Identity Text: an educational intervention to foster cultural interaction

Background: Sociocultural theories state that learning results from people participating in contexts where social interaction is facilitated. There is a need to create such facilitated pedagogical spaces where participants share their ways of knowing and doing. The aim of this exploratory study was to introduce pedagogical space for sociocultural interaction using ‘Identity Text’.
Methods: Identity texts are sociocultural artifacts produced by participants, which can be written, spoken, visual, musical, or multimodal. In 2013, participants of an international medical education fellowship program were asked to create their own Identity Texts to promote discussion about participants’ cultural backgrounds. Thematic analysis was used to make the analysis relevant to studying the pedagogical utility of the intervention.
Result: The Identity Text intervention created two spaces: a ‘reflective space’ helped
participants reflect on sensitive topics like institutional environments, roles in
interdisciplinary teams, and gender discrimination. A ‘narrative space’ allowed
participants to tell powerful stories that provided cultural insights and challenged cultural hegemony; they described the conscious and subconscious transformation in identity that evolved secondary to struggles with local power dynamics and social demands involving the impact of family, peers and country of origin.
Conclusion: Whilst the impact of providing pedagogical space using Identity Text on
cognitive engagement and enhanced learning requires further research, the findings of
this study suggest that it is a useful pedagogical strategy to support cross-cultural
education.

Mathematical prerequisites for learning statistics in Psychology: Assessing core skills of numeracy and mathematical reasoning among undergraduates.

This study sought to extend earlier work by Mulhern and Wylie (2004) to investigate a UK-wide sample of psychology undergraduates. A total of 890 participants from eight universities across the UK were tested on six broadly defined components of mathematical thinking relevant to the teaching of statistics in psychology - calculation, algebraic reasoning, graphical interpretation, proportionality and ratio, probability and sampling, and estimation. Results were consistent with Mulhern and Wylie's (2004) previously reported findings. Overall, participants across institutions exhibited marked deficiencies in many aspects of mathematical thinking. Results also revealed significant gender differences on calculation, proportionality and ratio, and estimation. Level of qualification in mathematics was found to predict overall performance. Analysis of the nature and content of errors revealed consistent patterns of misconceptions in core mathematical knowledge , likely to hamper the learning of statistics.

A new Jacobian matrix for optimal learning of single-layer neural networks

This paper investigates the learning of a wide class of single-hidden-layer feedforward neural networks (SLFNs) with two sets of adjustable parameters, i.e., the nonlinear parameters in the hidden nodes and the linear output weights. The main objective is to both speed up the convergence of second-order learning algorithms such as Levenberg-Marquardt (LM), as well as to improve the network performance. This is achieved here by reducing the dimension of the solution space and by introducing a new Jacobian matrix. Unlike conventional supervised learning methods which optimize these two sets of parameters simultaneously, the linear output weights are first converted into dependent parameters, thereby removing the need for their explicit computation. Consequently, the neural network (NN) learning is performed over a solution space of reduced dimension. A new Jacobian matrix is then proposed for use with the popular second-order learning methods in order to achieve a more accurate approximation of the cost function. The efficacy of the proposed method is shown through an analysis of the computational complexity and by presenting simulation results from four different examples.

Learning soft computing control strategies in a modular neural network architecture

Modelling and control of nonlinear dynamical systems is a challenging problem since the dynamics of such systems change over their parameter space. Conventional methodologies for designing nonlinear control laws, such as gain scheduling, are effective because the designer partitions the overall complex control into a number of simpler sub-tasks. This paper describes a new genetic algorithm based method for the design of a modular neural network (MNN) control architecture that learns such partitions of an overall complex control task. Here a chromosome represents both the structure and parameters of an individual neural network in the MNN controller and a hierarchical fuzzy approach is used to select the chromosomes required to accomplish a given control task. This new strategy is applied to the end-point tracking of a single-link flexible manipulator modelled from experimental data. Results show that the MNN controller is simple to design and produces superior performance compared to a single neural network (SNN) controller which is theoretically capable of achieving the desired trajectory. (C) 2003 Elsevier Ltd. All rights reserved.

