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This meta-analysis evaluated the effectiveness of data-driven learning (DDL) among low-proficiency L2 English learners, addressing the mixed results found in previous meta-analyses. The study incorporated 38 studies involving 2085 participants, yielding 37 effect sizes from control-experimental (C/E) studies and 42 from pre- and post-test (P/P) studies. The findings demonstrated that DDL had a medium effect in C/E studies (g = 0.71) and a large effect in P/P studies (g = 1.43). The moderator analyses, based on the corpus-based language pedagogy (CBLP) framework by Ma et al. (2022), examined 7 pedagogical moderators. The results reaffirmed the efficacy of DDL in teaching lexicogrammatical items and suggested DDL’s curriculum flexibility; the duration of DDL did not significantly impact its effectiveness. Unique to this meta-analysis were findings that DDL was more effective for low-proficiency L2 learners of English when employing the following pedagogical strategies that cater to the cognitive-social nature of DDL: (1) utilizing paper-based concordancing to facilitate the pedagogical processing of corpus resources, (2) leveraging learners’ first language (L1) to improve comprehension of concordance meanings, (3) applying interactive communication with teacher verbal guidance or teacher verbal feedback attuned to learner responsiveness, and (4) providing teacher support in collaborative work to reduce the collaborative cognitive load on learners. Finally, this study proposed a holistic approach to CBLP design tailored to low-proficiency L2 learners, which presents an essential frontier for future research.

Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefit that influence the learner' progress for self-regulated learning (SRL) and course achievements. However, learners tend to be unsure about how note-taking is performed using online learning materials and lack sequential patterns of e-book note-taking for SRL. Thus, in this paper, an exploratory study was conducted in an undergraduate course that implemented the BL design. The learning task for the blended learners in the present study was to study the learning material using BookRoll, an e-book system, during in-class and out-of-class learning sessions. Lag sequential analysis of the e-book learning behavior data was conducted to identify the blended learners' sequential behaviors of e-book note-taking for the cognitive strategy use of SRL. Moreover, the difference between higher- and lower-achievement blended learners in terms of their sequential behaviors of e-book note-taking for SRL was revealed. This study can help educators provide evidence-based educational feedback to learners regarding the identified sequential patterns of e-book note-taking that can be applied as effective strategies for promoting the cognitive strategy use of SRL and improvement of course achievement in BL.

Mobile technology is regarded as a helpful tool facilitating language learning. However, the success of mobile technology largely depends on learners’ acceptance. This study explored the factors that may affect students’ intention formation regarding mobile-assisted language learning (MALL) in the context of higher education through the lens of action control theory. The study adopted mixed methods: an online survey of 557 students and individual interviews with 70 students. The findings indicated factors in each of the three dimensions (preoccupation, hesitation, and volatility) of action control theory that positively or negatively influenced the students’ intention to use mobile technology for language learning. According to the findings, these influential factors may be related experiences in the preoccupation dimension, design and feature interference of MALL applications and teachers’ teaching style influence in the hesitation dimension, and overall appraisal and performance impact and other novelty interference in the volatility dimension. Students’ success in initiating and completing a MALL task depends on mainly depends on their acceptance of MALL, and this acceptance is affected by these factors in a positive or negative direction. The strengthening of the positive influence and the weakening of the negative influence caused by these factors should be paid attention to in the process of performing and engaging in a MALL task. Students’ concerns regarding the use of mobile technology in language education are addressed with suggestions for future research and practice in light of the findings.

This study employed drawing and co-word analysis techniques to explore students’ preferences for AI-assisted learning environments. A total of 64 teacher education students from a university in Taiwan participated in the study. The participants were asked to describe their perceptions of AI-assisted learning in the form of drawings and text descriptions. In order to analyze the content of the students’drawings, a coding scheme was developed based on the activity theory framework. Based on the results of the analysis, it was found that students placed more importance on personalized guidance and appropriate learning content provision. In addition, students acknowledged that AI technology can be used flexibly in different fields and situations. Interestingly, more than half of the students agreed that robots play important roles in AI-assisted learning. This indicates that the students expected a social AI learning companion. However, it was found that students’ expectations of an AI learning environment were less connected to the real environment and did not reveal learning activities with higher order thinking. In addition to the need for accurate and fast AI computing, this result indicated that professional instructional guidance is also an expectation that students have of AI education.

This study examined how information and communications technology (ICT) related factors and country-level economic status influence student academic achievement. Two-level structural equation modeling was employed to investigate both student-level and country-level variables, using the PISA 2015 data of ninth-grade students across 39 countries. The findings indicate that: (a) students’ interest in ICT, perceived ICT competence, and autonomy had positive impacts on academic performance; (b) GDP per capita had significant interaction effects on the relationship among ICT-related factors (ICT use for studying at school, for entertainment, and perceived ICT autonomy) and academic performance; and (c) a higher level of students’ perceived autonomy in ICT resulted in better learning outcomes in countries with less income inequality.

