The authors discuss empirically determined competence areas to K-12 computer science education, emphasizing the cognitive level of competence. The results of a questionnaire with 120 professors of computer science serve as a database. By using multi-dimensional scaling and cluster analysis, four competence areas to computer science education characterized by the degree of process-related coverage and educational accessibility can be defined: Information technology, computer communication and cooperation, model building, and software engineering. These competence areas consist of central content concepts of computer science (e.g., algorithm, system, and process) combined with central process concepts of computer science (e.g., analyzing, classifying, problem solving, and posing).
This study contributes on instructional methods from a validation technical point of view. The focus is on the assessment of instructional methods in relation to knowledge processes in the act of learning. By using questionnaires, computer science teachers, mathematics teachers, and English teachers assessed 20 instructional methods in terms of knowledge processes (build, process, apply, transfer, evaluate, and integrate). The findings show that computer science teachers and mathematics teachers differ compared to English teachers on the assessment of instructional methods with respect to knowledge processes. The findings represent important contributions for teacher education programs and theory construction concerning effectiveness of instructional methods.
This study contributes to an integrative view on STEM subjects from an educational point of view. The focus is on the assessment of instructional methods in relation to knowledge processes. By a questionnaire, computer science teachers and mathematics teachers assessed 20 instructional methods in terms of knowledge processes (build, process, apply, transfer, evaluate, and integrate). The findings show that computer science teachers and mathematics teachers differ on the rating of instructional methods. However, the findings also allow a common way of looking at instructional methods by computer science teachers and mathematics teachers. This is an important result for pre-and in-service training programs and for the introduction of computer science as a new school subject.
A process model (cpm.4.CSE) is introduced that allows the development of competence models in computer science education related to curricular requirements. It includes eight subprocesses: (a) determine competence concept, (b) determine competence areas, (c) identify computer science concepts, (d) assign competence dimensions to computer science, (e) code competences, (f) formulate competences, (g) formulate learning tasks, and (h) formulate test tasks. The competence definition by the Organisation for Economic Cooperation and Development gives the direction to cpm.4.CSE. This definition highlights four main dimensions of competence: knowledge, cognitive skills, practical skills, and attitudes. For the preparation of subprocesses and the exemplification of concepts, references have been used from the academic literature of computer science education. The processes, activities, and transformations of cpm.4.CSE are documented in Integration Definition for Function Modeling, a process modeling language that is standardized, widely used and easy to understand.
In this article empirically determined, process-related competence areas to computer science education at primary and secondary school levels are discussed. Based on the results of interviews with 120 professors of computer science and using multi-dimensional scaling and cluster analysis, six process-related competence areas to computer science education characterized by the degree of content-related coverage and educational accessibility can be defined: critical thinking, ordering, abstracting, problem solving, analyzing and collaborative construction. These competence areas consist of central process concepts of computer science (e.g., classifying, problem solving and posing) combined with central content concepts of computer science (e.g., algorithm, system, process).
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