Expert Status and Performance

Expert judgements are essential when time and resources are stretched or we face novel dilemmas requiring fast solutions. Good advice can save lives and large sums of money. Typically, experts are defined by their qualifications, track record and experience [1], [2]. The social expectation hypothesis argues that more highly regarded and more experienced experts will give better advice. We asked experts to predict how they will perform, and how their peers will perform, on sets of questions. The results indicate that the way experts regard each other is consistent, but unfortunately, ranks are a poor guide to actual performance. Expert advice will be more accurate if technical decisions routinely use broadly-defined expert groups, structured question protocols and feedback

Gliding saves time but not energy in Malayan colugos.

In this paper we consider the problem of maximizing information propagation in social networks. To solve it, we introduce a probabilistic maximum coverage problem, and further purpose a cluster-based heuristic and a neighborhood-removal heuristic for two basic diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model, respectively. Our proposed strategies are compared with the pure greedy algorithm and centrality-based schemes via experiments on large collaboration networks. We find that our proposed algorithms perform better than centrality-based schemes and achieve approximately the same performance as the greedy algorithm. Moreover, the computational load is significantly reduced compared with the greedy heuristic. © 2011 IEEE.published_or_final_versionProceedings of the IEEE Global Telecommunications Conference (GLOBECOM 2011), Houston, TX, USA, 5-9 December 201

Cooperative Learning Effects On Teamwork Attitudes In Clinical Laboratory Science Students

OBJECTIVE: To evaluate clinical laboratory science (CLS) student attitudes toward teamwork when using cooperative learning (CL) as compared to individual learning (IL) in a course and to determine if learning method affects student attitudes toward the course itself. DESIGN/SETTING/PARTICIPANTS: This was a multi-institutional study involving eight classrooms in seven states. The effects of CI and IL on student attitudes were compared for 216 student participants. INTERVENTION: One group of students learned the course material through a CI approach while a second group of students learned via a traditional IL approach. For each course, the instructor, class material, and examination content was identical for the CI and IL students; the only variable was learning method. MAIN OUTCOME MEASURE: Student attitudes toward teamwork and toward the course were evaluated with a 35-item Attitude Questionnaire administered as a post-test. Mean scores for the CI and IL groups were compared using the Student t-test for independent samples. RESULTS: No significant difference was seen between the CI and IL students when assessing the first 30 questions on student attitudes toward teamwork (means = 98.42 and 98.22, respectively) when all institutions were combined. Comparable results were seen for each of the eight institutions. For the five questions comparing attitudes toward the course itself, there usually was no significant difference in attitude between CI and IL students. The only classrooms where CI students had more positive attitudes were those with instructors who had more than 10 years experience with CL. CONCLUSION: Results suggest that CI produces similar student attitudes toward teamwork and toward a CLS course as does IL