Alison L Gibbs

Alison L Gibbs

Director, Centre for Teaching Support and Innovation and Professor, Teaching Stream, Department of Statistical Sciences, University of Toronto

Posts by Collection

publications

Lessons from Medicine for Training Professional Statisticians

Published in Proceedings of the 8th International Conference on Teaching Statistics, 2010

With Tim Guimond

Recommended citation: Gibbs, A.L. and Guimond, T. (2010). "Lessons from Medicine for Training Professional Statisticians." In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the Eighth International Conference on Teaching Statistics (ICOTS8, July, 2010), Ljubljana, Slovenia. Voorburg, The Netherlands: International Statistical Institute. https://iase-web.org/documents/papers/icots8/ICOTS8_6G1_GIBBS.pdf

Experiences Teaching an Introductory Statistics MOOC

Published in Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9), 2014

Recommended citation: Gibbs, A.L. (2014). "Experiences Teaching an Introductory Statistics MOOC." In K. Makar, B. de Sousa, & R. Gould (Eds.), Sustainability in statistics education. Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9, July, 2014), Flagstaff, Arizona, USA. Voorburg, The Netherlands: International Statistical Institute. https://icots.info/9/proceedings/pdfs/ICOTS9_9D1_GIBBS.pdf

A Comparison of Learner Intent and Behaviour in Live and Archived MOOCs

Published in The International Review of Research in Open and Distributed Learning, 2014

With Jen Campbell, Hedieh Najafi, and Cody Severinski

Recommended citation: Campbell, J., Gibbs, A.L., Najafi, H. and Severinski, C. (2014). "A Comparison of Learner Intent and Behaviour in Live and Archived MOOCs." The International Review of Research in Open and Distributed Learning, 15(5). http://www.irrodl.org/index.php/irrodl/article/view/1854/3097

Introducing Secondary School Students to Big Data and its Social Impact: A Study within an Innovative Learning Environment

Published in Proceedings of the Roundtable Conference of the International Association of Statistics Education (IASE), 2016

With Einat Gil

Recommended citation: E. Gil and A.L. Gibbs (2016). "Introducing Secondary School Students to Big Data and its Social Impact: A Study within an Innovative Learning Environment." In: J. Engel (Ed.), Promoting understanding of statistics about society. Proceedings of the Roundtable Conference of the International Association of Statistics Education (IASE), July 2016, Berlin, Germany. https://iase-web.org/documents/papers/rt2016/Gil.pdf

talks

Experiences teaching an introductory statistics MOOC

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A description of an early (2013) MOOC in introductory statistics and what we learned from data we collected about the learners, including what we know about who enrolled, their initial intentions, and how these are related to indicators of success in the course.

Changing with technology to facilitate learning: Ideas for using technology to find new ways to help students to achieve learning outcomes

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With Bethany White. In this breakout session, we selected some of the key learning outcomes from technology-enhanced courses we have taught, including blended, flipped, and completely online courses and describe ways we have integrated different kinds of technology (including Shiny applets and R, audience response systems, interactive videos, online quizzes, and existing online learning objects) to help students achieve these outcomes.

Does encouragement lead to better attitudes?

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With Nathan Taback. This poster reports on the results of a randomized encouragement study of the effect of weekly emails in an introductory statistics course, taught in both inverted classroom and fully online formats.

Is all that coffee I’m drinking hurting me? Talking about causality in Intro Stats

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In this talk, I addressed some of the problems I have observed that may be inhibiting students’ ability to make appropriate causal inferences, including ambiguity in language and lack of facility in multivariate thinking and I will consider what we might do in the first (and often last) non-mathematical course in statistics, to give our students a deeper, practical understanding.

Teaching statistics in the era of Data Science

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Some considerations on programs of study and the first course for students majoring in statistics given the rise of data science, and the importance of developing adaptive expertise.

Teaching Statistics in a Data Science World

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Some thoughts on teaching Statistics in the era of Data Science, including the process of developing an undergraduate curriculum designed for this context and the structure of the resulting program of study. Illustration of the progression of students’ acquisition of the knowledge, skills and attitudes that we want our graduating students to have is provided with examples from a first course in statistical reasoning and data science and from a capstone course in statistical practice.

Preparing our Students for Unknown Challenges

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Our world of rapidly changing technology, disruptive innovations, and accelerating complexity presents a challenge for how to prepare our learners for lifelong success. We need graduates with an extensive base of knowledge and problem-solving strategies who are also able to apply, adjust, and extend what they know in new environments and to new problems. These adaptive experts will be flexible, innovative, and continual learners, able to function effectively as the nature of their jobs and the way they work change. This talk addresses educational approaches to cultivate the development of adaptive expertise, illustrated with some examples of learning experiences from an introductory data science course.

Preparing our Students for Unknown Challenges

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We use the lens of adaptive expertise to consider how we might produce graduates who not only have an extensive base of knowledge and problem-solving strategies but are also able to apply, adjust, and extend what they know in new environments and to new problems, in preparation for a world of rapidly changing technology, disruptive innovations, and accelerating complexity. With Sotirios Damouras.

Beyond p<0.05: what should we teach about hypothesis testing?

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The realization that many scientific studies cannot be replicated has led to calls to retire statistical significance and a ban on P-values. For many years, our Statistics classrooms have too often incorporated a procedural approach to hypothesis testing, perhaps enhanced with some caveats about what P-values are not and what we should not do. But greater consideration of the development of broader statistical thinking, including how we ask questions, design studies, and collect data, may result in better practice and better understanding of what we can conclude from scientific studies. In this workshop we will discuss how we can engage students in considerations of the scientific and statistical issues that lead to appropriate conclusions and a deeper understanding of what P-values are, starting in students? first course in Statistics. We will work through some classroom-ready examples that illustrate problems with reproducibility, discuss possible reasons, and explore simulations to develop a deeper understanding of statistical testing, including the implications of small samples and p-hacking.

Navigating Whitewater: Preparing our Students for Unknown Challenges

Published:

A world of changing technology, accelerating complexity, and disruptive innovations presents a challenge for how to prepare our students for lifelong success. In addition to an extensive base of knowledge and problem-solving strategies, our graduates need the ability to apply, adjust, and extend what they know in new environments and to new problems. These adaptive experts will be flexible, innovative, and continual learners, able to function effectively as the nature of their jobs and the way they work change. I will discuss the development of learners who are able to thrive in an unpredictable world, and pedagogical approaches to cultivate the development of adaptive expertise. I’ll illustrate with some stories of learning experiences from an introductory data science course.