Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increasing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT skills and develop a model to describe the dimensions of CT. Through a systematic literature review, using the EBSCO Discovery Service and the ACM Digital Library search, this paper presents an overview of the dimensions of CT defined in scientific papers. A model for developing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills:

There is high demand for qualified Information and Communication Technology (ICT) practitioners in the European labor market. In Estonia, the problem is not a low number of ICT students but a high dropout rate. The aim of this study is to find how it is possible to predict first-year dropout in higher education ICT studies and possibly to engage methods to decrease dropout rate. Data was collected from 301 first-year ICT students in Estonia who filled in a questionnaire at the beginning of the first semester and after the first semester. Additionally, some information was collected electronically during the admission process. The results showed that on average, 32.2% of the ICT students in Estonia dropped out during the first study-year. It was found that students who dropped out had lower scores in the state mathematics exam. This means that the score of the mathematics exam is one characteristic that can predict dropout during the first study-year. At the beginning of the studies there were not many differences in students' perception of their interest and how well the studies met their expectations. However, the answers received after the first semester showed some statistically significant differences between the students who dropped out during the first study-year and those who did not. Differences occurred, e.g., in the case of the following questions: how big their interest in ICT was, how well the studies met their expectations, how pleasant studying was for them, and how high they felt was the probability of them finishing their studies. It can be concluded that asking questions after the first semester gives information to universities as to who are about to drop out. Based on the information universities can support their students to retain them. The results support some factors that were found in literature to be important for avoiding dropout (e.g., motivation, earned credit points, prior studies, expectations), but in some cases the results of this study are different than the literature suggests (e.g., age, gender, working during studies, number of friends in the ICT field). It could be that these factors are not that important in influencing first-year dropout in ICT studies.

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Learners who enrol in massive open online courses (MOOCs) have different backgrounds and tend to have different motivations than learners in traditional courses. Based on value-expectancy theory, an instrument was developed to measure motivation for enrolling in a programming MOOC. A study with 1229 adult participants in Estonian-language programming course "About Programming" was conducted to validate the instrument. Results of confirmatory factor analysis validated the 7-factor scale named factors influencing enrolment in MOOC (FIEM). FIEM comprises three factors of expectancies, three factors of values and one factor of social influence. The highest and lowest rated motivational factors influencing enrolment in programming MOOC are discussed in the paper. Interest in and expectations for the course, personal suitability of distance learning and suitability for family and work are the highest-rated motivational factors for those who enrol in MOOC. Usefulness related to own children, social influence and usefulness to related to certification were the lowest rated. The results of this study can be useful for designers of programming MOOCs and the developed scale might be used in future studies.

Learning programming has become more and more popular and organizing introductory massive open online courses (MOOCs) on programming can be one way to bring this education to the masses. While programming MOOCs usually use automated assessment to give feedback on the submitted code, the lack of understanding of certain aspects of the tasks and feedback given by the automated assessment system can be one persistent problem for many participants. This paper introduces troubleshooters, which are help systems, structured like decision trees, for giving hints and examples of certain aspects of the course tasks. The goal of this paper is to give an overview of usability (benefits and dangers) of, and the participants’ feedback on, using troubleshooters. Troubleshooters have been used from the year 2016 in two different programming MOOCs for adults in Estonia. These MOOCs are characterized by high completion rates (50–70%), which is unusual for MOOCs. Data is gathered from the learning analytics integrated into the troubleshooters’ environment, letters from the participants, questionnaires, and tasks conducted through the courses. As it was not compulsory to use troubleshooters, the results indicate that only 19.8% of the users did not use troubleshooters at all and 10% of the participants did not find troubleshooters helpful at all. The main difference that appeared is that the number of questions asked from the organizers about the programming tasks during the courses via helpdesk declined about 29%.

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