BFI) in modeling these three personality dimensions. Finally, this study provides several recommendations which can help researchers and practitioners develop effective smart learning environments for both learning and modeling. For example, it is needed to help identify more features of the hardest personality traits, such as agreeableness, using gamification courses.

Social networking sites (SNSs) are widely used to support online learning and knowledge exchange (KE) in projects that require the coordination of collaborative team-playing, especially in COVID-19 world pandemic. Paradoxically, while digital infrastructures enable instant communication, SNSs are not always conducive to KE behaviours, as learners are reticent to exchange knowledge with their peers online due to trust issues like personal privacy and Intellectual property rights protection, leads online users to conceal knowledge. Given that online-based KE practices are still in their infancy, one major weakness in current educational and knowledge management research is the lack of a reliable scale to measure knowledge-hiding online. This study develops and validates a novel Online Knowledge-hiding (OKH) scale that keeps into account the distinctive features of SNSs. While building on prior measurements of knowledge-hiding, we identify, test, and validate different indicators of the latent OKH construct. A mixed-method approach to scale development was used to validate this scale. Results show that the proposed scale is effective for organisations to assess whether online knowledge-hiding takes place during KE activities. The usefulness of this scale lies in improving the online collaborative learning environment while setting the foundation to address KE-related deviant behaviours at their incipit.

The purpose of this study is to develop and validate an authentic contextual learning framework with six constructs to model the relationships among essential factors of authentic contextual learning. In particular, this framework explores the relationships across six constructs, i.e. learning by applying, healthy learning, collaborative learning, creativity, sustainability, and scalability. A total of 99 learners participated in this study. All learners had to fill out a questionnaire; then, the data were analyzed using exploratory factor analysis and partial least square analysis. The result shows that the developed framework has a good predictive power for evaluating the effectiveness of a sustainable and scalable authentic contextual learning (SSACL) design. From the results, it was found that learning by applying not only can unlock learners' imagination and enhance high cognition development thereby helping creativity but also can sustain learning motivation (sustainability). In addition, learning by applying can make learners intend to do more learning tasks in different contexts, which promotes learners' interests to do more applications in authentic contexts (scalability). Moreover, learning by applying can also give learners a healthy learning experience by moving their body, walking, and breathe fresh air outside, thereby can help their health as well.

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 < 0.001, EG = 71.03, CG = 54.76; Smash: p < 0.01, EG = 82.79, CG = 72.22). Further analysis reveals that the multiple feedback is more beneficial for learners with lower initial skill levels (Clear: p < 0.05, Lower = 63.21, Higher = 46.99; Smash: p < 0.001, Lower = 77.67, Higher = 39.39)

Few studies have focused on the comparison between collaborative and individual digital game-based learning (DGBL) performance for students who study English as a Foreign Language (EFL). In collaborative DGBL, how the composition of foreign language anxiety (FLA) within groups of students affects students' performance remains uncertain. This study aimed to examine the effects of anxiety composition on DGBL performance of primary school students with different FLA levels to inform collaborative DGBL instruction. Ninety-six fifth-graders from four classes were identified as low, moderate, and high anxiety students according to Foreign Language Classroom Anxiety Scale (FLCAS). Two classes were randomly assigned into collaborative DGBL while the other two classes in individual DGBL. The students in the collaborative DGBL were further grouped into 12 heterogeneous groups with anxiety composition of Low & Moderate (LM), Low & High (LH), Moderate & High (MH), and Low, Moderate & High (LMH). Those in the individual DGBL underwent individual gameplay learning. It was found that either collaborative or individual DGBL effectively assisted the students to reduce their FLA. The results also showed that the students in the heterogeneous groups generally had better DGBL performance than those who worked individually. Particularly, groups with anxiety composition of LM outperformed those with LH, those with MH, and those with LMH. The study suggests group composition be considered a critical factor that may enhance students' learning with FLA in collaborative DGBL.

