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Analyses of teaching and learning in higher education are increasingly being based on a distinction between surface and deep learning. This distinction is helpful for investigating approaches used by teachers as well as student preferences for teaching and learning. Surface learning places an emphasis on memorizing facts and information as well as the relatively passive reproduction of content. In contrast, deep learning involves an intention to understand, the critical assessment of content and relating new information to past knowledge in meaningful ways. There has been an assumption that in the U.A.E. there is an orientation to surface learning in schools and higher education. To examine this assumption, an adaptation of questionnaires used with Western students (the Approaches to Study Skills Inventory for Students) was used with a small sample of ZU students. There are limitations in the use of this procedure and difficulties in interpreting the results. However, the results suggest that ZU students show strong beliefs and preference for deep learning approaches in addition to surface learning approaches. This finding is consistent with evidence obtained from student responses to assessment tasks, where there was evidence of deep learning. It was concluded that learning outcomes for ZU students could be enhanced by employing deep learning approaches to teaching and learning.

The purpose of this paper is to investigate the relationship between preferred choice of school subject and student information behaviour (IB).

Mixed methods were employed. In all, 152 students, teachers and librarians participated in interviews or focus groups. In total, 1,375 students, key stage 3 (11-14 years) to postgraduate, responded to a questionnaire. The research population was drawn from eight schools, two further education colleges and three universities. Insights from the literature review and the qualitative research phase led to a hypothesis which was investigated using the questionnaire: that students studying hard subjects are less likely to engage in deep IB than students studying soft subjects.

Results support the hypothesis that preferences for subjects at school affect choice of university degree. The hypothesis that a preference for hard or soft subjects affects IB is supported by results of an analysis in which like or dislike of maths/ICT is correlated with responses to the survey. Interviewees’ comments led to the proposal that academic subjects can be classified according to whether a subject helps students to acquire a “tool of the Mind” or to apply such a tool. A model suggesting how IB may differ depending on whether intellectual tools are being acquired or applied is proposed.

The “inner logic” of certain subjects and their pedagogies appears closely linked to IB. This should be considered when developing teaching programmes.

The findings offer a new perspective on subject classification and its association with IB, and a new model of the association between IB and tool acquisition or application is proposed, incorporating the perspectives of both teacher and student.

Previous studies on the associations between approaches to studying and outcomes have been conducted largely in Europe, where participants have been largely undergraduate-level students. The purpose of this study is to examine the associations between study approaches and academic outcomes of graduate occupational therapy students in the USA.

In this study, 120 masters- and doctoral-level occupational therapy students in their first and second study year in a large metropolitan city completed the short version of the Approaches and Study Skills Inventory for Students (ASSIST). A total of 18 items from the full 52-items version comprise the short version of the ASSIST, with 6 items belonging to each of the deep, strategic and surface scales. Associations between the study approach scales and exam grades were analyzed with single and multiple logistic regression analyses.

There was a direct association between the use of strategic studying and higher grade point average. Thus, students who want to perform well academically are encouraged to use study behaviours comprised by the strategic approach to studying.

This study appears to be the first to examine associations between study approaches and academic performance among occupational therapy students in the US context. More research on the study approaches of US American students is warranted.

The article aims to share the viewpoint of the author on the impact of the innovative teaching pedagogy adopted by him in teaching the accounting courses on the satisfaction of the participants in a premier Indian Business School.

The experiment was carried over by introducing an innovative teaching approach by the author among the first-year participants of the Management Program. At the end of the experiment, the participants were asked to rate the teaching approach on a five-point scale besides offering the qualitative feedback on the impact of the experiment on their learning outcomes.

The paper concludes that the participants were highly satisfied with the teaching approach adopted by the author.

The study contributes to the theory and practitioners on accounting education in many ways. First, it is offering evidence on the impact of teaching pedagogy on the satisfaction of the participants of the accounting courses of an emerging market business school. Second, the accounting educators of the emerging countries may replicate the experiment in their institutes. Third, the educators of other courses in the business schools may make an effort to measure the impact of the teaching approach on the feedback of their learners.

Teachers of primary education experience high levels of stress but lack rational coping strategies to reduce their work stress. The paper develops a value stream mapping for education approach and examines its use as a rational coping strategy for teams of teachers and other employees to overcome work-related stressors.

