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

CONFIRMING THE STRUCTURE OF THE SURVEY OF ATTITUDES TOWARD STATISTICS (SATS-36) BY SWEDISH STUDENTS

2019 ◽  
Vol 18 (1) ◽  
pp. 83-93
Author(s):  
INGER PERSSON ◽  
KATRIN KRAUS ◽  
LISBETH HANSSON ◽  
FAN YANG WALLENTIN
Keyword(s):  
Statistics Education ◽  
Original Model ◽  
Introductory Statistics ◽  
Model Structure ◽  
Research Journal ◽  
Statistics Course ◽  
Confirmatory Factor ◽  
Attitudes Toward Statistics ◽  
Students Attitudes ◽  
Analyze Data

Research on students’ attitudes toward statistics has attracted many statistics instructors and statistics education researchers. In this study, we use confirmatory factor analysis to analyze data collected from an introductory statistics course using the Survey of Attitudes toward Statistics. The results suggest that the items and six factors are conceptually relevant, confirming the six-factor structure of the pretest version of SATS-36 on this sample of Swedish students, with a few suggested modifications of the original model structure. Two items are excluded from the Difficulty component, two items on the Affect component are allowed to correlate, and two items on the Cognitive competence component are also allowed to correlate. First published May 2019 at Statistics Education Research Journal Archives

scholarly journals WHEN DO STUDENTS’ ATTITUDES CHANGE? INVESTIGATING STUDENT ATTITUDES AT MIDTERM

2017 ◽  
Vol 16 (2) ◽  
pp. 476-486
Author(s):  
APRIL T. KERBY ◽  
JACQUELINE R. WROUGHTON
Keyword(s):  
Education Research ◽  
Student Attitudes ◽  
Statistics Education ◽  
Student Attitude ◽  
Introductory Statistics ◽  
Individual Student ◽  
Statistics Education Research ◽  
Research Journal ◽  
Statistics Course ◽  
Students Attitudes

Statistics educators have been investigating how students’ attitudes change in the introductory statistics course for many years. Typically, an overall decrease in mean attitudes over the course has been noted. However, when and how do students’ attitudes change during the term? Do they steadily decrease or is there a point when students’ attitudes might actually be increasing? If so, can instructors use this to their advantage? This research introduced a mid-semester survey of attitudes. We found that students’ attitudes are not necessarily strictly declining from the beginning to the end of the semester. We also found it might be advantageous to follow individual student attitude trends throughout the semester instead of just looking at aggregate mean scores for the different surveys. First published November 2017 at Statistics Education Research Journal Archives

scholarly journals EVALUATING TWO MODELS OF COLLABORATIVE TESTS IN AN ONLINE INTRODUCTORY STATISTICS COURSE

2015 ◽  
Vol 14 (1) ◽  
pp. 36-59
Author(s):  
AUÐBJÖRG BJÖRNSDÓTTIR ◽  
JOAN GARFIELD ◽  
MICHELLE EVERSON
Keyword(s):  
Quantitative Methods ◽  
Statistics Education ◽  
Introductory Statistics ◽  
Qualitative And Quantitative Methods ◽  
Noticeable Increase ◽  
Research Journal ◽  
Significant Difference ◽  
Statistics Course ◽  
Students Attitudes ◽  
Collaborative Tests

This study explored the use of two different types of collaborative tests in an online introductory statistics course. A study was designed and carried out to investigate three research questions: (1) What is the difference in students’ learning between using consensus and non-consensus collaborative tests in the online environment?, (2) What is the effect of using consensus and non-consensus collaborative tests on students’ attitudes towards statistics?, and (3) How does using a required consensus vs. a non-consensus approach on collaborative tests affect group discussions? Qualitative and quantitative methods were used for data analysis. While no significant difference was found between groups using the two collaborative testing formats, there was a noticeable increase in students’ attitudes across both formats towards learning statistics. This supports prior research on the benefits of using collaborative tests in face-to-face courses. First published May 2015 at Statistics Education Research Journal Archives

scholarly journals THE IMPORTANCE OF ATTITUDES IN STATISTICS EDUCATION

2012 ◽  
Vol 11 (2) ◽  
pp. 57-71
Author(s):  
CAROLINE RAMIREZ ◽  
CANDACE SCHAU ◽  
ESMA EMMİOĞLU
Keyword(s):  
Education Research ◽  
Statistics Education ◽  
Value Theory ◽  
Expectancy Value Theory ◽  
Expectancy Value ◽  
Research Journal ◽  
Statistics Course ◽  
Attitudes Toward Statistics ◽  
Students Attitudes ◽  
Course Outcomes

People forget what they do not use. But attitudes “stick.” Our article emphasizes the importance of students’ attitudes toward statistics. We examine 15 surveys that purport to assess these attitudes and then describe the Survey of Attitudes Toward Statistics, a commonly used attitude survey. We present our conceptual model of Students’ Attitudes Toward Statistics (SATS-M), which is congruent with Eccles and colleagues’ Expectancy-Value Theory (Eccles’ EVT), as well as others. The SATS-M includes three broad constructs that impact Statistics Course Outcomes: Student Characteristics, Previous Achievement-Related Experiences, and Statistics Attitudes. We briefly describe Eccles’ EVT and other theories that support our SATS-M. We relate findings from research using the SATS to our model and end with implications for statistics education. First published November 2012 at Statistics Education Research Journal: Archives

scholarly journals COGNITIVE AND NON-COGNITIVE FACTORS RELATED TO STUDENTS’ STATISTICS ACHIEVEMENT

2010 ◽  
Vol 9 (1) ◽  
pp. 6-26
Author(s):  
FRANCESCA CHIESI ◽  
CATERINA PRIMI
Keyword(s):  
Education Research ◽  
Statistics Education ◽  
Cognitive Factors ◽  
Introductory Statistics ◽  
Cognitive Variables ◽  
Cognitive Variable ◽  
Research Journal ◽  
Statistics Course ◽  
Course Achievement ◽  
Attitudes Toward Statistics

