In het herstellen en behouden van zinvolle bezigheden voor mensen met een lichte of matige vorm van van de ziekte van Alzheimer is doelstelling van groot praktisch belang. De studies gericht op dit doel hebben vertrouwd op de verschillende strategieën van zelfmanagement van instruction cues. Zeven studies werden gevonden die plaats hadden in de periode 2008-2012 (dat wil zeggen, de periode waarin onderzoek op dit gebied daadwerkelijk vorm heeft gekregen). Die strategieën bestaan uit het gebruik van (1) verbale signalen aangeboden via audiorecorders, (2) visuele signalen aangeboden via computersystemen, en (3) een combinatie van verbale en visuele signalen gepresenteerd via computersystemen. Dit artikel geeft een overzicht van de hiervoor genoemde strategieën en bespreekt de resultaten daarvan, hun algemene doeltreffendheid, op prestaties en stemmingen, en hun geschiktheid en bruikbaarheid. Thema's voor toekomstig onderzoek werden eveneens onderzocht. ABSTRACT Helping people with mild or moderate Alzheimer's disease restore and maintain constructive occupations is an objective of great practical importance. Studies targeting this goal have relied on different strategies for self-management of instruction cues. Seven studies were identified in the period 2008- 2012 (i.e. the period in which research in this area has actually taken shape). These strategies consist of the use of (1) verbal cues presented via audio recording devices, (2) pictorial cues presented via computer-aided systems and (3) combinations of verbal and pictorial cues presented via computer-aided systems. This paper reviews these strategies and discusses their outcomes, their overall effectiveness on performance and mood, and their suitability and practicality. Issues for future research are also examined.
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In most primary science classes, students are taught science skills by way of learning by doing. Research shows that explicit instruction may be more effective. The aim of this study is to investigate the effects of explicit instruction in an inquiry-based learning setting on the acquisition of science skills for students in primary education. Participants included 705 Dutch 5th and 6th graders. Students were randomly assigned to either an explicit instruction condition including an 8-week intervention of explicit instruction on inquiry skills; an implicit condition in which students were taught by learning by doing; or a baseline condition in which students followed their regular science curriculum. To assess the effects, measurement instruments for evaluating the acquisition of science skills were developed. Results of a multi-level analysis indicated that explicit instruction facilitates development of science skills. Therefore, this study provides a strong argument for including an explicit teaching method for developing science skills in primary science education.
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Supplemental Instruction (SI) is a form of structured peer guidance attached to a specific course, provided by an experienced and trained student to a group of students. Previous studies show a positive effect of SI on learning outcomes, some found effects on well-being, and sense of belonging. However, literature on SI lacks randomized controlled trials and does not fully address the risk of self-selection bias. The current study tested whether SI has an effect on grades, mental well-being, and sense of belonging with a pre-registered randomized field experiment and a sample of 493 Dutch first-year students. Students who were offered SI obtained significantly higher grades (d = 0.26) but did not score significantly different on mental well-being or belonging.
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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.
