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.

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