Search results

In a fast evolving labour market, higher education graduates need to develop employability competences. Key in becoming employable is the ability to reflect on learning experiences, both within a curriculum as well as extra-curricular and work placements. This paper wants to conceptualise how an online learning platform might entail a reflective practice that systematically supports students in reflecting on their learning experiences.

When studying online learning platforms for developing students' employability competences, it became clear that the effectiveness of the platform depends on how the platform guides students' reflective practice. In turn, the authors studied which features (tools, services and resources) of the online learning platform are guiding the reflective practice.

This resulted in the introduction of an online learning platform, containing a comprehensive set of online learning tools and services, which supports students' reflective practice and, in turn, their employability competences. The online platform facilitates both feedback from curricular and work-related learning experiences and can be used as a start by students for showcasing their employability competences. The reflective practice consists of a recurrent, systematic process of reflection, containing various phases: become aware, analyse current state, draft and plan a solution, take action and, finally, reflect in and on action.

Future research revolves around studying the features of online learning platforms and their role in fostering students' reflection and employability competences.

The conceptual model provides concrete indicators on how to implement online learning platforms for supporting students' reflection and employability competences.

This is the first article that analyses an online learning platform that guides students' reflective practice and fosters their employability competences. The authors provide concrete suggestions on how to model the online platform, building further on reflective practice theory.

This study sought to understand the physical, educational and operational needs faced by healthcare workers (HCWs) (including physicians, nurses and allied health workers) during the COVID-19 pandemic.

A descriptive cross-sectional design was undertaken. HCWs working in hospitals, health centers, testing and quarantine areas in the Kingdom of Bahrain were invited to complete the online survey questionnaire developed by authors containing three domains: physical, educational, and perceived knowledge, and operational needs.

A convenient sample (N = 627) of volunteered participants responded to the online survey. The biggest challenges that HCWs were exposed to are physical needs (experiencing dry hands, difficulty breathing while on a mask, feeling hot and sweaty, and less fluid and food intake) which were reported as the higher level, followed by operational needs (limited communication due to Personal Protective Equipment - PPE - use, longer working hours, and preparation time to get ready for duty). Other challenges pertained to education and knowledge (the presence of multiple sources of information confused them during the care practices). Females faced more challenges than males, and Bahraini HCWs handled challenges more than non-Bahrainis. A negative relationship was found between age and years of experience with the challenges of the HCWs.

During the COVID-19 pandemic, health systems face rapidly increasing demands. HCWs face several challenges while providing patient care, particularly physical needs. This study provides adequate data for healthcare administrators to maintain a safe working environment during pandemics.

The shift from paper portfolios to e-portfolios has arrived in educational institutions worldwide. This study investigates e-portfolio systems as a means of improving performance-centered assessment, enriching students’ learning experiences and documenting the students’ progress and achievements. The current study reveals the experience of implementing a course-level framework for e-portfolios and an approach taken in initiating student electronic portfolios in the Department of Educational Technology (DET) at Ajman University of Science and Technology, UAE. Data was obtained in several ways, including Likert scale responses and interviews with the participants; students’ journals and final reports; notes from the Practicum site supervisor and the DET lab technician; and analysis of the electronic portfolio product. The work and responses of the Practicum students were compared for three consecutive Practicum classes. Analysis of the results showed that developing formative and summative portfolios fluctuated extensively between the three Practicum classes of DET graduates, as did the outcomes. In spite of this fact, the findings suggested that the use of e-portfolios could serve as an influential learning and assessment tool when driven by a clear understanding of the desired outcome and the specific skills to be assessed, and when sufficiently mentored, peer-reviewed, and based on sensible principles.

This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education.

The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020.

As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics.

The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

This chapter introduces the topic of cooperation and co-innovation between the audiovisual media and education sectors. It first discusses the emergence of educational film approximately a hundred years go – together with a new institutional framework, industry media, rulebooks, etc. It then discusses the ways public service media have addressed educational programming over the decades, including developing complex cross-media strategies and educational content databases more recently. The second half of the chapter is dedicated to the emergence of educational digital games, with their own institutional setups, production cultures, and training programmes. The chapter points, however, to a relative lack of cooperation between commercial game producers and educational institutions to date.

By envisioning the learning environment as an eco-social system, this study aims to map interrelated enablers of students’ sustainability-oriented learning (SoL) in the context of a university course at the interface of science and society.

A case-study approach was used to delineate what enables student learning in a university-wide transdisciplinary Master of Science course. A sample of 102 students, university and societal stakeholders participated to this study, by sharing their experiences and views through focus groups and questionnaires.

A main finding is the development of a configuration of six intertwined enablers that through their interplay help to cultivate students’ SoL, in the course under exploration.

This study paves the way for a re-orientation of how to explore learning in complex environments. It shows that adopting a relational, situated and systems approach is not only feasible but is also desirable to understand and guide learning practices in complex environments.

This chapter provides first insights into identities and communities of educational staff in one of the largest, multi-campus universities in Italy. This group of managers refers to those supporting teaching and learning in the light of emerging demands from the European strategy for universities which is positioning education at the frontline in today’s higher education institutions (HEIs).

These insights are compared with common issues surveyed among research managers and administrators (RMAs) working in the same as well as in other international HEIs using Evans’ ‘restricted’ and ‘extended’ models of professionalism.

Among findings, educational managers (EM) show awareness of their identity only as ‘professionals’ while RMAs may feel like ‘hybrid’ profiles. Unlike RMAs, EM report not having a strong sense of belonging to one community but feeling like they belong to a plethora of groups. In conclusion, there are no dominant ‘extended’ or ‘restricted’ traits for any of the two groups and they have both these attitudes to a certain extent as the results of this chapter will further explain.

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 paper aims to explore the effect of problem-based learning (PBL) embedding degree and education level on individual perception, as well as the moderating effect of nationality.

The paper first conceptualizes PBL embedding degree which means the extent of applying PBL. It takes an empirical study on an international MBA class in one of the first-class universities in China. An investigation is taken with the designed “PBL-based Cognitive Perception Scale” and an Ordered Probit Model is constructed.

The findings of this study are as follows: PBL embedding degree has a significant effect on the cognitive perception of student, which varies in different dimensions; the educational level of international student positively affects the cognitive perception toward PBL; and nationality may moderate the relationship between the PBL embedding degree and individual perception.

The paper replenishes the investigation and application of Bloom’s Taxonomy of Learning. By conceptualizing PBL embedding degree, the paper extends the research perspectives of PBL and proposes a subjective method on the evaluation of PBL. The paper also may provide a guidance for PBL curriculum design with sustainable development of education.