Research

  • Collaboration is an important feature of today's teacher education. According to several researchers, teachers are open to work collaboratively, although they often work in an individual and solitary manner. Since the nineties statistics education includes the need for children to be able to pose questions, collect, organize and represent data in a spirit of investigation and exploration. This is enhanced by implementing student projects. The main research question in this paper is to clarify how three pre-service math teachers planned and prepared classes and reflected afterwards upon them. A qualitative methodology was adopted. It is shown with empirical evidence that collaborative work between teachers is an important approach to solve difficulties in statistical and didactical knowledge. Our results also show that these are the areas teachers have to improve in order to become more confident teachers of Statistics.

  • Previous misconceptions about science may cause difficulties in the interpretation of scientific models. A Likert scale test was made and presented to part of the population in order to find out beliefs about science and technology that students who wanted to have a degree in engineering at Universidad Nacional de La Matanza had. Principal components analysis was performed to identify the testees' profile. We show the results referring to the beliefs and conceptions about probability, margin for error, accuracy, certainty, truth and validity. Although most of the people who answered the survey acknowledged the presence of probability in the results of a physical experiment, they also gave it accuracy and truth values which are not inherent. It is also remarkable that only a very low percentage has a posture that is coherent with the scientific vision of the terms.

  • The following three probabilities seem crucial when interpreting data, especially in the behavioral sciences:1) the probability that an effect is present in the population, 2) the probability that a replication is significant; and 3) the probability that the effect for a single individual in the population is in the expected direction. In our study, we asked 51 subjects (university students and lecturers in psychology) to estimate these probabilities after reading a short description of a hypothetical experiment with as outcomes only p-value and sample size given. Large variations in estimated probabilities were found. However estimates of the probabilities tended to increase as a positive function of sample size for a fixed p-value. Simulation studies show that , assuming a uniform prior distribution for the parameter, this turns out to be incorrect for all three probabilities.

  • This study concentrates on the analysis of responses to a questionnaire given to a sample of University Students in Portugal that concerns the teaching/learning of Statistics and Data Analysis. We first focus on the effectiveness of teaching Quantitative Methods at secondary level as regards increasing performance in the Introductory Statistical Course (ISC) at University level. The second question is related to the students' feelings towards Mathematics and whether these feelings imply a difference in students' performance on statistics. Even when results cannot be generalised, since the study is limited to our context, the data analysed suggest the need to rethink the goals of teaching statistics at secondary school level, at least in our context.

  • In this chapter, we use preparation for future learning assessments to work backwards to identify the types of prior knowledge that prepare students to learn. We highlight how to develop a specific form of prior knowledge that many current models of learning and instruction do not address very well.

  • This paper describes some preliminary results from a classroom teaching experiment at the college level, in which students were guided through activities to reveal and develop their notions of variability. Starting with their intuitions about variability, students were asked to speculate about the distributions of different variables, focusing on the informal ideas of whether they expected the variables to have larger or smaller variability. The class activities then guided students from their intuitions about variability as meaning "lots of different values" and overall range, to a dual understanding of variability as both range and clustering in the middle. A final goal of the teaching experiment was to help students make the transition from their initial ideas of variability to the ideas that variability has many dimensions, that formal measures (e.g., range, interquartile range, and standard deviation) all measure different aspects of variability, and that these measures are more useful or appropriate (e.g., to use in comparing groups) depending on the characteristics of the data. The use of assessment items to evaluate student learning will also be described.

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