Basic concepts of data literacy

In higher education, data collection and analysis are essential for understanding the learning and teaching process. Data is gathered using various methods, depending on research objectives, the type of educational activity, and the needs of the teaching process.

Understanding how data is collected is crucial for accurate interpretation and effective application to improve education. Some of the most common data collection methods are described below:

Automatic collection via digital systems
Digital educational platforms, such as e-learning systems, automatically record various student activities. Data is typically collected on time spent on tasks, number of test attempts, scores achieved, and participation in discussions. This information provides valuable insight into learning patterns and allows teachers to adapt content and methods to individual student needs.

Survey and test data
Data is often collected directly from teachers and students through surveys, questionnaires, or tests. These methods examine attitudes, motivation, satisfaction with the teaching process, and perceptions of the effectiveness of digital educational tools. In order to ensure reliable and useful results, it is necessary to carefully design data collection instruments, select an appropriate sample, and systematically analyse the collected information.

Biometric and sensor data
Advances in modern technology have enabled the collection of biometric and sensor data, providing deeper insight into learning processes. Examples include eye-tracking or measuring brain activity (EEG), which can investigate students' attention, engagement, and emotional state during learning. Although these methods offer highly precise insights, their use requires specialised equipment, professional expertise, and strict adherence to ethical standards, especially regarding privacy and personal data protection.

The role of artificial intelligence in data analysis
Artificial intelligence systems are increasingly used to analyse educational data collected by various methods. Artificial intelligence can identify patterns in student behaviour and progress, predict potential learning difficulties, and suggest personalised teaching strategies. However, the effectiveness of these systems largely depends on the quality, reliability, and representativeness of the data.

Therefore, it is crucial for educational experts to develop data literacy, which includes understanding data sources, processing methods, and the ethical and pedagogical implications of their use.