"How prepared are engineering educators to implement active learning?" - The emerging findings

This paper discusses the question of how well prepared Engineering Educators are to implement Active Learning approaches within Higher Education undergraduate Engineering Programmes in Malaysia. As the role of Higher Education has shifted from being that of a 'knowledge provider' to become primarily focused upon 'learning facilitation', so the role of teachers or academic staff has changed in that they have become the key to implementing successful Active Learning. Based upon the emergent findings from a case study conducted at a Malaysian institution of higher education, the paper reveals that the engineering educators within the institution concerned were neither prepared nor ready to implement Active Learning. Indeed, it is evident from the study findings that a huge effort is needed in terms of educational policy and practice to ensure that Malaysian institutions offering engineering education should move efficiently and effectively towards the unilateral adoption of Active Learning approaches.

The design and evaluation of distributed virtual environment to support learning in global operations management

The primary goal of this research is to design and develop an education technology to support learning in global operations management. The research implements a series of studies to determine the right balance among user requirements, learning methods and applied technologies, on a view of student-centred learning. This research is multidisciplinary by nature, involving topics from various disciplines such as global operations management, curriculum and contemporary learning theory, and computer aided learning. Innovative learning models that emphasise on technological implementation are employed and discussed throughout this research.

Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

The dynamics of a genetic algorithm for a simple learning problem

A formalism for describing the dynamics of Genetic Algorithms (GAs) using method s from statistical mechanics is applied to the problem of generalization in a perceptron with binary weights. The dynamics are solved for the case where a new batch of training patterns is presented to each population member each generation, which considerably simplifies the calculation. The theory is shown to agree closely to simulations of a real GA averaged over many runs, accurately predicting the mean best solution found. For weak selection and large problem size the difference equations describing the dynamics can be expressed analytically and we find that the effects of noise due to the finite size of each training batch can be removed by increasing the population size appropriately. If this population resizing is used, one can deduce the most computationally efficient size of training batch each generation. For independent patterns this choice also gives the minimum total number of training patterns used. Although using independent patterns is a very inefficient use of training patterns in general, this work may also prove useful for determining the optimum batch size in the case where patterns are recycled.

Post-observation feedback as an instigator of teacher learning and change

This paper investigates an on-going study into the nature of the discourse teacher educators (TEds) and student-teachers (Ts) use during postobservation feedback, and explores the feedback discourse in terms of the impact it may have on teacher learning and change. Two types of feedback, confirmatory and corrective, are briefly discussed, and the former is then made the focus of the paper in its role as a potential instigator of teacher learning and change.

Ethnic diversity as a multilevel construct:the combined effects of dissimilarity, group diversity, and societal status on learning performance in work groups

The authors present a model of the multilevel effects of diversity on individual learning performance in work groups. For ethnically diverse work groups, the model predicts that group diversity elicits either positive or negative effects on individual learning performance, depending on whether a focal individual’s ethnic dissimilarity from other group members is high or low. By further considering the societal status of an individual’s ethnic origin within society (Anglo versus non-Anglo for our U.K. context), the authors hypothesize that the model’s predictions hold more strongly for non-Anglo group members than for Anglo group members. We test this model with data from 412 individuals working on a 24-week business simulation in 87 four- to seven-person groups with varying degrees of ethnic diversity. Two of the three hypotheses derived from the model received full support and one hypothesis received partial support. Implications for theory development, methods, and practice in applied group diversity research are discussed.

A constructive learning algorithm based on back-propagation

There are been a resurgence of interest in the neural networks field in recent years, provoked in part by the discovery of the properties of multi-layer networks. This interest has in turn raised questions about the possibility of making neural network behaviour more adaptive by automating some of the processes involved. Prior to these particular questions, the process of determining the parameters and network architecture required to solve a given problem had been a time consuming activity. A number of researchers have attempted to address these issues by automating these processes, concentrating in particular on the dynamic selection of an appropriate network architecture.The work presented here specifically explores the area of automatic architecture selection; it focuses upon the design and implementation of a dynamic algorithm based on the Back-Propagation learning algorithm. The algorithm constructs a single hidden layer as the learning process proceeds using individual pattern error as the basis of unit insertion. This algorithm is applied to several problems of differing type and complexity and is found to produce near minimal architectures that are shown to have a high level of generalisation ability.

