Ethical issues and approach in e-learning

1. Ethical issues and approach in e-learning

Digital education brings many advantages such as flexibility, accessibility and innovative approaches to learning, but at the same time it raises a number of ethical questions. When students and teachers move to online environments, new responsibilities arise in terms of data protection, privacy and academic integrity. E-learning is not only a technical process of knowledge transfer, but also a space in which values, trust and professional culture are built. Of course, ethical problems such as copying also exist in contact teaching and are not new, but some of the solutions that e-learning offers us can help to some extent to combat such behavior.

It is therefore important to think about how data is collected, who has access to it and how it is used, as well as how to design tasks that encourage originality and responsibility. This text considers key aspects of ethics in e-learning, offering examples of good and bad practice and guidelines for creating a fair and safe digital learning environment.

2. Privacy and data protection

In digital e-learning environments, student privacy and data protection are key ethical issues. Systems such as Moodle LMS, Microsoft Teams or Google Workspace collect a wide range of information that includes not only basic identification data (name, surname, email), but also data on progress, learning patterns, time spent in the system, and even details about the technical devices from which the student connects. All of this information can be used to improve teaching, for example by monitoring student engagement or identifying difficulties, but at the same time it represents sensitive data whose leakage can have serious consequences.

One of the most important challenges is the issue of information: students must be clearly informed about what data is being collected and for what purpose. For example, if the time of login to the system is recorded, students need to know whether this will be used exclusively for analytical purposes (e.g. improving the user experience) or also for assessing participation. An example of good practice is the use of anonymous surveys to collect data on satisfaction with the teaching, while protecting the personal identity of the student.

It is also necessary to clearly define who has access to the data. As a rule, these are authorized teachers and system administrators, while only anonymized data is used for research purposes. A bad practice would be to share student results with third parties without the knowledge and consent of the students, which would be a violation of ethical and legal norms (e.g. GDPR).

Secure storage and use of data requires a combination of technical and organizational measures: encryption, regular backups, multi-factor authentication, but also educating teachers not to send sensitive information through unprotected channels. An example of good practice is the use of the university's AAI@EduHr identity verification system, which reduces the risk of unauthorized access.

Therefore, privacy protection in e-learning is not only a matter of meeting legal requirements, but also of creating a culture of trust. Students who know that their data is secure are more likely to embrace the digital environment and participate more actively in learning.

3. What data about students is collected?

Digital e-learning platforms collect diverse data about students. These are not only basic personal data, but also complex sets of information that enable detailed analysis of student behavior and engagement. The most commonly collected data include identification data (name, surname, username, e-mail), academic data (grades, test results, activity completion status), but also metadata (login time, session duration, number of clicks or frequency of viewing material).

For example, the Moodle LMS records how many times a student has accessed a particular resource, how much time they have spent completing a test, or whether they have participated in a discussion forum. This data can be useful for teachers to identify students who need additional support. If a teacher sees that a student is rarely logging in or never completing assigned activities, they can send them an individual message of support or suggest a consultation.

On the other hand, such data can be misused if it is used without a clear purpose or transparency. An example of unethical practice would be if metadata (e.g. IP address, access location) is used to monitor students' private lives.

A separate segment is data from communication and collaboration tools, such as recordings of online lectures, chat logs, or work on shared documents. These materials contain images of students' faces and therefore require additional attention in terms of protection. An example of good practice is when lecture recordings are stored only within the e-learning system and are only available to students enrolled in the course, and not to the general public.

Ultimately, while data collection can enhance the learning process, it is ethically imperative that students always know what data is being collected and that they are given the opportunity to give informed consent. This ensures a balance between the benefits of data access and the protection of student rights.

4. Who has access to this data?

Access to student data in e-learning is one of the most sensitive ethical issues. In theory, access should be limited only to those directly involved in the educational process: teachers, system administrators, student services staff, and possibly technical support. However, in practice, a different situation can sometimes occur.

For example, teachers should have access to student results in order to evaluate their work and plan lessons. However, they should not have access to private messages between students, even if these take place within the e-learning system. System administrators need to have technical access to maintain the platform, but their use of the data must be strictly limited to technical purposes and not to tracking individual students.

Researchers sometimes request access to data for scientific analysis, such as studying educational trends. In these cases, anonymization of the data is necessary: removing personal identifiers to protect students. An example of good practice would be when a researcher receives data on student engagement expressed as percentages or time intervals, but without any personal identifiers.

An unethical practice would be, for example, sharing student results with external partners (e.g. private companies) without the students' consent. This would be a violation of the law, but would also seriously undermine trust in the educational institution.

It is therefore important to establish access policies that clearly define who, when and under what conditions may access certain types of data. Institutions should also introduce audit systems (records of who accessed the data and when) to prevent abuse.

5. How to securely store and use student data?

The secure storage and use of student data is the foundation of ethical and legal e-learning. Technical and organizational measures must be in place to prevent leakage, loss, or unauthorized access.

Technical measures include data encryption, backups, regular software updates, and password protection. An example of good practice is when an institution uses two-factor authentication for teachers and administrators, which reduces the risk of account hacking. Another example is the use of secure servers in the European Union, which ensures compliance with the GDPR (we are talking about situations where some educational services are purchased by HEI from external providers).

Organizational measures include education of teachers and students. It is a common mistake when teachers share student data via email without encryption or when students use simple passwords. Education on digital security helps to reduce these risks.

The use of data must be proportionate and purposeful. If a teacher wants to analyze student engagement, they can use aggregated data on time spent in the system, but not detailed data on each individual session. Also, lecture recordings should only be used for learning purposes, not for disciplinary measures without the knowledge of the students.

