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This Scholarship of Teaching and Learning research is a part of the larger study grant to analyze written reflections through learning log among the third and final year students undertaking BPME 3073 Entrepreneurship module in University Utara Malaysia (UUM). The paper aims to discuss these issues.

The data collection techniques are researcher-directed textual data through reflective learning log, taken from 140 students from three classes. A thematic approach was utilized to present the reflections of the students and all data were recorded in a verbatim format.

The findings show that most students have never written a reflective log or essay in the formative assessment. As a consequence, they had difficulty in writing the reflection when being requested to do so. A total 75 (approximately 55 percent) of the reflective logs were identified as level 1 (from 1 to 5 percent) in which reflections were simply written in a descriptive manner, resulted in a balance of 61 learning logs being utilized for further analysis. The students’ reflections on their entrepreneurship’s experience systematically categorize into four different themes comprised of: the nature of entrepreneurship module, entrepreneurial characteristics, opportunity recognition, and creativity and innovation.

As for the limitation of the study, it is important to not to underestimate the challenges of introducing a grade assessment that most of them are not familiar with in their university academic journey. Students need guidance, assurance and confidence writing something that require personal opinion, own thinking, sensitive and personal nature of narration. For most students as found out in this study, self-confessional writing is hard to come by (they dare not attempt it in the first place), only a handful appreciating the writing start with “I,” “me” as first person. More research in this study should be conducted across the university to gauge the response from the students to see if the result of this study is only applicable to this group of students or to this discipline of studies. The researchers would also like to recommend for future studies which take the form of a longitudinal study of similar kind to examine the problems and challenges with regards to promoting learning reflection at the undergraduate level.

Based on the result of the 61 students who had demonstrated an ability in reflective writing, it is suggested that perhaps the university should consider offering coursework that contains a component of reflective writing as part of the assessment. As such, if this is implemented, students of such ability like the one in this sample group would have been benefitted from such assessment which look at reflective ability (Greene, 2014) and which they were allowed to form a broader perspective in relation to the module undertaken. This in turns will foster the growth of reflective ability which is recognized as a learned behavior (Gustafson and Bennett, 1999). In addition, for the future exercise of this reflective learning log, the researcher opined that we should encourage our students to engage with another student (e.g. close friend) in a way that encourages talking with, questioning, or confronting, helped the reflective process by placing the learner in a safe environment in which self-revelation can take place. In addition, students were able to distance themselves from their actions, ideas and beliefs, by holding them up for scrutiny in the company of a peer with whom they are willing to take such risks (Hatton and Smith, 1995).

The results of this research have strongly suggested the need to urgently develop among the students the skills in writing reflectively as they go through the process of higher education which is useful in molding their future professional and entrepreneurial behavior as when they entered the job market which requires a critical reasoning ability.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

The purpose of this paper is to analyse the impact of an action research intervention during a work-based learning (WBL) project among human resource management (HRM) students at a business college in London. The intervention was the researcher’s meeting with the nominated group leaders to facilitate reflection on their leadership and instil confidence.

This paper is based on an action research leadership intervention on a broader undergraduate WBL module taught across nine disciplines and numerous projects. The action learning involved the phases of action, reflection, learning and planning. The sample was five group leaders on one of the projects for HRM students. A content analysis of their assessment submissions was included in the reflection, learning and planning phases.

Based on a content analysis, most of the group leaders acknowledged the leadership intervention in their submissions in varying degrees of quantity and quality. The findings reflect the impact of the intervention upon leadership confidence and the application of theory on practice. Specific leadership input would enhance the impact. The intervention did address confidence which impacted self-directed learning.

The research is limited to a specific context and small sample. It is limited by the fact that reflective assessment work could not be used in comparison with the project assessment submissions.

The research demonstrates directly from the assessed submissions of students the benefit of WBL with a specific focus on confidence, leadership, reflection and self-directed learning. It demonstrates as an example the application of action research on a small WBL sample.

The research is the evidence of the importance of leadership and confidence among mature adults in WBL contexts.

This paper demonstrates the impact of WBL on the learning of mature adults and, furthermore, the impact of a leadership intervention on the motivation of students for self-directed learning.

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.

The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers’ explanations, conduct exercises, etc.

The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher’s PC. The teacher can easily grasp students’ status and whether or not students are following the teacher’s explanation.

Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences.

Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

Analysis of log message is very important for the identification of a suspicious system and network activity. This analysis requires the correct extraction of variable entities. The variable entities are extracted by comparing the logs messages against the log patterns. Each of these log patterns can be represented in the form of a log signature. In this paper, we present a hybrid approach for log signature extraction. The approach consists of two modules. The first module identifies log patterns by generating log clusters. The second module uses Named Entity Recognition (NER) to extract signatures by using the extracted log clusters. Experiments were performed on event logs from Windows Operating System, Exchange and Unix and validation of the result was done by comparing the signatures and the variable entities against the standard log documentation. The outcome of the experiments was that extracted signatures were ready to be used with a high degree of accuracy.

The aim of this paper is to analyze the links between leaders' creation of knowledge in the setting of a leadership development program and the transfer of knowledge to their own organizations.

This is a case study of a leadership development program conducted during 2020–2022. The program was focused on how to lead and manage learning and knowledge processes in organizations, and offered a mix of theoretical perspectives and practical collaborative sessions. Data were collected through interviews and the participants' written reflections on their learning experiences. Total number of interviews was 13.

Overall the participants showed many examples of how they applied theories and practical tools that they had learned during the program in their own organizations. The participants experienced different types of challenges regarding knowledge transfer, but also potential meta-knowledge transfer through dialogue.

Pedagogical organizing of leadership development point to a need for supplementary dialogue between the leader of the development program and both the participating leader and manager.

This study shows that meta-knowledge transfer is not a simple matter of moving codified knowledge from the development program to new settings. Knowledge about others' knowledge requires and stimulates subject-to-subject relations between people through which new knowledge potential is created. These findings confirm and enhance previous studies that indicate the need for social support for soft-skill knowledge transfer.