CaMeLOT: An educational framework for conceptual data modelling

Context Teaching conceptual data modelling (CDM) remains a challenging task for educators. Despite the fact that CDM is an integral part of software engineering curricula, there is no generally accepted educational framework for the subject. Moreover, the existing educational literature shows significant gaps when it comes to pursued learning outcomes and their assessment. Objective: In this paper, we propose an educational framework for conceptual data modelling, based on the revised Bloom's taxonomy of educational objectives, and provide necessary examples of systemized learning outcomes. Method: We utilized the revised Bloom's taxonomy to develop an adapted framework specifically for learning outcomes related to CDM. We validated the framework by mapping learning outcomes distilled from the existing course material to the framework, by presenting the framework for feedback to the experts in the field and further elaborating and refining it based on the feedback and experiences from these validation activities. Results: CaMeLOT is an adaptation of the Bloom's taxonomy specifically for learning outcomes related to CDM. We identified different content areas and indicated the necessary scaffolding. Based on the framework, we worked out 17 example tables of learning outcomes related to content areas at different levels of scaffolding, exemplifying the different knowledge and cognitive levels. We clarify the differences in learning outcomes related to different knowledge and cognitive levels and thereby provide a domain specific clarification of the classification guidelines. Conclusion: CaMeLOT gives educators an opportunity to enhance the CDM part of software engineering curricula with a systemized set of learning outcomes to be pursued, and open the path for creating more complete, useful and effective assessment packages. The adoption of our educational framework may reduce the time spent on designing educational material and, at the same time, improve its quality.