Digitalization enables public organizations to personalize their services, tuning them to the specific situation, abilities, and preferences of the citizens. At the same time, digital services can be experienced as being less personal than face-to-face contact by citizens. The large existing volume of academic literature on personalization mainly represents the service provider perspective. In contrast, in this paper we investigate what makes citizens experience a service as personal. The result are eight dimensions that capture the full range of individual experiences and expectations that citizens expressed in focus groups. These dimensions can serve as a framework for public sector organizations to explore the expectations of citizens of their own services and identify the areas in which they can improve the personal experiences they offer.
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The first year of study is very exciting for many students. Everything is new: the school, your schedule, the teachers, and your fellow students. How can a university ensure a smooth transition for first-year students? For this, Inholland launched the Students for Students (S4S) project in the 2019-2020 academic year. In this project, second-year students (studentcoaches) support first-year students with their studies. They do this based on their own experience and the training they receive during their year as studentcoaches. Research shows that peer-mentoring is very successful in aiding first-year students through their first year of the study program. Peer-mentoring has the potential to increase well-being, social bonding, the feeling of belonging, and student resilience. It also ensures smoother academic integration, as peer-mentoring focuses on developing academic skills as well. Additionally, a studentcoach is often a low threshold point of contact for students where they can go with questions.
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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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Obesity has become a major societal problem worldwide [1][2]. The main reason for severe overweight is excessive intake of energy, in relation to the individual needs of a human body. Obesity is associated with poor eating habits and/or a sedentary lifestyle. A significant part of the obese population (40%) belongs to a vulnerable target group of emotional eaters, who overeat due to negative emotions [3]. There is a need for self-management support and personalized coaching to enhance emotional eaters in recognising and self-regulating their emotions.Over the last years, coaching systems have been developed for behavior change support, healthy lifestyle, and physical activity support [4]-[9]. Existing virtual coach applications lack systematic evaluation of coaching strategies and usually function as (tele-)monitoring systems. They are limited to giving general feedback to the user on achieved goals and/or accomplished (online) assignments.Dialectical Behavior Therapy (DBT) focuses on getting more control over one’s ownemotions by reinforcing skills in mindfulness, emotion regulation, and stress tolerance [10]. Emotion regulation is about recognizing and acknowledging emotions and accepting the fact that they come and go. The behavior change strategies within DBT are based on validation and dialectics [11]. Dialectics changes the users’ attitude and behavior by creating incongruence between an attitude and behavior since stimuli or the given information contradict with each other.The ultimate goal of the virtual coach is to raise awareness of emotional eaters on their own emotions, and to enhance a positive change of attitude towards accepting the negative emotions they experience. This should result in a decrease of overeating and giving in to binges. We believe that the integration of the dialectical behavior change strategies and persuasive features from the Persuasive System Design Model by Kukkonen and Harjumaa [12] will enhance the personalization of the virtual coach for this vulnerable group. We aim at developing a personalized virtual coach ‘Denk je zèlf!’ (Dutch for ‘Develop a wise mind and counsel yourself’) providing support for self-regulation of emotions for young obese emotional eaters. This poster presents an eCoaching model and a research study protocol aiming at the validation of persuasive coaching strategies based on behavior change techniques using dialectical strategies. Based on the context (e.g., location), emotional state of the user, and natural language processing, the virtual coach application enables tailoring of the real-time feedback to the individual user. Virtual coach application communicates with the user over a chat timeline and provides personal feedback.The research protocol decribes the two weeks field study on validating persuasive coaching strategies for emotional eaters. Participants (N=30), recruited via a Dutch franchise organization of dietitian nutritionists, specialized in treating emotional eating behaviors, will voluntarily participate in this research study. Participants will be presented with short dialogues (existing questions and answers) and will be asked to select the preferred coaching strategy (validating or a dialectical), according to their (current) emotions. To trigger a certain emotion (e.g., the affect that fits best with the chosen coaching strategy), a set of pictures will be shown to the user that evoke respectively sadness, anger, fear, and disgust [13].Participants will be asked to fill out the demographics data ((nick) name, age, gender, weight, length, place of residence) and three questionnaires: • Dutch Eating Behavior Questionnaire (DEBQ) [14],• Five Factor Personality Inventory (FFPI) [15], • Quality of Life Index Questionnaire [16].This research study aims at answering the following research questions: “Which coaching strategies do users with a specific type of emotional eating behavior benefit most from while consulting their personalized virtual coach?; “Which coaching strategies are optimal for which emotions?” and “Which coaching approach do users prefer in which context, e.g. time of the day, before/after a craving?”
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In this article, the outcomes of a survey aimed to investigate how aware of and how capable coaches in higher vocational Dutch education perceive themselves to assist students displaying mental health and well-being issues are presented. Additionally, the article explores coaches’ perceptions regarding the frequency, form of help offered, topics to be tackled and the preferred form in which this help should be provided. The author conducted a survey that gathered qualitative and quantitative data from coaches (N 5 82) at a Dutch University of Applied Sciences in the north of the Netherlands. A differentiation in coaches’ number of years of teaching and coaching experience was considered.
