Collaborative concept mapping: an education research team leveraging their collaborative efforts

Collaborative concept mapping (CCM) has been a tool deployed by educators to enhance learning in such situations as primary science classes, supported learning environments and asynchronous computer-mediated learning. Of its outcomes, CCM has produced rich group discussion about ideas and possibilities pertinent to the topic or problem at hand. The majority of research into CCM has been explicitly pointed at enhancing learning. This chapter takes a different tack by reporting on how the authors used CCM to seek understandings of its utility in enabling collaborative research by creating synergies within a research team located in the Faculty of Education at the University of Southern Queensland. The following questions were used to focus the research: • What was the research team’s experience of collaborative concept mapping? • What propositions did the team construct about teamwork and collaboration? • How did the interactions among team members facilitate meaning-making about teamwork and collaboration? The data consisted of this team’s collaborative concept map and recordings of the dialogue during the process of constructing the map. Analysis revealed the team’s emerging propositions about teamwork and collaboration and also contributed understandings of the co-constructed patterns of talk that produced this dynamic map. The chapter concludes that collaborative concept mapping is a useful tool for research and other team development, and possibly for the collaborative conceptualisation of future team research projects

On the Determinants of the Reach of Innovation-related Collaboration in Small Firms

This paper takes as its starting point an item of relatively recent academic orthodoxy: the insistence that ‘…interactive learning and collective entrepreneurship are fundamental to the process of innovation’ (Lundvall, 1992, p. 9). From this, academics have frequently taken “interactive” to imply “inter-organisational” and, whilst one might be concerned by this too casual conflation, there is a growing consensus that firms’ embeddedness in collaborative networks matters for their innovative performance (Gilsing et al., 2008).

Neck-cooling improves repeated sprint performance in the heat

The present study evaluated the effect of neck-cooling during exercise on repeated sprint ability in a hot environment. Seven team-sport playing males completed two experimental trials involving repeated sprint exercise (5 × 6 s) before and after two 45 min bouts of a football specific intermittent treadmill protocol in the heat (33.0 ± 0.2°C; 53 ± 2% relative humidity). Participants wore a neck-cooling collar in one of the trials (CC). Mean power output and peak power output declined over time in both trials but were higher in CC (540 ± 99 v 507 ± 122 W, d = 0.32; 719 ± 158 v 680 ± 182 W, d = 0.24 respectively). The improved power output was particularly pronounced (d = 0.51–0.88) after the 2nd 45 min bout but the CC had no effect on % fatigue. The collar lowered neck temperature and the thermal sensation of the neck (P 0.05). There were no trial differences but interaction effects were demonstrated for prolactin concentration and rating of perceived exertion (RPE). Prolactin concentration was initially higher in the collar cold trial and then was lower from 45 min onwards (interaction trial × time P = 0.04). RPE was lower during the football intermittent treadmill protocol in the collar cold trial (interaction trial × time P = 0.01). Neck-cooling during exercise improves repeated sprint performance in a hot environment without altering physiological or neuroendocrinological responses. RPE is reduced and may partially explain the performance improvement

Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare

Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare