Basic concepts of data literacy

1. Introduction

In digital education, data plays a key role in understanding how learning and teaching occur. Large amounts of data are collected in digital environments every day, helping to identify students' needs, monitor their progress, and pinpoint areas where they need additional support. With these insights, teachers can adjust content, methods, and work formats to promote more active, effective, and meaningful learning.

The ability to understand and use data responsibly is becoming a core competency for every education stakeholder, and it is known as data literacy. It enables evidence-based decision-making, a deeper understanding of the learning and teaching process, and the development of a reflective approach to educational practice.

Artificial intelligence (AI) is playing an increasingly important role in analysing and interpreting educational data. As highlighted in the previous topic, AI-based systems can process large amounts of data, identify learning patterns, and provide personalised recommendations. However, their use requires critical thinking and an understanding of ethical principles, privacy protection, and transparency in information processing.

The following content offers fundamental knowledge about the role of data and AI in education, emphasising the importance of understanding and developing data literacy as a key competence for all educational stakeholders.

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2. What is data literacy?

Data literacy in practice includes the ability to:

• Recognise relevant data in a given context.
• Explain data sources and methods of collection.
• Analyse and interpret data to draw reliable conclusions.
• Apply data-based insights in planning and implementing teaching and research.
• Assess and act in accordance with ethical principles, including respecting privacy, protecting personal data, ensuring transparency in the use of information, and avoiding manipulation or bias in data interpretation.

In an era of increasing use of artificial intelligence (AI) in education, data literacy is becoming a key competency. AI-based systems analyse large amounts of data and generate recommendations about student progress, engagement, or performance. For such information to be useful and credible, teachers and students need to understand how these systems arrive at their results, what methodological assumptions they make, and what their limitations are. Critical reflection on how artificial intelligence collects and processes data is essential for preserving the integrity and fairness of the educational process.

Developing data literacy contributes to an academic culture based on evidence, understanding, and responsibility. In this approach, data is not an end in itself but becomes a tool that supports quality learning, teaching, and research.

In conclusion, being data literate means knowing how to read and understand data, using it to support learning and teaching, and acting responsibly, transparently, and in accordance with the ethical standards of the academic community at every step.

In an educational environment, data-literate teachers and students develop the ability to think critically and use data responsibly throughout the educational process.

3. Basic concept and data types

According to its structure and method of processing, data are usually divided into three basic groups.

Why is data classification important for teachers?

Distinguishing between data types allows teachers to:

• Select collection methods more effectively (such as automatic recording of activities in an e-learning system or manual entry of grades).
• Ensure proper storage and organisation (using databases, cloud solutions, or documentation systems).
• Apply appropriate analytical tools, including those based on artificial intelligence (AI).

Teachers who understand the nature and capabilities of different data types can make informed decisions in the teaching process, recognise student needs, and shape teaching strategies more effectively. This enhances the quality of the educational process and promotes a culture of evidence-based decision-making.

The role of data in the application of artificial intelligence (AI)

The use of artificial intelligence (AI) in education relies heavily on data as a key resource. AI-based systems analyse educational data to identify patterns, provide recommendations, and personalise the learning experience.

The quality and diversity of available data directly affect the accuracy and usefulness of AI systems. If data is unrepresentative or biased, it can lead to misinterpretations, such as incorrect recommendations for additional learning or inaccurate evaluation of student engagement.

Therefore, it is important for educational professionals to understand how data is generated, processed, and interpreted, and to develop a critical approach to its use.

Thoughtful and ethical use of data is the foundation for developing reliable AI systems, and it is essential for more equitable and effective education.

4. How does an IT professional interpret data literacy and why is it important?

What does data literacy mean for an IT professional?

An IT professional is someone with specialised knowledge and skills in computer systems, network technologies, programming, data security, and digital infrastructure. In education, their role is interdisciplinary, linking the technical aspects of information systems with the educational and research needs of teachers, students, and institutions.

For an IT professional, data literacy involves understanding how data is collected, structured, processed, stored, and protected.

An IT professional must ensure that data is accurate, accessible, secure, and ready for analysis, especially when used by artificial intelligence (AI) systems in education.

This role requires a combination of technical knowledge, analytical skills, and ethical responsibility in handling data, enabling the effective and responsible use of information in educational systems.

Key activities of IT experts in working with data

An IT specialist performs a series of activities that ensure reliable data management and responsible use of data in education on a daily basis:

The role of IT experts in applying artificial intelligence in education

An IT professional plays a central role in connecting technical systems with educational practice, ensures stable and secure functioning of information systems, and collaborates with instructional design experts to make sure that technological solutions effectively support pedagogical goals and meet the needs of teachers and students.

The role implies that AI systems:

• Use high-quality and relevant data,
• Produce reliable and understandable results,
• Respect user privacy and follow ethical principles.

This approach requires interdisciplinary understanding – a combination of technical knowledge, critical thinking, and awareness of educational values.

Why is this important?

Without IT professionals who understand the nature, quality, and limitations of data, AI systems may generate inaccurate, biased, or misinterpreted results. Their role is not only technical but also ethical and social: to ensure that technology serves education, not the other way around.

Data literacy among IT professionals is therefore the foundation for the responsible, transparent, and sustainable use of digital technologies that support learning, research, and decision-making in higher education.

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5. Digital data collection in education

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.

