The ‘smart’ classroom: a new frontier in the age of the smart university

Kwet, Michael; Prinsloo, Paul

doi:10.1080/13562517.2020.1734922

Cited by 123 publications

(77 citation statements)

References 47 publications

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“…However, increasingly critiques of AI in learning centre around the datafication of education (Jarke and Breiter, 2019;Williamson and Eynon, 2020;Selwyn, 2019a;Kwet and Prinsloo, 2020). A data driven educational system has the potential to be used or experienced as a surveillance system.…”

Section: Ai and Robotics In Research: Fiction 5 "The Research Management Suite Tm"mentioning

confidence: 99%

Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions

Cox

2021

Int J Educ Technol High Educ

107

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Artificial Intelligence (AI) and robotics are likely to have a significant long-term impact on higher education (HE). The scope of this impact is hard to grasp partly because the literature is siloed, as well as the changing meaning of the concepts themselves. But developments are surrounded by controversies in terms of what is technically possible, what is practical to implement and what is desirable, pedagogically or for the good of society. Design fictions that vividly imagine future scenarios of AI or robotics in use offer a means both to explain and query the technological possibilities. The paper describes the use of a wide-ranging narrative literature review to develop eight such design fictions that capture the range of potential use of AI and robots in learning, administration and research. They prompt wider discussion by instantiating such issues as how they might enable teaching of high order skills or change staff roles, as well as exploring the impact on human agency and the nature of datafication.

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Section: Ai and Robotics In Research: Fiction 5 "The Research Management Suite Tm"mentioning

confidence: 99%

Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions

Cox

2021

Int J Educ Technol High Educ

107

View full text Add to dashboard Cite

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“…Education (in whatever form) has always used data (such as demographic and behavioral data) to plan, operate and teach, and smart technology offers new opportunities to extend the "data gaze" (Kwet & Prinslo, 2020 ). In a smart learning environment, a large amount of learner behavior data is generated.…”

Section: Current Issues and New Challengesmentioning

confidence: 99%

“…Through artificial intelligence technologies in a smart learning environment, such as the internet of things, perception technology, video recording technology, image recognition technology and platform acquisition technology, multi-source, heterogeneous, multi-modal big data (for example, raising hands, facial expressions, bodily postures, and discussion) concerning with students’ learning process could be collected (Beer, 2019 ; Chatterjee et al, 2019 ; Kwet & Prinsloo, 2020 ). Such big data would generate new insights about students’ behavior and learning performance in the smart learning environment, which makes it possible to better understand and optimize the learning process and the teaching environments (Shorfuzzaman et al, 2019 ; Syafrudin et al, 2018 ).…”

Section: Opportunities and Development Trendsmentioning

confidence: 99%

Shaping the future learning environments with smart elements: challenges and opportunities

Cheung

Kwok

Phusavat

et al. 2021

Int J Educ Technol High Educ

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The evolving learning environments Unarguably, technology has become an irreversible force driving the transformation of teaching and learning practices. Cloud computing, learning analytics, big data, and artificial intelligence are being adopted in today's teaching and learning, though to different extents. For over a decade, educational researchers have been exploring how different innovative means could be integrated into traditional learning in order to enrich learning experience and enhance learning effectiveness. Enabled by various pedagogical and technological innovations, brand new learning environments can be created to optimize learners' ability to learn. They are collectively referred as the commonly known "smart learning environments" which can best delineate the future learning environments. Embracing a variety of concepts, including but not limited to flexible learning, personalized learning, mobile learning, adaptive learning, and blended learning, for obvious reasons, there are no one single form of smart learning environments. The concepts, and even definitions, of smart learning environments have continuously emerging. A smart learning environment can be conceptualized as a learning environment that emphasizes learning flexibility, effectiveness, efficiency, engagement, adaptivity, and reflectiveness (Spector, 2014), where both formal learning and informal learning are integrated (Gros, 2016). It is basically an adaptive system that improves learning experience based on learning traits, preferences and progress, features increased degrees of engagement, knowledge access, feedback and guidance, and uses rich-media with a seamless access to pertinent information, real-life and on-the-go mentoring with the use of technologies to continuously enhance the learning environment (Singh & Hassan, 2017). In recent years, educational researchers have been actively investigating a smart learning environment. As of March 2021, a simple search of the keyword, "smart learning environment", from Google Scholar and Scopus yields 1990 and 1773 results respectively. Over 80% of these results are published within 5 years, and almost all refer to the tertiary education settings, including higher education, further education and open education.

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“…Smart classroom emphasizes the application of smart technology and provides students with smart learning services (Song et al, 2014). Smart classrooms use cameras, sensors, smart devices, and Internet of Things systems to collect new sources of data about student participation (e.g., raising hands and discussion), facial expressions, bodily postures, and engagement (Kwet & Prinsloo, 2020). The information can help teachers understand the meaning behind individual and group behaviors in class more accurately and grasp the rhythm of classroom teaching more accurately.…”

Section: Research Foundation and Model Frameworkmentioning

confidence: 99%

Similar or Different? A Comparison of Traditional Classroom and Smart Classroom’s Teaching Behavior in China

Yun

Duan

et al. 2021

Journal of Educational Technology Systems

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In the “Internet+” era, to understand the difference between the traditional classroom and smart classroom, this study uses the current domestic and foreign classroom teaching behavior research methods as a starting point and analyzes the teaching behaviors in classrooms from six dimensions: resource sharing, teacher lecturing, teacher–student interaction, group cooperation, autonomous learning, and evaluation feedback. A data analysis method is used to conduct a complete statistical study on the teaching behaviors of the 40 lessons selected in the first smart classroom innovation teaching competition in Jiangsu Province, and the analysis results show that there are significant differences in teacher–student interaction, group cooperation, autonomous learning, and evaluation feedback in the smart classroom and the traditional classroom. There is no significant difference in data analysis between resource sharing and teacher teaching, but through further video observation and analysis, the two still show the difference in the actual classroom.

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The ‘smart’ classroom: a new frontier in the age of the smart university

Cited by 123 publications

References 47 publications

Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions

Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions

Shaping the future learning environments with smart elements: challenges and opportunities

Similar or Different? A Comparison of Traditional Classroom and Smart Classroom’s Teaching Behavior in China

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