Search results

Climate change, poverty and pandemics are some of the complex real-world problems that are increasingly challenging higher education institutions (HEIs) to equip future graduates with meta-competences that have hitherto not been demanded. These graduates need to be able to capture and operate within complex systems and relationships. By focusing on complex real-world problems, this study aims to systematically review competences and frame meta-competences supporting curricula development in HEIs.

This study applies a systematic literature review according to the review protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The review process resulted in the selection of 39 articles, which were subjected to qualitative synthesis to identify competences for tackling complex real-world problems. These competences were grouped into meta-competences and aligned with the Sustainable Development Goals (SDGs).

Scientific disciplines commonly describe four competences for tackling complex real-world problems including domain-specific competence, inter-relation competence, intrapersonal competence and normative competence.

This study found that inter-relation competence is in line with all the SDGs, which is considered important for tackling complex real-world problems across disciplines.

A study in a survey design across disciplines and a weighting of these competences in respect to complex real-world problems aligned with the SDGs could contribute to a more consolidated and common understanding of the meta-competences identified.

This study concerned aspiring educational leaders’ problem-solving skill development, specifically through classroom instruction with real-world scenarios. Professional educators obtaining an advanced degree in educational administration at a public university were surveyed in the fall and spring about their problem-solving abilities. Participants were also asked to respond to real-world principal scenarios. Focus group interviews were conducted in the spring. Results indicated that participants’ confidence in their problem-solving abilities did improve, though their ability to address the real-world problems did not improve significantly. Participants identified the value of learning from real-world scenarios and professors who had experience as administrators, and they also recognized the importance of learning from one another during discussions of the scenarios. Participants indicated that they still needed experience working in actual administrative contexts.

This paper presents an information technology based instructional model for effective learning in management information systems programs and modules for students in undergraduate education. This model is primarily centered on the traditional in-class learning model. Information technology is used in a unique and novel way in order to create a learning environment which not only engages students in the learning process but also enhances their problems solving skills, analysis and decision making abilities. The model embeds information technology in projects in order to hide complexities but without compromising achievement of learning objectives. It also uses information technology in a simulation mode in order to demonstrate key concepts or learning themes, to simulate main themes presented in case studies and to simulate problems behaviors. The model employs other creative techniques in order to deliver its overall goals.

The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement.

A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide.

The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy.

A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

The density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.

First, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.

The experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.

The authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.

This book review seeks to clarify the contribution of the (2022) edited collection, International Models of Changemaker Education, to the field of work-applied management. It proposes that the many international models of changemaker education described in this book offer management professionals an array of innovative methods which support workplace learning and change by fostering organizational and educational flexibility.

This book review frames the collection's contribution in the context of work-applied management by systematically reviewing its chapter in order to present their relevance to conversations in and adjacent to the field.

This book review provides insights into how changemaker education might be conceived of and utilized in work-applied management contexts as drawn from the many innovative and demiurgic methods described in the collection and its chapters.

Changemaker education is a wide-ranging theoretical perspective, which is often loosely defined across contexts. However, this lexical amorphousness provides important flexibility for an expanded range of theoretical application.

This review includes important implications for the development of work-applied management theories drawn from models of changemaker education that demonstrate methods for achieving organizational agility and flexibility.

This review provides new and innovate models of use to work-applied management theorists and professionals.

This study explores how problem-based learning (PBL) programs can address Sustainable Development Goals (SDGs) via the higher education (HE) curriculum, teaching materials and relevant assessments, supporting learning at scale for HE institutions.

Employing SDGs and their indicators as the coding framework, our two-phase study evaluates the curriculum and teaching materials of seven PBL programs at a leading higher education institution (HEI). The first phase involved a content analysis to assess the degree of sustainability integration in 156 relevant courses. The second phase applied a semi-automated mapping protocol to analyze learning and teaching materials in 120 relevant courses.

The school aligns with 17 SDGs (100%), covering 94 indicators (55.62%). On average, each program within the school addresses over ten of these goals and incorporates more than 24 associated indicators. However, the study reveals an imbalance in the incorporation of SDGs, with some goals not yet deeply and comprehensively embedded in the curriculum. While there is a substantial focus on sustainability theories, the practical implications of SDGs in emerging countries, particularly through case studies and assessments, require significant enhancement.

Mapping SDGs allows HEIs to identify strengths and gaps in SDG integration, thereby improving the PBL approach to enhance student work readiness in sustainability-focused careers.

Through the lens of transformative learning theory, this study provides evidence of SDG integration into PBL curricula. It highlights a mapping methodology that enables HEIs to evaluate their sustainability readiness in curriculum, teaching materials and relevant assessments.

It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event.

This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world.

Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent.

This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.

Many mathematical models have been shared to communicate about the COVID-19 outbreak; however, they require advanced mathematical skills. The main purpose of this study is to investigate in which way computational thinking (CT) tools and concepts are helpful to better understand the outbreak, and how the context of disease could be used as a real-world context to promote elementary and middle-grade students' mathematical and computational knowledge and skills.

In this study, the authors used a qualitative research design, specifically content analysis, and analyzed two simulations of basic SIR models designed in a Scratch. The authors examine the extent to which they help with the understanding of the parameters, rates and the effect of variations in control measures in the mathematical models.

This paper investigated the four dimensions of sample simulations: initialization, movements, transmission, recovery process and their connections to school mathematical and computational concepts.

A major limitation is that this study took place during the pandemic and the authors could not collect empirical data.

Teaching mathematical modeling and computer programming is enhanced by elaborating in a specific context. This may serve as a springboard for encouraging students to engage in real-world problems and to promote using their knowledge and skills in making well-informed decisions in future crises.

This research not only sheds light on the way of helping students respond to the challenges of the outbreak but also explores the opportunities it offers to motivate students by showing the value and relevance of CT and mathematics (Albrecht and Karabenick, 2018).

Micro-credentials, which have been widely used in re-skilling and skills development, provide advantages for improvement in the professional career. As a complementary approach to career and professional development in formal education, it can be said that micro-credentials are preferred for supporting professional development and/or to acquiring new skills to find a job in a new field (Fisher and Leder, 2022). This study aims to provide a detailed perspective on conceptual framework and the use of micro-credentials in higher education.

Many questions have arisen regarding micro-credentials, applicability, quality assurance, inclusion in formal educational settings and how to include them in traditional education systems, especially in higher education institutions. Oliver (2019) emphasizes that there is little research on micro-credentials or their derivatives in the literature. There is still uncertainty on micro-credential on behalf of students, employers or employees. In addition, although there are studies on micro-credentials in the USA, Europe and Australia, there is very little research around countries such as Turkey (Yilik, 2021). This study discusses the use of micro-credentials in the world and their reflections on higher education.

As it is shown in this article, micro-credentials have the potential to challenge or complement traditional ways of how learning is taking place, understood, recognized and certified. Research on micro-credentials is still in its infancy and could benefit a lot from more research. Especially research on employers' recognition and appreciation of digital credentials needs to be studied. Also, empirically confirming propositions of literature on micro-credentials could be beneficial.

This study is original in terms of discussing the use of micro-credential in the world and their reflections on higher education and presenting suggestions on this subject.