The integrative approach of teaching Science, Technology, Engineering and Mathematics (STEM) has been advocated as a pedagogical means to advance education for the 21st century. However, there is a lack of validated instruments that are theoretically grounded to account for the various forms o f knowledge that teachers need in order to effectively implement STEM education. This study adopts the technological pedagogical content knowledge (TPACK) framework to develop the Technological Pedagogical STEM Knowledge Survey to assess teachers’ self-efficacies of the proposed dimensions of knowledge. It also investigates the interrelationships of the four knowledge dimensions (i.e., technological pedagogical science knowledge [TPSK], technological pedagogical mathematics knowledge [TPMK], technological pedagogical engineering knowledge [TPEK] and integrative STEM) proposed in this paper. A total of 314 science, mathematics and technology teachers from China responded to the online survey. Both exploratory and confirmatory factor analyses indicated adequate validity and reliability of the survey for measuring teachers’ self-efficacies for STEM from the TPACK perspective. The structural equational model indicates that the teachers’ efficacies of integrating technology into science, mathematics and engineering subject predict their efficacy of integrative STEM teaching. Moreover, teachers’ TPEK is the strongest predictor of their efficacy for teaching integrative STEM. Overall, the findings support that the TPACK framework could be theoretically useful for promoting teachers’ efficacies for STEM education. Practical implications were discussed in this study.

Emerging research is highlighting the importance of fostering artificial intelligence (AI) literacy among educated citizens of diverse academic backgrounds. However, what to include in such literacy programmes and how to teach literacy is still under-explored. To fill this gap, this study designed and evaluated an AI literacy programme based on a multi-dimensional conceptual framework, which developed participants' conceptual understanding, literacy, empowerment and ethical awareness. It emphasised conceptual building, highlighted project work in application development and initiated teaching ethics through application development. Thirty-six university students with diverse academic backgrounds joined and completed this programme, which included 7 hours on machine learning, 9 hours on deep learning and 14 hours on application development. Together with the project work, the results of the tests, surveys and reflective writings completed before and after these courses indicate that the programme successfully enhanced participants' conceptual understanding, literacy, empowerment and ethical awareness. The programme will be extended to include more participants, such as senior secondary school students and the general public. This study initiates a pathway to lower the barrier to entry for AI literacy and addresses a public need. It can guide and inspire future empirical and design research on fostering AI literacy among educated citizens of diverse backgrounds.

With the increasing use of Artificial Intelligence (AI) technologies in education, the number of published studies in the field has increased. However, no large-scale reviews have been conducted to comprehensively investigate the various aspects of this field. Based on 4,519 publications from 2000 to 2019, we attempt to fill this gap and identify trends and topics related to AI applications in education (AIEd) using topic-based bibliometrics. Results of the review reveal an increasing interest in using AI for educational purposes from the academic community. The main research topics include intelligent tutoring systems for special education; natural language processing for language education; educational robots for AI education; educational data mining for performance prediction; discourse analysis in computer-supported collaborative learning; neural networks for teaching evaluation; affective computing for learner emotion detection; and recommender systems for personalized learning. We also discuss the challenges and future directions of AIEd.

Conversational agents (CAs) primarily adopt knowledge scaffolding (KS) or emotional scaffolding (ES) to intervene in learners' knowledge gain and emotional experience in online learning. However, the ill-defined design for KS and ES, as well as insufficient understanding of their interactive effects on learning outcomes, have hindered the advancement of CAs in theory and practice. This study proposed systematic KS and ES design principles based on Zone of Proximal Development and growth mindset theories. We investigated their individual and combined impacts on knowledge gain and emotional experience. A quasi-experiment was conducted with 128 undergraduate students divided into four groups, corresponding to four distinct CAs: a non-scaffolding control group (CG), ES, KS, and Knowledge and Emotional Scaffolding (K＆ES) CA. The results showed that K＆ES-based CA had a significant impact on knowledge gain and emotional experience, with both being slightly improved compared to CG. Besides, KS-based CA had a positive effect on knowledge gain and emotional experience, while ES-based CA only slightly improved emotional experience compared to CG. The results validated the effectiveness of the proposed ES and KS design principles. The fine-grained analysis revealed a significant correlation between the achievement positive emotion and knowledge transfer, highlighting the importance of integrating KS and ES. In conclusion, this study offers valuable theoretical, methodological, and empirical insights for utilizing CAs to optimize online learning experiences.

In this article the researchers explore the notion of emotional capital in relation to language teachers’ emotion labor and the role of reflection in understanding their emotional experiences. They draw on interview narratives with teachers (N = 25) working in higher education institutions in the United States and United Kingdom. During these interview conversations, the researchers elicited accounts of teachers’emotionally charged experiences that arise as part of their ongoing, mundane teaching practice and how they respond to these situations. The researchers argue that as language teachers struggle to orient to the feeling rules of their institutions, they develop the capacity to perform the emotions that they believe are expected of them. This capacity is further shaped through their reflective practice, as both individual reflection and collaborative reflection with colleagues. The researchers thus analyze how language teachers’ accruing emotional capital, developed through emotion labor and reflective activity, can be converted into social and cultural capital. The authors also point to how language teachers’ emotional capital is entangled in power relations and thus requires careful scrutiny.