title: A learning design framework for community resilience: International and transdisciplinary perspectives on a boundary object

authors: Brogden, Liz

abstract: To equip graduates to respond to sustainability challenges, higher education institutions are developing programs and innovative pedagogical approaches that target new types of 21st century knowledge and skills. Resilience is a core concept in sustainability discourse, and one that is also driving pedagogical innovation that targets specific learner attributes. We argue that the topic of community resilience – as a theoretical boundary object – presents rich opportunities for transdisciplinary Education for Sustainable Development (ESD). This paper highlights international and cross-disciplinary trends in learning design for community resilience-focused university programs. The results outline the survey responses of 111 educators, researchers, and practitioners from a range of disciplines. One-way analysis of variance showed consistent results across the different groups of participants, regardless of geographic location, disciplinary background, or employment context. The research offers a learning design framework for teaching community resilience in higher education institutions, highlighting the importance of academic—practitioner collaboration in program development, disciplinary expertise to inform transdisciplinary learning, hybridised pedagogical approaches, and fostering student agency through assessment tasks.

text: A learning design framework for community resilience: International and transdisciplinary perspectives on a boundary object To equip graduates to respond to sustainability challenges, higher education institutions are developing programs and innovative pedagogical approaches that target new types of 21st century knowledge and skills. Resilience is a core concept in sustainability discourse, and one that is also driving pedagogical innovation that targets specific learner attributes. We argue that the topic of community resilience – as a theoretical boundary object – presents rich opportunities for transdisciplinary Education for Sustainable Development (ESD). This paper highlights international and cross-disciplinary trends in learning design for community resilience-focused university programs. The results outline the survey responses of 111 educators, researchers, and practitioners from a range of disciplines. One-way analysis of variance showed consistent results across the different groups of participants, regardless of geographic location, disciplinary background, or employment context. The research offers a learning design framework for teaching community resilience in higher education institutions, highlighting the importance of academic—practitioner collaboration in program development, disciplinary expertise to inform transdisciplinary learning, hybridised pedagogical approaches, and fostering student agency through assessment tasks.

year: 2022

title: What wrist should you wear your actigraphy device on? Analysis of dominant vs. non-dominant wrist actigraphy for measuring sleep in healthy adults

authors: Driller, Matthew W.

subjects: polysomnography

abstract: OBJECTIVE: Differences in sleep results due to the placement of actigraphy devices (non-dominant vs. dominant wrist) are yet to be determined. 
METHODS: 65 nights of data from 13 adult participants was collected while participants wore two actigraphy devices, one on each wrist. Sleep indices including total sleep time (TST), total time in bed (TTB), sleep efficiency (SE%), sleep latency (SL), wake after sleep onset (WASO), sleep onset time (SOT) and wake time (WT) were assessed between the two devices. 
RESULTS: There were no significant differences between devices for any of the measured sleep variables (p>0.05). SE%, SL and WASO resulted in high correlations between devices (0.89, 0.89 and 0.76, respectively), with all other sleep variables resulting in very high correlations (>0.90) between devices.
CONCLUSIONS: Based on our results, it does not seem critical which wrist the actigraphy device is worn on for measuring key sleep variables.

text: What wrist should you wear your actigraphy device on? Analysis of dominant vs. non-dominant wrist actigraphy for measuring sleep in healthy adults OBJECTIVE: Differences in sleep results due to the placement of actigraphy devices (non-dominant vs. dominant wrist) are yet to be determined. 
METHODS: 65 nights of data from 13 adult participants was collected while participants wore two actigraphy devices, one on each wrist. Sleep indices including total sleep time (TST), total time in bed (TTB), sleep efficiency (SE%), sleep latency (SL), wake after sleep onset (WASO), sleep onset time (SOT) and wake time (WT) were assessed between the two devices. 
RESULTS: There were no significant differences between devices for any of the measured sleep variables (p>0.05). SE%, SL and WASO resulted in high correlations between devices (0.89, 0.89 and 0.76, respectively), with all other sleep variables resulting in very high correlations (>0.90) between devices.
CONCLUSIONS: Based on our results, it does not seem critical which wrist the actigraphy device is worn on for measuring key sleep variables.

year: 2017

title: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources?

authors: Death, Russell G.

abstract: Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

text: Does artificial intelligence modelling have anything to offer traditional management of freshwater food resources? Management of freshwater systems and the ecosystem services they provide has become a multi-stakeholder activity. This requires information on resources and how to manage them to be disseminated to a wide range of users. While artificial intelligence modelling can provide a powerful tool in managing and understanding resources and their drivers, they can be confusing to many users. In this study, we explored the potential use of two alternative modelling approaches (Boosted Regression Trees (BRT) and Bayesian Belief Networks (BBN)) for managing three species of freshwater mahinga kai species-kākahi or kaeo (freshwater mussel), koura (freshwater crayfish) and tuna (freshwater eel). While the BBN model I sbetter for stakeholder communication, the BRT produced more accurate models for all species. However, variables identified as being important for predicting abundance and biomass of these species were often environmental parameters that cannot be managed to improve yield. The artificial intelligence modelling does provide some accurate linkages between the target species and their environmental drivers. Nevertheless, translating these relationships into management plans remains challenging. The models are clearly not a panacea for better resource management, but provide one more tool that might assist multi-stakeholder understanding of how best to manage freshwater resources.

year: 2017