Abstract:

In today's data-driven society, data literacy is not just a desirable but a crucial skill. Organizations rely heavily on data to gain insights, identify patterns, and make strategic data-driven decisions, underscoring the practical importance of data literacy. The ability to collect, comprehend, analyze, interpret, and present data has become essential for students in any field. Specifically, students may be disadvantaged in a marketplace entirely reliant on data by lacking data literacy. Nevertheless, most students continue to struggle with comprehending and evaluating the data. That includes challenges in selecting the appropriate data processing method for the specific data type and inaccuracies in interpreting the outcomes. This paper delves into students' understanding of data and data analysis in two distinct fields: information technology and economics. IT students are assumed to have a solid knowledge of global data types and their implementation in various algorithms, while economics students understand the meaning of economic data. By examining students' data literacy in these diverse fields, the authors aim to provide a comprehensive understanding of the challenges and potential solutions to enhancing students' data literacy. The authors analyzed student projects on real data sets. The project activities included data preparation, setting appropriate queries for data analysis under the context to which the data belong, choosing a method/algorithm for data analysis, and interpreting and presenting the obtained results. Each of the students’ project activities was evaluated separately.

Since these are students from different fields and with different knowledge primarily related to IT (unlike economics students, IT students had subjects such as advanced databases, algorithms and data structures, programming, etc.), it was expected that an IT student would be better at preparing data and implementing an adequate method/algorithm, and an economics student would be better at setting appropriate queries for data analysis and interpreting and presenting results. However, it turned out that the grades for project activities were very similar for both groups of students. Namely, the biggest challenge for students from both faculties is data preparation: choosing the proper data processing technique relevant to the data type, interpreting and analyzing the data, and misinterpreting the results. One reason for this result is the disconnection between the materials from different subjects. For example, students listen to and pass the statistics course (in this case, in the second year for IT students and in the first year of study for economics students) and then apply statistical methods in only 2-3 subjects later during their studies. Therefore, it is logical that students forget and do not repeat part of the material often enough to acquire a specific routine and skillfulness in working with data. Educators can cultivate a generation of students skilled at utilizing data's potential by implementing practical and interactive learning experiences. However, for educators to be successful in this mission, it is necessary to raise the level of data literacy among teachers of all professions and empower them to use data analysis in the context of their subject as much as possible within their curricula.

Keywords:

Data literacy, data understanding, data preparation, data analysis, higher education.