The research process consists of two phases. First, a value stream mapping approach for education is developed, based on literature research. Next, the approach is validated in an action research study to reduce work stress of teachers in educational services. The processes that have been selected by the teachers relate to coping with increased variety, long and uncertain throughput times and unclear specifications.

Value stream mapping for education (VSM4EDU) is a well-structured improvement method based on principles of visualization, participation and process thinking, which helps teachers without background in lean thinking to analyse their processes. Using this method has enabled the team to develop rational coping strategies to reduce their work-related stress.

VSM4EDU has been validated using action research at a single school, which implicates deep insight, but further testing at other schools is welcome. Moreover, VSM4EDU has not been used to develop a future state map.

Value stream mapping is useful in educational settings as long as the educational context is respected in the approach.

VSM4EDU empowers teachers and helps to develop co-operation in teams.

The validation of value stream mapping for education is well-documented and original.

It is becoming more generally accepted that there is a need to develop a new kind of leader to meet the needs of our 21st century VUCA world. The bookcases are full of volumes that describe “what” great leaders should do, but “how” to develop such leaders is usually limited to a macro or systemic solution rather than focusing on granular behavioural change of the individual. This paper describes the qualities and characteristics of Transpersonal Leaders, then focuses on developing these leaders through a new coaching process and finally explains how experienced coaches can be trained to coach these leaders.

Our research over the last 20 years of working with leaders individually and in teams has focused on this issue. We have been developing “21st century ready” leaders, referred to as Transpersonal Leaders, for over 10 years in teams, but only recently have we been developing such leaders through a new coaching process. We have also developed a methodology that codifies the development of Transpersonal Leaders which, in turn, allows us to replicate the programme by training other professionals, potentially in large numbers.

Graduates of the Transpersonal Coach Training Programme say that it has been a transformational personal experience, enabling them to take their leader clients to a new level. Leaders who have been coached say the programme has equipped them to learn a practical approach to becoming an authentic, ethical, caring and more effective leader.

This is a unique approach to coaching leaders but based on proven learning principles.

The purpose of this paper is to examine conflicting institutional demands on individual frontline employees in hybrid public sector organisations. Specifically, it examines the competing accountability pressures professional and commercial logics exerted on academics at a business school, how individual lecturers responded to such pressures, and what drove these responses.

The paper draws on a case study of an English business school and is informed by the literatures on institutional logics and hybrid organisations.

The paper shows that the co-existence of professional and commercial logics at the case organisation exerted competing accountability pressures on lecturers. It moreover shows that sometimes deliberately and purposefully, sometimes ad hoc or even coincidentally, lecturers drew on a wide range of responses to these conflicting pressures, including compliance, defiance, combination and compartmentalisation.

The paper sheds light on individual level responses to competing institutional logics and associated accountability pressures, as well as on their drivers. It also highlights the drawbacks of user, customer or citizen accountability mechanisms, showing that a strong emphasis on them in knowledge-intensive public organisations can have severe dysfunctional effects.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The purpose of this papers is to provide an overview of how students and teachers in Taiwan conceptualize learning, especially in technology-enhanced learning environments. Their conceptions of learning reveal the extent to which the prevalence of technological use in education has facilitated students to cultivate a more advanced conception of learning and develop a deeper learning approach.

It reviews a total of nine relevant case studies, covering the contexts of conventional schools (from elementary schools to college, and cram schools) as well as technology-enhanced environments (internet-assisted learning and mobile learning); and participants from Grade 2 students to adult learners as well as teachers. Their conceptions of learning and preferred learning approaches are summarized.

Results of the studies show the Taiwanese students’ and teachers’ conceptions of learning in general and of technology-enhanced learning in particular. The students tended to be passive learners to receive instructions and considered examinations as a short-term goal for their study, with surface learning approaches commonly adopted. Despite technology may help to promote their cultivation of a more sophisticated conception of learning, many of them still opted for rote memorization and practice as the major ways to study. The potentials of technology in enhancing learning thus have not been fully realized.

The results shed light on an Asian-specific educational culture which is exam oriented. They reveal the challenges regarding the use of technology in education, which hinder the promotion of students’ advanced conceptions of learning. They also highlight the directions of future work to create a more accessible and gratifying technology-enhanced environment.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.