Zoekresultaten

A First Step Towards Learning Analytics

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."

Learning communities of practice in care

Lifelong learning is necessary for nurses and caregivers to provide good, person-centred care. To facilitate such learning and embed it into regular working processes, learning communities of practice are considered promising. However, there is little insight into how learning networks contribute to learning exactly and what factors of success can be found. The study is part of a ZonMw-funded research project ‘LeerSaam Noord’ in the Netherlands, which aims to strengthen the professionalization of the nursing workforce and promote person-centred care. We describe what learning in learning communities looks like in four different healthcare contexts during the start-up phase of the research project. A thematic analysis of eleven patient case-discussions in these learning communities took place. In addition, quantitative measurements on learning climate, reciprocity behavior, and perceptions of professional attitude and autonomy, were used to underpin findings. Reflective questioning and discussing professional dilemma's i.e. patient cases in which conflicting interests between the patient and the professional emerge, are of importance for successful learning.

Factors influencing intergenerational learning

The workforce in the EU is ageing, and this requires investment in older workers so that the organisations in which they work remain competitive and viable. One such investment takes the form of organising and facilitating intergenerational learning: learning between and among generations that can lead to lifelong learning, innovation and organisational development. However, successfully implementing intergenerational learning is complex and depends on various factors at different levels within the organisation. This multidisciplinary literature review encompasses work from the fields of cognitive psychology, occupational health, educational science, human resource development and organisational science and results in a framework that organisations can use to understand how they can create the conditions needed to ensure that the potential of their ageing workforce is tapped effectively and efficiently. Although not a comprehensive review, this chapter serves as a basis for further empirical research and gives practitioners an insight into solving a growing problem.

Career learning

This chapter explores qualitative career assessment as an identity learning process where meaning-oriented learning is essential and distinguished from conditioned or semantic types of learning. In order to construct a career identity in the form of a future-oriented narrative, it is essential that learners are helped through cognitive learning stages with the help of a dialogue about concrete experiences which aims to pay attention to emotions and broadens and deepens what is expressed.

Public Private Learning Communities

Tools for practice and theory: how to monitor collective learning in schools.

Primary schools are challenged to continuously improve their teachers' teaching and their students' learning. Through an iterative process, we have developed a method to stimulate teachers to collectively collect and analyze data, derive consequences from their analyses, take actions and evaluate outcomes. Such processes are referred to as collective learning (Verbiest, 2002; Dixon 2002) through collective action research. In order to study the impact of our method in practice, fifteen pilot-schools have applied this method of collective action research in their school-practice. Every pilot-school was followed intensively, both regarding processes as well as outcomes, leading to 15 case-description. Our research questions are: 1) what kind of framework is helpful to monitor collective learning in primary schools? and 2) which critical aspects are involved in the process of collective learning? There are three important aspects underlying our definition of collective learning: influence, vitality and evaluating. With 'influence' we refer to the way Lundy (2005) uses this: whose voice is heard and who has influence on the process? The second aspect is vitality, which refers to the way people interact with each other. Castelijns, Koster & Vermeulen (2009) describe vitality in relation to collective processes as a balanced situation in which individual- as well as collective needs are fulfilled. The third important aspect in collective learning is the way the process is finished: do the participants evaluate their process of working and learning together in order to learn from the process itself (double-loop learning as defined by Argyris & Schön, 1978)? This theoretical background is the frame for the analysis of our cases. Fifteen (qualitative) case-descriptions are the input for our analysis. We have developed an analytical framework, based on the three underlying principles for collective learning as described above. This framework consists of a set of descriptions (rubrics) which define each principle. Every case is read carefully and scores are adjusted for each principle leading to a quantitative measurement of collective learning. In addition to the quantitative scoring of case-descriptions, also a qualitative analysis is carried out by labelling text-fragments. The qualitative analysis helps to interpret the quantitative findings. By analysing our quantitative and qualitative data, we hope to be able to explore processes of collective learning. At this moment half of the fifteen case descriptions are ready for analysis, the other cases are being written. Our first impressions in applying the analytical framework are that the framework offers a useful tool to monitor and interpret a case. We think the framework itself might not only be used for research purposes, but could also serve as an instrument for schools to monitor their processes. Our first analysis shows that the framework is useful for analysing the process of collective learning in detail and that the framework is applicable to all kind of different cases.