Background: Given the demands posed by excessive practice quantities in modern dance, physical and mental health can be compromised. Therefore, there is a need to consider how quality of practice may be improved and possibly even reduce training times. Sports literature has shown that instructions and feedback given by coaches can have an effect on the quality of training and influence self-regulation and the performance of athletes. However, currently little is known about the use of instructions and feedback by dance teachers. The aim of the current study was, therefore, to examine the type of instructions and feedback given by dance teachers during various dance classes. Methods: A total of six dance teachers participated in this study. Video and audio recordings were made of six dance classes and two rehearsals at a contemporary dance university. The dance teacher’s coaching behavior was analyzed using the modified Coach Analysis and Intervention System (CAIS). Additionally, feedback and instructions were also examined in terms of their corresponding focus of attention. Absolute numbers, as well as times per minute (TPM) rates were calculated for each behavior before, during, and after an exercise. Absolute numbers were also used to calculate ratios of positive-negative feedback and open-closed questions. Results: Most feedback comments were given after an exercise (472 out of 986 total observed behaviors). Improvisation had the highest positive-negative feedback ratio (29) and open-closed questions ratio (1.56). Out of the focus of attention comments, internal focus of attention comments were used most frequently (572 out of 900). Discussion/conclusion: The results make clear that there is a large variability in instructions and feedback over teachers and classes. Overall, there is room for improvement toward a higher positive-negative feedback ratio, a higher open-closed question ratio and producing more comments eliciting an external focus of attention.
In light of increasing calls for transparent reporting of research and prevention of detrimental research practices, we conducted a cross-sectional machine-assisted analysis of a representative sample of scientific journals' instructions to authors (ItAs) across all disciplines. We investigated addressing of 19 topics related to transparency in reporting and research integrity. Only three topics were addressed in more than one third of ItAs: conflicts of interest, plagiarism, and the type of peer review the journal employs. Health and Life Sciences journals, journals published by medium or large publishers, and journals registered in the Directory of Open Access Journals (DOAJ) were more likely to address many of the analysed topics, while Arts & Humanities journals were least likely to do so. Despite the recent calls for transparency and integrity in research, our analysis shows that most scientific journals need to update their ItAs to align them with practices which prevent detrimental research practices and ensure transparent reporting of research.
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AIMS: The Canadian Occupational Performance Measure (COPM) can be used to support children to clarify their needs themselves. However, for pediatric occupational therapists it is not sufficiently clear how to effectively use the COPM with children from 8 years of age.This study aimed to formulate specific instructions for using the COPM with children themselves, based on the experience of children, parents, and occupational therapists. In addition, professional consensus on the instructions was reached.METHODS: A multi-stage approach was used to develop the instructions. Triangulation of methods was used to gather knowledge of how the COPM with children themselves is performed in daily practice: interviews with 23 children, questionnaires completed by 30 parents, interviews with 13 therapists, and 10 video recordings of COPM administration. Specific instructions were derived from this knowledge and consensus for these instructions was reached by Delphi method.RESULTS: The data were analyzed and resulted in 40 specific instructions. Consensus of at least 80% amongst 10 occupational therapists, who regularly use the COPM with children, was achieved on each instruction.CONCLUSION: There is consensus on 40 specific instructions for administering the COPM with children. Following these instructions might help children to formulate their own goals for intervention.
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In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
