Predictive analytics and the creation of the permanent present

Smithers, Laura E.

doi:10.1080/17439884.2022.2036757

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…We chose "predictive analytics" as our search keyword because there are many studies on LA using educational technology, and this is one of the core research areas [19]. Using LA to predict student success-with the help of educational technology products and other solutions offered by vendors-is commonplace in higher education [20,21]. This keyword allowed us to identify many articles about LA.…”

Section: Search Strategymentioning

confidence: 99%

“…The data included students' and teachers' strategies [42], affective outcomes [34], and experiences [39]. Log-based data were also linked with learning achievements, such as final grades [20,[32][33][34]39,42], language test results [43], tests of concepts [25], and teachers' assessments [40,44]. Finally, log-based data were linked with teacher and student demographic data [23,33,36].…”

Section: Social Relationsmentioning

confidence: 99%

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Deconstructing the Normalization of Data Colonialism in Educational Technology

Kohnke,

Foung

2024

Education Sciences

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As learning analytics and educational data mining have become the “new normal” in the field, scholars have observed the emergence of data colonialism. Generally, data colonialism can be understood as the process by which data were considered “free” to take and appropriate. Building on this theoretical understanding, this study aims to contextualize data colonialism in educational technology by identifying and reviewing learning analytics studies that adopted a predictive analytics approach. We examined 22 studies from major educational technology journals and noted how they (1) see data as a resource to appropriate, (2) establish new social relations, (3) show the concentration of wealth, and (4) promote ideologies. We found evidence of data colonialism in the field of educational technology. While these studies may promote “better” ideologies, it is concerning how they justify the authorities capitalizing on “free” data. After providing a contextualized view of data colonialism in educational technology, we propose several measures to decolonialize data practices, adopting a postcolonialist approach. We see data colonialism not only as a privacy issue but also as a culture that must be challenged.

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Section: Search Strategymentioning

confidence: 99%

Section: Social Relationsmentioning

confidence: 99%

Deconstructing the Normalization of Data Colonialism in Educational Technology

Kohnke,

Foung

2024

Education Sciences

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“…After finding that the system was biased against women and certain ethnicities, modifications were made to promote a fair admissions process [15]. Other institutions also employed predictive analytics for student support, necessitating a review of the system to ensure fair treatment of all students, regardless of their demographic background [35][36][37]. The University of North Carolina used predictive analytics to identify at-risk students, ensuring that the system did not unfairly target certain demographics [38].…”

Section: Education and Bias Mitigationmentioning

confidence: 99%

Navigating the ethical terrain of AI in higher education: Strategies for mitigating bias and promoting fairness

Barnes,

Hutson

2024

Forum Edu. Stud.

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Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their findings indicate that establishing an ethical AI environment in higher education is imperative and involves comprehensive efforts across policy regulation, governance, and education. The study emphasizes the significance of interdisciplinary collaboration in addressing the complexities of AI bias, highlighting how policy, regulation, governance, and education play pivotal roles in creating an ethical AI framework. Ultimately, the paper advocates for continuous vigilance and proactive strategies to ensure that AI contributes positively to educational settings, stressing the need for robust frameworks that integrate ethical considerations throughout the lifecycle of AI systems to ensure their responsible and equitable use.

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“…While the use of learning analytics is promoted as a way to improve learning and student performance (Rubel and Jones, 2016), these solutions are often implemented as “black boxes”, raising a number of concerns around inaccurate, oversimplified student data representation, perpetuating biases and affecting the power dynamics between institution and the learner (Slade and Prinsloo, 2013). Learning analytics can also result in diminishing autonomy, limiting possibilities (Smithers, 2023), inducing “chilling effects” on student behavior (Regan and Jesse, 2019; Rubel and Jones, 2016), violating students’ privacy expectations (Jones et al , 2020b; Zeide and Nissenbaum, 2018) and introducing conflicts of interests (Rubel and Jones, 2016).…”

Section: Background and Related Workmentioning

confidence: 99%

Privacy governance not included: analysis of third parties in learning management systems

Sanfilippo,

Apthorpe,

Brehm

et al. 2023

ILS

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Purpose This paper aims to address research gaps around third party data flows in education by investigating governance practices in higher education with respect to learning management system (LMS) ecosystems. The authors answer the following research questions: how are LMS and plugins/learning tools interoperability (LTI) governed at higher education institutions? Who is responsible for data governance activities around LMS? What is the current state of governance over LMS? What is the current state of governance over LMS plugins, LTI, etc.? What governance issues are unresolved in this domain? How are issues of privacy and governance regarding LMS and plugins/LTIs documented or communicated to the public and/or community members? Design/methodology/approach This study involved three components: (1) An online questionnaire about LMS, plugin and LTI governance practices from information technology professionals at seven universities in the USA (n = 4) and Canada (n = 3). The responses from these individuals helped us frame and design the interview schedule. (2) A review of public data from 112 universities about LMS plugin and LTI governance. Eighteen of these universities provide additional documentation, which we analyze in further depth. (3) A series of extensive interviews with 25 university data governance officers with responsibilities for LMS, plugin and/or LTI governance, representing 14 different universities. Findings The results indicate a portrait of fragmented and unobtrusive, unnoticed student information flows to third parties. From coordination problems on individual college campuses to disparate distributions of authority across campuses, as well as from significant data collection via individual LTIs to a shared problem of scope across many LTIs, the authors see that increased and intentional governance is needed to improve the state of student privacy and provide transparency in the complex environment around LMSs. Yet, the authors also see that there are logical paths forward based on successful governance and leveraging existing collaborative networks among data governance professionals in higher education. Originality/value Substantial prior work has examined issues of privacy in the education context, although little research has directly examined higher education institutions’ governance practices of LMS, plugin and LTI ecosystems. The tight integration of first and third-party tools in this ecosystem raises concerns that student data may be accessed and shared without sufficient transparency or oversight and in violation of established education privacy norms. However, these technologies and the university governance practices that could check inappropriate data handling remain under-scrutinized. This paper addresses this gap by investigating the governance practices of higher education institutions with respect to LMS ecosystems.

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Predictive analytics and the creation of the permanent present

Cited by 8 publications

References 27 publications

Deconstructing the Normalization of Data Colonialism in Educational Technology

Deconstructing the Normalization of Data Colonialism in Educational Technology

Navigating the ethical terrain of AI in higher education: Strategies for mitigating bias and promoting fairness

Privacy governance not included: analysis of third parties in learning management systems

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