Understanding how software works and writing a program are currently frequent requirements when hiring employees. The complexity of learning programming often results in educational failures, student frustration and lack of motivation, because different students prefer different learning paths. Although e-learning courses have led to many improvements in the methodology and the supporting technology for more effective programming learning, misunderstanding of programming principles is one of the main reasons for students leaving school early. Universities face a challenging task: how to harmonise students’ education, focusing on advanced knowledge in the development of software applications, with students’ education in cases where writing code is a new skill. The article proposes a conceptual framework focused on the comprehensive training of future programmers using microlearning and automatic evaluation of source codes to achieve immediate feedback for students. This framework is designed to involve students in the software development of virtual learning environment software that will provide their education, thus ensuring the sustainability of the environment in line with modern development trends. The paper’s final part is devoted to verifying the contribution of the presented elements through quantitative research on the introductory parts of the framework. It turned out that although the application of interactive features did not lead to significant measurable progress during the first semester of study, it significantly improved the results of students in subsequent courses focused on advanced programming.

In this article, we attempted to examine the issue of the existence of differences in eye move-ment of school-age students as they solve tasks of different difficulty levels in the sciences and natural sciences (computer science, mathematics, physics, biology). Categories of the task’s difficulty level were established on the basis of two types of criteria: subjective (an evaluation made by the subjects) and behavioural (connected to the correctness of their solution). The relationships of these criteria with the visual activity parameters, which were considered to be indicators of mental effort, were identified. An analysis of the data obtained enabled the observation of discrepancies in categorizing difficulties of the tasks on the basis of subjective and behavioural criteria. A significant and strong correlation was noticed between task difficulty level, determined by the percentage of correct answers, and the fixation parameters, although such a relationship with the blink parameters was not found. There was no correlation of the activity of the eye movement parameters, considered to be mental effort indicators, with a student’s opinion about the task’s difficulty level. On the basis of the investigations made, it can be stated that the fixation duration average can be taken as an index of the difficulty level of the task being solved.

The results of qualitative and quantitative investigations conducted with individuals who learned algorithms in school are presented in this article. In these investigations, eye-tracking technology was used to follow the process of solving algorithmic problems. The algorithmic problems were presented in two comparable variants: in a pseudocode and flowchart. The eye-tracking data resulted in both qualitative (films registering the gaze paths) and quantitative measures, which allowed the detection and interpretation of the differences in the task-solving strategies between those who found the correct answer and those that did not. The results confirmed a hypothesis that use of the formal notation characteristic of a programming language for presenting algorithms is often a practical difficulty in the process of solving even simple tasks. This study opens a new direction of research; they show that eye-tracking technology can be used to optimise the educational process of learning programming.

Prediction of visual attention is a new and challenging subject, and to the best of our knowledge, there are not many pieces of research devoted to the anticipation of students’ cognition when solving tests. The aim of this paper is to propose, implement, and evaluate a machine learning method that is capable of predicting saliency maps of students who participate in a learning task in the form of quizzes based on quiz questionnaire images. Our proposal utilizes several deep encoder–decoder symmetric schemas which are trained on a large set of saliency maps generated with eye tracking technology. Eye tracking data were acquired from students, who solved various tasks in the sciences and natural sciences (computer science, mathematics, physics, and biology). The proposed deep convolutional encoder–decoder network is capable of producing accurate predictions of students’ visual attention when solving quizzes. Our evaluation showed that predictions are moderately positively correlated with actual data with a coefficient of 0.547 ± 0.109. It achieved better results in terms of correlation with real saliency maps than state-of-the-art methods. Visual analyses of the saliency maps obtained also correspond with our experience and expectations in this field. Both source codes and data from our research can be downloaded in order to reproduce our results.

Computational thinking is a key universal competence, often taught using methods specific to computer science. One step towards achieving it is learning to analyse and create algorithms. Researchers have long been trying to establish how the form of representation of algorithms (pseudocode versus flowchart) affects its understanding and have reached varying, sometimes conflicting results. This article presents findings that provide objective new data on this topic. In our experiment, we used two different types of algorithmic tasks with three levels of complexity and a group of 114 research participants with varying programming skills. In addition, we used an eye tracking technique that allowed us to collect detailed information about the subjects’ attention distribution during analysis of algorithms. Our results show that subjects took significantly less time to analyse flowcharts (than they did with pseudocode), made much fewer errors, and had higher confidence in the correctness of their solution. Based on eye tracking data, a reduced number of both re-analyses of the algorithm and input data re-referencing was observed for graphically presented tasks. The difference in favour of flowcharts was revealed (with few exceptions) for all levels of algorithm complexity (simple, medium, complex), while regarding the duration of analysis the advantage of flowcharts increased with the growing complexity of algorithms. For complex algorithms, a significant relationship was observed between algorithm presentation and level of programming skills versus the duration of task solving and confidence level. Our study strongly supports the idea of using graphic representation of algorithms both when learning to code and in acquiring computational thinking skills.