Creating a domain model expert behavior is a key component of every tutoring system. Whether the process is manual or semi-automatic, the construction of the rules of expert behavior requires substantial effort. Once finished, the domain model is treated as a fixed entity that does not change based on scope, sequence modifications, or student learning parameters. In this paper, we propose a framework for automatic learning and optimization of the domain model expressed as condition-action rules based on designer-provided learning criteria that include aspects of scope, progression sequence, efficiency of learned solutions, and working memory capacity. We present a proof-of-concept implementation based on program synthesis for the domain of linear algebra, and we evaluate this framework through preliminary illustrative scenarios of objective learning criteria.
['Eric Butler', 'Emina Torlak', 'Zoran Popović']
A Framework for Parameterized Design of Rule Systems Applied to Algebra