Assessing university students' prior knowledge

The aim of this dissertation was to explore how different types of prior knowledge influence student achievement and how different assessment methods influence the observed effect of prior knowledge. The project started by creating a model of prior knowledge which was tested in various science disciplines. Study I explored the contribution of different components of prior knowledge on student achievement in two different mathematics courses. The results showed that the procedural knowledge components which require higher-order cognitive skills predicted the final grades best and were also highly related to previous study success. The same pattern regarding the influence of prior knowledge was also seen in Study III which was a longitudinal study of the accumulation of prior knowledge in the context of pharmacy. The study analysed how prior knowledge from previous courses was related to student achievement in the target course. The results implied that students who possessed higher-level prior knowledge, that is, procedural knowledge, from previous courses also obtained higher grades in the more advanced target course. Study IV explored the impact of different types of prior knowledge on students’ readiness to drop out from the course, on the pace of completing the course and on the final grade. The study was conducted in the context of chemistry. The results revealed again that students who performed well in the procedural prior-knowledge tasks were also likely to complete the course in pre-scheduled time and get higher final grades. On the other hand, students whose performance was weak in the procedural prior-knowledge tasks were more likely to drop out or take a longer time to complete the course. Study II explored the issue of prior knowledge from another perspective. Study II aimed to analyse the interrelations between academic self-beliefs, prior knowledge and student achievement in the context of mathematics. The results revealed that prior knowledge was more predictive of student achievement than were other variables included in the study. Self-beliefs were also strongly related to student achievement, but the predictive power of prior knowledge overruled the influence of self-beliefs when they were included in the same model. There was also a strong correlation between academic self-beliefs and prior-knowledge performance. The results of all the four studies were consistent with each other indicating that the model of prior knowledge may be used as a potential tool for prior knowledge assessment. It is useful to make a distinction between different types of prior knowledge in assessment since the type of prior knowledge students possess appears to make a difference. The results implied that there indeed is variation between students’ prior knowledge and academic self-beliefs which influences student achievement. This should be taken into account in instruction.

Bayesian fisheries stock assessment: integrating and updating knowledge

In this thesis the use of the Bayesian approach to statistical inference in fisheries stock assessment is studied. The work was conducted in collaboration of the Finnish Game and Fisheries Research Institute by using the problem of monitoring and prediction of the juvenile salmon population in the River Tornionjoki as an example application. The River Tornionjoki is the largest salmon river flowing into the Baltic Sea. This thesis tackles the issues of model formulation and model checking as well as computational problems related to Bayesian modelling in the context of fisheries stock assessment. Each article of the thesis provides a novel method either for extracting information from data obtained via a particular type of sampling system or for integrating the information about the fish stock from multiple sources in terms of a population dynamics model. Mark-recapture and removal sampling schemes and a random catch sampling method are covered for the estimation of the population size. In addition, a method for estimating the stock composition of a salmon catch based on DNA samples is also presented. For most of the articles, Markov chain Monte Carlo (MCMC) simulation has been used as a tool to approximate the posterior distribution. Problems arising from the sampling method are also briefly discussed and potential solutions for these problems are proposed. Special emphasis in the discussion is given to the philosophical foundation of the Bayesian approach in the context of fisheries stock assessment. It is argued that the role of subjective prior knowledge needed in practically all parts of a Bayesian model should be recognized and consequently fully utilised in the process of model formulation.