## 2 Dataset-Based Learning in the Literature
### Discussion  
In this SLR, the documents that deal with the use of datasets in higher-education active learning activities were reviewed. The analysis of these documents allowed us to answer three research questions: RQ1, RQ2, and RQ3. RQ1 speculated about the benefits of using datasets in higher-education active learning activities. Among the reviewed literature, only two documents answer this question with quantitative data collection and analysis [31; 32], while a third study [33] analyses the benefits of data visualization. The low number of documents that deal with this problematic is attributed to the fact that from our SLR, we excluded all the documents that do not include the term \"active learning\". Nevertheless, in the next section of this work, to establish the benefits of DBL, we consider additional literature studies on the benefits of working with datasets even though they do not include the term \"active learning\", since a DBL activity combines the benefits of active learning and working with datasets. RQ2 speculated about how datasets are used in higher-education active learning activities. To answer this question, we differentiate between two types of learning activities: the first ones [34; 35; 36; 37; 38; 39; 43; 44] are learning activities that are data-centred and data-driven, in which the data are analysed and the information that they provide is relevant to the development of the activity. The second ones [40; 41; 42] are active learning activities that use datasets as auxiliary elements to work with. In our understanding, only the first type of learning activities is considered DBL. RQ3 dealt with the characteristics of datasets used in higher-education active learning activities. The reviewed documents [31; 32; 34; 35; 37; 38; 39; 46] highlight the importance of using real-world data that are perceived by the students as authentic. Having students selecting or collecting their own data is also considered beneficial for the students [34; 37; 39]. The SLR also confirmed the absence of a definition of DBL. The published case studies are subject-specific, and as a result, there is not a well-established methodology for Dataset-Based Learning across a variety of domains. Consequently, we could not find, in the literature, basic guidelines to design a DBL activity in higher education or a reference to the common details and phases of this learning approach. As mentioned, there are many case studies that combine the use of datasets and other active methodologies, such as TBL, Project-BL, Problem-BL, IGL, or open-ended problem solving. After reviewing these case studies, we conclude that DBL is an active learning methodology that can be combined with others while adding a differential approach: the use of datasets as the focus of the activity. Combining active learning methodologies is a practice that can be easily found in the literature; this practice is referred to, in [49], as \"embedded\" methodologies. The authors of [49] mention that the consequence of applying simultaneously educational strategies in a learning activity multiplies their impact on motivation and academic results. However, to either use DBL as a stand-alone methodology or properly \"embed\" it with other active methodologies, we believe that it is important to establish a common approach. The absence of a shared model makes it extremely difficult to compare experiences and approaches or even to learn about DBL in teacher training courses. This common approach is addressed in the following section of this paper, and it was developed considering the case studies analysed in this SLR and other documents from the literature. After discussing the results of the SLR, it is also important to mention its limitations. The SLR resulted in only 16 reviewed documents. This is due to the fact that for locating studies, apart from the keyword \"data\", the keyword \"active learning\" was also included, which limited the results.