On Programming Competence and its Classification
Natalie Kiesler
Abstract
When the concept of competence entered the discourse on learning and teaching in computing education, modeling and classifying programming competences became a new research area. However, universal catalogs of competencies as a basis for curriculum design in Computer Science (CS) do not yet exist. In this paper several educational taxonomies are discussed with regard to their application in CS. Additionally, an empirical study is considered, that gathered expected competencies of introductory programming courses and by means of interviews with university professors. Both of these data sets were analyzed using Mayring’s qualitative content analysis and the categories of the Anderson Krathwohl Taxonomy (AKT) for learning, teaching and assessing. A summarized competency structure model is presented as a continuation of this empirical research. In terms of the knowledge and cognitive process dimensions of the AKT, the empirical analysis proved its suitability for the classification of programming competences. Therefore, this paper outlines the advantages and superiority of the AKT in contrast to other models proposed in the related works. Moreover, an adapted version of the AKT with sub-types and examples for programming is presented in order to help support CS educators in the design of future programming classes and assessments.