Design, Development and Evaluation of a Lightweight Knowledge-based System for Theoretically-grounded Math Error Classification - Avila-Santos, Anderson P.; Rodrigues, Luiz; Veloso, Thomaz E. V. da; Vieira, Thales; Marinho, Marcelo; Macario, Valmir; Dermeval, Diego; Bittencourt, Ig Ibert; Isotani, Seiji (Anais do Simpósio Brasileiro de Informática na Educação (SBIE); 2024: Anais do XXXV Simpósio Brasileiro de Informática na Educação, 2024)
Mathematical problem solving is essential for developing analytical skills and critical thinking among learners. Traditional methods of detecting and correcting errors in mathematical problem-solving are manual and time-consuming, leading to delayed feedback, which can hinder effective learning. Intelligent Tutoring Systems (ITS) provide real-time, error-specific feedback but require direct interaction with computational devices, limiting their use in classrooms with insufficient technological infrastructure. ITS unplugged (ITS-U) have been proposed to address these technological barriers, but no research has investigated how to design a lightweight error classificatin systems aligned to the resource-constrained setting of ITS-U. Therefore, this paper presents the Mathematical Solutions Error Classification (MSEC) API, a lightweight, knowledge-based tool designed to automate the classification of errors in mathematical solutions. This API categorizes errors into theoretically-grounded types, such as syntactical mistakes, conceptual misunderstandings, and calculation errors. MSEC is particularly suited for low-tech educational environments, aligning with the principles of ITS-U. We implemented MSEC within an ITS-U and conducted a case study involving 49 students, demonstrating its practical applicability and positive contribution to teaching and learning. The study highlights MSECs efficiency, ability to provide real-time feedback, and adaptability to various educational contexts, offering a significant advancement in automated error detection for ITS-U.