Do development and learning really decrease memory? On similarity and category-based induction in adults and children

In a recently published study, Sloutsky and Fisher [Sloutsky, V. M., & Fisher, A.V. (2004a). When development and learning decrease memory: Evidence against category-based induction in children. Psychological Science, 15, 553-558; Sloutsky, V. M., & Fisher, A. V. (2004b). Induction and categorization in young children: A similarity-based model. Journal of Experimental Psychology: General, 133, 166-188.] demonstrated that children have better memory for the items that they generalise to than do adults. On the basis of this finding, they claim that children and adults use different mechanisms for inductive generalisations;whereas adults focus on shared category membership, children project properties on the basis of perceptual similarity. Sloutsky & Fisher attribute children's enhanced recognition memory to the more detailed processing required by this similarity-based mechanism. In Experiment I we show that children look at the stimulus items for longer than adults. In Experiment 2 we demonstrate that although when given just 250 ms to inspect the items children remain capable of making accurate inferences, their subsequent memory for those items decreases significantly. These findings suggest that there are no necessary conclusions to be drawn from Sloutsky & Fisher's results about developmental differences in generalisation strategy. (C) 2007 Elsevier B.V. All rights reserved.

Accounting and business students' approaches to learning: A longitudinal study

This study reports the results of an investigation into the learning approaches of undergraduate accounting and business students from three universities in Ireland who were exposed to a learning environment, namely the case study method, which aimed to encourage a deep approach to learning. In order to assess the effectiveness of the intervention, the ASSIST research instrument was used to investigate changes in students’ approaches to learning over time. Consistent with previous research, this study considers whether gender is related to students’ approaches to learning over time and if there are differences between students majoring in accounting or business in their approaches to learning. The results of the study indicate that the case study intervention was successful in some respects. However, contrary to expectations, an overall statistically significant increase in students’ surface approach to learning was found. Additionally, it is reported that changes in students’ approaches to learning over time are not associated with gender or degree program. The results of this study offer insights to accounting educators developing educational interventions to encourage deep approaches to learning.

Chemosensory learning in the chicken embryo

Examined how exposure to a chemosensory stimulus prior to hatching affects subsequent chemosensory preferences of newly hatched chicks. The chicks' preferences were assessed at 2 days after hatching using an olfactory preference test (strawberry-smelling shavings vs water-coated shavings) and at 4 days after hatching using a gustatory preference test (strawberry-flavoured water vs unflavoured water). With no exposure to strawberry before hatching, strawberry was highly aversive to chicks after hatching. However, following exposure to strawberry before hatching, chicks expressed a greater preference for (or weaker aversion to) the strawberry stimulus. Chicks exposed to strawberry before hatching drank more strawberry-flavoured water and spent more time in a strawberry-scented area than chicks having no exposure before hatching. This change in preference was specific to the stimulus experienced before hatching and was present in the absence of any posthatching exposure to the stimulus. Results demonstrate that a chick's chemosensory preferences are changed as a result of experience with a stimulus before hatching and are suggestive of learning.

Transaction Costs and Organizational Learning in Strategic Oursourcing Relationships

The present paper examines the role of organisational learning and transaction costs economics in strategic outsourcing decisions. Interorganisational learning is critical to competitive success, and organisations often learn more effectively by collaborating with other organisations. However, learning processes may also complicate the process of forming interorganisational partnerships which may increase transaction costs. Based on the literature, the authors develop refutable implications for outsourcing supply chain logistics and a sample of 121 firms in the supply chain logistics industry is used to test the hypotheses. The results show that trust and transaction costs are significant and substantial drivers of strategic outsourcing of supply chain logistics (a strategic flexibility action). Learning intent and knowledge acquisition have no significant influence on the decision to outsource supply chain logistics. The paper concludes with a discussion of the different and often conflicting implications for managing interorganisational learning processes.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

Additivity Pretraining and cue competition effects: Developmental evidence for a reasoning account of causal learning

The effect of additivity pretraining on blocking has been taken as evidence for a reasoning account of human and animal causal learning. If inferential reasoning underpins this effect, then developmental differences in the magnitude of this effect in children would be expected. Experiment 1 examined cue competition effects in children's (4- to 5-year-olds and 6- to 7-year-olds) causal learning using a new paradigm analogous to the food allergy task used in studies of human adult causal learning. Blocking was stronger in the older than the younger children, and additivity pretraining only affected blocking in the older group. Unovershadowing was not affected by age or by pretraining. In experiment 2, levels of blocking were found to be correlated with the ability to answer questions that required children to reason about additivity. Our results support an inferential reasoning explanation of cue competition effects. (c) 2012 APA, all rights reserved.

A virtual surgery in general practice: evaluation of a novel undergraduate virtual patient learning package

Background: A suite of 10 online virtual patients developed using the IVIMEDS ‘Riverside’ authoring tool has been introduced into our undergraduate general practice clerkship. These cases provide a multimedia-rich experience to students. Their interactive nature promotes the development of clinical reasoning skills such as discriminating key clinical features, integrating information from a variety of sources and forming diagnoses and management plans.

