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Understanding the Development of Knowledge in Novice Database Modellers

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Thesis(23.42 MB)

Date

2023

Authors

Imbulpitiya, Asanthika Medawani

Supervisor

Whalley, Jacqueline
Senapathi, Mali

Item type

Thesis

Degree name

Doctor of Philosophy

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Publisher

Auckland University of Technology

Abstract

Database modelling is a skill any undergraduate student must master as part of a computer science degree program. For novices, it is a challenging task involving interconnected and abstract concepts. The aim of this research was to gain an in-depth understanding of how novices learn to model by examining their learning processes and barriers to learning. Modelling tasks typically given to novices were classified to understand their type and difficulty. Several difficulty metrics were explored, and a novel adaptation of the cognitive dimension of the revised Bloom’s taxonomy for novice database tasks was established. This work informed the design of tasks that comprised the instrument for this research. In designing the instrument, aspects of cognitive load theory were considered, including segmentation, redirected isolated element tasks, structured presentation of the case study, and sequenced sessions. Vygotsky’s zone of proximal development informed a novel intervention model consisting of redirected tasks to support the learners. The tasks of this research instrument were then attempted by novice modellers through a series of think-aloud interviews. Through the constructivist analysis methods of reflexive thematic analysis and narrative analysis, insights into the learning progressions of novice data modellers of different abilities were explored. This thesis presents an original investigation into the transfer of knowledge and its effect on learning data modelling. The research found challenges related to task-specific knowledge within the problem domain, as well as the taught modelling concepts and knowledge acquired through completing tasks in class. The thematic analysis revealed ‘fragile knowledge’ of concepts, terminology, and modelling syntax as the main challenges for the novices. Other difficulties included case study comprehension, lack of verification of design decisions and inability to recall and transfer on-task knowledge. Common tactics used by novices also emerged through the thematic analysis. The students who demonstrated inconsistent problem-solving processes and tactics tended to have weak or failed transfer. The narrative analysis provided an understanding of how the novices in this study constructed their knowledge. A unique visual representation was developed to explain and aid the interpretation of learning progressions. The findings of this study emphasise how fragile knowledge can affect novices regardless of their performance level. One student showed no progress due to fragile knowledge, while other students showed evidence of progression and learning with the support of a more knowledgeable other and redirected tasks, but not alone. This highlights that in addition to cognitive development, a sociocultural approach plays a pivotal role in progressing the learning of novice database modellers. Moreover, the results show that structure segmentation of the problem and an intervention model that isolates elements and progressively scaffolds the learner can be effective in promoting learning. The insights gained from this doctoral research strongly suggest a need for changes in the pedagogies for teaching database modelling and highlight the need for further empirical research in the area.

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http://hdl.handle.net/10292/17326

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Doctoral Theses