Just what and how eight experienced teachers in four coaching dyads learned during a 1-year reciprocal peer coaching trajectory was examined in the present study. The learning processes were mapped by providing a detailed description of reported learning activities, reported learning outcomes, and the relations between these two. The sequences of learning activities associated with a particular type of learning outcome were next selected, coded, and analyzed using a variety of quantitative methods. The different activity sequences undertaken by the teachers during a reciprocal peer coaching trajectory were found to trigger different aspects of their professional development.
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Just what and how eight experienced teachers in four coaching dyads learned during a 1-year reciprocal peer coaching trajectory was examined in the present study. The learning processes were mapped by providing a detailed description of reported learning activities, reported learning outcomes, and the relations between these two. The sequences of learning activities associated with a particular type of learning outcome were next selected, coded, and analyzed using a variety of quantitative methods. The different activity sequences undertaken by the teachers during a reciprocal peer coaching trajectory were found to trigger different aspects of their professional development.
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Dit paper is het eindproduct van leerarrangement 1 (Zin in Leren) van de HBO masteropleiding Leren en Innoveren. Het is een literatuurstudie naar blended learning en hoe blended learning kan bijdragen aan een beter leerresultaat van de student.
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Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.
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Het plan van aanpak gepresenteerd in deze handreiking is bedoeld als leidraad voor het ontwerpen, ontwikkelen, implementeren en evalueren van verschillende Learning Communities binnen het RAAK-5 project Het Nieuwe Telen: gas erop! Het is bedoeld om zowel inzichten als instrumenten te bieden aan coördinatoren en facilitatoren voor de implementatie van de lokale Learning Communities gedurende het project. Deze handreiking is een noodzakelijke aanvulling op het project vanwege de prominente rol van Learning Communities binnen het project, maar ook omdat er geen wetenschappelijk gebaseerde ontwerpprincipes voor LC’s te vinden zijn. Er zijn veel projecten die Learning Communities uitvoeren, maar een grondige zoektocht naar literatuur en internetbronnen resulteerde niet in ontwerpprincipes.
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One of the main causes of numerous health problemsis a lack of physical activity. To promote a more active lifestyle,the Hanze University started a health promotion program. Participants were motivated to reach their daily goal of physical activityby means of an activity tracker in combination with two-weeklycoaching sessions. Employing the data of the experiment, weinvestigated the manners in which the predictability of physicalactivity of a participant during the day can be improved. Thecollected step count data was used to construct personalisedmachine learning models, by taking into account the differencebetween physical activities during weekdays on the one handand weekends on the other hand. The training of algorithmsper participant in combination with the time-slices weekdays,weekend and the whole week improves the accuracy of theprediction model. The performance of the models improveseven further when the individualised time-sliced models arecombined. More contextual data, like free time and workinghours, might even extend the accuracy. The use of personalisedprediction models, based on machine learning and time slices,could become an addition in preventive personalized eHealthsystems and mobile activity monitoring. For instance, this canconstitute as a viable addition to a virtual coaching system to helpthe participants to reach their daily goal. As the individualisedmodels allow for predictions of the progression of the physicalactivity during the day, they enable the virtual coaching systemto intervene at the appropriate moment in time.
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Background: Physical activity is an important intervention for improving disease-related symptoms and systemic manifestations in rheumatic and musculoskeletal disease (RMDs). However, studies suggest that RMD patients report that the lack of individualized and consistent information about physical activity from managing doctors and healthcare professionals, acts as a barrier for engagement. On the other hand, managing doctors and healthcare professionals report lack of knowledge in this area and thus lack of confidence to educate and advise RMD patients about the beneficial effects of physical activity. The aim of the present study therefore, is to develop two e-Learning courses for RMD doctors and health professionals: a) the first one to provide consistent information about the collective benefits of physical activity in RMDs and b) the second on how to implement physical activity advice in routine clinical practice. Methods: An international collaboration of seven countries, consisting of one academic institution and one patient organization from each country, will co-develop the two e-Learning courses. The final e-Learning courses will primarily target to improve – through physical activity advice – RMD symptoms which are important for patients. Discussion: The main result of this study will be to co-develop two e-Learning courses that can be used by managing RMD doctors and healthcare professionals to be made aware of the overall benefits of physical activity in RMDs as well as how to implement physical activity advise within their practice.
