Measuring the Impact of Information Technology on Students’ Learning

Pilkington, Rachel M.

doi:10.1007/978-0-387-73315-9_63

Cited by 9 publications

(7 citation statements)

References 35 publications

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“…Evidence has shown that the design and use of an EdTech plays a big role in its impact on educational outcomes and on the nature of the evidence generated regarding its efficacy (see, for instance, Pilkington, ; Reeves, ). EDUCATE addresses the lack of access to, understanding of, and engagement with research evidence among most EdTech practitioners.…”

Section: Educate: Working Across Disciplines and Across Stakeholdersmentioning

confidence: 99%

Designing educational technologies in the age of AI: A learning sciences‐driven approach

Luckin¹,

Cukurova²

2019

Brit J Educational Tech

172

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Interdisciplinary research from the learning sciences has helped us understand a great deal about the way that humans learn, and as a result we now have an improved understanding about how best to teach and train people. This same body of research must now be used to better inform the development of Artificial Intelligence (AI) technologies for use in education and training. In this paper, we use three case studies to illustrate how learning sciences research can inform the judicious analysis, of rich, varied and multimodal data, so that it can be used to help us scaffold students and support teachers. Based on this increased understanding of how best to inform the analysis of data through the application of learning sciences research, we are better placed to design AI algorithms that can analyse rich educational data at speed. Such AI algorithms and technology can then help us to leverage faster, more nuanced and individualised scaffolding for learners. However, most commercial AI developers know little about learning sciences research, indeed they often know little about learning or teaching. We therefore argue that in order to ensure that AI technologies for use in education and training embody such judicious analysis and learn in a learning sciences informed manner, we must develop inter‐stakeholder partnerships between AI developers, educators and researchers. Here, we exemplify our approach to such partnerships through the EDUCATE Educational Technology (EdTech) programme. What is already known about this topic? The progress of AI Technology and learning analytics lags behind the adoption of these approaches and technologies in other fields such as medicine or finance. Data are central to the empirical work conducted in the learning sciences and to the development of machine learning Artificial Intelligence (AI). Education is full of doubts about the value that any technology can bring to the teaching and learning process. What this paper adds? We argue that the learning sciences have an important role to play in the design of educational AI, through their provision of theories that can be operationalised and advanced. Through case studies, we illustrate that the analysis of data appropriately informed by interdisciplinary learning sciences research can be used to power AI educational technology. We provide a framework for inter‐stakeholder, interdisciplinary partnerships that can help educators better understand AI, and AI developers better understand education. Implications for practice and/or policy? AI is here to stay and that it will have an increasing impact on the design of technology for use in education and training. Data, which is the power behind machine learning AI, can enable analysis that can vastly increase our understanding of when and how the teaching and learning process is progressing positively. Inter‐stakeholder, interdisciplinary partnerships must be used to make sure that AI provides some of the educational benefits its application in other areas promise us.

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Section: Educate: Working Across Disciplines and Across Stakeholdersmentioning

confidence: 99%

Designing educational technologies in the age of AI: A learning sciences‐driven approach

Luckin¹,

Cukurova²

2019

Brit J Educational Tech

172

View full text Add to dashboard Cite

show abstract

“…Educational technologies vary enormously and multiple researchers have made it clear that the design and use of an EdTech plays a big role in its impact on educational outcomes, and therefore on the nature of the evidence generated regarding its efficacy (Pilkington, ; Reeves, ). Not all EdTechs are equal in their potential to afford efficacy.…”

Section: The Educate Research Training: Taking a Pragmatic Approachmentioning

confidence: 99%

Creating the golden triangle of evidence‐informed education technology with EDUCATE

Cukurova¹,

Luckin²,

Clark-Wilson³

2018

Brit J Educational Tech

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EDUCATE is a London‐based programme that supports the development of research‐informed educational technology (EdTech), allowing entrepreneurs and start‐ups to create their products and services, and simultaneously grow their companies in a more evidence‐informed manner. The programme partners businesses with researchers who mentor, guide and support this research journey, a key aspect of which is the evaluation of the company’s EdTech product or service. However, conducting impact evaluations of technology in education is challenging, particularly for early stage technologies, as rapid cycles of innovation and change are part of their essence. Here, we present the pragmatic approach to evidence‐informed education technology design and impact evaluation, as developed and adopted by the EDUCATE programme. The research process is shaped by the core principles of evidence‐informed decision making detailed in the paper. The contributions of the paper are threefold. First, it defines and details an academia‐industry‐education collaboration model centred on a research training programme. Second, it presents emerging impact results of the programme. Third, it provides clear reflections on the challenges encountered during the implementation of the model in the EdTech ecosystem of London, which should be addressed if we are to move towards evidence‐informed EdTech globally.

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“… It is difficult to isolate the role technology plays in experimental studies carried out in real educational settings (Marshall & Cox 2008) and this can weaken the obtained conclusions (Pilkington 2008). There are substantial differences between the design and the actual implementation of ICT in education (Penuel 2005; Reeves 2008).…”

Section: Introductionmentioning

confidence: 99%

“…2006; Raudenbush 2008). Additionally, as the implementation scale grows, the effect size decreases (Slavin & Smith 2009), which means that finding evidence can be a complex and expensive process (Chatterji 2005; Marshall & Cox 2008; Pilkington 2008; Raudenbush 2008; Slavin 2008).…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary development: a model for the design, implementation, and evaluation of ICT for education programmes

Rodríguez

Nussbaum

Dombrovskaia

2011

Computer Assisted Learning

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The impact of information and communication technology (ICT) in primary and secondary education is still an open question. Following review of the available literature, we classify the causes of the lack of impact on students' attainment in four dimensions: (1) the design and implementation of ICT in educational settings; (2) the evaluation of its impact; (3) the scaling up of these kinds of innovations; and (4) the cost‐effectiveness of technology‐enhanced learning environments. Based on this evidence, we proposed the evolutionary development model (EDM), which aims to produce a cost‐effective and sustainable ICT for education (ICT4E) programme in three steps: efficacy, effectiveness, and efficiency. In each step, one component of the programme is built and validated in real educational settings. Therefore, the resultant ICT4E programme is ready to be replicated across the school system. We also show how the EDM guided the development of a programme based on mobile computer supported collaborative learning, known as Eduinnova. Finally, we discuss how EDM can serve as an analysis tool for researchers and policy makers.

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Measuring the Impact of Information Technology on Students’ Learning

Cited by 9 publications

References 35 publications

Designing educational technologies in the age of AI: A learning sciences‐driven approach

Designing educational technologies in the age of AI: A learning sciences‐driven approach

Creating the golden triangle of evidence‐informed education technology with EDUCATE

Evolutionary development: a model for the design, implementation, and evaluation of ICT for education programmes

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