Tepper, J ORCID: https://orcid.org/0000-0001-7339-0132, 2005. Measuring constructive alignment: an alignment metric to guide good practice. Innovation Learning and Teaching Journal. ISSN 1364-0607
Preview |
Text
185824_3507 Tepper postprint.pdf Download (489kB) | Preview |
Abstract
We present a computational model that represents and computes the level to which an educational design is constructively aligned. The model is able to provide ‘alignment metrics’ for both holistic and individual aspects of a programme or module design. A systemic and structural perspective of teaching and learning underpins the design of the computational model whereby Bloom’s taxonomy is used as a basis for categorising the core components of a teaching system and some basic principles of generative linguistics are borrowed for representing alignment structures and relationships. The degree of alignment is computed using Set theory and linear algebra. The model presented forms the main processing framework of a software tool currently being developed to facilitate teachers to systematically and consistently produce constructively aligned programmes of teaching and learning. It is envisaged that the model will have broad appeal as it allows the quality of educational designs to be measured and works on the principle of ‘practice techniques’ and ‘learning elicited’ as opposed to content.
Item Type: | Journal article |
---|---|
Alternative Title: | From theory to automata: a computational model of constructive alignment |
Publication Title: | Innovation Learning and Teaching Journal |
Creators: | Tepper, J. |
Publisher: | HE Academy for Information and Computer Sciences |
Date: | 2005 |
ISSN: | 1364-0607 |
Rights: | © 2006 HE Academy for Information and Computer Sciences |
Divisions: | Schools > School of Science and Technology |
Record created by: | EPrints Services |
Date Added: | 09 Oct 2015 10:25 |
Last Modified: | 04 Feb 2022 12:33 |
URI: | https://irep.ntu.ac.uk/id/eprint/12813 |
Actions (login required)
Edit View |
Statistics
Views
Views per month over past year
Downloads
Downloads per month over past year