## 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?
A combination of Project-BL and the use of datasets is also presented in [38], in which the authors present an undergraduate learning module that adopts a Project-BL approach to build students' skills on how data analysis and numerical modelling can be used to analyse and design water resources engineering projects. It is worth mentioning that the module was developed using a web-based design where all the datasets, instructions, learning activities, and supporting materials are integrated and accessible via a publicly available web interface. The utilization of dedicated software in learning activities involving datasets is widespread. In [45], the authors designed and implemented a web application to support active learning in data science. The application allows students to grasp individual knowledge discovery processes not just theoretically but also through practical experience. The app support students in analysing data, trying different models, solving problems, testing, and asking and discussing with others about the problem studied. An important aspect of the combination of active methodologies and the use of datasets is that students can be responsible for obtaining their own data, as in the activity presented in [37]. In this Inquiry-Guided Learning (IGL) activity, a research project was proposed to sociology students in which students generated the topics, questions, and data that were then used to complete a substantive research paper. The paper included an analysis of the data and a representation of them. As a novelty, in the activity, the students were responsible for collecting their own data, providing them with greater control over their research in comparison to traditional sociology activities, where the data are provided. The authors highlight that using pre-gathered data results in missing out on learning opportunities and skills associated with collecting data. The importance of having students gather their own data is also highlighted in [36]. In [36], the authors present a laboratory module in which students built their own spectrometer, collected data, and then proceeded to data analysis. The work highlights the relationships among instrument design, data acquisition, and data analysis. Learning activities involving datasets in which the students collect their own data can also take place as \"in-class modules\", combined with theory lectures, as in [39]. In the work, contextualized in a physics course, the modules were designed to be completed in the same class and required students to perform an experiment and analyse their data, to work through a set of calculations, or to perform some computer simulations. In addition, they also required the students to complete two mayor projects during the term, which involved measurements, data analysis, and discussion of results. In addition, the authors state that they found it successful to divide activities involving experiments to gather data in three parts: prediction of the results, measurement, comparison of the predicted results and the experimental results. On the other hand, in the reviewed literature, we also found case studies that use active methodologies to teach data science, in which the students work with datasets. However, in some of these activities, datasets are used as a supplement for the activity; but the information they provide is not relevant, nor it is analysed. For instance, in [41], the authors propose an intervention to develop divergent and convergent thinking skills through open problem solving in a data structures course. The students were presented with open-ended problems that were based on real-world problems, and they were asked to select appropriate data structure and operations for solving the problem. Similarly, the work presented in [42] studies the use of gamification in data science, using a model that works as an interpreter that takes both the algorithm and generated data as input and then interprets each step of the algorithm.