The Proceedings include all papers presented in the Plenary sessions, Invited Topics 1 to 10, Contributed Sessions and Posters.

Each link below navigates to a complete list of papers for that section, together with appropriate abstracts and further links to PDFs of the individual papers.

(Jump to Topic:   2   3   4   5   6   7   8   9   10 .   Also see: Contributed papers and Posters)


1On the relationships between statistics and other subjects in the K-12 curriculum
2What can we learn from real-world communication of risk and uncertainty?
3Teaching statistics to Real People: adventures in social stochastics
4Statistics in 2014: Reflections on the occasion of the 175th anniversary of the American Statistical Association
5Sustainable education for professional Statisticians

Topic 1  Sustaining strengths and building capacity in statistics education

1ABuilding the capacity of mathematics and science teachers to teach statistics
1A1Curriculum expectations for teaching science and statistics
1A2The Statistical Education of Teachers (SET): an American Statistical Association policy document
1A3Building high school pre-service teachers’ knowledge to teach correlation and regression
1BBuilding the capacity of new PhDs and graduate students to teach statistics (panel discussion)
1CStatistics education outreach across the globe
1C1Outreach efforts to enhance statistical education and statistical literacy in Hungary
1C2OZCOTS: Bringing statistical educators and statisticians together
1C3Japanese Inter-university Network for Statistical Education and new trials for development of students’ data analysis skills
1DBuilding the capacity to teach and understand statistics in emerging economies
1D1On the commencement of a culture of “statistics acceptance” in a higher education institution relatively new at research
1D2Building capacity for developing statistical literacy in a developing country
1D3Statistics education in Ethiopia: successes, challenges and opportunities
1D4Sustaining teachers’ capacity for teaching statistical inference through reflective practice
1EBuilding capacity in Statistics majors
1E1Skills needed for modern day statisticians
1E2Challenges and issues in developing real-world curriculum for data scientists in Japan
1E3Building capability in statistics majors: drawing strength from a diverse region
1FThe importance of attitudes in statistics education: sustaining learning processes and outcomes
1F1Student attitudes toward statistics from a randomization-based curriculum
1F2How do attitudes change from one stats course to the next?
1F3A fallacy in student attitude research: the impact of the first class
1F4Comparing attitudes toward statistics among students enrolled in project-based and hybrid statistics courses

Topic 2  Statistics education at school level

2AEarly years statistics education: ages 4 - 8
2A1Important ideas in statistics for children aged 4-8 years
2A2Establishing statistical foundations early: data modeling with young learners
2A3Exposing young children to activities that develop emergent inferential practices in statistics
2BMiddle school statistics education: ages 8 - 13
2B1Middle school (ages 10 – 13) students’ understanding of statistics
2B2Where’s your evidence? Challenging young students’ equiprobability bias through argumentation
2B3Student perspectives on being introduced to using Tinkerplots for investigations
2CSecondary school statistics education: ages 13 +
2C1On the delicate relation between informal statistical inference and formal statistical inference
2C2High school (ages 14 – 18) students’ understanding of statistics
2C3Teaching statistics at secondary education in Italy: some issues on large scale standardized test results
2C4Analysis of teachers’ understanding of covariation in the Vitruvian Man context
2DStatistical education at the Secondary/Higher Education interface
2D1What did they learn? Statistics skills: from French secondary school to university
2D2Bridging the statistical gap: creating successful secondary/higher education partnerships
2D3Preparing future teachers to teach statistics
2EUsing technology at school level to enhance statistical understanding
2E1From hat plots to box plots in Tinkerplots: supporting students to write conclusions which account for variability in data
2E2Constructing, refining and validating a task for developing reasoning on stabilized frequency distributions in the context of informal inferences
2E3Games of chance: tools that help enhance teachers’ notions of statistics and probability
2FInnovative approaches to improve pedagogical content knowledge at the school level
2F1Improving the perceived value and affect of statistics in elementary and middle school teachers through the development of pedagogical content knowledge
2F2Statistical knowledge for teaching: elementary preservice teachers
2F3High school teachers’ pedagogical content knowledge of variability
2GLinking research and practice in teaching and learning statistics at the school level
2G1Developing statistics teachers’ identity: a look at communities of practice
2G2Communities of practice: a theoretical framework to design for teachers’ statistical learning
2G3From observing and evaluating variation to measuring and comparing variation
2G4Teachers as key stakeholders in research in statistics education

