ObjectiveRepeated practice, or spacing, can improve various types of skill acquisition. Similarly, virtual reality (VR) simulators have demonstrated their effectiveness in fostering surgical skill acquisition and provide a promising, realistic environment for spaced training. To explore how spacing impacts VR simulator-based acquisition of surgical psychomotor skills, we performed a systematic literature review.MethodsWe systematically searched the databases PubMed, PsycINFO, Psychology and Behavioral Sciences Collection, ERIC and CINAHL for studies investigating the influence of spacing on the effectiveness of VR simulator training focused on psychomotor skill acquisition in healthcare professionals. We assessed the quality of all included studies using the Medical Education Research Study Quality Instrument (MERSQI) and the risk of bias using the Cochrane Collaboration’s risk of bias assessment tool. We extracted and aggregated qualitative data regarding spacing interval, psychomotor task performance and several other performance metrics.ResultsThe searches yielded 1662 unique publications. After screening the titles and abstracts, 53 publications were retained for full text screening and 7 met the inclusion criteria. Spaced training resulted in better performance scores and faster skill acquisition when compared to control groups with a single day (massed) training session. Spacing across consecutive days seemed more effective than shorter or longer spacing intervals. However, the included studies were too heterogeneous in terms of spacing interval, obtained performance metrics and psychomotor skills analysed to allow for a meta-analysis to substantiate our outcomes.ConclusionSpacing in VR simulator-based surgical training improved skill acquisition when compared to massed training. The overall number and quality of available studies were only moderate, limiting the validity and generalizability of our findings.
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BACKGROUND: Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem.OBJECTIVE: This study examines whether sensor-augmented toys can be used to assess children's fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction.METHODS: Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called "Futuro Cube." The game "roadrunner" focused on speed while the game "maze" focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05.RESULTS: The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046).CONCLUSIONS: The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.
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Jos Sanders (HAN), Melissa Selzener (Hanze) en Harm van Lieshout (Hanze) beschouwen de transformatie van een diplomagericht naar een skillsgericht ecosysteem van onderwijs en arbeidsmarkt. Ze zien binnen deze transformatie, indachtig het werk van nobelprijswinnares Elinor Ostrom (1990), ‘skills’ in onze samenleving als een zogenaamde ‘common’. Een common is een collectief goed dat zorgzaam wordt beheerd door een gemeenschap op basis van duidelijke afspraken en regels, gefundeerd in een duidelijk normen- en waardenpatroon. Ze zien het skillsgerichte ecosysteem van onderwijs en arbeidsmarkt als een ‘system of commons’ en gebruiken Ostrom’s acht principes voor ‘governing the commons’ (1990; 2000) om tot een realistisch toekomstperspectief te komen voor de verdere ontwikkeling van een succesvol skillsgericht ecosysteem van onderwijs en arbeidsmarkt. Zij roepen de overheid op om een veel actievere, aanjagende en coördinerende rol te pakken in deze transformatie: organiseer het skillsgerichte ecosysteem en zorg voor een goed geëquipeerde hoeder (‘marktmeester’) van dat ecosysteem.
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