6. Challenges of data collection in education

In education, a significant portion of data is collected digitally. Although such data offers numerous opportunities for analysis and improvement of educational practice, its collection and processing also present several challenges that must be recognised and understood to ensure reliability, ethics, and applicability. These challenges include:

Access to technology and the digital divide 
Unequal access to digital resources remains a challenge in education today. Not all students have equal internet connectivity, nor do they all possess the necessary equipment and technical knowledge to participate effectively in online environments. These disparities can lead to incomplete, biased, or uneven data, as some students are simply unable to participate in all digital activities in the same way.

Technical errors 
The reliability of digital data can be compromised by various technical problems, including software errors, network interruptions, or malfunctioning devices used by students and teachers. These issues can result in data loss, inaccuracy, or incompleteness, reducing the research and pedagogical value of the data. Therefore, it is necessary to provide technical support and regular maintenance of digital systems used in education.

Privacy and ethical challenges 
The collection, storage, and analysis of digital data in education must comply with applicable legal frameworks, such as the General Data Protection Regulation (GDPR). It is crucial that students and teachers are clearly informed about the purpose of data collection, how the data will be used, and the options for protecting privacy. Ethics in handling educational data requires transparency, respect for individual rights, and responsible information management. Only such an approach can ensure trust and create a foundation for the safe and ethical use of digital technologies in education.

7. Data processing and interpretation of results

After data collection, it is necessary to carry out systematic processing and analysis in order to obtain useful, reliable, and applicable information for improving learning and teaching. In this process (see image), artificial intelligence-based systems play an increasingly important role, enabling more efficient pattern recognition and a deeper understanding of complex educational data.

ChatGPT je rekao:Proces obrade i analize obrazovnih podataka uz podršku umjetne inteligencije

ChatGPT je rekaoProces obrade i analize obrazovnih podataka uz podršku umjetne inteligencije

Such situations indicate that, in addition to technical understanding and analytical skills, pedagogical knowledge and ethical judgment are necessary to properly interpret and apply the results. Artificial intelligence can be a valuable tool in education, but final decisions about students and teaching strategies should always be made by humans based on comprehensive insight into the educational context.

The integration of artificial intelligence into the processing and analysis of educational data brings many advantages, but also requires a high level of data and digital literacy among the stakeholders involved. Only a combination of technological understanding, pedagogical judgment, and ethical awareness can ensure that data and its analysis truly contribute to higher quality, more equitable, and more efficient education.

8. Ethical aspects and privacy protection in working with data

Developing data literacy involves not only understanding how data is collected, processed, and analysed, but also recognising the ethical responsibilities that arise from its use. In today’s educational environment, where artificial intelligence-based systems are increasingly implemented, ethics and privacy protection have become essential components of professional practice for both teachers and students. The way we manage data reflects not only technical expertise but also the core values of education – trust, responsibility, and respect.

Main ethical principles:

• Transparency: Participants in the educational process must clearly understand why, how, and for what purpose their data is collected and processed. It is especially important to ensure that processes involving artificial intelligence algorithms are understandable and can be explained, to prevent invisible automation of decisions that may affect students.
• Informed Consent: Before data is collected, students should be fully informed about the goals and methods of data use, including those applied in analytical artificial intelligence systems. Consent must be freely given, informed, and revocable at any time without negative consequences.
• Data Minimization: Only data necessary to achieve clearly defined educational objectives should be collected. This eliminates unnecessary collection of sensitive information that is not directly relevant to teaching and learning. This principle supports the responsible and purposeful use of information in education.
• Data Security and Protection: Technical and organisational measures must be in place to protect data from unauthorised access, loss, or misuse. This is especially important when working with artificial intelligence systems that rely on large volumes of data and complex processing models. Security infrastructure should include regular testing, encryption, and systems for monitoring potential threats.
• Anonymisation and Pseudonymisation: Whenever possible, personal data should be anonymised or pseudonymised to protect individual privacy and reduce the risk of identification.
  • Anonymisation includes permanently removing all identifying information so that individuals can no longer be identified.
  • Pseudonymisation replaces personal data with coded labels, so re-association with a person is only possible with additional information that is stored separately and securely.

Application in the educational environment:

Protecting the privacy of students and teachers must be a priority at all stages of educational data management – from collection to analysis and application of results. The introduction of artificial intelligence into education adds complexity to data management, as these systems learn from large amounts of information about students and teachers. Therefore, establishing clear policies on data collection, storage, and use is essential for maintaining high ethical standards and protecting the privacy of all those involved.

Reflective questions:

• In your educational environment, how do you strive to align the use of data and artificial intelligence technologies with ethical principles and the protection of student privacy and security?
• How challenging is this process for you when introducing new digital tools and approaches to educational processes?
• What steps could you take to make the use of artificial intelligence in teaching more accountable, transparent, and focused on student well-being?

9. Conclusion: Why is data literacy important in the practice of educational stakeholders?

Based on the above, a conclusion can be drawn that data literacy is extremely important for all stakeholders in the education system.

For teachers, data literacy is the ability to accurately interpret learning data, recognise patterns in student progress, and adjust teaching strategies based on reliable information.

It enables students to understand their own progress, participate more actively in learning, and critically reflect on automated recommendations from AI-based systems.

For administrators and decision-makers, data provides a foundation for planning and improving study programmes, managing resources more efficiently, and fostering innovation.

However, data literacy extends beyond technical skills. It develops the ability to think critically and ethically and to apply technology responsibly, ensuring that the use of data and AI in education remains aligned with the principles of fairness, transparency, and the well-being of all educational stakeholders.

Ultimately, only a data-literate teacher, student, or educational professional can use data effectively, responsibly, and meaningfully in the age of artificial intelligence – transforming it into knowledge that improves learning and teaching.

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10. Literature