An information policy perspective on learning analytics

Haythornthwaite, Caroline

doi:10.1145/3027385.3027389

Cited by 10 publications

(14 citation statements)

References 9 publications

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“…In order to define the characteristics of data sensitivity, this study refers to Family Educational Rights and Privacy Act (FERPA) regulation. The studies of FERPA generally are related to the privacy of students and staff in educational institutions [24]. Moreover, FERPA is also involved in developing a model for quantifying data sensitivity [25], [26].…”

Section: Fig 1 the Metrics Of Cvssmentioning

confidence: 99%

The Reliability Analysis for Information Security Metrics in Academic Environment

Ibnugraha

Satria

Nagari

et al. 2023

JOIV : Int. J. Inform. Visualization

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Today, academic institution involves digital data to support the educational process. It has advantages, especially related to ease of access and process. However, security problems appear related to digital data. There were several information security incidents in the academic environment. In order to mitigate the problem, metrics identification is required to determine the risk of incidents. There are many risks model and metrics to estimate the risk, such as DREAD, OWASP, CVSS, etc. However, specific metrics are required to obtain appropriate risk values. Therefore, this study aims to define metrics for an academic institution. The proposed metrics are obtained from The Family Educational Rights and Privacy Act (FERPA) regulation. It consists of directory information, educational information, personally identifiable information, and risk of information leakage. In order to achieve the objective, this study involves survey and reliability analysis to result in output. The survey is conducted by involving 90 respondents with various levels of education and jobs. The Cronbach's alpha and Test-retest are methods to determine this study's reliability. According to reliability analysis, the Cronbach's alpha method results in coefficients for the metrics between 0.730 - 0.911, while the Test-retest method results in coefficients between 0.630 - 0.797. These coefficients have a reliable category, so the proposed metrics are adequate for determining risk of information security incidents in academic environments. The reliable metrics will be developed as variables of the risk assessment model for the academic environment in the future study.

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Section: Fig 1 the Metrics Of Cvssmentioning

confidence: 99%

The Reliability Analysis for Information Security Metrics in Academic Environment

Ibnugraha

Satria

Nagari

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

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“…As the use of large-scale data in educational research became more prominent in recent years, there is an associated increase in concerns linked to the privacy of learner data and ethics of their collection for research and practice (Dawson et al, 2019). Early works in privacy and ethics, in educational research and learning analytics, primarily focus on developing policies and frameworks that would allow for exploiting data collected by various learning environments, while still respecting student privacy and building accountability into educational technology (Haythornthwaite, 2017;Hillman & Ganesh, 2019;Reidenberg & Schaub, 2018). Regardless of how the educational landscape evolves, privacy safeguards are necessary to build end-user trust and enable the broader adoption of learning analytics into practice (Reidenberg & Schaub, 2018).…”

Section: Policies and Frameworkmentioning

confidence: 99%

Privacy-Driven Learning Analytics

Joksimovíc

Marshall²,

Rakotoarivelo

et al. 2021

Manage Your Own Learning Analytics

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Data privacy is a central theme in the global dialogue around the application of data science in education. Despite the growing need, research organisations and private companies working on education and learning analytics solutions still rely on ad hoc, red-tape-heavy and inconsistent approaches to privacy protection. This chapter outlines the substantial and growing body of work on data privacy risk measurement and reduction which can help address this problem and enable better use of online learning data with improved privacy risk management. The combination of privacy risk measurement and reduction tools with a sound privacy risk management framework has the potential to manage privacy risk reliably and consistently across all datasets in a pragmatic and cost-effective way as tools evolve to integrate with standard data management infrastructure.

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“…An issue in need of examination is that of surveillance, or dataveillance (Haythornthwaite, 2017), both in student-student interplay and in student-teacher interplay. At the core lies the transfer of private and personal information to third parties.…”

Section: Ethical Considerationsmentioning

confidence: 99%

Visualizing online collaborative writing strategies in higher education group assignments

Sundgren

Jaldemark

2020

IJILT

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PurposeWorking together in groups is a common and emphasized feature in today's society, and higher educational settings often utilize group assignments to enable students to develop collaborative skills. Therefore, the purpose of this article is to describe and analyze applied strategies and the patterns that emerge during students online collaborative writing in higher education group assignments. The research questions that this article aims to answer are (1) which patterns of students online collaborative writing emerge in higher education group assignments and (2) what strategies of online collaborative writing do higher education students apply in group assignments?Design/methodology/approachThis study's design builds on Conversation Analysis to explore visualizations of Google Docs revision history of online collaborative writing documents. Documents from 25 student groups were the basis of the analysis. The visualizations used in this project are produced with the DocuViz Chrome extension.FindingsThe findings suggest that visualizations can provide a quick and fairly accurate estimate of collaborative strategies used when students write together online. Three patterns of document growth were identified, two of which could be directly linked to strategies for collaboration. Cramming patterns are indicative of low collaboration and concentrating patterns with high levels of collaboration.Practical implicationsThe findings provide useful insight for teachers regarding the nature of collaboration taking place during online collaborative writing tasks. By visualizing the revision history, much can be learnt about the nature of the collaboration and of the individual group member's contributions in a student group that otherwise remains largely invisible to the teacher.Originality/valuePrior studies have combined visualizations with extensive analysis of document content. This investigation shows that an examination of the visualization of the document's revision history can be used to draw conclusions about the nature of collaboration during the online writing process.

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An information policy perspective on learning analytics

Cited by 10 publications

References 9 publications

The Reliability Analysis for Information Security Metrics in Academic Environment

The Reliability Analysis for Information Security Metrics in Academic Environment

Privacy-Driven Learning Analytics

Visualizing online collaborative writing strategies in higher education group assignments

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