As computing has become an integral part of our world, demand for teaching computational thinking in K-12 has increased. One of its basic competences is programming, often taught by learning activities without a predefined solution using block-based visual programming languages. Automatic assessment tools can support teachers with their assessment and grading as well as guide students throughout their learning process. Although being already widely used in higher education, it remains unclear if such approaches exist for K-12 computing education. Thus, in order to obtain an overview, we performed a systematic mapping study. We identified 14 approaches, focusing on the analysis of the code created by the students inferring computational thinking competencies related to algorithms and programming. However, an evident lack of consensus on the assessment criteria and instructional feedback indicates the need for further research to support a wide application of computing education in K-12 schools.
The objective of this article is to present the development and evaluation of dETECT (Evaluating TEaching CompuTing), a model for the evaluation of the quality of instructional units for teaching computing in middle school based on the students' perception collected through a measurement instrument. The dETECT model was systematically developed and evaluated based on data collected from 16 case studies in 13 different middle school institutions with responses from 477 students. Our results indicate that the dETECT model is acceptable in terms of reliability (Cronbach's alpha ?=.787) and construct validity, demonstrating an acceptable degree of correlation found between almost all items of the dETECT measurement instrument. These results allow researchers and instructors to rely on the dETECT model in order to evaluate instructional units and, thus, contribute to their improvement and to direct an effective and efficient adoption of teaching computing in middle school.