To students and teachers: (1) the mechanization of tests-for instance, computer adaptive testing,Īnd automated essay grading (2) data mining of unstructured data-for instance, the texts of Go on to explore three kinds of application of computers to the task of providing evidence-of-learning Machine intelligence and the role of machines in supporting and extending human intelligence. We begin by examining, in general terms, the frame of reference of contemporary debates on One of the key features of recent developments has been popularly characterized as ‘bigĭata’. This article sets out to explore a shift in the sources of evidence-of-learning in the era of networkedĬomputing. The findings support the idea that previous ET-related experience can support positive attitudes and the implementations of ETs in university teaching in this study, university teachers had optimistic expectations towards ETs, accepting them as part of teaching practice development, while discussion about the negative effects of ETs was negligible. Deductive and inductive approaches with thematic analysis were used for the data analysis. The empirical data presented here were collected using written essays from 18 university teachers and students. Additionally, we analyzed the perceptions of university-level teaching staff about the potential of introducing ETs in education. In this paper, we identified the strengths and weaknesses, opportunities, and threats that are related to the adoption in higher education of the combination of two ETs: robotics together with artificial intelligence (AI). This paper is based on the results obtained in the project “My Future Colleague Robot”, an initiative that aimed to improve the competence of university teaching staff regarding the introduction of ETs in teaching practices at university level. Other practical implications of the findings of this work touch upon the design of teachers ‘development programs in Big Data and their analytics.Įmerging technologies (ETs) will most likely have a strong impact on education (starting with higher education), just like they have already had in so many economic and social areas. Consequently, a need arises for appropriate analytics and relevant privacy frameworks. Finally, it emerged that a small number of teachers is archiving digital multimedia. Also, it became clear that teachers use Big Data Analytics for two main distinctively different purposes: to cover teaching-learning aspects and to complete administrative tasks. Main findings reveal that the schoolteachers are storing and actively using student data as well as Big Data which involve the support of the teaching-learning process. The data were analysed using mixed methods. Thirty teachers who live in Greece participated in survey about their usage of (a) Big Data analytics and (b) online learning environments which capture student data. To compensate for this gap, this paper focuses on the actual uses of Big Data Analytics by active schoolteachers. Several recent articles in the field of technology enhanced learning concern this potential, yet little is known about how teachers actually make use of Big Data Analytics in their school to support themselves and their students.
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