EDIT, with its data-orientated and adaptive approach to design, reveals orthodox practices whilst revealing some unexpected incongruity between alignment theory and design practice. For example, as expected, increasing the amount of questioning, interaction and group-based activity effects higher levels of student satisfaction even though misalignment may be present. However, the model is relatively ambivalent towards the alignment of learning outcomes and learning objectives suggesting there is some confusion between practitioners as to how these are related. The presentation aims to present a model that has been developed to actually modify the module design to promote constructive alignment and higher student satisfaction. This includes examples of modifications made by the system for a number of science-based modules. Subsequently, the audience will be made aware of a tool, based on deep learning that is able to enhance their module designs.
KEYWORDS: Learning design, Constructive alignment, Bloom’s Taxonomy, Auto-encoder networks, Artificial Neural Network
Dalziel, J. (2003), Implementing Learning Design: The Learning Activity Management System (LAMS). Proceedings of the 20th Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education.
Laurillard, D., Charlton, P., Craft, B., Dimakopoulos, D., Ljubojevic, D., Magoulas, G., Masterman, E., Pujadas, R., Whitley, E.A. and Whittlestone, K. (2011), A constructionist learning environment for teachers to model learning designs. Journal of Computer Assisted Learning, 29: 15–30.
Tepper, J. A (2006), Measuring constructive alignment: an alignment metric to guide good practice,” in 1st UK Workshop on Constructive Alignment, Higher Education Academy Information and Computer Sciences (ICS), Subject Centre and Nottingham Trent University, UK.