scholarly journals Identifying Addressable Impediments to Student Learning in an Introductory Statistics Course

2012 ◽  
Vol 12 (3) ◽  
pp. 124-139 ◽  
Author(s):  
Scott P. Stevens ◽  
Susan W. Palocsay
Keyword(s):  
Student Learning ◽  
Introductory Statistics ◽  
Statistics Course

scholarly journals RETENTION OF STATISTICAL CONCEPTS IN A PRELIMINARY RANDOMIZATION-BASED INTRODUCTORY STATISTICS CURRICULUM

2012 ◽  
Vol 11 (1) ◽  
pp. 21-40 ◽  
Author(s):  
NATHAN TINTLE ◽  
KYLIE TOPLIFF ◽  
JILL VANDERSTOEP ◽  
VICKI-LYNN HOLMES ◽  
TODD SWANSON
Keyword(s):  
Education Research ◽  
Student Learning ◽  
Conceptual Understanding ◽  
Statistics Education ◽  
Introductory Statistics ◽  
Research Journal ◽  
Statistics Course ◽  
Additional Support ◽  
Improve Student Learning ◽  
Statistics Curriculum

Previous research suggests that a randomization-based introductory statistics course may improve student learning compared to the consensus curriculum. However, it is unclear whether these gains are retained by students post-course. We compared the conceptual understanding of a cohort of students who took a randomization-based curriculum (n = 76) to a cohort of students who used the consensus curriculum (n = 79). Overall, students taking the randomization-based curriculum showed higher conceptual retention in areas emphasized in the curriculum, with no significant decrease in conceptual retention in other areas. This study provides additional support for the use of randomization-methods in teaching introductory statistics courses. First published May 2012 at Statistics Education Research Journal: Archives

Comparing Two Models of Collaborative Testing for Teaching Statistics

2019 ◽  
Vol 47 (1) ◽  
pp. 68-73
Author(s):  
June A. Eastridge ◽  
Wendi L. Benson
Keyword(s):  
Student Learning ◽  
Student Attitudes ◽  
Thinking Skills ◽  
Critical Thinking Skills ◽  
Introductory Statistics ◽  
Control Group ◽  
Collaborative Testing ◽  
Statistics Course ◽  
Quasi Experimental ◽  
The Impact

Research on collaborative testing has shown that it decreases test anxiety, increases learning and critical thinking skills, and allows students to practice collaboration and teamwork. However, it has most often been used as a second test following traditional individual testing. This quasi-experimental study compared two models of collaborative testing in an introductory statistics course. Students who experienced collaborative testing as a pretest ( n = 35) and as a posttest ( n = 33) were compared with a control group ( n = 61) that experienced traditional individual testing only. Grade book data and a survey administered at the end of the semester provided information on student attitudes and perceptions toward learning statistics and use of collaborative testing, and the impact of each model on student learning. The same instructor taught all students and administered the same tests. The authors found that group-first testing was favored by students and provided the greatest benefit for reducing anxiety while still supporting student learning.

Statistical literacy and the news media

2017 ◽  
Author(s):  
Andrew Gelman ◽  
Deborah Nolan
Keyword(s):  
News Media ◽  
News Article ◽  
Introductory Statistics ◽  
News Story ◽  
Statistical Literacy ◽  
Process Information ◽  
Statistics Course ◽  
Reported Study ◽  
Original Report

An important theme in an introductory statistics course is the connection between statistics and the outside world. Described in this chapter are assignments that can be useful in getting students to learn how to gather and process information presented in the news and scientific reports. These assignments seem to work well only when students have direction about how to do this kind of research. Three versions of the assignment are provided. In all three, students read a news story and the original report on which the article was based, and they complete a worksheet with guidelines for summarizing the reported study. In some versions students are supplied the news story and report and in another each student finds a news article and tracks down the original report on her own. Included here are our guidelines, example instructional packets, and the process we use to organize each type of assignment.

INTRODUCTORY STATISTICS COURSE TERTIARY STUDENTS’ UNDERSTANDING OF P-VALUES

2014 ◽  
Vol 13 (1) ◽  
pp. 53-65 ◽  
Author(s):  
ROBYN REABURN
Keyword(s):  
Null Hypothesis ◽  
Probabilistic Reasoning ◽  
Statistics Education ◽  
Introductory Statistics ◽  
P Values ◽  
Testing Procedures ◽  
Research Journal ◽  
Null Hypothesis Testing ◽  
Statistics Course ◽  
Teaching Programs

This study aimed to gain knowledge of students’ beliefs and difficulties in understanding p-values, and to use this knowledge to develop improved teaching programs. This study took place over four consecutive teaching semesters of a one-semester tertiary statistics unit. The study was cyclical, in that the results of each semester were used to inform the instructional design for the following semester. Over the semesters, the following instructional techniques were introduced: computer simulation, the introduction of hypothetical probabilistic reasoning using a familiar context, and the use of alternative representations. The students were also encouraged to write about their work. As the interventions progressed, a higher proportion of students successfully defined and used p-values in Null Hypothesis Testing procedures. First published May 2014 at Statistics Education Research Journal Archives

scholarly journals Evaluating computer-based simulations, multimedia and animations that help integrate blended learning with lectures in first year statistics

2011 ◽  
Vol 27 (2) ◽  
Author(s):  
David L. Neumann ◽  
Michelle M. Neumann ◽  
Michelle Hood
Keyword(s):  
Blended Learning ◽  
Student Learning ◽  
First Year ◽  
Negative Attitudes ◽  
Learning Framework ◽  
Integration Of Technology ◽  
Statistics Course ◽  
Real World Applications ◽  
Computer Based ◽  
Enhance Student Learning

<span>The discipline of statistics seems well suited to the integration of technology in a lecture as a means to enhance student learning and engagement. Technology can be used to simulate statistical concepts, create interactive learning exercises, and illustrate real world applications of statistics. The present study aimed to better understand the use of such applications during lectures from the student's perspective. The technology used included multimedia, computer-based simulations, animations, and statistical software. Interviews were conducted on a stratified random sample of 38 students in a first year statistics course. The results showed three global effects on student learning and engagement: showed the practical application of statistics, helped with understanding statistics, and addressed negative attitudes towards statistics. The results are examined from within a blended learning framework and the benefits and drawbacks to the integration of technology during lectures are discussed.</span>

AN EXPLORATION OF STUDENT ATTITUDES AND SATISFACTION IN A GAISE-INFLUENCED INTRODUCTORY STATISTICS COURSE

2017 ◽  
Vol 16 (2) ◽  
pp. 487-510
Author(s):  
WARREN PAUL
Keyword(s):  
Student Attitudes ◽  
Structural Equation ◽  
Statistics Education ◽  
Student Feedback ◽  
Introductory Statistics ◽  
Group Discussions ◽  
Research Journal ◽  
Statistics Course ◽  
Attitudes Toward Statistics ◽  
Students Attitudes

We used the Survey of Attitudes Toward Statistics to (1) evaluate using pre-semester data the Students’ Attitudes Toward Statistics Model (SATS-M), and (2) test the effect on attitudes of an introductory statistics course redesigned according to the Guidelines for Assessment and Instruction in Statistics Education (GAISE) by examining the change in attitudes over the semester and, using supplementary data from an annual Student Feedback Survey, testing for a change in overall satisfaction following implementation of the redesigned course. We took an exploratory rather than confirmatory approach in both parts of this study using Bayesian networks and structural equation modelling. These results were triangulated with analysis of focus group discussions and the annual Student Feedback Survey. First published November 2017 at Statistics Education Research Journal Archives

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