This Proceedings CD includes 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.
. Also see:
| 1A | Evidence-based medicine |
| 1A1 | Evidence-generating research and evidence-based medicine |
| 1A2 | Divergent needs of learners in evidence based medicine |
| 1A3 | Drip-feed education: statistics notes in the British Medical Journal |
| 1B | Evidence-based policy making |
| 1B1 | Evidence-based policy making: the Pensions Commission and beyond |
| 1B3 | The evidence gap and its impact on public policy and decision-making in developing countries |
| 1C | Evidence-based management |
| 1C1 | Diagnosis, provision and assessment of quantitative skills for managers in local government |
| 1C2 | Information quality for process improvement |
| 1C3 | The evidence-based management of learning: diagnosis and development of conceptual thinking with Meaning Equivalence Reusable Learning Objects (MERLO) |
| 1C4 | Enhanced TESF methodology for course excellence |
| 1D | The researcher/practitioner gap |
| 1D3 | Bridging the researcher-practitioner gap: views from different fields |
| 1F | Creating an evidence-based society |
| 1F1 | Wikis, dynamic charts, videos and other innovative tools to transform statistics into knowledge |
| 1F2 | What we know and what we should know; examples of ways of helping real users of statistical information |
| 1F3 | Analysis of clustered measurements: a comparison of the performance of foundation year students, 1994 cohort, with those of direct students, 1995 cohort, at the University of Limpopo, South Africa |
| 1G | Lies, damn lies, statistics: lessons from past and present for the future |
| 1G1 | The “compleat” applied statistician |
| 1G2 | Unintentional lies in the media: don’t blame journalists for what we don’t teach |
| 2A | Learners’ first experiences of handling data — focusing on 7 to 13 year olds |
| 2A1 | National testing of data handling in years 3, 5 and 7 in Australia |
| 2A2 | Does context expertise make a difference when dealing with data? |
| 2A3 | Linking problems, conclusions and evidence: primary students’ early experiences of planning statistical investigations |
| 2A4 | Engaging young children in informal statistical inference |
| 2B | Secondary-level statistical education |
| 2B1 | Random walks in teaching probability at the high school |
| 2B2 | Data analysis: linking mathematics, science, and social studies |
| 2B3 | Helping mathematics teachers teach statistics: challenges and potentials |
| 2B4 | Making sense of statistical studies: a capstone experience for secondary students |
| 2C | Statistical education at the Secondary/Higher Education interface |
| 2C1 | Approaches to extra-curricular statistics support for non-statistics UG and PG: facilitating the transition to Higher Education |
| 2C2 | SPoC – Statistics Poster Challenge for schools |
| 2C3 | Creating a World population model to analyze the dynamics of change |
| 2D | Using technology at school level to enhance statistical understanding |
| 2D1 | Statistical software for teaching: relevant, appropriate and affordable |
| 2D2 | Enhancing students’ inferential reasoning: from hands on to “movie snapshots” |
| 2D3 | Fathom that!: an ethnography of the use of interactive data analysis software in a statistics class of a high school serving low-income students |
| 2D4 | Using data to make sense of statistics: the role of technology in scaffolding understanding |
| 2E | Improving the teaching of statistics at school level |
| 2E1 | Overcoming obstacles to supporting secondary teachers’ statistical content knowledge for teaching |
| 2E2 | Teachers’ understanding of students’ conceptions about chance: an expert-novice contrast |
| 2E3 | Using classroom video to identify development of teacher knowledge |
| 2E4 | Professional development through collaborative analysis of student work |
| 2F | Making connections between educational research and teaching statistics at the school level |
| 2F1 | Teacher knowledge and confidence in grade 8 and 9 data handling and probability |
| 2F2 | Helping teachers to make effective use of real-world examples in statistics |
| 2F3 | Exploring relations of Vitruvian Man to develop students’ reasoning about variation |
| 2F4 | Researchers cultivating a long-term relationship with schools |
| 3A | Professional development of teachers |
| 3A1 | Tools for fostering and guiding the statistics teachers’ reflection on their own practice |
| 3A2 | Statistics teacher of the new era: another specialized mathematician or a totally different person? |
| 3A3 | Teaching primary teachers to teach statistical investigations: the uniqueness of initial experiences |
| 3A4 | Training in-service teachers to develop statistical thinking |
| 3B | Pre-service preparation for primary teachers |
| 3B1 | Student teachers developing their knowledge about data handling using TinkerPlots |
| 3B2 | Preparing elementary school teachers to teach statistics — an international dilemma |
