Informatics in Education

The choice of programming language for education is an intensely debated topic. On the one hand, the programming language is supposed to be relevant in that its organisation, structures, and paradigms adhere to current standards and best practices in industry and academia. On the other hand, the programming language is expected to be simple, that is, easy to use and, of course, easy to learn. This increasingly means that we need an introduction to concepts shaped by a long history without all the historic baggage.

Massive Open Online Courses (MOOCs) have become hugely popular recently. MOOCs can offer high-quality education for anyone interested and equalize the whole education field. Still, there are different methodologies for running MOOCs. Coming up with the most suitable methodology benefits both students and teachers. In this study, we have limited the methodological focus to observing scheduled and unscheduled instances of similar MOOC courses. While unscheduled MOOC courses can provide flexibility, they also require self-regulated learning strategies for students to succeed. To observe this, we compare the effectiveness of scheduled and unscheduled programming MOOC courses to find the most effective methodology. For this, we compare the pass rates and grade averages of five instances (two unscheduled and three scheduled) of Python and Java programming MOOCs. The results show that while the attendance numbers are higher in the unscheduled versions, in the scheduled instances the pass rate is significantly better, and students’ progression is much swifter. It also seems that the higher proportion of university students enrolled in a MOOC course positively affects the retention rate. Moreover, the students in the recent unscheduled Python version seem to score significantly higher grades than in its scheduled counterpart. Based on our experiments, the scheduled and unscheduled versions complement each other. Hence, we suggest that, whenever feasible, the maximal benefits would be gained if both types of MOOCs are run simultaneously.

Programming is one of the basic subjects in most informatics, computer science mathematics and technical faculties' curricula. Integrated overview of the models for teaching programming, problems in teaching and suggested solutions were presented in this paper. Research covered current state of 1019 programming subjects in 715 study programmes at total of 218 faculties and 143 universities in 35 European countries that were analyzed. It was concluded that while most of the programmes highly support object-oriented paradigm of programming, introductory programming subjects are mainly based on imperative paradigm.

Nowadays, the use of information technology (ICT) in education is nothing new. But the question is where the limit is when the use of ICT does not have the desired effect.

We present an overview of the nature of academic dishonesty with respect to computer science coursework. We discuss the efficacy of various policies for collaboration with regard to student education, and we consider a number of strategies for mitigating dishonest behaviour on computer science coursework by addressing some common causes. Computer science coursework is somewhat unique, in that there often exist ideal solutions for problems, and work may be shared and copied with very little effort. We discuss the idiosyncratic nature of how collaboration, collusion and plagiarism are defined and perceived by students, instructors and administration. After considering some of the common reasons for dishonest behaviour among students, we look at some methods that have been suggested for mitigating them. Finally, we propose several ideas for improving computer science courses in this context. We suggest emphasizing the intended learning outcomes of each assignment, providing tutorial sessions to facilitate acceptable collaboration, delivering quizzes related to assignment content after each assignment is submitted, and clarifying the boundary between collaboration and collusion in the context of each course. While this discussion is directed at the computer science community, much may apply to other disciplines as well, particularly those with a similar nature such as engineering, other sciences, or mathematics.