## 2 Dataset-Based Learning in the Literature
### Results: Analysis and Synthesis
#### 2.3.1 RQ1: What Are the Benefits of Using Datasets in Higher-Education Active Learning Activities? As mentioned in Section 1, working with datasets at all levels of education is strongly related to the acquisition of soft skills, such as structural thinking [1] and critical thinking [2], and to the acquisition of other competences, such as data literacy skills, computational thinking, and data-driven thinking [4]. However, to answer RQ1, we only focus on the reviewed papers, which are contextualized in higher education and which, apart from using datasets in learning activities, use learning methodologies explicitly identified as active. Figure 1: Systematic literature review process overview. In this line, the work presented in [31] aims to assess the impact of data-driven didactic sessions for anaesthesiology residents on metrics including fund of knowledge, resident confidence in clinical topics, and stress, in addition to American Board of Anaesthesiology In Training Examination percentiles. For this purpose, they conducted an observational, mixed-methods, multi-institutional study among anaesthesiology residents who received a data-driven didactic session consisting of an evidenced-based review focused on lifelong learning while using frequently asked/missed topics (based on publicly available data) as a vehicle to drive the education. As a result of their work, the authors associate data-driven didactics with improved resident confidence, stress, and factors related to wellness. A similar study is presented in [32], where the authors created two different groups for an undergraduate hydrology course, which received different lectures. The lecture component was identical for both laboratory groups, and the only instructional difference was whether the students received a data-driven simulation lab exercise or a paper laboratory module (control group). The results of the study demonstrate that learning was enhanced in the data-driven simulation group in nearly every content area and that data-driven simulation can effectively bring the \"real world\" into the classroom and make it accessible, especially in the case of students at lower levels and across the general curriculum. In addition, the authors highlight that their data-driven activity was not excessively complex in the way of computer modelling and programming, as they only used a simple spreadsheet, combined with readily available online hydrological data. In another line, data visualization is an important aspect of DBL. The study presented in [33] investigates how data visualization learning experiences enhanced students' critical thinking and problem-solving skills. For that purpose, in an undergraduate course, the data visualization process was introduced as a seven-stage process. Students learned the key elements of each stage and demonstrated their understanding, competency, proficiency, and ultimately mastery of the process by completing visualization exercises that increased in difficulty to challenge their knowledge and skills. Data collected for the research included post-interviews with students who had enrolled in the course and their responses to class exercises. In simpler terms, the results of the research indicate that data visualization develops students' critical thinking skills. However, the authors highlight that their study was limited by the small number of participants. Two of the reviewed articles [31; 32] present studies with quantitative data collection and analysis to investigate the impact of working with data-driven approaches on students with concrete case studies. The third article [33] does not explicitly refer to data-driven approaches; in contrast, it studies how data visualization learning experiences enhance students' critical thinking and problem-solving skills. This study was also considered in this review due to the fact that data visualization is intrinsic to working with datasets.