Abstract
Contribution: Using threshold concepts as the framework for curriculum design, a project on neural network methods for solving differential equations is presented, with a rich set of transformative concepts from mathematics and computer science. Projects of this kind complement a typical curriculum with expertise that is crucial for critique and fundamental development of modern machine learning.
Background: The curricula of many schools of mathematics and computer science present a relatively shallow introduction to the other subject. Student projects, on the other hand, provide an effective environment for interdisciplinary research between the two disciplines.
Intended Outcomes: Providing students from computer science and mathematics the opportunity to obtain a deeper understanding and appreciation of the other subject, beyond the confines of the school curriculum.
Application Design: The project contains tasks that require acquisition, not just of knowledge, but also of effective strategies and mental models, relevant to a set of transformative concepts from both disciplines. The tasks require a spectrum of activities, ranging from rigorous theoretical work to coding.
Findings: Although the theory of threshold concepts needs further development, the existing paradigms provide a helpful framework for curriculum design. The continuous formative assessment proved effective in monitoring the participants' journeys through the liminal state.
Original language | English |
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Pages (from-to) | 139-145 |
Number of pages | 7 |
Journal | IEEE Transactions on Education |
Volume | 66 |
Issue number | 2 |
Early online date | 1 Sept 2022 |
DOIs | |
Publication status | Published - Apr 2023 |
Bibliographical note
Publisher Copyright:© 1963-2012 IEEE.
Keywords
- Differential equation
- mathematical foundations of computer science
- neural network
- summer project
- threshold concept
ASJC Scopus subject areas
- Education
- Electrical and Electronic Engineering