Search

Background: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work-integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal coordination of micro-level relations. Objectives: The primary aim of this study is to examine the cohesion of teams in learning environments using a learning analytics approach. Method: This study examines teams from higher education who participate in a WIL environment platform working in teams to develop their collaborative problem-solving skills. Here we show that temporal network motifs can be used as a proxy to measure cohesion. Results and Conclusions: We illustrate three clusters represented by team learning behaviours and found that each cluster has distinctive interactions with learning resources, performance outcomes, temporal network motif group characteristics and emergence over time using learning analytics. Implications: Applying temporal motifs as an analytics-based measure of cohesion is a starting point for understanding how cohesion develops over time without relying on surveys. We anticipate that the same approaches can be applied in most learning management systems containing trace data of teams and their interactions with learning resources to understand cohesion.

For the developers of next-generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology-mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth-spurt coincides with a growing global "Ethical AI" movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data-driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy-preserving analytics which will contribute to the goals of an ethical application of learning analytics methods.

Objectives: Adults with spinal cord injury (SCI) are often sedentary, increasing their risk of cardiometabolic diseases. Leisure-time Physical Activity (LTPA) is physical activity completed during recreation time for enjoyment. We aimed to quantify LTPA in people ≥45 years with SCI and to explore its relationship with participants' characteristics. Methods: This is a secondary analysis on a subset of the Australian International SCI Survey in participants ≥45 years, at least 12 months post-injury. We described levels of LTPA and used multivariable regressions to estimate the associations between participant characteristics and LTPA. Results: Of 1,281 participants (mean age: 62.7 years, mean time since injury: 18.7 years; 74% males) 44% reported no participation in LTPA. The average LTPA participation was 197 (SD 352) minutes per week (median: 50). Females (β = -62.3, 95% CI [-112.9, -11.7]), and participants with non-traumatic injuries (β = -105.2, 95% CI [-165.9, -44.6]) performed less LTPA. Time since injury was not associated with moderate-to-heavy LTPA (LR: Probability > F = 0.785). Conclusion: LTPA promotion in the SCI population ≥45 years focusing on females and non-traumatic injuries is warranted. [ABSTRACT FROM AUTHOR]

For the developers of next‐generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology‐mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth‐spurt coincides with a growing global "Ethical AI" movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data‐driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy‐preserving analytics which will contribute to the goals of an ethical application of learning analytics methods. Practitioner notesWhat is already known about this topic Privacy Enhancing Technologies, including a range of provable privacy risk reduction techniques (differential privacy) are effective tools for managing data privacy, though currently only pragmatically available to well‐funded early adopters.Learning Analytics is a relatively young but evolving field of research, which is beginning to deliver tangible insights and value to the Education and EdTech industries.A small number of procedural frameworks have been developed in the past two decades to consider data privacy and other ethical aspects of Learning Analytics.What this paper adds This paper describes the mechanisms for integrating Learning Analytics, Data Privacy Technologies and Ethical practices into a unified operational framework for Ethical and Privacy‐Preserving Learning Analytics.It introduces a new standardised measurement of privacy risk as a key mechanism for operationalising and automating data privacy controls within the traditional data pipeline;It describes a repeatable framework for conducting ethical Learning Analytics.Implications for practice and/or policy For the Learning Analytics (LA) and Education Technology communities the approach described here exemplifies a standard of ethical LA practice and data privacy protection which can and should become the norm.The privacy risk measurement and risk reduction tools are a blueprint for how data privacy and ethics can be operationalised and automated.The incorporation of a standardised privacy risk evaluation metric can help to define clear and measurable terms for inter‐ and intra‐organisational data sharing and usage policies and agreements (Author, Ruth Marshall, is an Expert Contributor on ISO/IEC JTC 1/SC 32/WG 6 "Data usage", due for publication in early 2022). [ABSTRACT FROM AUTHOR]