IASE - Designing Positive First Experiences with Coding for Introductory Statistics & Data Science Students
| Date | 22 Oct 2024 |
| Time | 20:00 GMT+02:00 - 21:30 GMT+02:00 |
| Level of instruction | Intermediate |
| Instructor |
Anna Fergusson
|
| Registration fee | |
IASE webinar
Join us on 22 October 2024 at 20:00 UTC (check your local time here) for a 90-minute webinar led by Anna Fergusson from the University of Auckland, New Zealand.
Teaching recommendations for implementing statistics and data science at the introductory level often promote coding (computer programming) as a tool for learning from data. However, there is minimal research concerned with how to design tasks that balance the demands of learning new code-driven tools at the same time as learning new statistical concepts. Using a design-based research approach, I developed four structured tasks for teaching statistical modelling at the same time as introducing the programming language R, which were implemented with high school statistics teachers. A main consideration in designing these tasks was to ensure that learners’ first experiences with coding were positive and inclusive. A task design framework for introducing code-driven tools was produced by using retrospective analysis on the four tasks to identify, evaluate, and refine key design principles and processes.
Concurrently with my research, I designed and implemented a new introductory level statistics course (STATS100) that introduced the programming language R alongside GUI-driven tools. The task design framework developed from my research explicates important features of the tasks used in the research, which include: using unplugged and GUI-driven tools before code-driven tools; extending the familiar into the unfamiliar; using the informal before the formal; and carefully targeting, sequencing and connecting specific human-computer interactions for statistical modelling. These features are also present in the tasks developed for STATS100. In this webinar, I will summarise my research approach, present the task design framework, and demonstrate some of the tasks and data technologies used with learners in both research and teaching contexts to further illustrate the task design principles. I will also discuss key conceptual and practical design considerations for creating and hosting web-based tasks that include videos, progressive revealing of task components, code exercises, and quiz questions.
Instructors
About the instructor
Anna Fergusson is passionate about teaching, data technologies, and developing inclusive, engaging, accessible, effective and fun ways to introduce people to learning statistics and data science. She has over 20 years teaching experience, 12 years at the high school level and nearly 10 years at the university level. Anna has worked with the New Zealand Ministry of Education and the New Zealand Qualifications Authority on the development of national curriculum frameworks, assessment standards, examination papers, project-based tasks, and teaching resources for statistics. At the university level, she has led several statistics and data science curriculum design projects, including the rewrite of the very large introductory-level statistics course (over 4000 students per year).
Anna completed her PhD in 2022, with a thesis focused on task design for introducing computer programming as part of data science at the high school level. She supports and advances her teaching, research and data analysis activities by creating new software tools and educational technologies. Her research specialty is data science and statistics education, with a focus on technology-based and technology-informed pedagogy, including but not limited to: large-scale teaching and assessment practices and tools; introduction of computer programming for data science and associated design principles for tool and task design; tool-mediated development of statistical concepts and reasoning, such as graphical and visual inference; frameworks for observable integrated statistical and computational thinking practices.