This study provides an illustration of a research design complementary to randomized controlled trial to evaluate program effects, namely, participatory peer research (PPR). The PPR described in current study was carried out in a small sample (N = 10) of young adults with mild intellectual disabilities (MID) and severe behavioral problems. During the PPR intervention, control and feedback to individuals is restored by training them to become participant-researchers, who collaborate in a small group of people with MID. Their research is aimed at the problems the young adults perceive and/or specific subjects of their interest. The study was designed as a multiple case study with an experimental and comparison group. Questionnaires and a semistructured interview were administered before and after the PPR project. Results of Reliable Change Index (RCI) analyses showed a decrease in self-serving cognitive distortions in the PPR group, but not in the comparison group. These results indicate that PPR helps to compensate for a lack of adequate feedback and control, and in turn may decrease distorted thinking and thereby possibly later challenging behavior.
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.
In het TEPE-jaarboek Teacher Education Policy in Europe : a Voice of Higher Education Institutions , beschrijven Marco Snoek, Ursula Uzerli en Michael Schratz hoe op Europees niveau een proces van peer learning tussen lidstaten impulsen probeert te geven aan de nationale beleidsvorming rond lerarenkwaliteit en lerarenopleidingen. In hun artikel beschrijven ze het peer learning proces van het Cluster Teachers & Trainers van de Europese Commissie en reflecteren ze op uitkomsten en belemmeringen van dit peer learning proces dat deel uitmaakt van de Open Coördination Method die de Europese Commisie gebruikt om impulsen te geven op het terrein van onderwijsbeleid.
Within the food industry there is a need to be able to rapidly react to changing regulatory requirements and consumer preferences by adjusting recipes, processes, and products. A good knowledge of the properties of food ingredients is crucial in this process. Currently this knowledge is available in scattered heterogeneous resources such as scientific peer-reviewed articles, databases, recipes, food blogs as well as in the experience of food-experts. This prevents, in practice, the efficient integration and use of this knowledge, leading to inefficiency and missed opportunities. In this project we will build a structured database of properties of food ingredients, focusing in particular on the taste and texture properties. By large-scale collection and text mining on a large number of textual resources, a comprehensive data set on ingredient properties will be created, along with knowledge on the relationships between these ingredients. This database will then be used for to find new potential applications for healthy and taste enhancing ingredient combinations by network-based discovery methods and artificial intelligence algorithms will be used. A concrete focus will be on application questions formulated by the industrial partners. The resulting hypothesis will be validated in a real life setting at the premises of the industrial partners. The deliverables of this project will be: - A reusable open-access ingredient database that is accessible via a user-friendly web portal - A set of state-of-the-art mining algorithms that can address a wide variety of industry driven use cases - Novel product formulations that can be further developed for the consumer and business2business market
