학술논문
Dublin Descriptors for Problem Based Learning Iterative Case Studies
Document Type
Conference
Author
Source
2024 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) Teaching, Assessment and Learning for Engineering (TALE), 2024 IEEE International Conference on. :1-7 Dec, 2024
Subject
Language
Abstract
In the context of problem based learning, problems find a significant role in teaching and learning. Learning outcomes are that are written for the course act as anchors to design and realize the reflections of the numerous designed case studies. A good learning outcome sets clarity on expectations. The course learning outcomes can be written using several available taxonomies. This work proposes to use the Dublin Descriptors. The ponders over the three research questions, namely how do writing learning outcomes and reflections differ from Bloom’s and Dublin Descriptors for complex case studies? How can we use Dublin Descriptors to design the learning outcomes for iterative case studies? And what are the recommendations for designing case studies to meet the Dublin Descriptor learning outcomes? With a pragmatic philosophical assumption, and higher order thinking skills conceptual framework, qualitative and quantitative research methods (multi-method) were adapted for analysis of case studies from 79 students. Two case studies were designed for the two written learning outcomes, one using Bloom’s and another using Dublin. A comparative analysis was carried out for both the case studies. Student t-test was used for quantitative study and descriptive, in-vivo coding was used for qualitative study. 53 of 79 students felt the learning from Dublin reflections were effective as compared to Bloom’s. Dublin can bring out structured reflections in case of iterative case studies. While an exercise case study can be effectively evaluated using a Bloom’s outcomes, an iterative case study is justified using Dublin. The qualitative analysis further provides the potential research questions that can be worked on the area for structured and ill-structured case study problems.