scholarly journals Teaching stats for data science

2017 ◽  
Author(s):  
Daniel T Kaplan
Keyword(s):  
20Th Century ◽  
Data Science ◽  
Statistics Education ◽  
Introductory Statistics ◽  
Applied Statistics ◽  
Causal Relationships ◽  
Chi Squared

The familiar mathematical topics of introductory statistics --- means, proportions, t-tests, normal and t distributions, chi-squared, etc. --- are a product of the first half of the 20th century. Naturally, they reflect the statistical conditions of that era: scarce, e.g. n < 10, data originating in benchtop or agricultural experiments; algorithms communicated via algebraic formulas. Today, applied statistics relates to a different environment: software is the means of algorithmic communication, observational and "unplanned" data are interpreted for causal relationships, and data are large both in n and the number of variables. This change in situation calls for a thorough rethinking of the topics in and approach to statistics education. This paper presents a set of ten organizing blocks for intro stats that are better suited to today's environment.

scholarly journals Teaching stats for data science

2017 ◽  
Author(s):  
Daniel T Kaplan
Keyword(s):  
20Th Century ◽  
Data Science ◽  
Statistics Education ◽  
Introductory Statistics ◽  
Applied Statistics ◽  
Causal Relationships ◽  
Chi Squared

The familiar mathematical topics of introductory statistics --- means, proportions, t-tests, normal and t distributions, chi-squared, etc. --- are a product of the first half of the 20th century. Naturally, they reflect the statistical conditions of that era: scarce, e.g. n < 10, data originating in benchtop or agricultural experiments; algorithms communicated via algebraic formulas. Today, applied statistics relates to a different environment: software is the means of algorithmic communication, observational and "unplanned" data are interpreted for causal relationships, and data are large both in n and the number of variables. This change in situation calls for a thorough rethinking of the topics in and approach to statistics education. This paper presents a set of ten organizing blocks for intro stats that are better suited to today's environment.

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

PROMOTING STATISTICS IN THE ERA OF DATA SCIENCE AND DATA-DRIVEN INNOVATIONS

2020 ◽  
Vol 19 (1) ◽  
pp. 226-237
Author(s):  
IBRAHIM SIDI ZAKARI
Keyword(s):  
Communication Skills ◽  
Data Science ◽  
Statistics Education ◽  
Effective Interaction ◽  
Oral Communication ◽  
Public Institutions ◽  
Future Generations ◽  
Data Driven ◽  
Policy Makers ◽  
Research Journal

This paper aims at highlighting initiatives in developing future statisticians directed at high-school and university levels in Niger. More specifically, it focuses on collaborations, partnerships, outreach initiatives and supporting mechanisms, which may contribute to increase engagement and interest in and attraction to the field of statistics in the era of data science and data-driven innovations. Providing sufficient exposure to modern statistical analysis, computational and graphical tools, written and oral communication skills, and the ever-growing interdisciplinary use of statistics are key activities for building future generations of statisticians. Furthermore, current curricula as well as pedagogical approaches, teaching materials, and assessment methods need to be re-thought in order tomeet the requirements of the skills needed in the 21st century ensuring effective interaction with scientists, public institutions, industry, civil society, and policy makers. First published February 2020 at Statistics Education Research Journal Archives

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

PAINT-BY-NUMBER OR PICASSO? A GROUNDED THEORY PHENOMENOGRAPHICAL STUDY OF STUDENTS’ CONCEPTIONS OF STATISTICS

2021 ◽  
Vol 19 (2) ◽  
pp. 76-102
Author(s):  
NICOLA JUSTICE ◽  
SAMANTHA MORRIS ◽  
VERONIQUE HENRY ◽  
ELIZABETH BRONDOS FRY
Keyword(s):  
Education Research ◽  
Grounded Theory ◽  
Undergraduate Students ◽  
Statistics Education ◽  
Qualitative Approach ◽  
Introductory Statistics ◽  
Preliminary Results ◽  
Research Journal ◽  
Survey Responses