Therefore, data security is not only a technical issue, but also a question of a responsible culture of use. When students know that their data is stored and used securely, their trust in the institution and willingness to use digital tools increases.

6. Academic integrity

Academic integrity implies adhering to the principles of honesty, responsibility and originality in the learning process. In digital environments, the challenge of plagiarism and cheating is particularly pronounced, as the availability of online resources and tools makes it easier than ever to copy someone else's work. However, at the same time, the digital environment offers opportunities to encourage ethical behavior through good task design and the development of a culture of responsibility. 

An example of a problem is an online exam in the form of a standard multiple choice. Students can easily share answers with each other or use external sources during the exam. If exams are based only on memorizing facts, superficial learning is encouraged and the risk of cheating increases. 

To avoid this, teachers can use a variety of tasks: reflective journals, case studies, project tasks or tasks that require personal examples. For example, a student can be assigned to analyze a problem from their own work or social environment by applying theoretical knowledge. Such tasks cannot be simply copied from the Internet. 

It is also useful to use personalized tasks: in Moodle LMS, the teacher can generate random variations of mathematical problems or cases in business economics, so that each student receives different parameters. This makes cheating more difficult and encourages individual work.

Artificial intelligence (AI) is increasingly present in e-learning, where it brings significant benefits, but also a number of ethical challenges. AI systems can personalize learning, recommend materials, automate feedback, and analyze student progress, thereby improving teaching efficiency and the learning experience. However, the use of AI in education raises questions of privacy, transparency, and academic integrity. Systems that collect data on student behavior can violate privacy if the data is not stored and used responsibly.

Another ethical challenge is the increasing use of generative artificial intelligence (genAI), which students can use to write essays, solve assignments, or automate information searches. While these tools can support learning, their unsupervised and unlabeled use can lead to plagiarism, reduce the development of critical thinking, and violate academic integrity. It is therefore crucial to clearly communicate the rules for using AI tools and encourage transparency in the creation of student work.

Educators should thoughtfully integrate artificial intelligence (AI) into e-learning, choosing tools that are ethically designed, non-discriminatory, and provide explainable results. Educating students about the responsible use of AI, encouraging authentic tasks, and developing digital literacy are important steps toward creating a safe, equitable, and ethically informed digital learning environment.

In addition to the design of assignments, education about the concept of academic integrity is also important. Students should be taught what is considered plagiarism, how to properly cite sources, and why honesty is important not only in their studies but also in their professional lives. An example of good practice is when a course includes a workshop on academic honesty at the beginning of the semester, using plagiarism checking tools as a support. 

Ultimately, integrity is best ensured through a combination of good design, clear rules, and a culture of trust. Students should feel that grades are the result of their efforts, not surveillance or repression. When assignments encourage critical thinking and creativity, academic integrity naturally becomes part of the learning process.

If you want to learn more, we recommend the free online SRCE course called "Autorstvo, plagiranje i citiranje: što, kako, zašto?"

7. How to design tasks that make unethical behavior more difficult?

Having considered ways in which academic integrity can be promoted through a culture of trust and education in ethical principles, the next logical step is to consider specific strategies for designing teaching tasks. It is precisely the way in which we structure and present tasks to students that has a direct impact on their motivation, engagement, and ethical behavior. A well-designed task can act as a protective mechanism against cheating, but also as a tool for deeper learning. The following analyzes methods of designing tasks that reduce the risk of unethical behavior and encourage independent, responsible, and creative thinking.

Task design is a key tool in preventing unethical behavior in e-learning. If the tasks are reduced to the reproduction of facts, students will find it easier to find ready-made answers on the Internet or share solutions with each other. However, if tasks are designed to require the application of knowledge in new situations, cheating becomes significantly more difficult.

One effective method is to contextualize tasks. For example, in a management course, students might be given an analysis of a real-life situation in a local community, where they must apply theoretical models. Such an assignment is difficult to replicate because it requires personal engagement.

Another method is to individualize tasks through random variations. In quizzes, it is possible to generate equations with different numbers, cases with different parameters, or essay questions that vary in focus. This reduces the possibility that students will simply share solutions.

Group work can also be a powerful tool: when students work in smaller teams on a shared project, each member is required to contribute, and the work process becomes transparent. For example, in the Moodle LMS, a teacher can require students to keep a collaboration journal in which they record who contributed what to the project.

Additionally, assignments that encourage critical thinking and creativity naturally reduce cheating. Instead of reproducing a definition, students can discuss the advantages and disadvantages of a particular model or devise their own solution to a problem. Such assignments require originality and thought, which reduces the possibility of plagiarism.

A practical example: instead of a standard essay, one history teacher assigned students to write a “letter to a historical figure” in which they expressed their own views based on the sources they had studied. The results showed greater student engagement and an almost complete absence of plagiarism.

Thus, well-designed tasks not only prevent unethical behavior, but also encourage deeper learning, engagement, and competency development.

8. Conclusion

Ethics in e-learning form the foundation for the sustainable development of digital education. Ensuring the privacy and security of student data builds trust, while promoting academic integrity fosters a culture of honesty and accountability. Teachers and educational institutions must recognize their role not only as transmitters of knowledge but also as guardians of ethical standards.

Transparency in data collection and use, clearly defined access rules, and thoughtfully designed assignments are key elements in preventing unethical practices and encouraging meaningful learning. When digital learning environments are built on these principles, e-learning becomes more than a technical tool; it becomes a space in which values, critical thinking, and professional integrity are cultivated, skills that extend beyond academic contexts and support students in their future professional and personal lives.

9. References

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