| 3B3 | Teaching statistics at the primary level: identifying obstacles and challenges in teacher preparation from looking at teaching |
| 3C | The impact of technology on learning to teach statistics |
| 3C1 | A model for teacher knowledge as a basis for online courses for professional development of statistics teachers |
| 3C2 | Students’ understanding and reasoning about sample size and the law of large numbers after a computer-intensive introductory course on stochastics |
| 3C3 | An attempt to reconcile teaching content, pedagogy, and software in an online course for teachers |
| 3C4 | High school teachers’ reasoning about data analysis in a dynamics statistical environment |
| 3D | Learning to use context in teaching statistics at school and tertiary level |
| 3D1 | The multiple roles of context in the development of students’ informal inferential reasoning |
| 3D2 | Structuring contexts for statistical treatment: initializing statistical reasoning |
| 3D3 | Educational versions of authentic practices as contexts to teach statistical modeling |
| 3E | Learning to teach data-based statistics at school and tertiary level |
| 3E1 | Towards evaluation criteria for coherence of a data-based statistics curriculum |
| 3E2 | Some issues of data production in teaching statistics |
| 3E3 | Models of teacher preparation designed around the GAISE Framework |
| 3F | Similarities and contrasts in teaching mathematical and statistical thinking |
| 3F1 | Chance and necessity: the languages of probability and mathematics |
| 3F2 | Mathematical logic and statistical or stochastical ways of thinking: an educational point of view |
| 3F3 | Exploration and induction versus confirmation and deduction |
| 3G | Diversity in types of teaching at the tertiary level |
| 3G1 | How technology can help or complicate the teaching of statistics depending on the class size |
| 3G2 | Diversity and differentiated instruction and learning |
| 3G3 | Equity and the increasingly diverse tertiary student population: challenges and opportunities in statistics education |
| 3I | Practicum learning to teach statistics: perspectives from young staff |
| 3I1 | Testing, testees, and tested: practical lessons from the first years at a small teaching-focused university |
| 3I2 | Novice experience from teaching service courses in statistics |
| 3I3 | How young statistics academics learn to teach statistics |
| 4A | A taxonomy of statistics courses |
| 4A1 | Banishing the theory-applications dichotomy from statistics education |
| 4A2 | Accommodating specialists and non-specialists in statistics courses |
| 4A3 | Specialized basic courses for engineering students: a necessity or a nuisance? |
| 4B | Less parametric methods in statistics |
| 4B1 | The use of statistical software to teach nonparametric curve estimation: from Excel to R |
| 4B2 | On teaching bootstrap confidence intervals |
| 4B3 | Exploring data with non- and semiparametric models |
| 4C | Methods for ordinal data analysis |
| 4C1 | Teaching: a way of implementing novel statistical methods for ordinal data to researchers |
| 4C2 | Fitting transition models to longitudinal ordinal response data using available software |
| 4C3 | An illustration of multilevel models for ordinal response data |
| 4D | Innovations in teaching statistics at the tertiary level |
| 4D1 | Real-life module statistics: a happy Harvard experiment |
| 4D3 | Enriching statistics courses with statistical diversions |
| 4D4 | Stats2: An applied statistics modeling course |
| 4E | Heterogeneity of student levels |
| 4E1 | Teaching critical thinking to first year university students |
| 4E2 | Medical students and statistics challenges in teaching, learning and assessment |
| 4E3 | An overview of techniques used in the teaching and assessing of knowledge and application of statistical skills across undergraduate levels |
| 4F | Sensible use of multivariate software |
| 4F1 | Effect sizes and confidence intervals for multivariate analysis: how complete are published accounts of research in psychology? |
| 4F2 | A sampling of analyses and software use for cluster randomized trials over the last decade |
| 4F3 | Applying idiographic research methods: two examples |
| 4F4 | Exploratory factor analysis in Mplus, R and SPSS |
| 4G | Learning statistics through projects |
| 4G1 | Incorporating a research experience into an early undergraduate statistics course |
| 4G2 | Student discovery projects in data analysis |
| 4G3 | Formulating statistical questions and implementing statistics projects in an introductory applied statistics course |
| 4H | Integrating consulting with graduate education |
| 4H1 | Experiences with research teams comprised of graduate students, faculty researchers, and a statistical consulting team |
| 4H2 | Communication in statistical consultation |
| 4H3 | Lessons we have learned from post-graduate students |
| 4I | Integrating Bayesian methods with traditional statistics education |
