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This paper is a case study of student response systems used in large-class teaching. It considers the benefits, including the engagement of students and academic gains such as reduced administration. The constraints and impacts in classroom teaching are noted, drawing upon the experience of two teachers with their learning captured as a means of dissemination of practice to support other teachers who may be considering adopting and later adapting such practices (Gribble and Beckmann, 2023).

An autoethnographical account was undertaken using an action-learning approach as a sense-making exercise. These accounts enabled a depth of insight beyond the anecdotal evidence experienced by an individual teacher alone.

The findings show that while student response systems have constraints, these can be addressed by putting pedagogical concerns in front of any technology deployment, reaping benefits for students and teachers. Once engaged in using the system, students become more willing to enter further discussions. However, the limitations of both systems indicate that there may be a need for multiple systems to be available based on the pedagogical needs of the class.

The exploration of student response systems and outcomes of positive engagement by students in classroom settings provides insight to those wishing to explore such systems for use in large-class teaching settings.

This work extends discussions surrounding interactivity using student response systems. Additionally, practical insights from the users into their experiences with their students in using such systems provide alternatives for engagement in delivering large-class learning at scale.

The purpose of this study is realizing human-like motions and performance through musculoskeletal robots and brain-inspired controllers. Human-inspired robotic systems, owing to their potential advantages in terms of flexibility, robustness and generality, have been widely recognized as a promising direction of next-generation robots.

In this paper, a deep forward neural network (DFNN) controller was proposed inspired by the neural mechanisms of equilibrium-point hypothesis (EPH) and musculoskeletal dynamics.

First, the neural mechanism of EPH in human was analyzed, providing the basis for the control scheme of the proposed method. Second, the effectiveness of proposed method was verified by demonstrating that equilibrium states can be reached under the constant activation signals. Finally, the performance was quantified according to the experimental results.

Based on the neural mechanism of EPH, a DFNN was crafted to simulate the process of activation signal generation in human motion control. Subsequently, a bio-inspired musculoskeletal robotic system was designed, and the high-precision target-reaching tasks were realized in human manner. The proposed methods provide a direction to realize the human-like motion in musculoskeletal robots.

In this study, the authors explore students' and industry’s perceptions about the challenges and opportunities of participating in a large-scale, non-compulsory, individual, in-person and unpaid business placement programme at an Australian university. The placement programme aims to support students' workplace transition by emphasising the development of key employability skills through reflective learning and linking theory to practice.

Utilising a case study methodology and integrating survey questionnaires, the authors collected both quantitative and qualitative data with large sample sizes.

The results highlight curriculum areas for improvement, emphasising tailored feedback to manage placement expectations and addressing employability skill strengths and weaknesses.

Recommendations include co-partnering with students to develop short, tailored and hot tip videos along with online learning modules, including the presentation of evidence-based statistics to inform students about post-programme employment prospects.

The study contributes to benchmarking good practices in non-compulsory, individual, in-person and unpaid placement pedagogy within the business education context.

Smart urban governance research has progressed over the past few decades following changes and increasingly complicated city management difficulties. Therefore, the purpose of this paper is to use a scoping review and bibliometric analysis to examine all the publications on smart urban governance, especially in Asia.

A total of 1,900 smart urban governance articles indexed in the Scopus database was analyzed through scoping review and bibliometric analysis. The articles were analyzed by the number of publications per year, contributing countries, subject areas, authors, cited documents, related issues and cited papers. Furthermore, VOSviewer was used to provide a visual analysis of the co-occurrence of keywords.

This study indicated that urban smart governance publications continue to increase yearly. Even though the area of analysis is Asia, the USA and China seriously contributed to the analysis. Therefore, the topic of smart urban governance has become a discussion for scholars in the international. From the Scopus database analysis, the top three subject areas are social sciences (28%), environmental science (20%) and medicine (16%). The synthesis using bibliometric analysis by VOSviewer obtained 13 clusters.

This study only focuses on the Scopus database and one specific topic, using one bibliometric analysis tool. Meanwhile, national and international index databases are not used.

This paper examined publication trends on smart urban governance. This paper provided a comprehensive analysis of topic-specific knowledge areas based on previous studies. Additionally, this paper suggested the direction of the development of smart urban governance in the future.

