Piloting Multimodal Learning Analytics using Mobile Mixed Reality in Health Education

© 2019 IEEE. Mobile mixed reality has been shown to increase higher achievement and lower cognitive load within spatial disciplines. However, traditional methods of assessment restrict examiners ability to holistically assess spatial understanding. Multimodal learning analytics seeks to investigate how combinations of data types such as spatial data and traditional assessment can be combined to better understand both the learner and learning environment. This paper explores the pedagogical possibilities of a smartphone enabled mixed reality multimodal learning analytics case study for health education, focused on learning the anatomy of the heart. The context for this study is the first loop of a design based research study exploring the acquisition and retention of knowledge by piloting the proposed system with practicing health experts. Outcomes from the pilot study showed engagement and enthusiasm of the method among the experts, but also demonstrated problems to overcome in the pedagogical method before deployment with learners

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals

SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget

In the context of industrial engineering, it is important to integrate efficient computational optimization methods in the product development process. Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients, highly non-linear objectives and a limited function evaluation budget. Although a huge variety of different optimization algorithms is available, the development and selection of efficient algorithms for problems with these industrial relevant characteristics, remains a challenge. In this communication, a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics. The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations. The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions. The operations used in the proposed mutation scheme, are computationally inexpensive, and can be easily implemented in existing differential evolution variants or other population-based optimization algorithms by a few lines of program code as an non-invasive optional setting. Besides the applicability of the presented algorithm by itself, the described concepts can serve as a useful and interesting addition to the algorithmic operators in the frameworks of heuristics and evolutionary optimization and computing

Train-the-trainers in hand hygiene : a standardized approach to guide education in infection prevention and control

Background Harmonization in hand hygiene training for infection prevention and control (IPC) professionals is lacking. We describe a standardized approach to training, using a “Train-the-Trainers” (TTT) concept for IPC professionals and assess its impact on hand hygiene knowledge in six countries.Methods We developed a three-day simulation-based TTT course based on the World Health Organization (WHO) Multimodal Hand Hygiene Improvement Strategy. To evaluate its impact, we have performed a pre-and post-course knowledge questionnaire. The Wilcoxon signed-rank test was used to compare the results before and after training.Results Between June 2016 and January 2018 we conducted seven TTT courses in six countries: Iran, Malaysia, Mexico, South Africa, Spain and Thailand. A total of 305 IPC professionals completed the programme. Participants included nurses (n = 196; 64.2%), physicians (n = 53; 17.3%) and other health professionals (n = 56; 18.3%). In total, participants from more than 20 countries were trained. A significant (p < 0.05) improvement in knowledge between the pre- and post-TTT training phases was observed in all countries. Puebla (Mexico) had the highest improvement (22.3%; p < 0.001), followed by Malaysia (21.2%; p < 0.001), Jalisco (Mexico; 20.2%; p < 0.001), Thailand (18.8%; p < 0.001), South Africa (18.3%; p < 0.001), Iran (17.5%; p < 0.001) and Spain (9.7%; p = 0.047). Spain had the highest overall test scores, while Thailand had the lowest pre- and post-scores. Positive aspects reported included: unique learning environment, sharing experiences, hands-on practices on a secure environment and networking among IPC professionals. Sustainability was assessed through follow-up evaluations conducted in three original TTT course sites in Mexico (Jalisco and Puebla) and in Spain: improvement was sustained in the last follow-up phase when assessed 5 months, 1 year and 2 years after the first TTT course, respectively.Conclusions The TTT in hand hygiene model proved to be effective in enhancing participant’s knowledge, sharing experiences and networking. IPC professionals can use this reference training method worldwide to further disseminate knowledge to other health care workers.peer-reviewe