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This study investigated undergraduate preparation of English as a Second Language (ESL) students through English for Academic Purposes courses, in relation to recipient subject lecturers’ expectations. Qualitative data were gathered from 36 faculty teaching ESL undergraduates in nine countries. A two-phase approach included seeking discursive responses to questionnaires from faculty and information about curricula. Outcomes highlighted difficulties with material selection for EAP tutors. Tutors chose between ‘general interest’ or ‘discipline-specific’ material, but reported that the former could lead to oversimplification or discipline irrelevance, while the latter usually requires some specialized subject knowledge which may be beyond tutors’ remit. Addressing this, it is suggested that articles about EAP-related topics can form the subject matter of EAP courses with significant benefits. In particular, they can simultaneously provide students with models of academic writing, while the content reinforces skills needed for successful study.

This study aimed to examine the similarities and differences between the ability to analyze the environment and exploit new knowledge (absorptive capacity) and the skills to generate value from innovation (appropriation). These fields have similar origins and are sometimes confused by practitioners and academics.

A review was conducted based on a full-text analysis of 681 and 431 papers on appropriation and absorptive capacity, respectively, from Scopus, Science Direct and Lens, using methodologies such as text mining, backward citation analysis, modularity clustering and latent Dirichlet allocation analysis.

In business disciplines, the fields are considered different; however, in other disciplines, it was found that some authors defined them quite similarly. The citation analysis results showed that appropriation was more relevant to absorptive capacity, or vice versa. From the dimension perspective, it was found that although appropriation was considered a relevant element for absorptive capacity, the last models did not include it. Finally, it was found that studies on both topics identified the importance of appropriation and absorptive capacity for innovation performance, knowledge management and technology transfer.

This is one of the first studies to examine in-depth the relationship between appropriation and absorptive capacity, bridging a gap in both fields.

Drawing on the theoretical concept of organisational fit, this paper questions the relevance of employees' participation in the link between continuous improvement (CI) and operational performance. The literature has long emphasised that to be successful, CI implementation needs to rely on employees' involvement as soon as its inception. This paper argues that this approach is not generalisable.

Based on a database of 330 firms across 15 countries, regression analyses were used to hypothesise that the fit between CI and employee participation is positively associated with operational performance, and that the fit between CI and centralisation of authority is negatively associated with operational performance. The authors also ran a robustness check with polynomial regression analyses and the response surface methodology.

CI–employee participation fit is positively associated with operational performance, suggesting that there is less need for employees to be involved when a firm has scarcely developed CI. Employee participation becomes gradually more relevant as CI progresses. Moreover, the results demonstrate that the CI–centralisation of authority fit is negatively associated with operational performance, suggesting that a top-down management approach with centralised authority is preferable when CI is low, whereas a bottom-up management approach is helpful when a firm has extensively developed CI.

This research draws on the concept of organisational fit to explore the relationships between internal practices in the operations management literature. The authors suggest that managers should dynamically balance the practices of employee participation and centralisation of authority as CI improves. This study highlights that CI has different evolutionary levels that require different managerial approaches and practices.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

The purpose of this paper is to lay down the methodological structure of the epistemology of tawhid (Oneness of Allah). In this paper, the meaning of tawhid also refers to the monotheistic unity of knowledge (consilience) in the cast of its organic pairing by circular causation relations between the moral and material possibilities. The paper thereby raises the critique of mainstream economic reasoning and its imitation by existing Islamic economics. Consequently, by the ontological, epistemological and phenomenological foundation of tawhidi methodological worldview, an altogether new socio-scientific reasoning in generality and economic reasoning in particular is introduced.

The socio-scientific methodological reasoning of unity of knowledge according to the tawhidi methodological worldview is introduced contrary to the inept rational choice postulates of mainstream economic reasoning and its imitation by existing notions of Islamic economics. The method of instructing students in the light of this approach according to Tawhidi Islamic Economics (TIE) is introduced from the existing literature.

The existing nature of mainstream economics and its imitation by Islamic economics is critically deconstructed and replaced by the true epistemological, ontological and phenomenological perspectives of TIE in the world of learning. Some inner properties of such a methodological study of TIE are laid bare for further investigation.

