Individualized Learning Experience Framework: Teaching of Data Science to Non-Computer Science Students

Proceedings of The 6th International Conference on Research in Teaching and Education

Year: 2022

DOI:

[PDF]

Individualized Learning Experience Framework: Teaching of Data Science to Non-Computer Science Students

Gholamreza Rafiee

 

ABSTRACT: 

Data science is a difficult subject to learn because it requires a wide range of prerequisite skills. This paper describes a data science teaching practice that has been running for over three years for students in two different non-computer science pathways including CIT (computing and information technology) and BIT (business information technology) at the school of Electronics, Electrical Engineering and Computer Science (EEECS) in Queen’s University Belfast (QUB). In this paper, a novel framework is proposed by which student learning experiences are personalized before the module begins in an academic year. By identifying each student’s requirements and skills to focus on using a multilevel assessment approach, individualized content recommendations for each student will be provided in the first week of teaching. This enables each learner to concentrate on essential customized learning content materials and self-directed learning. Some preliminary evidence for the efficacy of this framework in improving student learning experience is discussed.

keywords: content recommendation, data science, individualized learning experience framework, prerequisite skills, self-directed learning