The paper's contribution is a methodology that integrates two basic technologies (GLO and LEGO robot) to teach Computer Science (CS) topics at the school level. We present the methodology as a framework of 5 components (pedagogical activities, technology driven processes, tools, knowledge transfer actors, and pedagogical outcomes) and interactions among the components. GLOs are meta-programmed entities to generate LO instances on demand depending on the context of use and learning objectives. A GLO is a black-box entity, which is integrated in the framework through the generating process to source the teaching and learning process via robot-based visualization to demonstrate how programs and algorithms are transformed into real-world tasks and processes. The methodology is tested in the real e-learning setting. The pedagogical outcomes are evaluated by empirical data showing the increase of student engagement level, higher flexibility and reuse enhancement in learning.
Aggregating and sequencing of the content units is at the core of e-learning theories and standards. We discuss the aggregating/sequencing problems in the context of using generative learning objects (GLOs). Proposed by Boyle, Morales, Leeder in 2004, GLOs provide more capabilities, focus on quality issues, and introduce a solid basis for a marked improvement in productivity. We use meta-programming techniques to specify GLOs and then to automatically generate LO units on demand. Aggregating of the generated units to form a compound at a higher granularity level can be performed in various ways depending on the selected criteria or their trade-offs (e.g., complexity, granularity level, semantic density, time constraints, capabilities of modelling the learning process, etc.) that enable to evaluate units in advance. We describe aggregating as an internal sequencing of the content units derived from a GLO. Our contribution is a formal graph-based model to specify the problem when the variability of LO units is large. First we formulate the problem and consider properties of the proposed model; and then we analyze a case study, implementation capabilities, and evaluate the approach for e-learning.
Learning Objects (LOs) play a key role for supporting eLearning. In general, however, the development of LOs remains a vague issue, because there is still no clearly defined and widely adopted LO specification and development methodology. We combined two technological paradigms (feature diagrams (FDs) and generative techniques) into a coherent methodology to enhance reusability and productivity in the development of LOs. FDs are used for knowledge representation, modelling variability of the LO content and relationships between its features, and as a high-level specification for generative reuse. The paper describes the specification of LOs using FDs and some design principles to design generative LOs.