Topic 3  Education and development of staff who teach statistics

3AStatistics instructors’ content knowledge
3A1Developing pre-service teachers’ technological pedagogical content knowledge (TPACK) of sampling
3A2Analysis of teachers’ understanding of variation in the dot-boxplot context
3A3Beyond calculations: fostering conceptual understanding in statistics graduate teaching assistants
3BStatistics instructors’ knowledge of students’ development of fundamental statistics concepts
3B1A certification system for statistics knowledge and skills by Japanese Statistical Society
3B2Teachers’ knowledge of students’ conceptions and their development when learning linear regression
3B3The impact of a teachers’ attention deriving on students’ statistical discourse
3CStatistical instructors’ knowledge of assessing students’ learning of statistics
3C1Trends in students’ conceptual understanding of statistics
3C2Discerning students’ statistical thinking: a researcher’s perspective
3C3The LOCUS assessment at the college level: conceptual understanding in introductory statistics
3DStatistics instructors’ use of technology for teaching statistics
3D1Using bootstrap dynamic visualizations in teaching
3D2Reflections on using technology to teach statistics in Kenya
3D3Using the Open Learning Initiative (OLI) to support teaching statistics to international politics students
3D4The use of technology in a mentor teacher course in statistics education
3EProfessional development of statistics instructors
3E1Building the capacity of mathematics and science teachers to teach statistics
3E2Project-SET materials for the teaching and learning of sampling variability and regression
3E3Implementing GAISE recommendations through “doing statistics” tasks
3FTheory and practice of statistics: curriculum for statistics teachers
3F1Suitability criteria for teachers’ education programs in mathematics and statistics education
3F2Relationships between curriculum knowledge of in-service Mexican teachers and statistics
3F3Preparing teachers to teach statistics: developing professional knowledge and practice

Topic 4  Statistics education at the post-secondary level

4ARandomisation and bootstrapping: the quick way to inference
4A1Accepting the challenge: constructing a randomisation pathway for inference into our traditional introductory course
4A2Using simulation/randomization to introduce p-value in week 1
4A3Intuitive introduction to the important ideas of inference
4BUse of student response systems in teaching statistics at the university level
4B1Clickers, simulations, and conceptual understanding of statistical inference
4B2Teaching data analysis in large classes using clicker assessment
4B3Teaching discrete distributions using contingent teaching with clickers
4B4Personal response systems as a learning aid in an epidemiology course for postgraduate statistics students
4CRank-based inference, association measures and nonparametric statistics
4C1Is the real world normal?
4C3Combining nonparametric inferences using data depth, bootstrap and confidence distribution
4C4Should we still teach rank-based distribution-free procedures?
4DExchanging pedagogy between post-secondary and secondary school statistics courses
4D1Exchanging statistics pedagogy between the master teacher and the future teacher
4D2Statistics for all students
4D3Exchanging pedagogy between post-secondary and secondary school statistics courses
4D4Exchanging pedagogy between post-secondary and secondary school statistics courses: facilitating meaningful professional development
4EWe know you need to know statistics, do you?
4E1Measuring university students’ approaches to learning statistics: a cross-cultural and multilingual version of the ASSIST
4E2A comparison of attitudes between traditional and hands-on classes in an introductory statistics course
4E3Turkish ASSIST: measuring university students’ approaches to learning statistics
4FOpening up the data world wider and faster
4F1Introductory statistics in the 21st century
4F2DataFest: celebrating data in the data deluge
4F3Middleware for Middle Earth
4F4Open data, civil society and monitoring progress: challenges for statistics education

Topic 5  Statistics education in the disciplines and the workplace

5AEvidence-based policy making
5A1The use of official statistics in evidence based policy making in New Zealand
5A2Challenges to evidence-based policy making in the decentralized U.S. statistical system
5A3Statistics education, collaborative research, and LISA 2020: a view from Nigeria
5A4International statistical standards as enabler for evidence-based policy making: the case of tourism statistics
5BEvidence-based management
5B2The prevalence of statistics and data mining in management journals
5B3Bringing the workplace into a National Certificate in Official Statistics
5B4How do school principals understand and use the statistics in reports from national large-scale assessments?
5CStatistics education beyond qualification (panel discussion)
5DDevelopment of statistical thinking in the workplace
5D1Improving statistical literacy at university
5D2The contributions of Six Sigma to the development of statistical thinking in the workplace
5D3Development of training methods to accelerate the competencies in Weibull analysis: case study in the automotive industry
5EMentoring young statisticians in the workplace
5E1The importance of inter-personal skills on statistical teams
5E2Best practices in mentoring and training young statisticians
5E3Mentoring advanced and newly graduated masters and Ph.D students
5FBridging the gap between current statistical practice in the workplace and modern statistics
5F1Tradition should not supplant understanding and insight
5F2Once were warriors: the need of re-education in mathematics and computing for life “scientisticians”
5F3Training to develop modern statistics in the workplace using R and R Commander – experiences from the New Zealand government sector
5HIn search of evidence: exploring the relationship between real workplace based data and statistics education
5H1Workplace and official statistics: how can higher education contribute to a better relationship?
5H2Experiences with real and accessible recent data in context to motivate student learning at higher levels in statistics
5H3Supporting statistical consultant decision-making within a case-based learning environment

