A computational model for task-adapted knowledge organisation: improving learning through concept maps extracted from lecture slides.

Abstract

This thesis presents a framework for automatically generating concept maps from lecture slides. A concept map is recognised as a valuable educational visualisation tool, which assists students in organising, sharing and representing knowledge. Expert maps (also known as expert concept maps) are prepared by domain experts with the intention to serve as scaffolding to facilitate learning. Automated concept map generation provides an alternative solution to the labour-intensive and time-consuming process of manually constructing expert maps. Therefore, the main objective of this thesis is to develop techniques to extract maps from lecture slides, ensuring that auto-generated concept maps may be utilised as a positive alternative to expert maps. This process is known as concept map mining (CMM). The particular interest of this thesis is on CMM from lecture slides, due to their wide usage within the teaching context and the poor support of sequentially-structured lecture slides in aiding learners in identifying relationships between information. In general, semantically and syntactically missing and ambiguous text in lecture slides make it undesirable for adopting previously developed algorithms for CMM. Within this thesis, a set of Natural Language Processing (NLP) algorithms are developed to support concept-relation-concept triple extraction to form concept maps. To support knowledge extraction and to overcome the noise associated with text, this work utilises contextual features specific to lecture slides. The natural layout of the lecture slides is incorporated to organise the extracted triples in a hierarchy. Structural (e.g. co-occurrence, term frequency) and graph-based features (e.g. degree of centrality) are utilised to rank the triples according to their importance within the domain. A series of evaluation studies in this thesis identify promising results, with several case studies demonstrating a strong positive correlation between auto-generated concept maps and human generated maps. These results indicate that this research provides an effective and efficient alternative to expert maps. Auto-generated concept maps can be utilised to provide scaffolding in the problem solving context, in particular supporting students who are lacking the required skills. Even though this application has been studied previously, these studies do not specifically focus on the relevance of information to learning. To fill this gap, this thesis investigates an approach to provide more relevant concept maps to a given problem. In pursuit of this goal, a framework capable of automatically extracting concept maps according to the given problems (named task-adapted concept maps) is developed, utilising auto-generated concept maps from lecture slides as domain knowledge. In order to investigate the effect of task-adapted concept maps as scaffolding for learning, an evaluation study was undertaken, with students in the task-adapted concept map scaffolding group demonstrated statistically significant learning gain compared to the students who received lecture slides or full concept maps as scaffolding.