The aim of this study was to investigate students’ achievement in introductory statistics courses taking into account the relationships between cognitive and non-cognitive factors. It was hypothesised that achievement was related to background in mathematics (a cognitive variable), as well as to attitudes toward statistics and anxiety (non-cognitive variables). Students were presented with measures assessing their attitudes, mathematical competence, and anxiety toward courses and examinations at the beginning and at the end of their statistics course. Achievement was assessed by tasks assigned during the course, as well as by students’ final grades and the number of exam failures. The results reveal the reationships between cognitive and non-cognitive factors, their changes during the course, and how both interact in predicting achievement. First published May 2010 at Statistics Education Research Journal: Archives

scholarly journals USING THE EXPECTANCY VALUE MODEL OF MOTIVATION TO UNDERSTAND THE RELATIONSHIP BETWEEN STUDENT ATTITUDES AND ACHIEVEMENT IN STATISTICS

2012 ◽  
Vol 11 (2) ◽  
pp. 72-85 ◽  
Author(s):  
MICHELLE HOOD ◽  
PETER A. CREED ◽  
DAVID L. NEUMANN
Keyword(s):  
Student Attitudes ◽  
Statistics Education ◽  
Past Performance ◽  
Expectancy Value ◽  
Final Model ◽  
Research Journal ◽  
Statistics Course ◽  
Attitudes Toward Statistics ◽  
Value Model ◽  
The Relationship

We tested a model of the relationship between attitudes toward statistics and achievement based on Eccles’ Expectancy Value Model (1983). Participants (n = 149; 83% female) were second-year Australian university students in a psychology statistics course (mean age = 23.36 years, SD = 7.94 years). We obtained demographic details, past performance, attitudes, and expectancies in Week 2, and attendance records (effort) and course marks (achievement) at the end of semester. Path analysis was conducted via AMOS 19. The final model fit well and explained 40% of the variance in achievement. Past performance (22%), effort (8%), and expectancies (2%) made significant direct contributions. There were significant indirect contributions by each attitude component. Cognitive competence made the largest indirect contribution. First published November 2012 at Statistics Education Research Journal: Archives

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 STUDENTS’ ATTITUDES TOWARD STATISTICS ACROSS THE DISCIPLINES: A MIXED-METHODS APPROACH

2012 ◽  
Vol 11 (2) ◽  
pp. 45-56
Author(s):  
JAMES D. GRIFFITH ◽  
LEA T. ADAMS ◽  
LUCY L. GU ◽  
CHRISTIAN L. HART ◽  
PENNEY NICHOLS-WHITEHEAD
Keyword(s):  
Mixed Methods ◽  
Undergraduate Students ◽  
Qualitative Methodology ◽  
Statistics Education ◽  
Business Majors ◽  
Negative Attitudes ◽  
Mixed Methods Approach ◽  
Research Journal ◽  
Attitudes Toward Statistics ◽  
Students Attitudes

Students’ attitudes toward statistics were investigated using a mixed-methods approach including a discovery-oriented qualitative methodology among 684 undergraduate students across business, criminal justice, and psychology majors where at least one course in statistics was required. Students were asked about their attitudes toward statistics and the reasons for their attitudes. Five categories resulted for those with positive and negative attitudes and were separated on the basis of discipline. Approximately 63% of students indicated a positive attitude toward statistics. Business majors were most positive and were more likely to believe statistics would be used in their future career. Multiple methodological approaches have now provided data on the various domains of attitudes toward statistics and those implications are discussed. First published November 2012 at Statistics Education Research Journal: Archives

scholarly journals COGNITIVE TRANSFER OUTCOMES FOR A SIMULATION-BASED INTRODUCTORY STATISTICS CURRICULUM

2017 ◽  
Vol 16 (2) ◽  
pp. 419-440
Author(s):  
MATTHEW D. BECKMAN ◽  
ROBERT C. DELMAS ◽  
JOAN GARFIELD
Keyword(s):  
Learning Outcomes ◽  
Statistics Education ◽  
Assessment Instrument ◽  
Introductory Statistics ◽  
Far Transfer ◽  
Research Journal ◽  
Cognitive Transfer ◽  
Simulation Based ◽  
Statistics Course ◽  
New Applications

Cognitive transfer is the ability to apply learned skills and knowledge to new applications and contexts. This investigation evaluates cognitive transfer outcomes for a tertiary-level introductory statistics course using the CATALST curriculum, which exclusively used simulation-based methods to develop foundations of statistical inference. A common assessment instrument administered at the end of each course measured learning outcomes for students. CATALST students showed evidence of both near and far transfer outcomes while scoring as high, or higher than, on the assessed learning objectives when compared with peers enrolled in similar courses that emphasized parametric inferential methods (e.g., the t-test). First published November 2017 at Statistics Education Research Journal Archives

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

Sign in / Sign up

Export Citation Format

Share Document