Towards a Competence Model for the Novice Programmer Using Bloom's Revised Taxonomy - An Empirical Approach

Abstract

This work addresses the demand of an empirically developed competence model for programming as challenging core tier of computer science curricula. The presented paper investigates the application of Bloom's revised taxonomy for learning, teaching and assessing by Anderson and Krathwohl for the specification of currently used learning objectives in programming education. Accordingly, 129 module descriptions of beginner level programming courses from 35 German universities constitute the sample. Learning goals are evaluated using Mayring's qualitative content analysis. In addition, seven guided interviews with computer science professors as experts are categorized according to Mayring's qualitative analysis method. As a result, a model comprised of deductively-inductively built cognitive categories is proposed, proving the adequacy of Bloom's revised taxonomy for computer science and programming in particular. The categories depict current operationalized learning objectives and cognitive competencies of novice programmers, as well as additional non-cognitive competencies. Thus, the results can help classify competency levels and support the didactic design of introductory programming classes and assessment. This research also constitutes a basis for the development of a measuring instrument of programming competence in the future.