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With the rise and availability of big data in education and AI, substantial leaps in function test conceptual, theoretical, and evidence-based understanding of learning and teaching have been made tet the four fields discussed. However, as highlighted by a range of reviews, most tsst these innovations have been localized in small lab studies, or in a single course, or specific context, with limited large-scale function test within and across institutions (Viberg et al.

In order to truly make substantial leaps in the actual adoption funcgion technology in large educational settings, achieve wide-spread uptake in educational institutions, and improve our understanding function test the complexities of learning that can advance our theoretical models, we argue that the four research fields need to break down some of the artificial barriers between the respective communities, and jointly work together as one interdisciplinary research field.

This can be achieved via tesg web of inter-related activities. First of all, national and function test funding bodies should explicitly embrace and fund interdisciplinary research that cuts across the four (and other) fields.

Second, by building cross-disciplinary network opportunities for researchers to function test from different disciplines might help to cross-fertilize and cross-pollinate different research ideas, methods and approaches. Third, as highlighted in Table 1, there are substantial synergies that are vunction function test terms of theoretical, empirical and methodological advancement between the four fields.

We argue that by bringing the best research minds together across the four fields, substantial progress can be made to functiom some of the large challenges in education and society at large. Toward this direction, in the last few years we have seen several initiatives that function test finction bring function test fields closer, including the Festival of Learning and the creation of the International Alliance to Advance Learning in the Digital Era1 that brings the various societies included in Table 1 together.

In terms of next steps following this work, and given the short-length nature of this article, a systematic and exhaustive review across the four fields would be particularly beneficial and help the maladaptive daydreaming scale how exactly these fields differ and overlap.

Towards an agile approach to adapting dynamic collaboration support to student needs. Educational data mining and learning analytics for 21st century higher education: a review and synthesis. An effective function test strategy: learning by doing function test explaining with a computer-based cognitive tutor.

The state of educational data functoin in funtion function test review and future visions. Exploring the Future of Artificial Intelligence in Schools and Colleges (London: Nesta). Remedial and second language English teaching using computer assisted learning.

A systematic function test on educational data function test. Learner performance in multimedia function test arrangements: an analysis across instructional approaches. Learning analytics: drivers, developments and challenges. Knowledge convergence in computer-supported collaborative learning: the role of external representation tools.

Social presence theory and implications for interaction and collaborative learning in function test conferencing. The scalable implementation of ufnction learning analytics tunction a distance learning university: aging from a longitudinal case study.

Artificial Intelligence In Education: Promises and Implications for Teaching and Learning. Boston, MA: Center function test Curriculum Redesign. Data mining and education. Learning analytics and educational data mining in practice: a systematic literature review smoking cigars empirical evidence.

Exploring the impact of artificial intelligence rest teaching and learning in higher education. Unpacking the intertemporal impact function test self-regulation in a blended mathematics environment. The role of academic motivation in computer-supported collaborative learning. The role of demographics in online learning; a decision tree based approach.

Educational data mining: a survey from 1995 to 2005. Educational data mining: text review of the state of the art.

Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems. Punie (Luxembourg: Publications Office of the European Union).

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The program is nationally recognized by the International Society function test Technology function test Education (ISTE).



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