Investigating Turkey's national innovation and learning system

The national systems of innovation (NIS) approach focuses on the patterns and the determinants of innovation processes from the perspective of nation-states. This paper reports on continuing work on the application of an NIS model to the development of technological capability in Turkey. Initial assessment of the literature shows that there are a number of alternative conceptualisations of NIS. An attempt by the Government to identify a NIS for Turkey shows the main actors in the system but does not pay sufficient attention to the processes of interactions between agents within the system. An operational model should be capable of representing these processes and interactions and assessing the strengths and weaknesses of the NIS. For industrialising countries, it is also necessary to incorporate learning mechanisms into the model. Further, there are different levels of innovation and capability in different sectors which the national perspective may not reflect. This paper is arranged into three sections. The first briefly explains the basics of the national innovation and learning system. Although there is no single accepted definition of NIS, alternative definitions reviewed share some common characteristics. In the second section, an NIS model is applied to Turkey in order to identify the elements, which characterise the country’s NIS. This section explains knowledge flow and defines the relations between the actors within the system. The final section draws on the “from imitation to innovation” model apparently so successful in East Asia and assesses its applicability to Turkey. In assessing Turkey’s NIS, the focus is on the automotive and textile sectors.

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.

"Ignorance was bliss, now I'm ignorant and that is far more difficult":transdisciplinary learning and reflexivity in responsible management education

Purpose – The collapse of world economic systems brought the interconnectedness between business and global events sharply into focus. As Starkey points out: “leading business schools need to overcome their fascination with a particular form of finance and economics […] to broaden their intellectual horizons […] (and to) look at the lessons of history and other disciplines”. The purpose of this paper is to provide evidence from three years of research on the Aston MBA suggesting that an emphasis on developing capabilities within a far broader, connected and reflexive business curriculum is what business students and practitioners now recognise as an essential way forward for responsible management education. Design/methodology/approach – This research paper examines the reflective accounts of 300 MBA students undertaking a transdisciplinary Business Ethics, Responsibility and Sustainability core module. Findings – As Klein argues, transdisciplinarity is simultaneously an attitude and a form of action. The student reflections provide powerful discourses of individual learning and report a range of outcomes from finding “the vocabulary or the confidence” to raise issues to acting as “change agents” in the workplace. Originality/value – As responsibility and sustainability requires learners, researchers and educators to engage with real world complexity, uncertainty and risk, conventional disciplinary study, especially within business, often proves inadequate and partial. This paper demonstrates that creative and exploratory frames need to be developed to facilitate the development of more connected knowledge – informed by multiple stakeholders, able to contribute heterogeneous skills, perspectives and expertise.

Increased salience of gains versus decreased associative learning differentiate bipolar disorder from schizophrenia during incentive decision making

Background Abnormalities in incentive decision making, typically assessed using the Iowa Gambling Task (IGT), have been reported in both schizophrenia (SZ) and bipolar disorder (BD). We applied the Expectancy-Valence (E-V) model to determine whether motivational, cognitive and response selection component processes of IGT performance are differentially affected in SZ and BD. Method Performance on the IGT was assessed in 280 individuals comprising 70 remitted patients with SZ, 70 remitted patients with BD and 140 age-, sex-and IQ-matched healthy individuals. Based on the E-V model, we extracted three parameters, 'attention to gains or loses', 'expectancy learning' and 'response consistency', that respectively reflect motivational, cognitive and response selection influences on IGT performance. Results Both patient groups underperformed in the IGT compared to healthy individuals. However, the source of these deficits was diagnosis specific. Associative learning underlying the representation of expectancies was disrupted in SZ whereas BD was associated with increased incentive salience of gains. These findings were not attributable to non-specific effects of sex, IQ, psychopathology or medication. Conclusions Our results point to dissociable processes underlying abnormal incentive decision making in BD and SZ that could potentially be mapped to different neural circuits. © 2012 Cambridge University Press.

Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Using cognitive load theory to interpret student difficulties with a problem-based learning approach to engineering education:a case study

This article reports on an investigationwith first year undergraduate ProductDesign and Management students within a School of Engineering and Applied Science. The students at the time of this investigation had studied fundamental engineering science and mathematics for one semester. The students were given an open ended, ill-formed problem which involved designing a simple bridge to cross a river.They were given a talk on problemsolving and given a rubric to follow, if they chose to do so.They were not given any formulae or procedures needed in order to resolve the problem. In theory, they possessed the knowledge to ask the right questions in order tomake assumptions but, in practice, it turned out they were unable to link their a priori knowledge to resolve this problem. They were able to solve simple beam problems when given closed questions. The results show they were unable to visualize a simple bridge as an augmented beam problem and ask pertinent questions and hence formulate appropriate assumptions in order to offer resolutions.

Do educators need educating?

Actual text: I was recently at the Spanish College of Optometry biennial conference and attended a meeting of contact lens lecturers from around Spain and Portugal. We discussed various ideas, mainly about how to share good practice and improve standards. What came to my mind was ‘is there a blueprint for training trainers?’ Well probably not but there are many things that we need to acknowledge such as the way students learn for example. Many educators themselves were taught by lecturers who would write on a blackboard or use acetate on an overhead projector, then came the 35 mm slide era followed by the Powerpoint era. More recently there is a move towards a much more integrated approach of various teaching methods. At my university our contact lens and anterior eye lectures generally follow a format where a narrated Powerpoint lecture is uploaded onto our internal virtual learning environment. This narrated version of the slides is designed to give the didactic element of the topic. The students listen to that before attending an interactive seminar on that topic. The seminar is also recorded so that students can listen to that afterwards. The seminar is designed to give additional information, such as case reports, or to clarify key points or for live demonstrations. It is a good way of doubling the contact time with the students without imposing further on an already packed formal timetable as the students can work in their own time. One problem that we noticed with this approach was that attendance can vary. If the students feel that they will gain something from the interactive seminar then they are more likely to attend – exam tips usually win them over! At the Spanish meeting the educators decided that they wanted to have regular meetings. The industry colleagues in attendance said that they were happy to help but could not necessarily give money, but they could offer meeting rooms, pay for lunch and evening meals. They even said that that they were happy to host meetings and invite other companies too (except to manufacturing plants). In the UK the British Committee of Contact Lens Educators (BUCCLE) meets for one day on three occasions in the year. The American Optometric Contact Lens Educators (AOCLE) meets annually at a three day event. Both these organisations get some help from industry. BUCCLE usually has one of its meetings at a university, one at a company training centre/manufacturing plant/national headquarters and one meeting the day before the BCLA annual conference. BUCCLE usually has its pre-BCLA meeting in conjunction with the International Association of Contact Lens Educators (IACLE). So when educators meet what would they discuss; well probably the focus should be on education rather than actual contact lens knowledge. For example sharing ideas on how to teach toric lens fitting would be better than discussing the actual topic of toric lenses itself. Most universities will have an education department with an expert who could share ideas on how to use the internet in teaching or how to structure lectures or assessments etc. In the past I have helped with similar training programmes in other countries and sharing good practice in pedagogy is always a popular topic. Anyone who is involved in education in the field of contact lenses should look at the IACLE web page and look out for the IACLE World Congress in 2015 in the days preceding the BCLA. Finally, IACLE, AOCLE and BUCCLE all exist as a result of generous educational grants from contact lens companies and anyone interested in finding out more about should refer to their respective web pages.