| 4I1 | Psychology students’ understanding of elementary Bayesian inference |
| 4I2 | Comparing the Bayesian and likelihood approaches to inference: a graphical approach |
| 4I3 | The very beginning of a class on inference: classical vs Bayesian |
| 4I4 | Teaching young grownups how to use Bayesian networks |
| 4J | Sampling populations |
| 4J1 | Teaching survey sampling with the “sampling” R package |
| 4J2 | The use of Monte Carlo simulations in teaching survey sampling |
| 4J3 | Understanding sample survey theory with the “replicates-duplicates” approach |
| 5A | Assessing progress and performance with authentic and alternative assessment techniques |
| 5A1 | Assessment within Census at School: a pilot program in the United States |
| 5A2 | Contrasting cases: the “B versus C” assessment tool for activating transfer |
| 5A3 | Assessing pre-service teachers’ conceptions of randomness through project work |
| 5B | Methods for large scale assessment of meaningful knowledge of statistics |
| 5B1 | The statistics items in the Brazilian National Student Performance Exam (ENADE) |
| 5B2 | What do you know? Assessment beyond the traditional roles |
| 5B3 | Text analytic tools for the cognitve diagnosis of student writings |
| 5D | The use of innovative technologies to enhance assessment of statistical knowledge |
| 5D1 | Statistics assessment: the good, the bad, and the ugly |
| 5D2 | Issues for the assessment and measurement of statistical understanding in a technology-rich environment |
| 5D3 | Technologies for enhancing project assessment in large classes |
| 5E | Assessing statistical literacy and critical understanding of real-world messages related to statistics, probability, and risk |
| 5E1 | Assessing the interpretation of two-way tables as part of statistical literacy |
| 5E2 | It’s not what you know, it’s recognising the power of what you know: assessing understanding of utility |
| 5E3 | Post secondary and adult statistical literacy: assessing beyond the classroom |
| 5F | Assessing statistical reasoning and statistical thinking |
| 5F1 | Assessing student learning about statistical inference |
| 5F2 | Development of an instrument to assess statistical thinking |
| 5F3 | Towards assessing understanding of prerequisite knowledge for sampling distributions |
| 6A | Environmental statistics |
| 6A1 | The need for teaching weighted distribution theory: illustrated with applications in environmental statistics |
| 6A2 | Amarillo by morning: data visualization in geostatistics |
| 6A3 | Statistics education in a conservation organisation — towards evidence based management |
| 6C | Statistics training for researchers in other disciplines |
| 6C1 | Some different models for interacting with researchers and students in other disciplines |
| 6C2 | Statistics education at Russian agrarian universities: problems and prospects |
| 6C3 | Statistics for postgraduates and researchers in other disciplines: case studies and lessons learned |
| 6C4 | Communicating the value of statistical thinking in research |
| 6D | Medical statistics |
| 6D1 | Continuous variables: to categorise or to model? |
| 6D2 | Control in clinical trials |
| 6D3 | The applied statistical scientist in a high-profile academic environment |
| 6E | Statistical applications in the workplace |
| 6E1 | Training and conducting economic evaluation in public health |
| 6E2 | Issues in training physicians in the uses of statistics: what do they think they need to know? |
| 6E3 | Helping make government policy analysts statistically literate |
| 6F | Service learning and statistics: integrating statistics education into the workplace |
| 6F1 | Combining on- and off-campus service-learning in a statistics methods course |
| 6F2 | Service-learning for statistics students in the global health arena |
| 6F3 | STATCOM @ UHASSELT: yet another benefit for all parties |
| 6F4 | Promoting opportunities for statistics service-learning at a large urban university |
| 6G | Preparing for the world of work: lessons for statistics education from beyond the field |
| 6G1 | Lessons from medicine for training professional statisticians |
| 6G2 | Applying a model of professional learning to case studies in statistics education |
| 6G3 | Statistical training for non-statistical staff at the Office for National Statistics |
| 6G4 | The importance of teaching statistics in a professional context |
| 7A | Statistics and the media |
| 7A1 | Association-causation problems in news stories |
| 7A2 | Spinning heads and spinning news: the American media’s gap in quantitative reasoning skills |
| 7A3 | Statistics on national radio: some insights from working with professional broadcasters |
| 7B | Statistics and sports |
| 7B2 | Statistical models for student projects with sports themes |
| 7B4 | Using sports data to motivate statistical concepts: experiences from a freshman course |
| 7C | Statistics in psychology and the social sciences |