Aims: To evaluate the usefulness and usability of a set of online virtual patients in an undergraduate general practice clerkship.
Method: Online questionnaire completed by students after their general practice placement incorporating the System Usability Scale questionnaire.

Results: There was a 57% response rate. Ninety-five per cent of students agreed that the online package was a useful learning tool and ranked virtual patients third out of six learning modalities. Questions and answers and the use of images and videos were all rated highly by students as useful learning methods. The package was perceived to have a high level of usability among respondents.

Conclusion: Feedback from students suggest that this implementation of virtual patients, set in primary care, is user friendly and rated as a valuable adjunct to their learning. The cost of production of such learning resources demands close attention to design.

A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models

The majority of reported learning methods for Takagi-Sugeno-Kang fuzzy neural models to date mainly focus on the improvement of their accuracy. However, one of the key design requirements in building an interpretable fuzzy model is that each obtained rule consequent must match well with the system local behaviour when all the rules are aggregated to produce the overall system output. This is one of the distinctive characteristics from black-box models such as neural networks. Therefore, how to find a desirable set of fuzzy partitions and, hence, to identify the corresponding consequent models which can be directly explained in terms of system behaviour presents a critical step in fuzzy neural modelling. In this paper, a new learning approach considering both nonlinear parameters in the rule premises and linear parameters in the rule consequents is proposed. Unlike the conventional two-stage optimization procedure widely practised in the field where the two sets of parameters are optimized separately, the consequent parameters are transformed into a dependent set on the premise parameters, thereby enabling the introduction of a new integrated gradient descent learning approach. A new Jacobian matrix is thus proposed and efficiently computed to achieve a more accurate approximation of the cost function by using the second-order Levenberg-Marquardt optimization method. Several other interpretability issues about the fuzzy neural model are also discussed and integrated into this new learning approach. Numerical examples are presented to illustrate the resultant structure of the fuzzy neural models and the effectiveness of the proposed new algorithm, and compared with the results from some well-known methods.

Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Primary motor cortex involvement in initial learning during visuomotor adaptation

Human motor behaviour is continually modified on the basis of errors between desired and actual movement outcomes. It is emerging that the role played by the primary motor cortex (M1) in this process is contingent upon a variety of factors, including the nature of the task being performed, and the stage of learning. Here we used repetitive TMS to test the hypothesis that M1 is intimately involved in the initial phase of sensorimotor adaptation. Inhibitory theta burst stimulation was applied to M1 prior to a task requiring modification of torques generated about the elbow/forearm complex in response to rotations of a visual feedback display. Participants were first exposed to a 30° clockwise (CW) rotation (Block A), then a 60° counterclockwise rotation (Block B), followed immediately by a second block of 30° CW rotation (A2). In the STIM condition, participants received 20s of continuous theta burst stimulation (cTBS) prior to the initial A Block. In the conventional (CON) condition, no stimulation was applied. The overt characteristics of performance in the two conditions were essentially equivalent with respect to the errors exhibited upon exposure to a new variant of the task. There were however, profound differences between the conditions in the latency of response preparation, and the excitability of corticospinal projections from M1, which accompanied phases of de-adaptation and re-adaptation (during Blocks B and A2). Upon subsequent exposure to the A rotation 24h later, the rate of re-adaptation was lower in the stimulation condition than that present in the conventional condition. These results support the assertion that primary motor cortex assumes a key role in a network that mediates adaptation to visuomotor perturbation, and emphasise that it is engaged functionally during the early phase of learning.

Predicting organic acid concentration from UV/vis spectrometry measurements - A comparison of machine learning techniques

The concentration of organic acids in anaerobic digesters is one of the most critical parameters for monitoring and advanced control of anaerobic digestion processes. Thus, a reliable online-measurement system is absolutely necessary. A novel approach to obtaining these measurements indirectly and online using UV/vis spectroscopic probes, in conjunction with powerful pattern recognition methods, is presented in this paper. An UV/vis spectroscopic probe from S::CAN is used in combination with a custom-built dilution system to monitor the absorption of fully fermented sludge at a spectrum from 200 to 750 nm. Advanced pattern recognition methods are then used to map the non-linear relationship between measured absorption spectra to laboratory measurements of organic acid concentrations. Linear discriminant analysis, generalized discriminant analysis (GerDA), support vector machines (SVM), relevance vector machines, random forest and neural networks are investigated for this purpose and their performance compared. To validate the approach, online measurements have been taken at a full-scale 1.3-MW industrial biogas plant. Results show that whereas some of the methods considered do not yield satisfactory results, accurate prediction of organic acid concentration ranges can be obtained with both GerDA and SVM-based classifiers, with classification rates in excess of 87% achieved on test data.