How to trigger emergence and self-organisation in Learning Networks

The original publication is available at www.springerlink.com. Brouns, F., Fetter, S., & Van Rosmalen, P. (2009). How to trigger emergence and self-organisation in Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp. 57-72). Berlin, Germany: Springer Verlag.In the current chapter, we describe an example of a peer support Learning Network Service based on the mechanism of peer tutoring in ad-hoc transient communities.The work on this publication has been sponsored in part by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Global Optimization strategies for two-mode clustering

Two-mode clustering is a relatively new form of clustering that clusters both rows and columns of a data matrix. To do so, a criterion similar to k-means is optimized. However, it is still unclear which optimization method should be used to perform two-mode clustering, as various methods may lead to non-global optima. This paper reviews and compares several optimization methods for two-mode clustering. Several known algorithms are discussed and a new, fuzzy algorithm is introduced. The meta-heuristics Multistart, Simulated Annealing, and Tabu Search are used in combination with these algorithms. The new, fuzzy algorithm is based on the fuzzy c-means algorithm of Bezdek (1981) and the Fuzzy Steps approach to avoid local minima of Heiser and Groenen (1997) and Groenen and Jajuga (2001). The performance of all methods is compared in a large simulation study. It is found that using a Multistart meta-heuristic in combination with a two-mode k-means algorithm or the fuzzy algorithm often gives the best results. Finally, an empirical data set is used to give a practical example of two-mode clustering.algorithms;fuzzy clustering;multistart;simulated annealing;simulation;tabu search;two-mode clustering

Development and User Experiences of a Biopsychosocial Interprofessional Online Course on Persistent Somatic Symptoms

Background: Communication between healthcare providers and patients with persistent somatic symptoms (PSS) is frequently hampered by mutual misunderstanding and dissatisfaction. Methods: We developed an online, interprofessional course to teach healthcare providers the knowledge, skills, and attitude they need to diagnose and treat PSS in a patient-centered manner based on the biopsychosocial model. The course consisted of six modules of 45–60 min. Each module contained different types of assignments, based on six cases: videos, discussion boards, reading assignments, polls, and quizzes. For this study, we included (1) medical residents, following the course as part of their residency training, and (2) healthcare providers (general practitioners, medical specialists, physiotherapists, nurses, and psychologists), following the course as continuing vocational training. Throughout the course, participants were asked to fill out online surveys, enquiring about their learning gains and satisfaction with the course. Results: The biopsychosocial approach was integrated across the modules and teached health care workers about recent insights on biological, psychological and social aspects of PSS. In total, 801 participants with a wide variety in clinical experience started the course; the largest groups of professionals were general practitioners (N = 400), physiotherapists (N = 124) and mental healthcare workers (N = 53). At the start of the course, 22% of the participants rated their level of knowledge on PSS as adequate. At the end of the course, 359 participants completed the evaluation questionnaires. Of this group, 81% rated their level of knowledge on PSS as adequate and 86% felt that following the course increased their competencies in communicating with patients with PSS (N = 359). On a scale from 1 to 10, participants gave the course a mean grade of 7.8 points. Accordingly, 85% stated that they would recommend the course to a colleague. Conclusion: Our course developed in a co-design process involving multiple stakeholders can be implemented, is being used, and is positively evaluated by professionals across a variety of health care settings

Observing, Coaching and Reflecting: A Multi-modal Natural Language-based Dialogue System in a Learning Context

The Metalogue project aims to develop a multi-modal, multi-party dialogue system with metacognitive abilities that will advance our understanding of natural conversational human-machine interaction and dialogue interfaces. This paper introduces the vision for the system and discusses its application in the context of debate skills training where it has the potential to provide learners with a rich, immersive experience. In particular, it considers a potentially powerful learning analytics tool in the form of a performance reflection dashboard

Toward Automatic Interpretation of Narrative Feedback in Competency-Based Portfolios

Self-directed learning is generally considered a key competence in higher education. To enable self-directed learning, assessment practices increasingly embrace assessment for learning rather than the assessment of learning, shifting the focus from grades and scores to provision of rich, narrative, and personalized feedback. Students are expected to collect, interpret, and give meaning to this feedback, in order to self-assess their progress and to formulate new, appropriate learning goals and strategies. However, interpretation of aggregated, longitudinal narrative feedback has been proven to be very challenging, cognitively demanding, and time consuming. In this article, we, therefore, explored the applicability of existing, proven text mining techniques to support feedback interpretation. More specifically, we investigated whether it is possible to automatically generate meaningful information about prevailing topics and the emotional load of feedback provided in medical students' competence-based portfolios (N = 1500), taking into account the competence framework and the students' various performance levels. Our findings indicate that the text-mining techniques topic modeling and sentiment analysis make it feasible to automatically unveil the two principal aspects of narrative feedback, namely the most relevant topics in the feedback and their sentiment. This article, therefore, takes a valuable first step toward the automatic, online support of students, who are tasked with meaningful interpretation of complex narrative data in their portfolio as they develop into self-directed life-long learners

Physician assistants in intensive care units in the Netherlands:a narrative review with recommendations

This review is an overview of the current status of the advanced practice provider (APP) working in critical care. After describing the history of the profession, the paper focuses on the literature available. Although a lot of literature is available, the papers are often heterogeneous and comparison with other clinicians remains difficult. The paper zooms in on the situation in the Netherlands and describes the training courses for the physician assistant (PA), the equivalent of the APP, together with the legislation in place. Furthermore, the review elaborates on the potential superimposed value of the PA for the ICU. Because of the limited amount of studies performed in the Dutch situation this review finishes with the conclusions of 15-year-experience and the possible issues which could arise when implementing a PA on the ICU