The Politics of Learning Analytics

Al-Mahmood, Reem

doi:10.1007/978-3-030-47392-1_2

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…In addition, a full development of LA would lie in a combination of quantitative and qualitative analyses (Al-Mahmood 2020 ). Qualitative studies could help in overcoming some of the main challenges that LA face such as the simplification of learning processes or the critique that LA is insufficiently sensitive to the time and place of the students’ learning.…”

Section: Recommendations and Limitationsmentioning

confidence: 99%

Learning analytics in higher education: a preponderance of analytics but very little learning?

Guzmán-Valenzuela

Gómez-González

Tagle

et al. 2021

Int J Educ Technol High Educ

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In a context where learning mediated by technology has gained prominence in higher education, learning analytics has become a powerful tool to collect and analyse data with the aim of improving students’ learning. However, learning analytics is part of a young community and its developments deserve further exploration. Some critical stances claim that learning analytics tends to underplay the complexity of teaching-learning processes. By means of both a bibliometric and a content analysis, this paper examines the publication patterns on learning analytics in higher education and their main challenges. 385 papers that were published in WoScc and SciELO indexes between 2013 and 2019 were identified and analysed. Learning analytics is a vibrant and fast-developing community. However, it continues to face multiple and complex challenges, especially regarding students’ learning and their implications. The paper concludes by distinguishing between a practice-based and management-oriented community of learning analytics and an academic-oriented community. Within both communities, though, it seems that the focus is more on analytics than on learning.

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Section: Recommendations and Limitationsmentioning

confidence: 99%

Learning analytics in higher education: a preponderance of analytics but very little learning?

Guzmán-Valenzuela

Gómez-González

Tagle

et al. 2021

Int J Educ Technol High Educ

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“…Working with fairness and bias in LA with a focus on equity would imply applying the DF principle of reflexivity in LA data science methods. While accounting for reflexivity in research is a well-known methodological choice in fields like science and technology (Haraway, 1988), reflecting on the positionality of researchers, developers, or practitioners is not as common in LA; however, there is increasing acknowledgment of these politics, pedagogies, and practices in the LA community (Al-Mahmood, 2020;Buckingham Shum & Luckin, 2019). In this vein, considering reflexivity in the collection, classification, analysis, interpretation, and communication of LA data bound to ADM systems might contribute to discussing equity in the community; by doing so, we may be able to "develop an ability to reflect and take responsibility for one's position within the multiple, intersecting dimensions of the matrix of domination" (D'Ignazio & Klein, 2020, p. 64).…”

Section: Implication 3: Whose Ethics Are Mobilized In La Data Science...mentioning

confidence: 99%

New Vistas on Responsible Learning Analytics

et al. 2023

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The focus of ethics in learning analytics (LA) frameworks and guidelines is predominantly on procedural elements of data management and accountability. Another, less represented focus is on the duty to act and LA as a moral practice. Data feminism as a critical theoretical approach to data science practices may offer LA research and practitioners a valuable lens through which to consider LA as a moral practice. This paper examines what data feminism can offer the LA community. It identifies critical questions for further developing and enabling a responsible stance in LA research and practice taking one particular case — algorithmic decision-making — as a point of departure.

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“…Due to the remaining challenges of implementing meaningful AI in educational contexts, especially for more sophisticated tasks, the reciprocal collaboration of humans and AI might be a suitable approach for enhancing the capacities of both (Baker, 2016). However, the importance of understanding how AI, as a stakeholder among humans, selects and acquires data in the process of learning and knowledge creation, learns to process and forget information, and shares knowledge with collaborators is yet to be empirically investigated (Al-Mahmood, 2020;Zawacki-Richter et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners

Ifenthaler,

Majumdar,

Gorissen

et al. 2024

Tech Know Learn

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One trending theme within research on learning and teaching is an emphasis on artificial intelligence (AI). While AI offers opportunities in the educational arena, blindly replacing human involvement is not the answer. Instead, current research suggests that the key lies in harnessing the strengths of both humans and AI to create a more effective and beneficial learning and teaching experience. Thus, the importance of ‘humans in the loop’ is becoming a central tenet of educational AI. As AI technology advances at breakneck speed, every area of society, including education, needs to engage with and explore the implications of this phenomenon. Therefore, this paper aims to assist in this process by examining the impact of AI on education from researchers’ and practitioners' perspectives. The authors conducted a Delphi study involving a survey administered to N = 33 international professionals followed by in-depth face-to-face discussions with a panel of international researchers to identify key trends and challenges for deploying AI in education. The results indicate that the three most important and impactful trends were (1) privacy and ethical use of AI; (2) the importance of trustworthy algorithms; and (3) equity and fairness. Unsurprisingly, these were also identified as the three key challenges. Based on these findings, the paper outlines policy recommendations for AI in education and suggests a research agenda for closing identified research gaps.

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The Politics of Learning Analytics

Cited by 3 publications

References 29 publications

Learning analytics in higher education: a preponderance of analytics but very little learning?

Learning analytics in higher education: a preponderance of analytics but very little learning?

New Vistas on Responsible Learning Analytics

Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners

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