This is the first paper of its kind in this journal to expound the original and most creative methodological worldview that Islamic economics must bear. This is the foundation of the development of the true stance of Islamic economics and finance.

The objective of the presented pilot study was to test the applicability of a metric to specifically measure performance improvement via a hands-on workshop about collaborative robotics.

Candidates interested in acquiring basic practical skills in working with a collaborative robot completed a distance learning exercise in preparation for a hands-on training workshop. The candidates executed a test before and after the workshop for recording the parameters compiled in the tested performance index (PI).

The results reflected the potential of the tested PI for applications in detecting improvement in practical skill acquisition and revealed potential opportunities for integrating additional performance factors.

The low number of candidates available limited in-depth analyses of the learning outcomes.

The study outcomes provide the basis for follow-up projects with larger cohorts of candidates and control groups in order to expedite the development of technology-assisted performance measurements.

The study contributes to research on performance improvement and prediction of learning outcomes, which is imperative to this emerging field in learning analytics.

The development of the presented PI addresses a scientific gap in learning analytics, i.e. the objective measurement of performance improvement and prediction along skill-intensive training courses. This paper presents an improved version of the PI, which was published at the 12th Conference on Learning Factories, Singapore, April 2022.

Because of the increasingly higher expectations of accrediting organizations, calls for greater accountability from state governments, and students’ demand for an education that prepares them for a career, most hospitality programs are now required to have an effective assessment of learning outcomes process. The increasing popularity of the assessment of learning outcomes process is viewed as highly positive because it can be considered as best practices in higher education. The paper aims to discuss this issue.

This is Part 1 of a two-part article that provides an overview of the justifications for implementing an assessment of learning outcomes process, the steps that were developed by two hospitality programs and the experiences of the two programs during implementation of the seven steps. Part 1 includes foundational principles of the process and the first three of the seven steps.

The steps in a closed-loop assessment of learning outcomes process are relatively detailed; however, because of changes in expectations of stakeholders and the requirements of accreditors, they are now mandatory for most hospitality programs. Therefore, the choice is not whether to implement them, but when to implement them. From a competitive standpoint, it is to the program’s advantage to begin as soon as possible. Another factor to consider is that the implementation of an effective closed-loop assessment of learning outcomes process will take several years to complete.

This paper is presenting a critical view of one of, if not the most important concepts in higher education, the closed-loop assessment of learning outcomes process. Hopefully, the information on the process that is provided and the experiences of the two programs can shorten the learning curve for other hospitality programs.

In this study, reasons for proving the relevancy of personalisation of e-learning systems to act as a knowledge management system in which tacit to tacit type of knowledge (socialisation) can be delivered, are being provided. Nonaka’s knowledge conversion model is being used as the basis of the investigation. The relationship between ‘the strategic knowledge conversion model’ drawn from the ‘identifying list of strategies’ and ‘an individual’s decision-making method’ has been investigated in relation to knowledge transferring systems and individual’s learning styles. The outcome of the qualitative as well as quantitative investigation defines a set of frameworks in which different types of e-learning systems utilizing different learning philosophies and learners learning preferences to support the learner’s learning curve.

Because of the increasingly higher expectations of accrediting organizations, calls for greater accountability from state governments and students’ demand for an education that prepares them for a career, most hospitality programs are now required to have an effective assessment of learning outcomes process. The increasing popularity of the assessment of learning outcomes process is viewed as highly positive because it can be considered as best-practices in higher education. The paper aims to discuss this issue.

This is Part 2 of a two-part article that provides an overview of the justifications for implementing an assessment of learning outcomes process, the steps that were developed by two hospitality programs, and the experiences of the two programs during implementation.

The steps in a closed-loop assessment of learning outcomes process are relatively detailed; however, because of changes in expectations of stakeholders and the requirements of accreditors, they are now mandatory for most hospitality programs. Therefore, the choice is not whether to implement them, but when. From a competitive standpoint, it is to the program’s advantage to begin as soon as possible. Another factor to consider is that the implementation of a closed-loop assessment of learning outcomes process will take several years to complete.

This paper is presenting a critical view of one of, if not the most important concepts in higher education, the closed-loop assessment of learning outcomes process. Hopefully, the information on the process that is provided and the experiences of the two programs can shorten the learning curve for other hospitality programs.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.