Broader terms curriculum mapping: Using natural language processing and visual-supported communication to create representative program planning experiences
This method aims to improve curriculum planning for universities by enabling better communication between faculty and non-faculty stakeholders.
This paper addresses the challenge of creating inclusive curriculum development processes by using natural language processing and data visualization to classify learning objectives. The method generates universal, self-explanatory program plan representations, demonstrated through a simple example and confirmed for accuracy and utility in a case study.
Accreditation bodies call for curriculum development processes open to all stakeholders, reflecting viewpoints of students, industry, university faculty and society. However, communication difficulties between faculty and non-faculty groups leave unexplored an immense collaboration potential. Using classification of learning objectives, natural language processing, and data visualization, this paper presents a method to deliver program plan representations that are universal, self-explanatory, and empowering. A simple example shows how the method contributes to representative program planning experiences and a case study is used to confirm the method's accuracy and utility.