University of Duisburg-Essen (GERMANY)
About this paper:
Appears in: INTED2020 Proceedings
Publication year: 2020
Pages: 4335-4341
ISBN: 978-84-09-17939-8
ISSN: 2340-1079
doi: 10.21125/inted.2020.1202
Conference name: 14th International Technology, Education and Development Conference
Dates: 2-4 March, 2020
Location: Valencia, Spain
Computer Science (CS) is on the way to become common ground in the public and private space. Defining core competences within CS is indissoluble connected to further developments in the way human users interact with computational machines. The way of usual interactions with intelligent machines, like smart phones or smart cars, is progressing very fast and user interfaces (UIs) are adopted to natural interactions and natural language, like todays man-machine interaction with Siri, Cortana or Alexa for example. Thus while user interaction with computing machines was a base level competence in the past, this may not in the future. It is the mission of education to enable the subject having a deeper understanding of the smart changing world around him and to give him the ability to design the smart environment. Taking this next level of designing and developing the world behind UIs into account leads to programming languages. Thus the idea of a model of programming competence is on the horizon. Therefor the project COMMOOP (suppressed) started to take a deeper look on the concepts of the high-level programming language JAVA and the personal abilities of novice and advanced programmers.

The current empirical study presents the first steps in discovering encoding and decoding competencies of novice and expert programmers. In the context of natural language texts exists a long research history on reading and writing skills, whereas in a formal language text, such as the syntax of a programming language, this is not the case. In order to close this gap, we developed an encoding/decoding test comprising both formal and natural language texts and conducted a study with 42 participants. We found tendencies that textual programming language knowledge influences the encoding and decoding of formal language texts (ANOVA, F(1, 35)= 14.32, p<.001). Therefore, we anticipate our study to be a starting point for a deep analysis of the encoding and decoding competencies of computer science students.

The significant results of this first explorative study discussed above allow a positive impression of the attempt to develop a text comprehension scale of programming languages. This scale will help to improve diagnostics of programming competences as well as improving trainings for the smart systems designers of the public future.
Learning Analytics, Programming, Digital Competences, Empirical Study.