Investigating ‘Churnalism’ in real time news

Language Use in Past and Presen

Support of the collaborative inquiry learning process: influence of support on task and team regulation

Regulation of the learning process is an important condition for efficient and effective learning. In collaborative learning, students have to regulate their collaborative activities (team regulation) next to the regulation of their own learning process focused on the task at hand (task regulation). In this study, we investigate how support of collaborative inquiry learning can influence the use of regulative activities of students. Furthermore, we explore the possible relations between task regulation, team regulation and learning results. This study involves tenth-grade students who worked in pairs in a collaborative inquiry learning environment that was based on a computer simulation, Collisions, developed in the program SimQuest. Students of the same team worked on two different computers and communicated through chat. Chat logs of students from three different conditions are compared. Students in the first condition did not receive any support at all (Control condition). In the second condition, students received an instruction in effective communication, the RIDE rules (RIDE condition). In the third condition, students were, in addition to receiving the RIDE rules instruction, supported by the Collaborative Hypothesis Tool (CHT), which helped the students with formulating hypotheses together (CHT condition). The results show that students overall used more team regulation than task regulation. In the RIDE condition and the CHT condition, students regulated their team activities most often. Moreover, in the CHT condition the regulation of team activities was positively related to the learning results. We can conclude that different measures of support can enhance the use of team regulative activities, which in turn can lead to better learning results

The power-series algorithm for Markovian queueing networks

A newversion of the Power-Series Algorithm is developed to compute the steady-state distribution of a rich class of Markovian queueing networks. The arrival process is a Multi-queue Markovian Arrival Process, which is a multi-queue generalization of the BMAP. It includes Poisson, fork and round-robin arrivals. At each queue the service process is a Markovian Service Process, which includes sequences of phase-type distributions, setup times and multi-server queues. The routing is Markovian. The resulting queueing network model is extremely general, which makes the Power-Series Algorithm a useful tool to study load-balancing, capacity-assignment and sequencing problems.Queueing Network;operations research

Aluminium sheet forming simulations: influence of the yield surface

The accuracy of simulations of the plastic deformation of sheet metal depend to a large extend on\ud the description of the yield surface, the hardening and the friction. In this paper simulations of deep drawing of\ud an AlMg alloy with a shell model are presented. The yield surface is described by a Von Mises, a Hill ’48 and a\ud Vegter yield function. The parameters for the model are based on biaxial experiments. It is concluded that the\ud shape of the yield locus has a minor influence on the prediction of the punch force–displacement diagram and a\ud large influence on the prediction of the thickness strains. The Vegter model performs much better than the Hill\ud ’48 model, based on the same R-values

A comparison of methods for converting DCE values onto the full health-dead QALY scale

Cardinal preference elicitation techniques such as time trade-off (TTO) and Standard Gamble (SG) receive criticism for their complexity and difficulties in using them in more vulnerable populations. Ordinal techniques such as discrete choice experiment (DCE) and Best Worst Scaling (BWS) are easier, but values generated by them are not anchored onto the full health-dead 1-0 QALY scale required for use in economic evaluation. This paper explores new methods for converting modelled DCE latent values onto the full health-dead QALY scale: (1) anchoring assuming worst state is equal to being dead; (2) anchoring DCE values using dead as valued in the DCE; (3) anchoring DCE values using TTO value for worst state; (4) mapping DCE values onto TTO; (5) combining DCE and TTO data in a hybrid model. We use postal DCE data (n=263) and TTO data (n=307) collected by interview in a general population valuation study of an asthma condition-specific measure (AQL-5D). Methods (4) and (5) using mapping and hybrid models perform best; the anchor-based methods perform relatively poorly. These new methods have a useful role for producing values on the QALY scale from ordinal techniques such as DCE and BWS for use in cost utility analyses