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In recent years, Game-Based Learning (GBL) has been widely adopted in various educational settings. This paper aims to review empirical studies that adopt GBL in the field of AI education and explore its future research perspectives. After a systematic keyword search in the online database and a snowballing approach, a total of 125 empirical papers with 133 studies were targeted as samples. Results indicated that the games in AI education are mainly fell into five categories: Puzzle games are the most used in the curriculum (27.07%), followed by Reasoning strategy games (23.31%), Robot games (18.05%), Role-playing games (9.02%) and Simulation games (6.77%). Among them, 22.39% of games were with real characters, 11.94% were with virtual characters and 64.18% were with no characters. Besides, games were used in three main forms in AI education: games as teaching tools (78.95%), games as student works (12.03%), and games as a competing mechanism (9.02%). Researchers mainly paid attention to the effect of GBL on students' Opinions and Attitude (52.96%) and Learning achievement (24.04%), while the other three categories such as Skills and ability, Interaction, and Cognition were not extensively measured. The cross-sectional analysis, research gaps, and potential directions for future research were also discussed. [ABSTRACT FROM AUTHOR]

In recent years, the emerging trend of mobile learning (hereinafter "m-learning") has obtained increasing attention from educators and higher popularity among students worldwide. Partly due to its short history, there seems to be a lack of comprehensive and in-depth understanding on the distinctiveness of m-learning. It is maintained in this paper that m-learning should be described and researched in three intertwined aspects: the mobility of devices, learners and materials. The multi-faceted mobility may help achieve highly portable and genuinely personal learning, which distinguishes m-learning from other forms of technology-enhanced education. This paper also reports an empirical study on m-learning for academic English. With a total of 60 participants (and six in the pilot study) over three progressive phases, the data obtained from mixed methods of automatic logging, questionnaire and interview were used to assess multidimensional learner engagement in the self-directed process of m-learning. The triangulated data can reflect and highlight some known features of m-learning, and they may also imply learners' expectations towards m-learning. Based on the literature review and data analysis, this paper aims to contribute to the (re)conceptualisation of "m-learning" and also provide some technical and pedagogical implications on future and further development of m-learning. [ABSTRACT FROM AUTHOR]

Lecture recording is an increasingly common practice in UK universities, whereby audio, video, and multimedia content from lecture theatres can be captured and distributed online. Despite a large body of recent lecture capture literature, much of the empirical research adopts positivist paradigms, which overlooks the complex and unpredictable nature of teaching and learning. Addressing this knowledge gap, this exploratory case study adopts sociomaterial approaches, specifically perspectives from the domain of actor-network theory (ANT), to view learning technologies as complex assemblages involving heterogeneous human and non-human entities or actors. This paper explores the entanglements involved in enacting online pedagogy and learning across spatiotemporal dimensions using trace ethnography and visualisation mapping. Examining the student-led study practices revealed that multitasking and fluid task switching, between contrasting networks and spaces, was a significant activity during the playback of lecture recordings. Exploring an innocuous and ubiquitous practice, such as video pausing, affords nuanced perspectives into the sociomaterial entanglements involved in enacting study practices. Moreover, adopting multimodal sensitivities reveals how often overlooked modes, such as iconography, can become actors within an assemblage. This may offer new insights into how modes help produce or stabilise configurations and advance efforts in attending to the non-human within actor-networks. [ABSTRACT FROM AUTHOR]