Understanding of Smart Content for STEM-Driven CS Education

By this chapter, we start considering a thorough analysis of the first component of our approach, i.e. smart content to support STEM-driven CS education. We define the smart content as a compound of generative learning objects (GLOs), i.e. robot control programs implemented as meta-programs to generate program instances on demand, and component-based learning objects, i.e. quizzes, movies, instructions and other supporting material. We call GLOs smart (SLO) if their structure implements enhanced capabilities for reuse, adaptation or enforced functionality using agent-based technology. GLOs/SLOs are dynamic entities. They have evolved over 7–8 years in the context of our research. For better understanding of GLOs/SLOs, we present an evolution curve and framework of those entities. Using the evolution curve, we have identified three models, i.e. initial M0, intermediate M1 and current M2, to define the growth of GLOs/SLOs functionality. The model M0 is the simplest. The model M1 specifies more functions but without the explicit use of STEM features. The model M2 implements explicit STEM attributes with enhanced functionality. The framework defines the understanding of these entities, using the following visions: learners, teachers, designers and researchers.

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