Search results

This study aims to assess the knowledge and attitudes toward recovering citizenship (RC)/5 Rs and mental illness of people aged ≥18 years in Hong Kong using a telephone survey approach.

A questionnaire comprised the Mental Health Knowledge Schedule (MAKS), Short Form-Community Attitudes Toward Mental Illness (SF-CAMI) and questions on attitudes toward RC/5 Rs, was administered on the phone.

A total of 1,009 respondents completed the telephone survey. A high mean score of MAKS (4.37 ± 1.08) was found with 68%–94% answering the knowledge items correctly. The mean score of SF-CAMI was 46.50 ± 8.74 with the most positive attitude toward fear and exclusion. Approximately half had heard about a similar concept of RC and 79%–94.3% agreed with people in recovery to possess the 5 Rs. Those with greater knowledge or more positive toward mental illness, or knowing someone in recovery were more supportive toward 5 Rs. Those aged 18–44 years, attained a post-secondary education, were employed, and received a monthly income of US$3,861–6,434 were significantly more positive toward 5 Rs.

To the best of the authors’ knowledge, this is the first study assessing the views of RC of people in the community. The sample had a good knowledge of mental illness but recognition of recovery from mental illness and a sympathetic view toward people in recovery can be further improved. Besides promotion programs, dissemination of the concept of RC and having people in recovery take up valued roles in the community could potentially facilitate the acceptance of social inclusion and acceptance in the community.

The Singapore construction sector is promoting performance improvement by implementing collaborative contracting (CC). For CC's successful implementation, there is a need to sieve out the critical success factors (CSFs). Hence, this paper aims to explore factors influencing the adoption of CC and investigate the potential performance improvement by implementing CC.

Based on the literature review, 23 CSFs were identified and packaged to a questionnaire to evaluate the CSF's priority as well as CC's potential impact on the project performance. After prior validation, 165 potential respondents were drawn from survey list by using simple random sampling. Finally, 31 valid responses were received.

The survey result showed that the top five CSFs are mutual trust, all parties to commit fully, openness between parties, commitment to a win–win attitude and resource availability. The majority of respondents hold that CC implementation would improve overall project performance, decision effectiveness, cost, time, quality and risk performance.

Directors, project managers, contractor managers and site engineers have different perceptions of CSFs. Also rather than meeting the same requirements in the implementation of CC, there are differences among projects with different values.

This study provides an in-depth understanding of the CSFs for industry practitioners adopting CC and CC's potential impact on project performance. This study is also helpful for authorities to formulate effective policies to push the implementation of CC.

As universities grapple with the question of how to prepare students for increasingly uncertain futures, the development of evidence-based frameworks to guide academic program¹ design is critical. Here, we propose a strategic framework with a high impact on program design, implementation, and evaluation as well as mentoring and support for academic program leaders. High impact can be achieved when program leaders are enabled to embed key future-focused capabilities and skills across the curriculum in a program. In order to ensure that these capabilities are systematically and cohesively embedded in students’ learning journeys, we suggest that academic program leaders (e.g., Directors of Studies, Subject Area Coordinators, etc.) require strategic approaches to program design, implementation, and evaluation, as well as mentoring and support. Such approaches would ensure that high impact practices are consistently employed, rather than being the exception in isolated courses.² At Queensland University of Technology, we have developed a holistic model to support “whole-of-program” design for award programs across faculties and disciplines, in a coherent and strategic way. The model we use is based on a framework for curriculum design called the Future Focused Curriculum Design Framework (FFCF), and is an iterative model that places learners at the center of their learning to enable meaningful change to the design of programs. The adoption of the framework is supported by curriculum design studios situated within each discipline-specific faculty,³ which are made up of curriculum and learning designers, working closely with academics in different faculties. A key element of the process is that curriculum design studios enable relationships and communities to develop (Wenger et al., 2002), which in turn allows for contextualized practice. This holistic model supports whole-of-program design for award programs, in a coherent and strategic way and enables communities of practice to emerge in an iterative manner. In this chapter, we share our experiences with using this model and the impacts it has achieved, and we reflect on ways it be adapted for future use and in other contexts.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.