In-service teachers' professional development regarding technology adoption in education is often focused on only one specific technology. To maximize the utilization of technologies, teachers need to be able to leverage different technologies seamlessly. Technological Pedagogical Content Knowledge (TPACK) is one of the most important frameworks depicting teachers' competences for effectively teaching with technology. The TPACK Support-Stimulate-Seek model (3S-TPACK) was used to design training programs with learning activities using robots, IoT-based sensors, actuators, tablets, and computer programming tools. The results show that the training programs designed by adapting the 3S-TPACK model not only can improve the teachers' learning outcomes but also can support positive attitudes toward adopting robots and IoT technologies in the teaching and learning process.

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.

The purpose of this study is to review journal papers on educational big data research published from 2010 to 2018. A total of 143 papers were selected. The papers were characterized based on three dimensions: (a) educational goals; (b) educational problems addressed; and (c) big data analytical techniques used. A qualitative content analysis approach was conducted to develop a coding scheme for analyzing the selected papers. The results identified four types of educational goals, with a clear predominance of quality assurance. The identification of the most mentioned educational problems resulted in four main concerns: the lack of detecting student behavior modeling and waste of resources; inappropriate curricula and teaching strategies; oversights of quality assurance; and privacy and ethical issues. Concerning the most mentioned big data analytical techniques, the coding scheme revealed that the majority of the papers focused on the educational data mining technique followed by the learning analytics technique. The visual analytics technique was mentioned in a few papers. The results also indicated that the educational data mining technique is the most suitable technique to use for quality assurance and to provide potential solutions for the lack of detecting student behavior modeling and the waste of resources in institutions.

This review study investigates the appropriation of sensing technology in context-aware ubiquitous learning (CAUL) in the fields of sciences, engineering, and humanities. 40 empirical studies with concrete learning outcomes across mandatory and higher education have been systematically reviewed and thematically analyzed with an outcomes-based teaching and learning approach. Four derived themes have been found to describe the design and implementation of CAUL, including learner-centeredness, technological facilitation, learning ecology, and research evaluation. The learning processes enabled by context-aware sensing technology have been explicated, revealing specific ways to apply new technologies in formal and informal environments. The analysis based on intended learning outcomes suggest that more efforts should be directed to fostering competence in analyzing and creating in mandatory education, and to creating in tertiary settings. Finally, unequal distribution of CAUL implementation across world regions calls for more technological appropriation in Southeast Asia and Africa. Specific suggestions on how to improve CAUL are also provided to better prepare learners in the twenty-first century.

The use of technology such as online software has been examined in English as a foreign language (EFL) writing contexts. However, few studies have incorporated context-aware ubiquitous technology into EFL writing instruction. To develop multimodal and digital literacy of target EFL undergraduates in this pilot project, the researchers designed and implemented an augmented-reality context-aware ubiquitous writing (ARCAUW) application, which aimed to increase long-term memory, motivation, and self-regulated cognition in participants' writing development. The pilot project compared the writing outcomes and learner perceptions of the proposed ARCAUW writing mode against the baseline mobile-assisted, classroom-based writing mode. The two writing modes differed in the way that metacognitive scaffolding tasks were carried out. Pre- and post-test results showed that (a) although both modes led to significant improvement in writing the process analysis essay, ARCAUW was conducive to the development of task schema in long-term memory, motivation, and self-regulation in writing, and (b) additional cognitive processing during AR-based learning led to mixed results in writing performance. It was concluded that ARCAUW should be incorporated into mobile-assisted writing courses to reach optimal instructional outcomes. Finally, a five-step procedure was offered to help EFL practitioners design and implement ubiquitous writing in their own teaching contexts.

