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The lesson study aims to examine college English teachers' growth in technological pedagogical content knowledge (TPACK) and the improvement of students' learning outcomes in the context of MOOC-based and AI-powered flipped teaching and assessment of EFL writing (MAFTA).

Three college EFL teachers and their students (66 in total) participated in three cycles of MAFTA instruction. Triangulated analysis was conducted by considering all relevant data sources, including the teachers' discussions and reflections, the argumentative essays produced by the students before and after the MAFTA instruction, as well as the data gathered through questionnaires and interviews.

The three teachers demonstrated varying degrees of growth in TPACK, as evidenced by their increased knowledge of the technology tools and skills in utilizing the tools to realize their pedagogical beliefs on teaching EFL writing. Substantial improvements were detected in students' essays. The students generally have affirmative perceptions on the MAFTA model and the questionnaire and interviews specified the benefits they gained from each stage of the model.

Firstly, the lesson study is grounded in an innovative approach to teaching EFL writing that incorporates multiple technological affordances. Secondly, it closely scrutinizes the dynamics of both teachers' and students' growths during the innovative practice. The findings could offer insights into teachers' TPACK development and effective integration of technological advancements in EFL education at the tertiary level.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.

The purpose of this study is to investigate students’ attitudes, self-efficacy and emotional behaviour associated with online learning and the effectiveness of online learning.

A research model was formulated and analysed with the structural equation modelling technique. The respondents consist of 843 students pursuing their studies at a private university’s foundation, undergraduate and postgraduate levels. A two-step systematic approach was used using the SmartPLS version 3 software to conduct statistical analysis and draw meaningful insights.

The study’s findings have demonstrated that students’ attitudes and self-efficacy exhibit a positive relationship with online learning behaviour (OLB). It is observed that the students’ emotions are related to online learning effectiveness (OLE) and mediate the relationship between OLB and OLE. Furthermore, OLB partially mediates the relationship between attitude and OLE and between self-efficacy and OLE.

The operational instructions and digital resources have proven to be highly effective in providing valuable learning experiences to the students. As a result, the students are now expanding and applying their new encounters to a broader range of learning opportunities. This study has provided valuable insights for stakeholders, including scholars, higher education institutions and the Ministry of Higher Education, in providing the ideas of online learning or Web-based education.

The originality of this study sheds light on the role of OLB as a mediator. It was underlined that emotion is critical in improving students’ OLE. Thus, students’ attitudes and self-efficacy have been essential in reassuring OLB and enhancing OLE.

This study aimed to investigate learning behaviors deeply in flipped classrooms. In addition, it is worth considering how to help learners through recognition technology with natural language processing (NLP) when learners have question and answer (Q&A). In addition, the Internet of Things (IoT) can be utilized to make the physical learning environment more comfortable and smarter.

The authors developed smart learning environment (SLE) with smart mechanisms supported by recognition technology, NLP and IoT to help learners and employed scaffolding to facilitate their group discussions. This study is an explanatory research to investigate graduate learners' learning behavior when they are collaborating with group members and interacting with the environment in flipped classroom using the proposed SLE.

The results revealed that learners who collaborated more while coediting had significant learning achievement, and NLP sufficiently addressed their questions. Physical conditions of the SLE were comfortable for learners. They perceived that SLE could facilitate group discussions with scaffolding.

This study suggests to utilize flipped classrooms with technologies, e.g. Google Slides integration, to help learners to do more collaboration and use smart mechanisms, e.g. Q&A with NLP, to make learners more interacting during the discussion process.

The proposed SLE can record and analyze smartly their collaboration meaningfully with group members and interact with the environment. Accordingly, researchers found that collaboration in flipped classrooms can help their learning achievement, and it is worth being widely promoted.

COVID-19 played a crucial role in the development and enlargement of learning via electronic media. Still, the recent fourth industrial revolution [Industry 4.0 (I4.0)] paved the road toward Education 4.0. In this regard, several research challenges arise, involving the preparation of gamification strategies in online learning environments. In this sense, this paper aims to investigate the relationship between technologies of I4.0 and gamification practices in online learning around the world. Specifically, to categorize the studies of the scientific literature in the area into knowledge domains and the mention of I4.0 technologies and to verify the relationship of these technologies with the different educational levels.

The preferred reporting items for systematic reviews and meta-analysis protocol was used as a research method with 130 papers included for full content analysis and obtained from the Web of Science.

The leading I4.0 technologies mentioned in the analyzed papers were simulation, Internet of Things and augmented/virtual reality, in this order. Although there are more mentioned technologies, the domain of knowledge to be applied and the educational level interfere in choosing these pillars. With this, the main findings of this relationship were exposed in a singular, modern, active, realist, technological framework to demonstrate how I4.0 relates to the practice of gamification in online educational environments.

To the best of the authors’ knowledge, this is the first study that brings together the relationship of gamification applied in e-learning with I4.0 technologies.

The flipped classroom model is an emerging teaching pedagogy in universities, colleges and secondary schools. This model will likely be successful if students prepare and acquire basic knowledge before class hours. Pre-class video lectures are common for students to access knowledge before class hours. However, students often do not watch the pre-class videos or do so only immediately before class hours due to poor engagement and supporting strategies, which can have detrimental effects on their learning achievement. To address this issue, embedding quiz questions into pre-class recorded videos may increase the completion of pre-class activities, students' engagement and learning success. This study examines the effect of a quiz-based flipped classroom (QFC) model to improve students' learning achievement and engagement in a computer science course.

The study involved 173 participants divided into experimental and control groups. The experimental group consisted of 78 students who used the QFC model, while the control group consisted of 73 students who used the conventional flipped classroom (CFC) model.

The 10-week experiment showed that the QFC model effectively improved students' learning achievement and engagement (both behavioral and agentic) compared to the CFC model.

Embedding quiz strategy into the pre-class video demonstrated the potential support to enhance the efficacy of the CFC model. Based on the results of this research, the authors recommended that flipped educators can use the quiz strategy to minimize pre-class issues (especially students' disengagement).

This research adds to the existing literature by evaluating the effect of the newly proposed model on students' learning outcomes and engagement. This study's results can guide colleges and universities intending to implement a blended learning or flipped learning model. The research also gives design, content and course implementation guidelines, which can help engage students to achieve their learning objectives.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

The purpose of the case study is to investigate the perception of computer science learners at Krishna Kanta Handiqui State Open University (KKHSOU) regarding the use of gamification and to identify the challenges in implementing gamified teaching-learning processes in open and distance learning (ODL) institutions.

The case study was carried out by observing the views of the learners on the use of gamification in distance learning and studying the participation level of computer science learners before and after the gamification environment was added to a specific topic through the learning management system (LMS). The pre-game and post-game effects on learners' participation against various factors using t-tests were also investigated in this study.

The study reveals that gamification had a positive and highly significant influence on the learners' participation and engagement in the e-content provided via the LMS. The post-gamification mean score (5.62) was higher than the pre-gamification mean score (2.36), and the overall paired t-test (5.301) value was significant at the 1% level of significance. Some of the challenges faced during the study were communication with the learner, lack of technical skills of the learner, duration of study, etc.

This study will help in understanding the perception and effect of gamification on computer science learners in ODL mode in India. The study succeeded in demonstrating that the use of gamified elements results in increased learner participation.

This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia.

A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education.

The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated.

The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.

This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.

This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.