Gui-driven intelligent tutoring system with affective support to help learning the algebraic method

Abstract

Despite many research efforts focused on the development of algebraic reasoning and the resolution of story problems, several investigations have reported that relatively advanced students experience serious difficulties in symbolizing certain meaningful relations by using algebraic equations. In this paper, we describe and justify the Graphical User Interface of an Intelligent Tutoring System that allows learning and practising the procedural aspects involved in translating the information contained in a story problem into a symbolic representation. The application design has been driven by cognitive findings from several previous investigations. First, the process of translating a word problem into an algebraic form has been treated in isolation, and clearly separated from algebraic manipulation. Second, the user interface has been devised to force a systematic approach to problem solving, and also avoid the use of a non-algebraic reasoning. Third, sensor-free affective support has been added by using a machine learning approach that relies on data captured from a series of experimental sessions involving 48 subjects. The evaluation of the resulting application has revealed a positive and significant impact in learning gains.