Using Problem Statement Parameters and Ranking Solution Difficulty to Support Personalization.
Rômulo César Silva, Alexandre Ibrahim Direne, Diego Marczal · International Conference on User Modeling, Adaptation, and Personalization · 2015
The work approaches theoretical and implementation issues of a framework aimed at supporting human knowledge acquisition of mathematical concepts. We argue that personalization support can be achieved from problem statement parameters, defined/set during the creation of Learning Objects (LOs) and integrated with the skill level of learners and problem solution difficulty. The last two are formally defined here as algebraic expressions based on fundamental principles derived from extensive consultations with experts in pedagogy and cognition. Our implemented prototype framework, called ADAPTFARMA, includes a collaborative authoring and learning environment that allows shortand long-term interactions. We present our ongoing research about student modeling to support personalization. Finally, we draw conclusions about the suitability of the claims and briefly direct the reader’s attention to future research.