## 2 Dataset-Based Learning in the Literature
### Results: Analysis and Synthesis
#### 2.3.2 RQ2: How Are Datasets Used in Higher-Education Active Learning Activities?
In [41; 42], the datasets are only accessories, with the primary focus of the activities being on the algorithms, rather than the analysis of the data. Other examples of the use of datasets as accessories in an active learning activity are presented in [40], where experiential learning methods are used to teach data science. The reviewed documents present case studies in which the use of data in learning activities is combined with the use of active learning methodologies. However, there are differences in how data are used. Some of these case studies [34; 35; 37; 38; 43; 44] implement well-known active methodologies, such as TBL, Problem-BL, Project-BL, or IGL. On the other hand, the case studies [36; 39] present, as active learning activities, laboratory modules and in-class modules in which students work with data gathered by themselves. These and the previous case studies share the characteristic of keeping the focus of the activity on the data and the information that they contain. On the other hand, the case studies presented in [40; 41; 42] use active methodologies to work with data; but the information of the datasets is not relevant or analysed, nor it is important how it was gathered. According to our understanding, using datasets in an active learning teaching approach does not make it a DBL activity. In contrast, a DBL activity should be data-centred and data-driven. For this reason, only the first cases are considered to have established a common approach to Dataset-Based Learning in the next section. 3.3 RQ3: How Are the Characteristics of Datasets Used in Higher-Education Active Learning Activities? In the reviewed literature, there is a document that explores what aspects of an exercise database contribute to student engagement in database education [46]. Their results indicate that although complex exercise databases may pose obstacles to learning, they also enhance student engagement, because they are perceived as more realistic. In their words, \"students are engaged by highly diverse yet easily understood database business domains, relatively simple database structures, and conceivable yet seemingly realistic amounts of data\" [46] (p. 1). The importance of data authenticity is also highlighted in the reviewed documents: [31; 32; 33; 34; 35; 37; 38; 39]. As stated in [32], real data contextualized within a specific example present an opportunity to apply the course concepts within a formally structured and valid situation, again consistent with professionals in the field. In addition, in [38], it is stated that the appreciation of uncertainties and challenges imposed by data scarcity contributes to students' learning and potential value for future careers. The benefits of having students selecting their own data are also highlighted in [34; 37; 39]. Students may select or gather their own data in different manners, such as consulting existing data sources, performing experiments, and taking measurements, or running their own surveys. Providing the dataset to the students may result in missing out on learning opportunities and skills associated with collecting data or researching databases.