As the use of online learning has continued to expand, there has been a growing demand for advanced software or social sites that can enhance learning outcomes in online settings. It is important to explore how learner-learner, learner-teacher and learner-content interactions affect on learning outcomes while using the social sites such as Facebook, Twitter, WeChat, Blogs, etc. Although some prior research highlight that there is a demand for interaction in online courses, specific design tactics and empirical evaluation of the effects of interaction on learning outcomes in blog-based courses are lacking. Thus, a blog learning platform named "Learner's Digest Blog" (LDB) was developed to facilitate the three types of interactions and enhance learning outcomes. A total of 26 learners participated in the blog-based interactive learning environment. The study addressed two research questions, and regression analysis was used to analyze the data. The findings indicated a significant influence between learner-learner interaction, learner-teacher interaction and learner-content interaction on subjective learning outcome. In contrast, there was no significant influence of learner-teacher interaction on objective learning outcomes, but learner-learner and learner-content did significantly affect objective learning outcomes. A significant relationship was also found between students' subjective and objective learning outcomes.

The aim of this study was to develop a badminton teaching system using the wearable technology for improving badminton teaching and learning. This system can provide the similarity scores automatically by comparing a student's movement and strength while playing badminton with a well-trained expert model. A quasi-experiment was conducted for eight weeks to evaluate the effectiveness of the developed system. A paired-sample t-test was analyzed to evaluate whether there is a significant difference between the pretest and posttest for the experimental group (EG) and the control group (CG) respectively. Also, an independent sample t test was utilized to compare the posttests between EG and CG. The results show that the badminton teaching system developed in this study could enhance the learning effectiveness of novice badminton learners. The results not only show that applying wearable technology, the Myo armband, can benefit learners' badminton training, but also can demonstrate the importance of providing instant and adaptive feedback in motor skill learning.

To address the challenges of limited language proficiency and provide necessary feedback in the implementation of task-based language teaching (TBLT), a mobile-supported TBLT application was developed to provide linguistic and task scaffolding. Sixty-six English as a foreign language (EFL) university learners participated in a three-week experiment as part of a general English course. They were assigned to either an experimental group (mobile-supported TBLT), which received TBLT with scaffolds built into the application, or a control group (traditional TBLT), which received traditional paper-based TBLT without the scaffolds. At the end of the experiment, an English achievement test of vocabulary, grammar, and conversation comprehension was administered to determine if the technological scaffolds enhanced the learning outcomes for the course. Students' self-perceived use of oral communication strategies was also measured to explore how these scaffolds affected the conversational interaction essential for task performance. Results showed that the mobile-supported TBLT group outperformed the traditional TBLT group on the vocabulary and conversation comprehension tests but not so much on the grammar test. Also, the mobile-supported TBLT group reported greater awareness of fluency- and accuracy-oriented strategies for speaking than the traditional TBLT group. Implications for designing mobile learning to enhance TBLT in an EFL setting are drawn.

This study aims to design an authoring system of robots and IoT-based toys for creating a scenario-based interactive learning environment for young English as a Foreign Language learners. This study adopts a design-based research approach to investigate the pedagogical needs, the critical features and usability of such a authoring system. Twelve adult participants, including EFL teachers, parents, non-technical, and technical professionals, voluntarily participated in this study to co-develop and evaluated the authoring system through iterations of needs collection, system design, system testing and refinements. An authoring system, consisting of IoT-based toys, a robot and a script editor, was finally developed taking four cycles of iterative improvements in 18 weeks. Eleven pedagogical needs concerning children's language and cognitive development, better children-robot interaction and practical issues were identified. The modifications made across different versions of the authoring system were presented in detail. The results also show that the issues of effectiveness, robustness and efficiency were the most frequently identified, and that the system had achieved good usability after four rounds of design and refinements. Design principles and guidelines also merged as a result providing insights into the effective design of Robot Assisted Language Learning in particular and Technology-Enhanced Language Learning in general.

The learning analytics dashboard (LAD) has been recognised as a useful tool to facilitate teachers' diagnosis and intervention in collaborative learning. However, little empirical evidence has been reported concerning the effects of LAD on teachers in computer-supported collaborative learning (CSCL). The purpose of this study was to evaluate the effectiveness of a LAD system called the Knowledge-Behavior-Social Dashboard (KBSD) for supporting teachers' diagnosis and intervention of CSCL activities. Thirty-five pre-service teachers participated in the experiment. The results indicated that teachers using KBSD used more 'cross-group' strategies to diagnose students' learning problems. Teachers in the experimental group also performed more interventions involving 'cognitive guidance', 'scaffold instructing', 'positive evaluation' and 'individual facilitating'. Moreover, the analysis of the interview revealed that the KBSD tool has the potential to assist teachers in detecting learning problems in a group in an easy and convenient manner. Pedagogical implications and future research are also addressed.

