JOURNAL ARTICLE
Lecturers don't know everything: students listening to the thought processes of lecturers on unfamiliar ground.
Published In: Teaching Mathematics & its Applications, 2025, v. 44, n. 1. P. 68 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Steckles, Katie; Ketnor, Claire; Porter, Ros; Shukie, Alex; Corner, Alexander S 3 of 3
Abstract
This article examines an educational intervention in a level five undergraduate mathematics module at a post-92 university, where lecturers attempted unseen A-level mathematics problems live in front of students to demonstrate the problem-solving process, including making mistakes, getting stuck, and correcting errors. The main aim was to challenge students' perceptions of lecturers as infallible mathematicians and to normalize struggle and error-making as part of mathematical learning. Student responses collected via questionnaires and focus groups indicated that while many were initially surprised to see lecturers struggle, the session helped about half of them feel more comfortable with making mistakes and tackling unfamiliar problems, fostering a more realistic and relatable view of lecturers. However, some students preferred traditional content delivery, and concerns were raised about potential impacts on lecturers' perceived credibility. The study highlights the delicate balance in presenting lecturers' fallibility to support student development without undermining authority, suggesting such activities can positively influence mathematical resilience if carefully implemented.
Additional Information
- Source:Teaching Mathematics & its Applications. 2025/03, Vol. 44, Issue 1, p68
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2025
- ISSN:0268-3679
- DOI:10.1093/teamat/hrae008
- Accession Number:183846406
- Copyright Statement:Copyright of Teaching Mathematics & its Applications is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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