Using digital tools to solve pedagogical problems

Data processing and interpretation are perhaps the most sensitive stages of the evaluation process, as this is where raw data are transformed into meaningful insights. Digital tools used in higher education can generate vast amounts of data; however, without appropriate processing and interpretation, these data remain of limited value.

Quantitative data are typically processed using statistical tools and methods. For example, an instructor may analyze the relationship between time spent in the Moodle learning management system and students’ final exam performance. If the analysis shows that students who are more active in discussion forums achieve higher grades, this finding may be interpreted as evidence that digital interaction has a positive effect on learning outcomes.

Qualitative data require a different analytical approach. Student reflections posted in blogs or discussion forums, for instance, can be examined through thematic analysis, in which recurring patterns and categories are identified, such as satisfaction with digital tools, challenges in their use, or suggestions for improvement.

Interpretation must be conducted carefully and within the appropriate context. For example, a low frequency of logins does not necessarily indicate low engagement, as students may rely on alternative resources or work offline. For this reason, findings should always be considered alongside complementary data sources.

A practical example illustrates this point. In the evaluation of a course that incorporated interactive quizzes in Moodle, quantitative analysis revealed that students who completed the quizzes regularly achieved better results. However, qualitative analysis of student feedback showed that the quizzes also helped learners monitor their own progress, rather than serving solely as exam preparation. This combined interpretation provides instructors with valuable insights for improving course design.

In this way, data processing and interpretation extend beyond the mere presentation of statistics and contribute to a deeper understanding of the broader impact of digital technologies on teaching and learning.