| 7C1 | Learning probability and statistics: cognitive and non-cognitive factors related to psychology students’ achievement |
| 7C2 | Human sciences student’s difficulties in parametric tests: a contribution to statistics education |
| 7C3 | A cross-cultural psychometric evaluation of the attitude toward Estrada’s statistic scale in teachers |
| 7D | Statistics education for engineering |
| 7D1 | Individualised learning for engineers — combining face-to-face teaching with non-linear web learning |
| 7D3 | The impact of problem-based learning on statistical thinking of engineering and technical high school students |
| 7D4 | Using directed online tutorials for teaching engineering statistics |
| 7E | Statistics for biology and the health sciences |
| 7E1 | Promoting autonomous learning in statistics among undergraduate medical students |
| 7E2 | A model to optimise statistical independence and critical thinking amongst researchers in a diverse disciplinary setting |
| 7E3 | Statistics for the biological and environmental sciences: improving service teaching for postgraduates |
| 7F | Statistics in business |
| 7F2 | High dimensional data – a growing business |
| 7F3 | Some arguments for integration of qualitative methods into business statistics courses |
| 7G | Statistics for non-quantitative majors |
| 7G1 | Using media reports to promote statistical literacy for non-quantitative majors |
| 7G2 | Luring non-quantitative majors into advanced statistical reasoning (and luring statistics educators into real statistics) |
| 7G3 | A five step framework for interpreting tables and graphs in their contexts |
| 7G4 | How we can all learn to think critically about data |
| 7H | Official statistics in statistics education:links between IASE and IAOS |
| 7H2 | Improving statististical literacy by national and international cooperation |
| 7H3 | Some case studies on the links between National Statistical Offices and statistical educators: what are the main developments? |
| 8A | Research on developing students’ statistical reasoning in primary and middle school |
| 8A1 | Developing primary students’ ability to pose questions in statistical investigations |
| 8A2 | How students’ spontaneous use of statistical tools shapes their thinking about precision |
| 8A3 | Emergence of reasoning about sampling among young students in the context of informal inferential reasoning |
| 8A4 | Developing statistical reasoning facilitated by TinkerPlots |
| 8B | Research on developing students’ statistical reasoning at secondary and tertiary levels |
| 8B1 | Inferential reasoning: learning to “make a call” in theory |
| 8B2 | Inferential reasoning: learning to “make a call” in practice |
| 8B3 | Developing tertiary-level students’ statistical thinking through the use of model-eliciting activities |
| 8B4 | Students’ statistical reasoning about distribution across grade levels: a look from middle school through graduate school |
| 8C | Making sense of risk |
| 8C1 | Teaching uncertainty and risk in mathematics and science |
| 8C2 | Conditions for risk assessment as a topic for probabilistic education |
| 8C3 | Exploring risk through simulation |
| 8D | Research on technology in statistics education |
| 8D1 | Introducing concepts of statistical inference via randomization tests |
| 8D2 | Development of ideas in data and chance through the use of tools provided by computer-based technology |
| 8D3 | Developing students’ computer-supported simulation and modelling competencies by means of carefully designed working environments |
| 8D4 | Conceptual issues in quantifying expectation: insights from students’ experiences in designing sampling simulations in a computer microworld |
| 8E | Theoretical frameworks in statistics education research |
| 8E1 | Quality in statistics education: applying expectancy value models to predict student outcomes in statistics education |
| 8E2 | Reasoning about variation: rethinking theoretical frameworks to inform practice |
| 8E3 | The transformation process from written curricula to students’ learning |
| 8F | Research methodologies in statistics education |
| 8F1 | Multilevel modeling of educational interventions: educational theory and statistical consequences |
| 8F2 | Randomized controlled trials and PhD level training in educational research |
| 8F3 | Qualitative methods in statistics education research: methodological problems and possible solutions |
| 8I | Research into learning statistics in vocational educational and training |
| 8I1 | The use of statistical tools by sales managers: forms of rationality and decision-making |
| 8I2 | Evaluating statistics education in vocational education and training |
| 8I4 | The influence of technology on what vocational students need to learn about statistics: the case of lab technicians |
| 8J | Evidence-based statistical practice |
| 8J2 | The influence of presentation on the interpretation of inferential results |
