Students practice Data Query, Manipulation, and Definition Language statements in a standard introductory relational database course. When teaching large classes, the teacher needs to review many student solutions. Doing it by hand is laborious. However, this task can be quickly and accurately completed using a learning management system (LMS). Moodle is a widely used LMS that can be enhanced with the CodeRunner plugin to facilitate the evaluation of programming tasks. Unfortunately, a basic setup supports only the embedded database engine SQLite, which often is not the preferred choice for a course of this type. We present an open-source implementation that extends CodeRunner with popular client-server database engines, i.e., Microsoft SQL Server, MySQL, and PostgreSQL. Our method checks the correctness of a student's query in terms of output and validates the query by investigating the parse tree derived from the grammar of a given Structured Query Language (SQL) dialect. We present the system's evaluation results during a database course along with the implementation.

Learning materials provided to students during remote learning are characterized by different technical forms and interactivity levels. The structure of the materials is often enforced by the tools used for their creation, it could also result from the preferences and experiences of their authors. This article presents the effect of the structure of learning materials on the student's performance as well as the dynamics of all interactions of students with such materials. The experiment used learning materials provided to students for self-study as part of classes from a 15-week database course. The classes were held in the form of weekly lectures and teaching labs. The course comprised several larger sections, beginning with the introduction to the SQL SELECT command. Each learning materials module for this part of the course contained approx. 25 programming tasks which were assessed automatically using the Jobe/CodeRunner environment. It ended with a quiz with random questions of single- or multiple-choice, fill-in the gap, or calculate the result. Before the course started, 88 students were randomly divided into two groups, each numbering 44 students. The first group received learning materials with a granular structure, which were divided into small, thematically cohesive chunks of knowledge. The second group received the same learning materials organized in the manner webpages are constructed (long form), i.e., all the material content from one class was presented on one webpage. This is a solution known, for example, from Jupyter Notebook documents. The deadline for completing one module and taking the quiz at the end of it was one week and was the same for both groups. The self-learning materials replaced traditional weekly teaching labs, but students were allowed to contact the teachers remotely to obtain help or additional explanations. The results show that the structure of the materials did not have a significant effect on the performance of the students, yet they were more involved in learning and spent more time in the course when they worked with granular materials. Their levels of satisfaction were also higher. The results presented in the article may be of interest to teachers when deciding on the form of learning materials to be created.

The outbreak of the COVID-19 pandemic gave rise to a need to change course syllabi in order to completely transition to a remote learning model. In the case of subjects comprising programming tasks, taking into account the availability of tools and resources that provide opportunities for independent work without teacher supervision, it was necessary to decide whether students should be allowed to work under self-paced learning, or, similar to traditional classes, fixed-schedule learning. Experiences from MOOCs demonstrated that course scheduling is not without impact on the learning process, and may affect the level of student satisfaction with the course. This study determines how course scheduling affected the performance of students attending a database course. The students were divided into two groups, and completed the first module without teacher supervision. During the learning process, they solved programming tasks which were assessed automatically, and then took quizzes to verify what they learned. One of the groups worked with the materials and took the quizzes in accordance with a schedule, and the other group did so without any time constraints. The results demonstrate that students perform better when working under the fixed-schedule model, without any impact on their level of satisfaction with the course. The system of learning not only affected the quiz results in the module where different scheduling was used, but student performance in later parts of the course as well. The results presented in the paper should be of interest to teachers designing remote courses involving self-learning.