## 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? The reviewed literature shows that datasets are used in higher education, presenting many benefits as detailed in the previous subsection. However, datasets can be used in different types of activities and for different purposes. To answer RQ2, we are going to analyse the reviewed papers that present experiences or case studies of active learning methodologies that use datasets in higher education. The work presented in [44] combines the active methodology of Team-Based Learning (TBL) with the use of datasets to teach data science. TBL represents a well-organized adaptation of the small-group cooperative learning models advocated in [47,48]. TBL transforms the traditional classroom experience by encouraging students to actively collaborate with their fellow classmates, applying the course material in practical data science applications to solve real-world problems. In the paper, the authors present an overview of the TBL pedagogical approach and its effectiveness. Although the aim of the paper is to promote the adoption of TBL within the data science education community, one of the intended learning outcomes of their case study is \"becoming familiar with statistical thinking concepts in the context of doing interesting things with data, and doing all of this in a setting promoting the learning of communication, teamwork, and collaboration skills while considering and incorporating discussion of ethics\" [44] (p. 280). For this purpose, the students learn skills such as data visualization, transforming data, Exploratory Data Analysis, importing, and tidying data. Similarly, in [35], the authors explore the effectiveness of the Problem-Based Learning (Problem-BL) methodology with various sensor data in aviation technology courses on aircraft maintenance. The students started working with aircraft engine data collected using numerous sensors, and they analysed them to find a possible problem in the turbine engine, to find a possible solution for the effective maintenance of aircraft. Another example of the use of the methodology of Problem-BL with datasets can be found in [43]. In the work, the authors present a series of DBL exercises that use Green Maps, a data-rich environment, as a tool to conduct inventory, frame analysis, and communicate environmental assets of a community, all within the frame of Problem-BL. They present two case studies, one of them in K-12 education, and the other in higher education. The case study contextualized in higher education consisted of four projects in which students had access to more data than they could consume but they also needed to develop new data to solve the problem assigned. To solve their problem, students needed to reflect critically on the aim of their project and find ways to use existing and new data. The activity ended with the creation of spatial data and maps, which are a method of data visualization. In the same line, the work presented in [34] combines the active methodology of Project-Based Learning (Project-BL) with the use of datasets to teach data science. The paper describes a new data science course designed to emphasize these analytical skills using individual Project-BL in a large-scale cohort. In the course, the students are required to develop a project consisting of interviewing experts, surveying existing data sources, developing a plan to analyse a dataset, cleaning a gathered dataset, and analyse it using visualization. The solution to the proposed project is a dataset cleaned and analysed,in addition to a report that represents the stages of the project. The authors state that with this activity the students develop the following skills: resourcefulness, pro-activity, creativity, abstract thinking, critical thinking, problem solving.