While Audience Response System (ARS) such as Kahoot! can promote learner self-regulation and enhance comprehension by engaging cognitive processing on multiple levels, learning behavior in questioning with ARS was understudied. The present study explores the use of Kahoot! with student-generated questioning, to report the effect on students' reading comprehension. This study involved two intact sixth grade elementary school classes, with a total of 48 students which were divided into an experimental group and a control group. All students first took the simulated GEPT (General English Proficiency Test)-Kids reading test as a pre-test. Each team was assigned a story commensurate with their reading level by the instructor. The experimental group used Kahoot! as an ARS to generate questions, while the control group presented their questions by PowerPoint. The data were analyzed in terms of the students' pre-test and post-test GEPT-Kids scores, and the worksheets. The results indicated that although little significant improvement was recorded from the GEPT scores, the quality of the questions generated by the group using Kahoot! increased at a greater rate over time compared to the control group. The question-generation process showed that the Kahoot! group indicated a higher level of engagement and collaboration and also stimulated an active learning environment.

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.

With advances in learning technologies, new pedagogical models are being developed to improve students' learning performance. One notable model is the flipped classroom, which has attracted the attention of many researchers, particularly in K-12 education. However, research on effective approaches for managing the flipped classroom model to improve mathematics learning is lacking. This study explored approaches to manage the flipped classroom in a Chinese context and designed an approach to improve the mathematical learning performance of middle school students. In a flipped classroom teaching and learning, students took notes while watching videos at home and then teacher utilized the notes for in-class discussion. A total of 88 sixth-grade students in a secondary school in mainland China participated in this study. An experiment was conducted to evaluate the effectiveness of the proposed approach by situating the experimental and control groups in flipped and traditional classrooms, respectively. The results show that the proposed flipped classroom approach significantly improves the students' mathematical learning performance. The proposed approach is more beneficial to students at the middle mathematics level comparing to those at high or low levels. Furthermore, some suggestions are provided for teachers to manage flipped classroom more effectively.

Recent research has focused on students' knowledge sharing behavior and its driving factors, which promote the knowledge transmission and exchange in different educational settings. However, little research has focused on factors that inhibit knowledge sharing. Based on social learning theory and social cognitive theory, the current study investigates the relationships between knowledge hiding behaviors and students' belief about interactional justice and professional commitment. Based on 312 reliable questionnaires collected from Chinese and international postgraduate students in China, this study explored postgraduate students. Three knowledge hiding behaviors include playing dumb, evasive hiding, and rationalized hiding, all of which is negatively related to interactional justice. Furthermore, the moderating effect of professional commitment was found to influence evasive hiding more significantly than the other two aspects of knowledge hiding. Based on the findings of the study, implications are elaborated for learners, supervisors, and higher education.

A cyber-flipped course was conducted with the flipped classroom pedagogy by using a wholly online approach for all learning activities in asynchronous and synchronous class sessions. Literature suggests that traditional flipped courses can effectively enhance students' learning outcomes in comparison to non-flipped courses. However, conducting all asynchronous and synchronous learning activities using a wholly online approach has not been reported. This paper aimed to investigate how student engagement in four different types of learning activities affects their learning outcomes in a cyber-flipped course. Results show that the learning activities with the flipped classroom pedagogy can be successfully implemented and conducted in a wholly online course along with time and space flexibility for learners. This study also found that students who watched more pre-recorded video lectures tended to participate in the synchronous learning activities more actively and obtained a higher semester grade; higher completion of asynchronous learning activities had benefited students' understanding of the learning concepts. Furthermore, students who had a high level of readiness by attending synchronous class sessions on time and keeping their webcams activated had more frequent and proactive interactions with their peers and instructor.