Session Information
28 SES 17 A, (Un)Making (In)Equitable EdTech Futures in Schools
Symposium
Contribution
In many education systems digital technologies are seen as an important way to address educational inequity. Yet despite this enduring emphasis on equity in policy and popular discourse, the research evidence is complex to navigate. It is multifaceted, wide ranging and relatively disparate. This paper presents a systematic thematic (as opposed to meta-analytic) review of the peer-reviewed academic literature that explores the relationships between technology, equity, and teaching and learning in secondary schools, identifying 73 studies from the Global North based on an initial review of 15,000 abstracts from three academic databases (Google Scholar, Scopus, and EBSCO Host). The thematic analysis of all 73 included studies identified four overlapping themes: 1. Digital equity: work that provides an increasingly nuanced understanding of the constituent aspects of the ‘digital divide’ (Dolan, 2016), that has implications for the learning experiences of secondary school pupils (Robinson, et al., 2018), that have intensified and reconfigured during the pandemic (Greenhow et al. 2021) 2. Data driven systems: work that addresses the equity implications of the use of algorithmic systems in education, including growing concerns about the multiple ways that these systems can lead to unjust practices and outcomes along different social axes (Baker and Hawn, 2021) 3. Socio-technical interactions: work that examines the equity implications of the relationships between technology, teachers, pupils, and school administration, including how schools in wealthier areas tend to use technology differently to schools in less well-off areas (Rafalow and Puckett, 2022) 4. Equity-orientated pedagogies: work that attempts to make learning environments more equitable, including digital access schemes (Adhikari et al., 2017); the fostering of digital and data literacies (Choi and Cristol, 2021); and the use of Universal Design for Learning (Griggs and Moore, 2023) The paper presents a synthesis of these themes, and highlights important gaps in the evidence base: a need for greater clarity in the definitions of equity; a need for greater attention to the underpinning logic, biases and accountability structures in commercial EdTech products; and a need for richer, context-specific understandings of how and for what purpose technologies are employed in the learning experiences of secondary school pupils, especially outside of the U.S. We suggest the need for an explicit focus on the ways in which complex patterns of digital inequity, algorithmic bias, and interactions between teachers, pupils and technologies can exacerbate existing social and educational inequities or, indeed, create new ones in specific school contexts.
References
Adhikari, J., Scogings, C., Mathrani, A. & Sofat, I. (2017). Evolving digital divides in information literacy and learning outcomes: A BYOD journey. International Journal of Information and Learning Technology 34, 290–306. Baker, R.S & Hawn, A. (2022). Algorithmic Bias in Education. Int J Artif Intell Educ 32, 1052–1092. Choi, M. & Cristol, D. (2021). Digital citizenship with intersectionality lens: Towards participatory democracy driven digital citizenship education. Theory Into Practice 60, 361–370. Dolan, J.E. (2016). Splicing the divide: A review of research on the evolving digital divide among K-12 students. Journal of Research on Technology in Education, 48, 16–37. Greenhow, C., Lewin, C. & Staudt Willet, K.B., (2021). The educational response to Covid-19 across two countries. Technology, Pedagogy & Education 30, 7–25. Griggs, N. & Moore, R. (2023). Removing Systemic Barriers for Learners with Diverse Identities: Antiracism, UD for Learning, and Edpuzzle. J Spec Educ Technol 38, 15–22. Rafalow, M.H. & Puckett, C. (2022). Sorting Machines: Digital Technology and Categorical Inequality in Education. Educational Researcher 51, 274–278. Robinson, L., Wiborg, Ø. & Schulz, J. (2018). Interlocking Inequalities: Digital Stratification Meets Academic Stratification. American Behavioral Scientist 62, 1251–1272.
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