Since May 2021, the University of Zaragoza has been working in the development of the toolbox on Dataset-based learning (DBL). Here are the essential points of this active pedagogy.
What is DBL?
Dataset-based learning (DBL) is a student-centred pedagogy in which students learn about a subject through the experience of working directly with dataset taken from real situations. The DBL process does not focus on giving the problems to be solved by students directly. It works over the approach that students should be able to identify problems (guided by teachers) in the dataset or analyse this dataset provided in order to solve the problems proposed or to obtain behaviour patterns useful to learn deeply in some topics. DBL can be considered a subset of Active learning because “students are actively or experientially involved in the learning process”.
Phases of dataset-based learning
DBL can be divided into three main phases:
- Previous to the lesson. Before developing the lesson, the teacher has to prepare material for it:
- Choose a topic to be solved or analysed containing a reference dataset for the learning process.
- Prepare a report with the topic concepts in which the students are going to work.
- The dataset has to be adapted to the objectives of the lesson. In some cases, this implies a previous work with them: establish data collection relations, check the data quality, format the data to be given to the students.
- During the lesson. During the development of the lesson, the teachers have to work with students in:
- Making “reverse engineering” for going from the dataset to the concepts in which the students should work.
- Inferring them from the dataset/ finding them into the dataset/ work with the dataset to obtain another data …
- After the lesson. Once the lesson has been worked with students, it is necessary to:
- Give other datasets for exercising the students in the concepts presented.
- Obtain conclusions and summarize the work done and establish relationships with the learning objectives proposed
Challenges
The main challenges of developing this learning approach are:
- Identifying key dataset resources according to the topics of the subject.
- The previous work done by the teachers to transform and analyse deeply the dataset can be very hard.
Benefits
The main benefits of this approach are:
- Enhance student-centred learning: students are actively involved because they have to have an active participation in the development of the lesson.
- Prominence on comprehension not facts: students have to be able to identify the problems and their nature, and then to develop a solution for them. In this method, collaborative research through discussion forums takes the place of lecturing.
- In-depth learning and constructivist approach: DBL fosters learning by involving students with the interaction of learning materials. They relate the concept they study to real situations and problems.
- Augments self-learning: the after lesson datasets confront students to new scenarios that they have to resolve. This makes them take more interest and responsibility in their learning.
- Better understanding and adeptness: giving more significance to the meaning, applicability and relevance to the learning materials provides better understanding of the subjects learnt. When students are given more challenging and significant problems, it makes them more proficient.
- Reinforce interpersonal skills and teamwork: DBL provides a good scenario for teamwork and collaborative learning. The teams or groups work with shared datasets in collaboration which fosters and reinforces student interaction, teamwork and interpersonal skills.
- Enrich the teacher-student relationship: the use of resources closed to the reality improves the image that students have of teachers in relation with how far away “they live” from the reality. In many cases, students see their lessons as theoretical concepts that they are never going to apply in reality. This generates doubts in the utility of the lessons, as well as in the quality of the teachers.
- Self-motivated attitude: as well as in the previous point, the use of resources closed to the reality also improves the utility of the lessons and motivates students for learning.
In addition, this methodology is a very useful approach for subjects related to data management because it makes it “easier” to empathize with the problem and to understand the effects of the data management concepts learnt.
