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Article
Publication date: 27 September 2023

Emily Zoe Mann, Stephanie A. Jacobs, Kirsten M. Kinsley and Laura I. Spears

Building on past studies of library privacy policies, this review looks at how privacy information is shared at universities and colleges in the state of Florida. Beyond the…

Abstract

Purpose

Building on past studies of library privacy policies, this review looks at how privacy information is shared at universities and colleges in the state of Florida. Beyond the question of whether a library-specific privacy policy exists, this review evaluates what is covered in the policies – whether topics such as how student data is stored, retained, de-identified and disposed of are broached in the statements, and whether specific data sets covering instruction, reference and surveillance are mentioned. The purpose of this study is to open the door to directed exploration into student awareness of privacy policies and spark conversation about positionality of libraries regarding privacy.

Design/methodology/approach

This review was done using a cross-sectional study design through observation of public-facing library privacy policies of higher education institutions in Florida.

Findings

Findings include that the majority of Florida academic libraries do not have a public-facing privacy policy. Only 15 out of the 70 schools reviewed had one. A large portion of those came from doctoral universities with associate’s colleges having none, and baccalaureate/associate’s colleges having only two. The policies that were in place tended to be institution-centered rather than patron-centered. Most categories of listed data collected were in the area of collections, website or computer usage.

Originality/value

The value of this review is that it adds to the literature studying privacy policies in academic libraries. Going forward, this research could address statewide practice in privacy policies as well as helping to lay pathways for working with students and other library patrons to gauge their interests and concerns about privacy.

Details

Information and Learning Sciences, vol. 124 no. 9/10
Type: Research Article
ISSN: 2398-5348

Keywords

Article
Publication date: 26 September 2023

Madelyn Rose Sanfilippo, Noah Apthorpe, Karoline Brehm and Yan Shvartzshnaider

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…

Abstract

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.

Open Access
Article
Publication date: 11 November 2022

Chulapol Thanomsing and Priya Sharma

Social media are increasingly being used in teaching and learning in higher education. This paper aims to explore multiple case studies to better understand how instructors decide…

1595

Abstract

Purpose

Social media are increasingly being used in teaching and learning in higher education. This paper aims to explore multiple case studies to better understand how instructors decide to incorporate social media into learning.

Design/methodology/approach

This qualitative case study used the technology acceptance model (TAM) to explore five instructors' use of social media for teaching and learning, particularly the pedagogical reasons and goals driving their use of social media. Participant interviews, course documentation and social media observation data were collected to answer the research questions.

Findings

Findings suggest that an instructor's social media knowledge and awareness of instructional goals are important for the use of social media in learning. Three pedagogical objectives of the use of social media were found across five participants: collaborative learning, dialog and discussion, and authentic learning.

Originality/value

Previous studies have explored potential pedagogical uses of social media tools, however studies that attempt to understand how and why instructors decide to use particular social media tools are underreported.

Details

Journal of Research in Innovative Teaching & Learning, vol. 17 no. 1
Type: Research Article
ISSN: 2397-7604

Keywords

Article
Publication date: 26 September 2023

Stacey Lynn von Winckelmann

This study aims to explore the perception of algorithm accuracy among data professionals in higher education.

Abstract

Purpose

This study aims to explore the perception of algorithm accuracy among data professionals in higher education.

Design/methodology/approach

Social justice theory guided the qualitative descriptive study and emphasized four principles: access, participation, equity and human rights. Data collection included eight online open-ended questionnaires and six semi-structured interviews. Participants included higher education professionals who have worked with predictive algorithm (PA) recommendations programmed with student data.

Findings

Participants are aware of systemic and racial bias in their PA inputs and outputs and acknowledge their responsibility to ethically use PA recommendations with students in historically underrepresented groups (HUGs). For some participants, examining these topics through the lens of social justice was a new experience, which caused them to look at PAs in new ways.

Research limitations/implications

Small sample size is a limitation of the study. Implications for practice include increased stakeholder training, creating an ethical data strategy that protects students, incorporating adverse childhood experiences data with algorithm recommendations, and applying a modified critical race theory framework to algorithm outputs.

Originality/value

The study explored the perception of algorithm accuracy among data professionals in higher education. Examining this topic through a social justice lens contributes to limited research in the field. It also presents implications for addressing racial bias when using PAs with students in HUGs.

Details

Information and Learning Sciences, vol. 124 no. 9/10
Type: Research Article
ISSN: 2398-5348

Keywords

Article
Publication date: 18 October 2023

Langdon Holmes, Scott Crossley, Harshvardhan Sikka and Wesley Morris

This study aims to report on an automatic deidentification system for labeling and obfuscating personally identifiable information (PII) in student-generated text.

Abstract

Purpose

This study aims to report on an automatic deidentification system for labeling and obfuscating personally identifiable information (PII) in student-generated text.

Design/methodology/approach

The authors evaluate the performance of their deidentification system on two data sets of student-generated text. Each data set was human-annotated for PII. The authors evaluate using two approaches: per-token PII classification accuracy and a simulated reidentification attack design. In the reidentification attack, two reviewers attempted to recover student identities from the data after PII was obfuscated by the authors’ system. In both cases, results are reported in terms of recall and precision.

Findings

The authors’ deidentification system recalled 84% of student name tokens in their first data set (96% of full names). On the second data set, it achieved a recall of 74% for student name tokens (91% of full names) and 75% for all direct identifiers. After the second data set was obfuscated by the authors’ system, two reviewers attempted to recover the identities of students from the obfuscated data. They performed below chance, indicating that the obfuscated data presents a low identity disclosure risk.

Research limitations/implications

The two data sets used in this study are not representative of all forms of student-generated text, so further work is needed to evaluate performance on more data.

Practical implications

This paper presents an open-source and automatic deidentification system appropriate for student-generated text with technical explanations and evaluations of performance.

Originality/value

Previous study on text deidentification has shown success in the medical domain. This paper develops on these approaches and applies them to text in the educational domain.

Details

Information and Learning Sciences, vol. 124 no. 9/10
Type: Research Article
ISSN: 2398-5348

Keywords

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