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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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Present study focuses on revealing and developing personal constructs regarding problem behaviour in classrooms. The main idea is that teachers opinions about their students and themselves influence the way they interact with them. Their thoughts and ideas about students - their personal constructs - are generally unconscious. We used the Personal Construct Theory from Kelly (1955) and his Repertory Grid Technique for exploration mental constructs. They can give an impulse to the development of thinking and acting of teachers. We think it can help them to build up their professional identity towards problem children. Twenty-nine teachers formed the sample that worked with this method. We investigated the number of unique construct pairs mentioned by the teachers. This number happened to be remarkably high. While assessing pupils, the teachers use primarily personality characteristics. There is hardly any agreement between the teachers constructs, which complicates their communication about their pupils. We considered the number of construct pairs named by one participant. This number seems to depend on the type of education the teacher is involved in. The type of the school the teacher is working at also influences the average scores on the constructs. We shall also turn to the issue of pupils sex and its role if any in the teachers scores. No significant differences have been found.
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A considerable amount of literature on peer coaching suggests that the professional development of teachers can be improved through experimentation, observation, reflection, the exchange of professional ideas, and shared problem-solving. Reciprocal peer coaching provides teachers with an opportunity to engage in such activities in an integrated form. Even though empirical evidence shows effects of peer coaching and teacher satisfaction about coaching, the actual individual professional development processes have not been studied extensively. This article offers a way to analyze and categorize the learning processes of teachers who take part in a reciprocal peer coaching trajectory by using the Interconnected Model of Teacher Professional Growth as an analytical tool. Learning is understood as a change in the teacher's cognition and/or behavior. The assumption underlying the Interconnected Model of Teacher Professional Growth is that change occurs in four distinct domains that encompass the teacher's professional world: the personal domain, the domain of practice, the domain of consequence and the external domain. Change in one domain does not always lead to change in another, but when changes over domains do occur, different change patterns can be described. Repeated multiple data collection methods were used to obtain a rich description of patterns of change of four experienced secondary school teachers. The data sources were: audiotapes of coaching conferences, audiotapes of semi-structured learning interviews by telephone, and digital diaries with teacher reports of learning experiences. Qualitative analysis of the three data sources resulted in two different types of patterns: including the external domain and not including the external domain. Patterns of change within a context of reciprocal peer coaching do not necessarily have to include reciprocal peer coaching activities. When, however, patterns do include the external reciprocal peer coaching domain, this is often part of a change process in which reactive activities in the domains of practice and consequence are involved as well. These patterns often demonstrate more complex processes of change.
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Background: Around 13% of the world’s population suffers from obesity. More than 40% of people with obesity display emotional eating behaviour (eating in response to negative emotions or distress). It is an alternate to moreeffective coping strategies for negative emotions. Our study explored the opportunities for helping adults with emotional overeating using a virtual coach, aiming to identify preferences for tailored coaching strategies applicable in a personal virtual coach environment. Three different coaching strategies were tested: a validating, a focus-on-change, and a dialectical one – the latter being a synthesis of the first two strategies. Methods: A qualitative study used vignettes reflecting the two most relevant situations for people with emotional eating: 1. experiencing negative emotions, with ensuing food cravings; and 2. after losing control to emotional eating, with ensuing feelings of low self-esteem. Applied design: 2 situations × 3 coaching strategies. Participants: 71 adult women (Mage 44.4/years, range 19–70, SD = 12.86) with high scores on the DEBQ-emotional eating scale (Memo 3.65, range 1.69–4.92, SD = .69) with mean BMI 30.1 (range 18–46, SD = 6.53). They were recruited via dieticians’ practices, were randomly assigned to the conditions and asked how they would face and react to thepresented coaching strategies. Data were transcribed and a thematic analysis was conducted. Results: Qualitative results showed that participants valued both the validating coaching strategy and the focus-onchange strategy, but indicated that a combination of validation and focus-on-change provides both mental supportand practical advice. Data showed that participants differed in their level of awareness of the role that emotions play in their overeating and the need for emotion-regulation skills. Conclusion: The design of the virtual coach should be based on dialectical coaching strategies as preferred by participants with emotional eating behaviour. It should be tailored to the different stages of awareness of their emotions and individual emotion-regulation skills.
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Nowadays, one of the major current health risks is excessive sitting during work hours. Furthermore, the coronavirus disease 2019 (COVID-19) pandemic and the corresponding government state of emergency forced many people to work from home. These constraints carried out an important change in the lifestyle of people; for instance, the proportion of sitting time in front of a computer during working hours has increased considerably worldwide, particularly through the implementation of teleworking.In order to motivate people to lead a less sedentary life, the Hanze University of Applied Sciences Groningen developed an automated recommender system. We investigated the possibility of automated coaching in order to increase physical activity and help people to reach their daily step goal. By monitoring people’s activity level and progress during the day, we predict personalized recommendations. The effect of these recommendations on the individual’s activity level forms the basis for a personalized coaching approach.Step count data is used to train a machine learning algorithm that estimates the hourly probability of the individual achieving the daily steps goal. The outcome of this prediction is combined with the effect of the type recommendation for the individual to deliver the best recommendation for the individual. To show the practical usefulness, we constructed a platform to manage the data, rules, machine learning algorithms and clustering of participants. Results of initial pilots using the platform and app have given insight in the performance of and challenges associated with algorithm selection and personal model generation for the coaching package caused by the nature of the data. Further research will therefore be done in optimizing machine learning algorithms and tuning for human datasets.
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