Evaluating the Factors that Facilitate a Deep Understanding of Data Analysis


  • Oliver Burmeister Swinburne University of Technology, Melbourne, Australia




deep learning, data analysis, tertiary education


Ideally the product of tertiary informatic study is more than a qualification, it is a rewarding experience of learning in a discipline area. It should build a desire for a deeper understanding and lead to fruitful research both personally and for the benefit of the wider community. This paper asks: 'What are the factors that lead to this type of quality (deep) learning in data analysis?' In the study reported in this paper, students whose general approach to learning was achieving or surface oriented adopted a deep approach when the context encouraged it. An overseas study found a decline in deep learning at this stage of a tertiary program; the contention of this paper is that the opposite of this expected outcome was achieved due to the enhanced learning environment. Though only 15.1% of students involved in this study were deep learners, the data analysis instructional context resulted in 38.8% of students achieving deep learning outcomes. Other factors discovered that contributed to deep learning outcomes were an increase in the intrinsic motivation of students to study the domain area; their prior knowledge of informatics; assessment that sought an integrated, developed yet comprehensive understanding of analytical concepts and processes; and, their learning preferences. The preferences of deep learning students are analyzed in comparison to another such study of professionals in informatics, examining commonalties and differences between this and the wider professional study.




How to Cite

Burmeister, O. (1995). Evaluating the Factors that Facilitate a Deep Understanding of Data Analysis. Australasian Journal of Information Systems, 3(1). https://doi.org/10.3127/ajis.v3i1.390



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