A stochastic growth model applied to repeated tests of academic knowledge

In the course of the medical program at the University of Limburg, students complete a total of 24 progress tests, consisting of items drawn from a constant itembank. A model is presented for the growth of knowledge reflected by these results. The Rasch model is used as a starting point, but both ability and difficulty parameters are taken to be random, and moreover the logistic distribution is replaced by the normal. Both individual and group abilities are estimated and explained through simple linear regression. Application to real data shows that the model fits very well.     

The effect of guiding questions on students’ performance and attitude towards statistics

Background. In this study, the effect of guidance on students’ performance was investigated. This effect was hypothesized to be manifested through a reduction of cognitive load and enhancement of self‐explanations. Aim. The goal of this study was to investigate the effect of guiding questions on students’ understanding of statistics. Sample and Method. In an experimental setting, two randomly selected groups of students (N= 49) answered achievement and transfer questions on statistics as a measure of performance. Students in the intervention condition were given guiding questions to direct their way of reasoning before they answered the achievement questions. The students in the control condition were asked to write down their way of thinking before they answered the same achievement questions. In this way, both groups were stimulated to self‐explain, but only the reasoning processes of the students in the intervention condition were guided. Results and Conclusion. It was found that students in the intervention condition performed significantly better on achievement and transfer questions and that this effect of guidance was mediated by self‐explanations. Attitude towards statistics was positively related to performance.     

The effect of directive tutor guidance on students' conceptual understanding of statistics in problem‐based learning

Background. Education is aimed at students reaching conceptual understanding of the subject matter, because this leads to better performance and application of knowledge. Conceptual understanding depends on coherent and error‐free knowledge structures. The construction of such knowledge structures can only be accomplished through active learning and when new knowledge can be integrated into prior knowledge. Aims. The intervention in this study was directed at both the activation of students as well as the integration of knowledge. Sample. Undergraduate university students from an introductory statistics course, in an authentic problem‐based learning (PBL) environment, were randomly assigned to conditions and measurement time points. Method. In the PBL tutorial meetings, half of the tutors guided the discussions of the students in a traditional way. The other half guided the discussions more actively by asking directive and activating questions. To gauge conceptual understanding, the students answered open‐ended questions asking them to explain and relate important statistical concepts. Results and conclusions. Results of the quantitative analysis show that providing directive tutor guidance improved understanding. Qualitative data of students' misconceptions seem to support this finding. Long‐term retention of the subject matter seemed to be inadequate.