Statistics students’ conceptions of the work of statisticians and the discipline of statistics may play an important role in the topics to which they attend and their interest in pursuing further study. To learn about students’ conceptions, we collected open-ended survey responses from 44 undergraduate students who had completed introductory statistics courses. We used a grounded theory phenomenographical qualitative approach to identify several themes in students’ conceptions. In addition to the test-and-procedure conception, we offer several other themes, such as acknowledgement of variation and the role of ethical integrity. We use a metaphor of painting styles to compare to experts’ conceptions of statistics. By identifying “seeds” of what may be developed into expert conceptions, these preliminary results set possible foundations to explore trajectories that may help shape students’ conceptions of statistics. First published June 2020 at Statistics Education Research Journal Archives

scholarly journals Post–Modern Epidemiology: When Methods Meet Matter

2019 ◽  
Vol 188 (8) ◽  
pp. 1410-1419 ◽  
Author(s):  
George Davey Smith
Keyword(s):  
Causal Inference ◽  
20Th Century ◽  
Data Science ◽  
Disease Risk ◽  
Epidemiologic Studies ◽  
High Income ◽  
Noncommunicable Diseases ◽  
Primary Concern ◽  
Disease Etiology ◽  
Major Progress

Abstract In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post–Modern Epidemiology world.

scholarly journals COMPARING STUDENT SUCCESS AND UNDERSTANDING IN INTRODUCTORY STATISTICS UNDER CONSENSUS AND SIMULATION-BASED CURRICULA

2018 ◽  
Vol 17 (1) ◽  
pp. 103-120
Author(s):  
LAURA A. HILDRETH ◽  
JIM ROBISON-COX ◽  
JADE SCHMIDT
Keyword(s):  
Student Success ◽  
Statistics Education ◽  
Student Understanding ◽  
Introductory Statistics ◽  
Success Rates ◽  
Traditional Curriculum ◽  
Montana State University ◽  
Research Journal ◽  
Simulation Based ◽  
Using Data

This study examines the transferability of results from previous studies of simulation-based curriculum in introductory statistics using data from 3,500 students enrolled in an introductory statistics course at Montana State University from fall 2013 through spring 2016. During this time, four different curricula, a traditional curriculum and three simulation-based curricula, were used. Student success rates and understanding of six key statistical concepts are compared among these curricula using mixed logistic regression. Results indicate that after controlling for salient covariates, differences in student success rates are minimal while student understanding under the simulation-based curricula are similar to or better than student understanding under the traditional curriculum suggesting simulation-based curricula may help increase student understanding of several key statistical concepts. First published May 2018 at Statistics Education Research Journal Archives

scholarly journals EVALUATION OF THEORETICAL AND EMPIRICAL CHARACTERISTICS OF THE COMMUNICATION, LANGUAGE, AND STATISTICS SURVEY (CLASS)

2018 ◽  
Vol 17 (1) ◽  
pp. 141-164
Author(s):  
AMY E. WAGLER ◽  
LAWRENCE M. LESSER
Keyword(s):  
Statistics Education ◽  
Linguistically Diverse ◽  
Culturally And Linguistically Diverse ◽  
Introductory Statistics ◽  
Class Iii ◽  
Research Journal ◽  
Diverse Student Populations ◽  
Diverse Student ◽  
Improve Instruction ◽  
User Friendly

The interaction between language and the learning of statistical concepts has been receiving increased attention. The Communication, Language, And Statistics Survey (CLASS) was developed in response to the need to focus on dynamics of language in light of the culturally and linguistically diverse environments of  introductory statistics classrooms. This manuscript presents a cross-cultural evaluation of the characteristics of the CLASS III (third generation of the  instrument) and provides a user-friendly cross-culturally valid version of the CLASS. Mixed methods are employed to investigate further characteristics of the CLASS III and provide a scale (CLASS IV) that may be utilized in diverse settings. These research results have implications for instructors, professional developers, and researchers to improve instruction with culturally and linguistically diverse  student populations. First published May 2018 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

Sign in / Sign up

Export Citation Format

Share Document