The use of an e-learning constructivist solution in workplace learning

We wished to investigate whether an e-learning approach which uses constructivist principles can be successfully applied to train employees in a highly specialised skill thought to require expert individuals and extensive prolonged training. The approach involved the development of an e-learning package which included simulations and interactivity, then experimental testing in a case study workplace environment with the collection of both quantitative and qualitative data to assess the effectiveness of the package. Our study shows that this e-learning strategy improved the skills of the inexperienced operator significantly. We therefore propose that such programmes could be used as a work based training aid and used as a model system for the training of employees in complex skilled tasks in the workplace. This research demonstrates that the e-learning can be applied outside the traditional learning environment to train unskilled employees to undertake complex practical tasks which traditionally would involve prohibitively expensive instruction. This work also illustrates that simulations and interactivity are powerful tools in the design of successful e-learning packages in preparing learners for real world practical situations. Finally this study shows that workplace learners can be better served by elearning environments rather than conventional training as they allow asynchronous learning and private study which are valued by employees who have other demands on their time and are more comfortable receiving tuition privately Relevance to industry: E-learning using constructivist principles, and incorporating simulations and interactivity can be used successfully in the training of highly specialised and skilled tasks required in the modern workplace

Multi-label classification using ensembles of pruned sets

This paper presents a Pruned Sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By pruning these sets, PS focuses only on the most important correlations, which reduces complexity and improves accuracy. By combining pruned sets in an ensemble scheme (EPS), new label sets can be formed to adapt to irregular or complex data. The results from experimental evaluation on a variety of multi-label datasets show that [E]PS can achieve better performance and train much faster than other multi-label methods

Multi-label classification using ensembles of pruned sets

This paper presents a Pruned Sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By pruning these sets, PS focuses only on the most important correlations, which reduces complexity and improves accuracy. By combining pruned sets in an ensemble scheme (EPS), new label sets can be formed to adapt to irregular or complex data. The results from experimental evaluation on a variety of multi-label datasets show that [E]PS can achieve better performance and train much faster than other multi-label methods

Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model

Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation. ,is paper proposes a nonlinear spring-mass-damper model of train frontal crash, which achieves high accuracy and maintains low complexity. ,e Convolutional Long-short-term-memory Generation Adversarial Network (CLGAN) model is applied to study the nonlinear parameters dynamic variation of the key components of a rail vehicle (e.g., the head car, anticlimbing energy absorber, and the coupler buffer devices). Firstly, the nonlinear lumped model of train frontal crash is built, and then the physical parameters are deduced in twenty different cases using D’Alembert’s principle. Secondly, the input/output relationship of the CLGAN model is determined, where the inputs are the nonlinear physical parameters in twenty initial conditions, and the output is the nonlinear relationship between the train crash nonlinear parameters under other initial cases. Finally, the train crash dynamic characteristics are accurately estimated during the train crash processes through the training of the CLGAN model, and then the crash processes under different given conditions can be described effectively. ,e estimation results exhibit good agreement with finite element (FE) simulations and experimental results. Furthermore, the CLGAN model shows great potential in nonlinear estimation, and CLGAN can better describe the variation of nonlinear spring damping compared with the traditional model. ,e nonlinear spring-mass-damper modeling is involved in improving the speed and accuracy of the train crash estimation, as well as being able to offer guidance for structure optimization in the early design stage

Dominance and G×E interaction effects improvegenomic prediction and genetic gain inintermediate wheatgrass (Thinopyrumintermedium)

Genomic selection (GS) based recurrent selection methods were developed to accelerate the domestication of intermediate wheatgrass [IWG, Thinopyrum intermedium (Host) Barkworth & D.R. Dewey]. A subset of the breeding population phenotyped at multiple environments is used to train GS models and then predict trait values of the breeding population. In this study, we implemented several GS models that investigated the use of additive and dominance effects and G×E interaction effects to understand how they affected trait predictions in intermediate wheatgrass. We evaluated 451 genotypes from the University of Minnesota IWG breeding program for nine agronomic and domestication traits at two Minnesota locations during 2017–2018. Genet-mean based heritabilities for these traits ranged from 0.34 to 0.77. Using fourfold cross validation, we observed the highest predictive abilities (correlation of 0.67) in models that considered G×E effects. When G×E effects were fitted in GS models, trait predictions improved by 18%, 15%, 20%, and 23% for yield, spike weight, spike length, and free threshing, respectively. Genomic selection models with dominance effects showed only modest increases of up to 3% and were trait-dependent. Crossenvironment predictions were better for high heritability traits such as spike length, shatter resistance, free threshing, grain weight, and seed length than traits with low heritability and large environmental variance such as spike weight, grain yield, and seed width. Our results confirm that GS can accelerate IWG domestication by increasing genetic gain per breeding cycle and assist in selection of genotypes with promise of better performance in diverse environments

An e-learning supported Train-the-Trainer program to implement a suicide practice guideline. Rationale, content and dissemination in Dutch mental health care

AbstractAn e-learning supported Train-the-Trainer program was developed to implement the Dutch suicide practice guideline in mental health care. Literature on implementation strategies has been restricted to the final reporting of studies with little opportunity to describe relevant contextual, developmental and supporting work that would allow for a better interpretation of results and enhance the likelihood of successful replication of interventions. Therefore, in this paper we describe the theoretical and empirical background, the material and practical starting points of the program. We monitored the number of professionals that were trained during and after a cluster randomized trial in which the effects of the program have been examined.Each element of the intervention (Train-the-Trainer element, one day face-to-face training, e-learning) is described in detail. During the trial, 518 professionals were trained by 37 trainers. After the trial over 5000 professionals and 180 gatekeepers were trained. The e-learning module for trainees is currently being implemented among 30 mental health care institutions in The Netherlands.These results suggest that an e-learning supported Train-the-Trainer program is an efficient way to uptake new interventions by professionals. The face-to-face training was easily replicable so it was easy to adhere to the training protocol. E-learning made the distribution of the training material more viable, although the distribution was limited by problems with ICT facilities. Overall, the intervention was well received by both trainers and trainees. By thoroughly describing the material and by offering all training materials online, we aim at further dissemination of the program