Teaching novice conceptual data modellers to become experts

Abstract

This paper describes teaching practices designed to help novice data modellers become expert data modellers. We base these practices on extant empirical research which highlights the strengths of expert data modellers and reveals the weaknesses of novices. After reviewing this research and analysing the causes of the novices' difficulties, we describe the strategy and specific techniques for helping novices to overcome their weaknesses and acquire the strengths and skills of expert data modellers. Techniques recommended include explicit comparison and teaching of novice and expert characteristics and behaviours, providing students with a realistic plan for how to acquire expert data modellers' capabilities, exposure to and comparison of a wide variety of data modelling approaches and topics, extensive amounts of practice on a wide variety of application domains, and critique of practical work in light of the understanding of novice errors and expert behaviours. Our intent is not just to make significant progress during a course, but to provide students with a means to continue to learn and improve in the long term.