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Presentation 6F1. Modelling Students' Learning of Introductory Statistics

Dirk Tempelaar (the Netherlands) D.Tempelaar@KE.UNIMAAS.NL


Presentation Abstract

In this project we followed about 1000 students Economics, International Economics and International Business in their freshman year at the University of Maastricht (Netherlands). Those students attend three compulsory courses Quantitative Methods, each having an important component of statistics (descriptive statistics and probability theory, inferential statistics and the regression model, respectively). Our population of students exhibits a strong heterogeneity with respect to several aspects: attitude towards and prior knowledge of mathematics and statistics (like in most universities), but also with regard to nationality (a majority of Dutch students, but a large minority of about 300 German students and a small minority of students from all over the world), type of prior education and the mastery of languages. And as a result of several changes in both the secondary and tertiary educational system in the Netherlands, the heterogeneity of the inflow of our faculty will further increase next years. To study the impact of this heterogeneity on learning introductory statistics, the development of a model of students' learning of introductory statistics was chosen as the goal of the project. In order to develop a relational model (see e.g. Prosser & Trigwell, 1999), several surveys were taken or data sources used with regard to students characteristics, learning context, students' perceptions and students' approaches. Most surveys are based on self-assessment; some are scored by tutors (the dominant educational system of our faculty is that of problem-based learning, with tutorial groups of 12-14 students, coached by staff in the role of tutor). The data collected contains:

- Personal data of students (nationality, sex, language)
- Prior education
- Assessment of the mastery of prior knowledge
- Survey of Attitudes Towards Statistics (SATS) (Schau ea.)
- Statistical Reasoning Assessment (SRA) (Garfield ea.), assessing the presence of both statistical misconceptions as correct conceptions
- Inventory Learning Styles (ILS) (Vermunt), assessing the four domains of processing strategies (scales: surface approach, deep approach and elaborative approach), regulation strategies (scales: external regulation, self-regulation and lack of regulation), learning orientations (scales: certificate directed, self-test directed, vocation directed, personally interested, ambivalent), and mental models or conceptions of learning, education and cooperation (scales: intake of knowledge, construction of knowledge, use of knowledge, stimulating education, cooperation)
- Personality Inventory (50 item version of Big Five)
- Student's interaction in tutorial groups (assessed by tutors)
- Assessment of group dynamics (by tutor)
- Academic and social self-concept (both as self-assessment and scored by tutor)
- Students' effort in learning (hours study per week, both for statistics as for other courses)
- Students' effort in learning statistics (measured by quiz results and obtained bonus for home work)
- Returns of learning measured in terms of the grades on the Knowledge Tests (that assess the mastery of statistics at the more basic levels of understanding)
- Returns of learning measured in terms of the grades on the OverAll Tests (that assess the mastery of statistics at the more advanced levels of understanding).


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