A Comprehensive Primer to Library Learning Analytics Practices, Initiatives, and Privacy Issues

Jones, Kyle M. L.; Briney, Kristin; Goben, Abigail; Salo, Dorothea; Asher, Andrew; Perry, Michael

doi:10.5860/crl.81.3.570

Cited by 21 publications

(23 citation statements)

References 42 publications

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“…Students are willing to share data related to their academic performance, but they are not inclined to share nonacademic data, such as their personal information and data trails regarding on‐ and offline behaviors (datapoints LA proponents find immensely valuable) (Ifenthaler & Schumacher, ). Other research on student privacy perceptions signals that students are able to parse data and information flows to make specific privacy arguments regarding access, control, and informed consent (Jones et al, ). The problem is that students have few legal options and few (if any) technical tools to control representative data and how their university uses such data (Zeide, ).…”

Section: Student Perceptions and Moral Questionsmentioning

confidence: 99%

“…Students will become more aware of LA practices and the potential harm as they become more common in the educational experience, but if HEIs remain silent about their data practices, it is plausible that students will change their actions to game the data analytics system or chill their behavior out of fear. Student perceptions research suggests that individuals are already changing their behaviors or are will willing do so in response to knowledge of LA and other data‐mining practices (Jones et al, ). If students choose to suppress their educational and personal interests, then LA will effectively limit intellectual freedom and conflict with higher education's larger mission to develop an educated, free‐thinking citizenry.…”

Section: Higher Education Institutions As Information Fiduciariesmentioning

confidence: 99%

“…Second, even if advisor expertise is still supported, the pressure to use data‐driven insights and demonstrate to outside stakeholders the use thereof may override the guidance advisors could provide to students. About this, Jones et al () write:

We can understand “personalized” education as being less about the needs of the learners and more about serving the interests of higher education institutions—namely improving profits and their position with accountability hawks—by surfacing analyzable data for the purposes of demonstrating politically prudent outcomes. (p. 453)…”

Section: Data Practices At Variance With Information Fiduciary Responmentioning

confidence: 99%

See 2 more Smart Citations

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Jones

Rubel

LeClere

2019

Asso for Info Science & Tech

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Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.

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Section: Student Perceptions and Moral Questionsmentioning

confidence: 99%

Section: Higher Education Institutions As Information Fiduciariesmentioning

confidence: 99%

Section: Data Practices At Variance With Information Fiduciary Responmentioning

confidence: 99%

See 1 more Smart Citation

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Jones

Rubel

LeClere

2019

Asso for Info Science & Tech

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“…The points made by IFLA ( 2019) reflect continuity in debates about AI from previous ethical dilemmas in the profession. Another pre-existing debate that could help us grasp the dimensions of ethical issues of AI in information work is that around learning analytics, which are data about learning behaviours including library use (Jones and Salo, 2017;Jones et al, 2020). Since AI is based on data many of the same challenges exist with AI, at least in an educational setting.…”

Section: Lis Specific Ethics Issues Already Debatedmentioning

confidence: 99%

The Ethics of AI for Information Professionals: Eight Scenarios

Cox

2022

Journal of the Australian Library and Information Association

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Artificial Intelligence (AI) is central to transformative changes happening in many industries, perhaps potentially to a fourth industrial revolution, but it has also raised a storm of ethical concerns. Information professionals need to navigate these ethical issues effectively because they are likely to use AI in delivering services as well as contributing to the process of adoption of AI more widely in their organisations. Professional ethical codes are too high level to offer precise or complete guidance. In this context the purpose of this paper is to review the relevant literature and describe eight ethics scenarios of AI which have been developed specifically for information professionals to understand the issues in a concrete form. The paper considers how AI might be defined and presents some of the applications relevant to the information profession. It thensummarises the key ethical issues raised by AI in general both those inherent to the technology and those arising from the nature of the AI industry. It considers existing studies that have discussed aspects of the ethical issues specifically for information professionals. It then describes a set of eight ethics scenarios that have been developed and shared in an open form to promote their reuse.

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“…It might also reflect library professional concerns about the ethical implications of using the technologies in terms of privacy, consent and (in the case of nudging) human agency. These raise serious questions that have been mostly fully explored to date in the literature on learning analytics (Jones et al, 2020). Such ethical issues are not the only barrier to the smart campus: other significant obstacles are legacy systems, interoperability between systems, skills shortages, and organisational culture issues.…”

Section: Co-design With Studentsmentioning

confidence: 99%

Factors Shaping Future Use and Design of Academic Library Space

Cox

2022

New Review of Academic Librarianship

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COVID is having immediate and long-term impacts on the use of library space through social distancing and shifts in how campus space is used. But these changes will probably not alter the importance of the academic library as a space. In the decade pre COVID libraries saw a growing number of visits, despite the increasing availability of material digitally. The first part of the paper offers an analysis of the main factors driving this historic growth, such as changing pedagogies, diversification in the student body, new technologies plus tighter estates management. Barriers to change such as academic staff attitudes and skills, cost, and slow decision making are also presented. Then, the main body of the paper discusses a number of emerging factors which are likely to further shape the use of library space, namely: concerns with student well-being; sustainability; equality, diversity and inclusion, and decolonisation; increasing co-design with students; and new technologies. A final model captures the main inter-related factors shaping use and design of library space post COVID.

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A Comprehensive Primer to Library Learning Analytics Practices, Initiatives, and Privacy Issues

Cited by 21 publications

References 42 publications

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

The Ethics of AI for Information Professionals: Eight Scenarios

Factors Shaping Future Use and Design of Academic Library Space

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