Date: Friday 30 October, 12:00–13:00 GMT (UK time)
Where: Online via Zoom (joining link will be circulated to those registered nearer the time)
How do we get students to stop treating regression output as a black box and start thinking like analysts? This online INERME seminar brings together two approaches that put real data at the centre of teaching. You’ll hear about puzzles, redaction tasks and replication of published research that get students working through the logic of inference, and about an introductory module rebuilt around an authentic data-analysis project using Bank of England data.
Register here to attend (free)
Speakers/Abstracts:
Peter Dawson (University of East Anglia)
Structured Replication in Econometrics Teaching: From Bespoke Exercises to Research-Led Learning
A recurring challenge in teaching econometrics is moving students beyond passive consumption of software output toward genuine engagement with the mechanics and logic of statistical inference. This session showcases a body of teaching resources, published via the Economics Network, built around a shared threefold framework of replication (direct, step, and flexible) applied across two complementary strands of teaching practice.
At introductory level, bespoke, artificial datasets support exercises designed to deepen active learning while minimising quantitative anxiety: redaction-based tasks requiring multi-step reconstruction of missing output; cross-number puzzles demanding precise numerical answers derived from regression results; and alternative diagnostic testing exercises that connect graphical, tabular, and mechanical elements of output in non-standard ways.
At a more advanced level, the session considers the replication and reproduction of published research as a vehicle for research-led teaching. This strand shows how working with real data and original findings shifts students from passive recipients of research to active participants in it, with clear links to employability and data literacy.
The session discusses the shared pedagogical rationale behind both strands, including cognitive load, self-efficacy, and active learning research, and offers practical guidance on sequencing them across a curriculum.
Jingyi Mao (University of Leicester)
Bridging Theory and Practice in Introductory Economics: Embedding Work-Related Learning through Authentic Data Analysis and Assessment Design
Statistics and data analysis are fundamental to economics education, yet students can experience introductory statistics as abstract, formula-driven, and disconnected from how data are used beyond the classroom. This presentation explores how work-related learning can be embedded within an introductory economics module to make these connections more explicit.
The module was redesigned around an integrated theory-to-practice approach, combining core statistical concepts with hands-on computer-based activities using Excel. Students engage with the full analytical process, from data cleaning and analysis to interpretation and communication of findings, helping them understand both how statistical methods work and why they matter.
The presentation will also discuss the redesign of assessment from a traditional examination to an authentic group data-analysis project using real Bank of England data. Students undertake data cleaning, descriptive analysis, visualisation, confidence intervals, hypothesis testing, and open-ended analytical tasks, developing both statistical knowledge and broader employability skills.
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