Resumen de A framework for introducing personalisation into stem-driven computer science education

Vytautas Štuikys, Renata Burbaite, Vida Drasute, Giedrius Ziberkas, Sigitas Drasutis

• Currently two approaches, personalised learning and STEM, are intensively researched worldwide; however, we still know littleabout how they should or could be integrated seamlessly. This paper is just about that, proposing a framework for introducingpersonalised learning in STEM-driven Computer Science (CS) education. We motivate the framework by presenting themethodology and theoretical background for creating personalised content. This framework outlines basic activities relevant topersonalised learning in STEM and focuses on the content personalisation and learner’s knowledge assessment and self-assessment.We propose a generic structure of Personalised Learning Objects (PLOs) in three categories: component-based LO, generative LOand smart LO (the latter is a combination of the first two). The generic structure integrates those entities with the assessmentmodules and specifies the distributed interface for connecting them with digital libraries. Firstly, we have developed the learner’sassessment model that integrates attributes defined by the revised BLOOM taxonomy and computational thinking skills with theadequate tasks. Then, using this model and applying meta-programming techniques, we have implemented the assessment modulesand integrated them with PLOs. We illustrate and motivate this approach by presenting two case studies taken from the realeducational setting at the high school. Finally, we evaluate our approach. As STEM relates to technology and engineeringdisciplines and CS-based modules are within most engineering curricula, our approach contributes to engineering education too.