Enhancing Student Understanding in Statistical Inference - Assessing the Effectiveness of a Computer Interaction (original) (raw)
Related papers
Developing a computer interaction to enhance student understanding in statistical inference
2006
Prior investigation of student experiences with a computer interaction indicated that the simulation was only partly successful in facilitating developmental learning of statistical inference. The simulation was re examined in the light of subsequent multimedia design research and cognitive theory. A new simulation was developed with less extraneous information and reduced on screen text. In addition the new simulation incorporated audio narration and a higher degree of student control in progressing through signalled stages of development.
The Impact of Interactive Visual Simulations on Learning Statistics
In previous studies non-interactive visual simulations in learning tasks have improved learners' conceptual understanding of statistical principles. To explore the impact of interactive visual simulations on conceptual understanding of statistical principles, an online tutorial where students could either manipulate or only observe changes of statistical graphs was developed. Overall, the tutorial supports students in learning statistical concepts immediately after working with the tutorial and two weeks after. In addition, if students could manipulate the graphs on their own, they were faster. Implications and opportunities for further investigations of interactive simulations are discussed.
… of the Fifth International Conference on …, 1998
We shall describe episodes of middle school students working on Exploratory Data Analysis (EDA) developed within an innovative curriculum. We outline the program and its rationale, analyze the design of the tasks, present extracts from students' activities and speculate about their learning processes. Finally, from our observations, we propose a new construct --learning arena, which is suggested as a curriculum design principle, which may also facilitate research.
The Role of Dynamic Interactive Technology in Teaching and Learning Statistics
2011
Dynamic interactive technology brings new opportunities for helping students learn central statistical concepts. Research and classroom experience can be help identify concepts with which students struggle, and an "action-consequence" pre-made technology document can engage students in exploring these concepts. With the right questions, students can begin to make connections among their background in mathematics, foundational ideas that undergrid statistics and the relationship these ideas. The ultimate goal is to have students think deeply about simple and basic statistical ideas so they can see how they lead to reasoning and sense making about data and about making decisions about characteristics of a population from a sample.Technology has a critical role in teaching and learning statistics, enabling students to use real data in investigations, to model complex situations based on data, to visualize relationships using different representations, to move beyond calculations to interpreting statistical processes such as confidence intervals and correlation, and to generate simulations to investigate a variety of problems including laying a foundation for inference. Thus, graphing calculators, spreadsheets, and interactive dynamic software can all be thought of as tools for statistical sense making in the service of developing understanding. NEW OPPORTUNITIES Dynamic interactive technology has the potential to extend this tool to help students understand central statistical concepts. The ability to link representations, where changes in one representation are reflected in the others, enables students to take an action, immediately see the consequences and reflect on the meaning of these consequences to make sense of the statistics-an action-consequence principle (Dick & Burrill, 2006). To maximize this potential and allow students to explore statistical concepts in deeper ways, it is possible to impose constraints on what they can do, in essence creating action-consequence "microworlds" in which students can play with a statistical concept in a variety of ways but where the opportunity to go astray, both mathematically and operationally, is limited. An action-consequence document is similar to an applet (e.g
Simulation as a Tool to Develop Statistical Understanding
2002
Statistical reasoning is often presented through a variety of statistical "tests"-usually leaving many students bewildered. A foundation for understanding what statistical reasoning is and how it works can help students understand how to make sensible decisions from data before they move to formal techniques. Simulations, made possible by technology such as graphing calculators or computer software, can provide students with a conceptual basis for inference. By generating sampling distributions, students can analyze the behavior of a given statistic, explore whether a given observation is likely, investigate the effect of changing sample size, and consider how distributions differ. Such experiences give students a sense of how to reason from data and help to explain what is behind some of the formal tools of inference. Examples from the world outside of the classroom illustrate how simulation can be a tool in making sensible decisions giving students opportunities to see why statistics is important.
2014
We report preliminary results from an ongoing study of the development of tertiary students’ reasoning related to statistical inference and uncertainty during a one-semester modeling and simulation-based statistics course. Comparisons of students’ performance on assessments of statistical reasoning will be presented for both students enrolled in this course and those enrolled in courses that used conventional parametric methods of inference. Summaries of qualitative data from nine students who participated in problem-solving interviews will also be presented to illustrate the development of students' reasoning related to statistical inference and uncertainty. Analyses of the data indicate that students taking the modeling and simulation-based course demonstrate better understanding of the principles of study design and statistical inference and begin developing these understandings within the first few weeks of the course.
The purpose of this study was to (1) examine the effects of a storyline on learners’ factual, conceptual and application knowledge with the use of a simulation for teaching introductory statistical skills and to (2) explore students’ subjective enjoyment of various learning activities often used in statistics education. In order to conduct the study, two versions of a simulation were developed that differed in the presence or absence of a storyline attribute. Sixty-four graduate students were randomly assigned to one of the two intervention conditions. Both intervention groups demonstrated significantly higher learning gains after interacting with the simulation. Particularly, both simulation-based interventions had a positive significant effect on the acquisition of application knowledge and skills. However, no significant differences between the intervention groups on any learning outcome explored in the study were found. Results also showed that students rated the simulation used in the study as a more enjoyable learning activity in comparison to reading a textbook, lecture or teamwork. Students from the simulation without a storyline intervention reported higher enjoyment than the other intervention group. Implications of the findings for understanding the instructional benefits and shortcomings of embedding a storyline in digital learning content are discussed.
2006
This paper reports a comparison of two separate studies using the same task and simulation software but with different age groups and abilities of students who have had different curricula experiences. One study examined how middle school students used computer simulation tools to reason between empirical data and theoretical probability. The second study replicated the first with secondary school students who had just completed an Advanced Placement statistics course. This comparison includes the similarities and the differences in the way each group approached the task and used the simulation software, given their background and prior knowledge.
Student Performance in Curricula Centered on Simulation-Based Inference: A Preliminary Report
Journal of Statistics Education
Simulation-based inference" (e.g., bootstrapping and randomization tests) has been advocated recently with the goal of improving student understanding of statistical inference, as well as the statistical investigative process as a whole. Preliminary assessment data have been largely positive. This article describes the analysis of the first year of data from a multi-institution assessment effort by instructors using such an approach in a college-level introductory statistics course, some for the first time. We examine several pre-/post-measures of student attitudes and conceptual understanding of several topics in the introductory course. We highlight some patterns in the data, focusing on student level and instructor level variables and the application of hierarchical modeling to these data. One observation of interest is that the newer instructors see very similar gains to more experienced instructors, but we also look to how the data collection and analysis can be improved for future years, especially the need for more data on "nonusers."
Learning statistical inference through computer-supported simulation and data analysis
2008
iv This dissertation explored the effects of two different interventions on the learning of statistics. Each intervention corresponded to a different conception of statistical learning and used a particular type of computer-tool. One intervention used data analysis tools and focused on authentic situations of statistical activity. The other intervention used simulations and focused on formal aspects of probability. Data Analysis (data) and Probability (chance) are the constituent parts of statistical inference and the two lens from which is possible to present this topic. In this study, both perspectives were compared in their effectiveness to teach ANOVA, a central topic in inferential statistics. The results of this study showed that the intervention that used simulations improved students' knowledge about probability, sampling and sample size effects.