Topic 6  Innovation and reform in teaching probability within statistics

6ABayesian inference (probability) goes to school: meanings, tasks and instructional challenges
6A1Will the real Bayesian probablity please stand up!?
6A2Proto-Bayesian reasoning of children in fourth class
6A3Exploring realistic Bayesian modeling situations
6BProbability and p-values — probing the problems
6B1Impact of a simulation/randomization-based curriculum on student understanding of p-values and confidence intervals
6B2Teaching probability: using levels of dialogue and proportional reasoning
6B3The interpretation of effect size in published articles
6CInterdisciplinarity and innovation
6C1Sampling in the wild
6C2Using re-sampling and sampling variability in an applied context as a basis for making statistical inferences with confidence
6C3A case study of an elementary school student’s understanding of stochastic prognoses
6DTeaching probability to future teachers of mathematics and statistics
6D1Step-by-step activities in the classroom preparing to teach the frequentist definition of probability
6D2Learning and teaching probability in the 21st century
6D3Transforming media items into classroom tasks in the context of a study group
6EModeling distributions to connect chance processes, data production, and data distributions
6E1Model-based informal inference
6E2Visual representations of empirical probability distributions when using the granular density metaphor
6E3Multidirectional modelling for fostering students’ connections between real contexts and data, and probability distributions
6FTeachers’ awareness of conceptual connections between probability and statistics
6F1Teachers and students: from an intuitive approach to a rational evaluation of probability
6F2Challenges for learning about distributions in courses for future Mathematics teachers
6F3Odds that don’t add up

Topic 7  Statistical literacy in the wider society

7AStatistical literacy beyond the classroom
7A1Odyssey: a journey to lifelong statistical literacy
7A2Teaching statistics for engagement beyond classroom walls
7A3Taking statistical literacy to the masses with YouTube, blogging, Facebook and Twitter
7BStatistical literacy requirements for teachers
7B1Statistical literacy requirements for teachers
7B2Developing statistical knowledge for teaching of variability through professional development
7B3Teachers’ views related to goals of the statistics classroom – from global to content-specific
7CAssessment of statistical literacy
7C1Towards statistical literacy - relating assessment to the real world
7C2Establishing the validity of the LOCUS assessments through an evidenced-centered design approach
7C3Sufficiently assessing teachers’ statistical literacy
7DDeveloping statistical literacy: Case studies and lessons learned
7D1Students’ beliefs about the benefit of statistical knowledge when perceiving information through daily media
7D2Changing the course: from boring numeracy to inspiring literacy
7D3A numeracy infusion course for higher education (NICHE): strategies for effective quantitative reasoning (QR) instruction
7D4Implementing a quantitative literacy core competency requirement in the College of Arts and Science at Miami University
7EFactors that affect statistical literacy
7E1Critical thinking as an impact factor on statistical literacy – theoretical frameworks and results from an interview study
7E2A multilevel perspective on factors influencing students’ statistical literacy
7E3Sustaining communication of the value of statistics in the humanities
7FFactors that affect statistical literacy II
7F1Reconceptualizing statistical literacy: Developing an assessment for the modern introductory statistics course
7F2Improving statistical literacy through supplemental instruction
7F3Interpreting variation of data in risk-context by middle school students

