David A. Tobinski

Modelling Competency in the Field of OOP: From Investigating Computer Science Curricula to Developing Test Items

In this paper, we describe the results of a thorough analysis of 44 K12 computer science curricula and standards documents conducted as part of an ongoing research project aiming at the development of a competency structure model and measurement instruments in the field of object-oriented programming (OOP). The curricula analysis builds upon a first model draft derived theoretically from a literature analysis in prior work. The model draft is 4-dimensional and consists of the four competency dimensions (1) OOP knowledge and skills, (2) Mastering representation, (3) Cognitive processes and (4) Metacognitive processes. We used these dimensions and the belonging sub-dimensions as a coding scheme and coded competency facets concerning OOP contained in the curricula and standards documents using the method of qualitative content analysis according to Mayring. This way, we could firstly successfully prove the curricular validity of our model draft and secondly, after a step of paraphrasing the identified competency facets, use these descriptions to initiate the process of item development to operationalize our competency model draft.

What do the terms computer, internet, robot, and CD have in common?: an empirical study on term categorization with students

The ability to categorize concepts is an essential capability for human thinking and action. This is why both psychology and subject-specific educational research deal with this topic. For computer science education, there have been no corresponding studies available so far. This paper reports on an empirical study in which around 500 German students from primary to higher education were presented with 23 IT-related terms (such as computer, Facebook, hard drive, virus) with a request to assign these to self-defined categories and to give the categories individual names. This paper gives a first insight into the categorization behavior.

Code structure difficulty in OOP: an exploration study regarding basic cognitive processes

On the way to a model of object-oriented programming competency this study focuses on the interaction of different code structures, in concrete objects and algorithms, and different levels of semantical knowledge on a basic cognitive level of encoding and decoding. Therefore a computer-based instrument has been invented and the first study with 42 students has been conducted. The results seem to be promising to unravel an assumed hierarchy of code structure difficulty

Understanding the Differences Between Novice and Expert Programmers in Memorizing Source Code

This study investigates the difference between novice and expert programmers in memorizing source code. The categorization was based on a questionnaire, which measured the self-estimated programming experience. An instrument for assessing the ability to memorize source code was developed. Also, well-known cognitive tests for measuring working memory capacity and attention were used, based on the work of Kellog and Hayes. Forty-two participants transcribed items which were hidden initially but could be revealed by the participants at will. We recorded all keystrokes, counted the lookups and measured the lookup time. The results suggest that experts could memorize more source code at once, because they used fewer lookups and less lookup time. By investigating the items in more detail, we found that it is possible that experts memorize short source codes in semantic entities, whereas novice programmers memorize them line by line. Because our experts were significantly better in the performed memory capacity tests, our findings must be viewed with caution. Therefore, there is a definite need to investigate the correlation between working memory and self-estimated programming experience.

Measuring Learners’ Interest in Computing (Education): Development of an Instrument and First Results

So far, there is hardly any empirical research on the question of what raises or influences the interest of school learners in computer science or computing education. Aspects to be considered are for example pedagogical decisions of the teacher concerning contexts, phenomena, situations, or concepts to which a lesson or a lesson sequence refers, planned learner activities and many others. This paper analyses a model for describing interest in physics on its transferability to computer science, reports about the development of an online questionnaire for investigating the computing-related interests of school learners and gives results of a first empirical pilot study (based on N = 141 datasets). Based on the participants’ answers concerning socio-demographical aspects, the computing interest of different groups of learners was analyzed. A higher level of computing interest was found at male pupils, learners who indicated that they were striving for a computing-related job, that computing was their favorite school subject, or that they had good or very good school marks in mathematics or computing.