| 8J3 | The role of external representations in understanding probabilistic concepts |
| 8J4 | Understanding, teaching, and using p values |
| 9A | New paradigms in teaching statistics through technology |
| 9A1 | Learning to apply statistics using a virtual environment |
| 9A2 | Learning from the statistician’s lab notebook |
| 9A3 | Pupils reasoning with information and misinformation |
| 9B | Rethinking the statistics curriculum: computing skills our students need |
| 9B1 | Developing introductory computing for stats undergraduates |
| 9B2 | Integrating computing and data technologies into the statistics curricula |
| 9B3 | Introducing undergraduates to probability using the open-source programming language R |
| 9C | Virtual environments and experimental learning in statistics education |
| 9C1 | Setting up experiments in veterinary science: an example of virtual experimentation |
| 9C2 | A visual approach in the teaching of statistics and probability |
| 9C3 | Statlab: learning DOE by doing! |
| 9D | Advancing statistics education through visualization technologies |
| 9D1 | Using visualisation to teaching data analysis and programming |
| 9D2 | Statistical cartoons: the role of graphics in understanding statistics |
| 9E | e-learning tools: evaluation and the role of the instructor |
| 9E1 | Using blended learning environments in teaching introductory statistics to a strong diversity of students: the role of background factors |
| 9E2 | Using simulations for active learning: the query-first method in practice |
| 9E3 | KNOU mobile learning for innovation in statistics education |
| 9E4 | Creating active learning in a large introductory statistics class using clicker technology |
| 9F | Sharing data for educational purposes (standards, databases, case studies) |
| 9F2 | Promoting statistical literacy: a European pilot project to bring official statistics into university and secondary school classrooms |
| 9F3 | On and off-line dynamic data interrogation |
| 9G | Effective online educational materials |
| 9G1 | Online learning materials: are they put to different uses by online and on campus students? |
| 9G2 | In search of the “perfect” blend between an instructor and an online course for teaching introductory statistics |
| 9G3 | Improving lectures with CAST applets |
| 10A | Statistics teaching in the Asian context |
| 10A1 | Statistics education in India: a review |
| 10A2 | Teaching experiments for a course in introductory statistics |
| 10A3 | Meeting the statistical training needs of statistical offices of countries in Asia and the Pacific region: the experience of the statistical research and training centre (SRTC) of the Philippines |
| 10A4 | The teaching of statistics in the Philippines: moving to a brighter future |
| 10B | Statistics education in Africa |
| 10B3 | Statistics in Ugandan schools: challenges on instruction and assessment |
| 10C | Statistics education in developing countries |
| 10C1 | Assessment of graduate students’ conception of statistical inference: Philippine perspective |
| 10C2 | Training teachers to teach statistics in South Africa: realities and attitudes |
| 10C3 | Comparing teachers’ statistical knowledge in Botswana and South Africa: some preliminary results |
| 10C4 | Opportunities, challenges and statistical cooperation in the implementation of a statistical literacy project in Mendoza, Argentina |
| 10D | International projects that improve statistics education |
| 10D1 | Developing a statistical learning environment: Japanese CensusAtSchool project |
| 10D4 | An international quantitative education initiative and its impact on statistics education |
| 10E | The role that National Statistics Offices play in promoting statistics literacy |
| 10E1 | Beyond the data: exploiting the IT tools young and adult people use in their everyday life |
| 10E2 | The lecture series “Economic statistics: data production and data analysis in the official statistics” from the Federal Statistical Office |
| 10E3 | Statistical literacy assessment and training of government personnel using data from National Statistics Office: Philippine context |
| 10F | Statistics education in South America |
| 10F1 | Trajectory and prospects of statistics education in Brazil |
| 10F2 | The impact of an instance of quaternary education |
| 10F3 | Different views of a basic statistics course |
| 10F4 | Teaching statistics to physicians: a five-years experience |
| 10G | One hundred years of progress — teaching statistics 1910 to 2010: what have we learned? |
| 10G2 | Evolutions and revolutions in government statistics, and what we need to teach and learn |
| 10G3 | One hundred years of progress — teaching statistics 1910 to 2010: what have we learned? Part 1: It’s not mathematics but real data in context |
| 10G4 | One hundred years of progress — teaching statistics 1910 to 2010: what have we learned? Part 2: Problem solving, pedagogy and employees |