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To adequately deal with the challenges faced within residential care for older people, such as the increasing complexity of care and a call for more person-centred practices, it is important that health care providers learn from their work. This study investigates both the nature of learning, among staff and students working within care for older people, and how workplace learning can be promoted and researched. During a longitudinal study within a nursing home, participatory and democratic research methods were used to collaborate with stakeholders to improve the quality of care and to promote learning in the workplace. The rich descriptions of these processes show that workplace learning is a complex phenomenon. It arises continuously in reciprocal relationship with all those present through which both individuals and environment change and co-evolve enabling enlargement of the space for possible action. This complexity perspective on learning refines and expands conventional beliefs about workplace learning and has implications for advancing and researching learning. It explains that research on workplace learning is itself a form of learning that is aimed at promoting and accelerating learning. Such research requires dialogic and creative methods. This study illustrates that workplace learning has the potential to develop new shared values and ways of working, but that such processes and outcomes are difficult to control. It offers inspiration for educators, supervisors, managers and researchers as to promoting conditions that embrace complexity and provides insight into the role and position of self in such processes.
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This paper reports on the first stage of a research project1) that aims to incorporate objective measures of physical activity into health and lifestyle surveys. Physical activity is typically measured with questionnaires that are known to have measurement issues, and specifically, overestimate the amount of physical activity of the population. In a lab setting, 40 participants wore four different sensors on five different body parts, while performing various activities (sitting, standing, stepping with two intensities, bicycling with two intensities, walking stairs and jumping). During the first four activities, energy expenditure was measured by monitoring heart rate and the gas volume of in‐ and expired O2 and CO2. Participants subsequently wore two sensor systems (the ActivPAL on the thigh and the UKK on the waist) for a week. They also kept a diary keeping track of their physical activities, work and travel hours. Machine learning algorithms were trained with different methods to determine which sensor and which method was best able to differentiate the various activities and the intensity with which they were performed. It was found that the ActivPAL had the highest overall accuracy, possibly because the data generated on the upper tigh seems to be best distinguishing between different types of activities and therefore led to the highest accuracy. Accuracy could be slightly increased by including measures of heartrate. For recognizing intensity, three different measures were compared: allocation of MET values to activities (used by ActivPAL), median absolute deviation, and heart rate. It turns out that each method has merits and disadvantages, but median absolute deviation seems to be the most promishing metric. The search for the best method of gauging intensity is still ongoing. Subsequently, the algorithms developed for the lab data were used to determine physical activity in the week people wore the devices during their everyday activities. It quickly turned out that the models are far from ready to be used on free living data. Two approaches are suggested to remedy this: additional research with meticulously labelled free living data, e.g., by combining a Time Use Survey with accelerometer measurements. The second is to focus on better determining intensity of movement, e.g., with the help of unsupervised pattern recognition techniques. Accuracy was but one of the requirements for choosing a sensor system for subsequent research and ultimate implementation of sensor measurement in health surveys. Sensor position on the body, wearability, costs, usability, flexibility of analysis, response, and adherence to protocol equally determine the choice for a sensor. Also from these additional points of view, the activPAL is our sensor of choice.
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From the article: "The educational domain is momentarily witnessing the emergence of learning analytics – a form of data analytics within educational institutes. Implementation of learning analytics tools, however, is not a trivial process. This research-in-progress focuses on the experimental implementation of a learning analytics tool in the virtual learning environment and educational processes of a case organization – a major Dutch university of applied sciences. The experiment is performed in two phases: the first phase led to insights in the dynamics associated with implementing such tool in a practical setting. The second – yet to be conducted – phase will provide insights in the use of pedagogical interventions based on learning analytics. In the first phase, several technical issues emerged, as well as the need to include more data (sources) in order to get a more complete picture of actual learning behavior. Moreover, self-selection bias is identified as a potential threat to future learning analytics endeavors when data collection and analysis requires learners to opt in."
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