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.
Early and precisely predicting the students’ dropout based on available educational data belongs to the widespread research topic of the learning analytics research field. Despite the amount of already realized research, the progress is not significant and persists on all educational data levels. Even though various features have already been researched, there is still an open question, which features can be considered appropriate for different machine learning classifiers applied to the typical scarce set of educational data at the e-learning course level. Therefore, the main goal of the research is to emphasize the importance of the data understanding, data gathering phase, stress the limitations of the available datasets of educational data, compare the performance of several machine learning classifiers, and show that also a limited set of features, which are available for teachers in the e-learning course, can predict student’s dropout with sufficient accuracy if the performance metrics are thoroughly considered. The data collected from four academic years were analyzed. The features selected in this study proved to be applicable in predicting course completers and non-completers. The prediction accuracy varied between 77 and 93% on unseen data from the next academic year. In addition to the frequently used performance metrics, the comparison of machine learning classifiers homogeneity was analyzed to overcome the impact of the limited size of the dataset on obtained high values of performance metrics. The results showed that several machine learning algorithms could be successfully applied to a scarce dataset of educational data. Simultaneously, classification performance metrics should be thoroughly considered before deciding to deploy the best performance classification model to predict potential dropout cases and design beneficial intervention mechanisms.
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