The impact of extreme heat on prisons and carceral facilities is becoming increasingly visible, yet remains overlooked by scholars, practitioners and policymakers. Prisons are a unique type of infrastructure designed to severely limit and control the movement of hundreds and even thousands of individuals as a form of punishment. This leads to many significant challenges to mitigating the risk of heat-related illness in prisons and other carceral spaces that have remained overlooked across many disciplines including emergency management, disasters, corrections and public health.

For this study, we analyzed 192 surveys from incarcerated persons in state prisons throughout Texas to understand how incarceration and the punitive prison environment create challenges to managing extreme heat in prisons.

We found that characteristics of modern incarceration, including communal distribution of resources, crowded conditions and a lack of agency for incarcerated people, create barriers to accessing resources during periods of extreme heat. Furthermore, the punitive nature of the prison environment as manifested in the relationship between staff and incarcerated persons and certain prison policies also create barriers to incarcerated persons accessing resources to reduce their risk of heat-related illness and death.

These issues are particularly relevant to the health and safety of incarcerated persons during periods of extreme temperatures but also speak broadly to the implications of incarceration, disaster risk, and the advancement of human rights for incarcerated people.

This article addresses a gap in the literature by including the perspectives of persons incarcerated in Texas prisons experiencing extreme heat and implicates the characteristics of incarceration and punishment in the production of disaster risk.

Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.

This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.

The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.

The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.

This study aims to identify the key factors that influence household pro-environmental behaviors (HPEBs) and explore the differences caused by the same influencing factors between household waste management behavior (HWM) and household energy-saving behavior (HES).

A meta-analysis was conducted on 90 articles about HPEBs published between 2009 and 2023 to find the key factors. HPEBs were further categorized into HWM and HES to investigate the difference influenced by the above factors on two behaviors. The correlation coefficient was used as the unified effect size, and the random-effect model was adopted to conduct both main effect and moderating effect tests.

The results showed that attitude, subjective norms, and perceived behavioral control all positively influenced intention and HPEBs, but their effects were stronger on intention than on HPEBs. Intention was found to be the strongest predictor of HPEBs. Subjective norms were found to have a more positive effect on HES compared to HWM, while habits had a more positive effect on HWM. Furthermore, household size was negatively correlated with HWM but positively correlated with HES.

The same variables have different influences on HWM and HES. These results can help develop targeted incentives to increase the adoption of HPEBs, ultimately reducing household energy consumption and greenhouse gas emissions and contributing to the mitigation of global warming.

Utilizing the Social Cognitive Theory framework, this research aims to illuminate how self-efficacy acts as a mediating factor, enhancing employability among working university students by fostering essential capabilities in digital leadership and intercultural competence.

Quantitative study with 206 working undergraduates from an Indonesian university. Questionnaires and structural equation modeling (SEM) with SPSS and SmartPLS were used.

Findings showed that working undergraduate students possess a high level of digital leadership capability and intercultural competence and tend to display increased confidence in their ability to perform challenging tasks and attain anticipated outcomes, thereby enhancing their employability. The integration of these skills, coupled with a solid self-efficacy belief, emerges as a powerful combination in the modern employment landscape in the digital transformation and Artificial Intelligence age.

This research is conducted in one university in Indonesia, potentially limiting its generalizability. Future studies could expand its regions to multiple universities across different countries for a more comprehensive implication.

This research answered the call for strategies from the university level on enhancing the development of essential competencies for working undergraduate students’ employability. It further highlights the necessity for shaping the curriculum to prioritize digital leadership capability, intercultural competence, and the cultivation of self-efficacy among working students.

This insight of the research has profound social implications, indicating that educational institutions must adapt their curricula to prioritize these competencies. By doing so, universities can better prepare students to navigate the complexities of the modern workplace, thereby contributing to a workforce that is not only technologically proficient but also culturally adept and resilient in the face of challenges. This adaptation is essential for fostering a generation of graduates who are well-equipped to contribute positively to a diverse and rapidly evolving labor market, ultimately benefiting both the individuals and the wider society by promoting economic development and cultural understanding.

Adopting a quantitative approach, this research offers a data-driven lens of employability determinants, deepening the understanding of how intertwined competencies shape employment outcomes in the AI age.