Topic 8  Research in statistics education

8AResearch on developing students’ reasoning using simulation methods for introductory statistical inference: Session I
8A1Students’ visual reasoning and the randomization test
8A2Designing and implementing an alternative teaching concept within a continuous professional development course for German secondary school teachers
8A3Quantitative evidence for the use of simulation and randomization in the introductory statistics course
8BResearch on developing students’ reasoning using simulation methods for introductory statistical inference: Session 2
8B1The symbiotic, mutualistic relationship between modeling and simulation in developing students’ statistical reasoning about inference and uncertainty
8B2Bootstrapping for learning statistics
8B3From data to decision-making: using simulation and resampling methods to teach inferential concepts
8CResearch on developing students’ informal statistical inferential reasoning
8C1Informal statistical inference revisited
8C2Students’ reasoning about uncertainty while making informal statistical inferences in an “integrated pedagogic approach”
8C3Exploring informal inferential reasoning through data games
8DResearch on developing students’ statistical reasoning
8D1Long-term impact on students’ informal inferential reasoning
8D2Statistical reasoning with the sampling distribution
8D3Extending the curriculum with TinkerPlots: opportunities for early development of informal inference
8EResearch on developing students’ probabilistic reasoning
8E1Empirical research on understanding probability and related concepts — a review of vital issues
8E2Reasoning development of a high school student about probability concept
8E3Characteristics of students’ probabalistic reasoning in a simulation-based statistics course
8FResearch on professional development of teachers in statistics
8F1Teachers’ professional development in a stochastics investigation community
8F2A teacher development program in statistics within a community of practice
8F3Professional development for teaching statistics: a collaborative action research project with middle-school mathematics teachers
8F4Learning and teaching statistical investigations: a case study of a prospective teacher
8GTheoretical frameworks in statistics education research
8G1Describing distributions
8G3Cultural diversity in statistics education: bridging uniqueness
8G4About central issues of mental model theory in context of learning statistics
8HPublishing in education research journals (panel discussion)
8IResearch on risk literacy
8I1Getting alternative representations for risk into the school syllabus
8I3Risk literacy: first steps in primary school
8I4Comparing fast and frugal trees and Bayesian networks for risk assessment
8JResearch on technology in statistics education
8J1Constructing inferential concepts through bootstrap and randomization-test simulations: a case study
8J2Measuring the effectiveness of using computer assisted statistics textbooks in Kenya
8J3Comparing groups by using TinkerPlots as part of a data analysis task — tertiary students’ strategies and difficulties

Topic 9  Technology in statistics education

9AThe design of digital tools and technology-enhanced learning environments for teaching statistics
9A1Expressions of uncertainty when variation is partially-determined
9A2A learning trajectory on hypothesis testing with TinkerPlots – design and exploratory evaluation
9A3Students’ emergent roles in developing their reasoning about uncertainty and modeling
9BModeling, randomization and simulation tools for connecting data and chance
9B1Hierarchical data visualization as a tool for developing student understanding of variation of data generated in simulations
9B2StatKey: online tools for bootstrap intervals and randomization tests
9B3Teaching resampling in an introductory statistics course
9CThe emerging concepts of “data science” and “big data” for educational purposes
9C1Exploring “white flight” via open data and big data
9C2Teaching data science to teenagers
9C3Integrating big data into the science curriculum
9DE-learning, E-teaching and E-assessment in fully online, blended and open virtual web-based courses
9D1Experiences teaching an introductory statistics MOOC
9D2Statistical reasoning’s new look
9D3Using Carnegie Mellon’s Open Learning Initiative (OLI) to support the teaching of introductory statistics: experiences, assessments and lesson learned
9D4A mobile web for enhancing statistics and mathematics education
9ESupporting teachers’ use of new statistics technology in their classrooms and development of their technological-pedagogical content knowledge
9E1Data and Chance with FATHOM — teaching material for implementing computer-based stochastic courses
9E2Designing technology-rich learning environments for secondary teachers to explore and prepare to teach statistics
9E3How a curriculum may develop technological statistical knowledge: a case of teachers examining relationships among variables using Fathom
9FTechnology for developing statistical thinking, reasoning, and literacy
9F1Year six students’ reasoning about random “bunny hops” through the use of TinkerPlots and peer-to-peer dialogic interactions
9F2Technology for developing statistical thinking: a psychological perspective
9F3Integrating technology in regular statistics courses and assessments of pre-service teachers
9GEducational software for helping students learn statistics
9G1An early look at rich learning analytics: statistics students playing “Markov”
9G2Improving the attitudes of high school students towards statistics: an island-based approach
9G3Using on-line quizzes to help students learn probability and statistics
9HFuture trends for technology in statistics education (panel discussion)

Topic 10  Innovative collaboration in statistics education

10ACollaborations between Statistics agencies and academia (schools/universities/colleges)
10A2Developing statistical literacy amongst in-service teachers through a collaborative project
10A3SMARTCensus – making sense of census data
10A4More ways to Heaven than one: improving statistical literacy in Ireland
10CCollaboration among countries
10C1Statistics and probability curriculum development for future elementary teachers in Chile: collaboration among countries
10C3How the curriculum shapes teachers’ thinking: a comparison of New Zealand and Australian teachers’ thinking about statistics
10C4Building strength from compromise: a case study of five year collaboration between the Statistical Services Centre of the University of Reading, UK, and Maseno University, Kenya
10EResearch projects collaborations
10E1Open lessons impact statistics teaching teachers’ beliefs
10E2Conducting successful cross-institutional research in statistics education
10E3Peer learning in statistics beyond the University curriculum