Snap-drift neural network for selecting student feedback

Abstract

This paper investigates the application of the snap-drift neural network (SDNN) to the provision of guided student learning in formative assessments. SDNN is able to adapt rapidly by performing a combination of fast, convergent, minimal intersection learning (snap) and Learning Vector Quantization (drift) to capture both precise sub-features in the data and more general holistic features. Snap and drift are combined within a modal learning system that toggles its learning style between the two modes. In this particular application the SDNN is trained with responses from past students to Multiple Choice Questions (MCQs). The neural network is able to categorise the learner's responses as having a significant level of similarity with a subset of the students it has previously categorised. Each category is associated with feedback composed by the lecturer on the basis of the level of understanding and prevalent misconceptions of that category-group of students. The feedback addresses the level of knowledge of the individual and guides them towards a greater understanding of particular concepts. The trained snap-drift neural network is integrated into an on-line Multiple Choice Questions (MCQs) system. This approach has been implemented and trialled with two cohorts of students using data sets of student answers related to a topic from an Introduction to Computer System module. Results indicate that significant learning support is provided for the students.