Berufungsvortrag Dr. Bernhard Standl: Utilizing Computational Thinking for Real-Life Applications
Berufungsvortrag Dr. Bernhard Standl
Institut für Informatik, SR K40
9:15 - 13:00 Uhr
Titel :Utilizing Computational Thinking for Real-Life Applications
Just as Nickerson et. al. argued in : Definitions of computational thinking vary, there is only little consent in a definition for computational thinking and so far a common understanding can only be identified in Wing’s view : Computational Thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent. Rich et al. are highlighting this ongoing process in , mentioning that computational thinking emerged during the last decade to perhaps to the most cited topic in the field of computer science education and is still developing in different directions.
Moreover, there are a multiplicity of different areas where computational thinking is being implemented within computer science education. Generally said, for the most part, computational thinking is either understood as umbrella term for learning coding concepts as e.g. loops, variables, recursions or as a term describing a problem-solving approach as used in computer science. Considering Yadav’s recent contribution , suggesting computational thinking can be integrated for everyday life challenges, this presentation introduces an approach for computational thinking which combines computer science algorithms with real-life applications (as e.g. finding the shortest path to the school) addressing these questions:
- How can a definition for computational-thinking be defined, which is aimed at the integration of algorithms and
- How can such definition put in practice classroom teaching?
As a result, a problem-solving process was identified and further integrated in classroom lessons, carried out with four student groups and analysed with qualitative (analysis of worksheets) and quantitative methods (questionnaire) at a sample size of n=75. Results showed that students frequently discovered a good approximation to real computer science algorithms and furthermore developed their attitudes for approaching challenges in the context of computational thinking.
Further outcomes of the questionnaires, evaluating students’ attitudes for problem-solving showed, that the intervention had some positive impact, even if it is likely that students didn’t adopt their dispositions but rather had an increased awareness which dispositions are required for computational thinking problem-solving.
 Hilarie Nickerson, Catharine Brand, and Alexander Repenning.
Grounding Computational Thinking Skill Acquisition Through Contextualized Instruction.
Proceedings of the eleventh annual International Conference on International Computing Education Research, pages
 Peter J. Rich and Matthew B. Langton.
Computational Thinking: Toward a Unifying Definition.
In Competencies in Teaching, Learning and Educational Leadership in the Digital Age, pages 229–242.
Springer International Publishing, Cham, 2016.
 Brandon Rodriguez, Cyndi Rader, and Tracy Camp.
Using Student Performance to Assess CS Unplugged Activities in a Classroom Environment. pages 95–100, 2016.
 Jeannette M Wing.
Computational Thinking: What and Why? thelink - The Magazine of the Carnegie Mellon University
School of Computer Science, 2011.
 Aman Yadav, Hai Hong, and Chris Stephenson.
Computational Thinking for All: Pedagogical Approaches to Embedding 21st Century Problem Solving in K-12
TechTrends, pages 1–4, 2016.
Zeit & Ort
28.04.2017 | 09:00 c.t. - 12:00
Institut für Informatik,UG, SR K40