Privacy and data protection in learning analytics should be motivated by an educational maxim—towards a proposal

Hoel, Tore; Chen, Weiqin

doi:10.1186/s41039-018-0086-8

Cited by 41 publications

(22 citation statements)

References 15 publications

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“…In recent years, educational institutions have started systematically collecting and analyzing digital data about their students, often in the form of digital breadcrumbs from digital educational services or WiFi use, with the aim of improving understanding and prediction student behavior. The COVID-19 pandemic is projected to dramatically accelerate this process, intensifying already existing worries about privacy and transparency, as well as broader ethical and legal concerns ( 2 , 26 , 27 ), over the collection of such high-resolution data.…”

Section: Discussionmentioning

confidence: 99%

Task-specific information outperforms surveillance-style big data in predictive analytics

Bjerre-Nielsen

Kassarnig

Lassen

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Increasingly, human behavior can be monitored through the collection of data from digital devices revealing information on behaviors and locations. In the context of higher education, a growing number of schools and universities collect data on their students with the purpose of assessing or predicting behaviors and academic performance, and the COVID-19–induced move to online education dramatically increases what can be accumulated in this way, raising concerns about students’ privacy. We focus on academic performance and ask whether predictive performance for a given dataset can be achieved with less privacy-invasive, but more task-specific, data. We draw on a unique dataset on a large student population containing both highly detailed measures of behavior and personality and high-quality third-party reported individual-level administrative data. We find that models estimated using the big behavioral data are indeed able to accurately predict academic performance out of sample. However, models using only low-dimensional and arguably less privacy-invasive administrative data perform considerably better and, importantly, do not improve when we add the high-resolution, privacy-invasive behavioral data. We argue that combining big behavioral data with “ground truth” administrative registry data can ideally allow the identification of privacy-preserving task-specific features that can be employed instead of current indiscriminate troves of behavioral data, with better privacy and better prediction resulting.

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Section: Discussionmentioning

confidence: 99%

Task-specific information outperforms surveillance-style big data in predictive analytics

Bjerre-Nielsen

Kassarnig

Lassen

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…The authors in [144] con-sidered a scenario where learning analytics (LA) could be used to track students and their performance could be flagged to deny a student access to future education programs based on the pre-conceived student ability for institution decisionmaking leading to unintended outcomes. The authors in [145] remarked that LA presents significant student privacy challenges for higher education institutions. In their work, the authors also posited four proponents that LA must justify in relation to the use of student data: (1) LA systems should provide controls for differential access to private student data;…”

Section: A Social Challengesmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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“…Contemporary conversation has focused on this tension-technologies that are presented as neutral or beneficial, yet present risks to privacy. Learning analytics, for example, represents a new technology that offers complicated tradeoffs of privacy and data (Hoel and Chen 2018;Hathcock 2018). For-profit companies that sell learning analytics products to institutions are also selling an overlysimplistic idea that more data enables greater insights, that students today would rather trade their privacy for efficiency, and that by purchasing data-collecting systems, colleges and universities are embracing innovation and the future (Jones and Salo 2018).…”

Section: Contemporary Technologies and New Risks To Privacymentioning

confidence: 99%

Participatory Approaches for Designing and Sustaining Privacy-Oriented Library Services

Young

Walker

Swauger

et al. 2020

JIFP

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Privacy is a long-held value of information professionals, but new technologies of the contemporary digital age pose new risks to privacy. In an effort to build participatory, profession-wide action in support of designing privacy-oriented library services, two groups were formed with the goal of generating ideas and sustaining action: the National Forum on Web Privacy and Web Analytics, and Digital Library Federation Technologies of Surveillance Working Group. In this paper, members of these two groups present case study descriptions and analysis via reflective self-assessment. The authors discuss how these groups can serve as models of participatory action for integrating the value of privacy into the design of library services, technologies, policy, and outreach.

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Privacy and data protection in learning analytics should be motivated by an educational maxim—towards a proposal

Cited by 41 publications

References 15 publications

Task-specific information outperforms surveillance-style big data in predictive analytics

Task-specific information outperforms surveillance-style big data in predictive analytics

Big Educational Data & Analytics: Survey, Architecture and Challenges

Participatory Approaches for Designing and Sustaining Privacy-Oriented Library Services

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