## 4 Discussion
Furthermore, fuzzy logic facilitates the transition between different performance levels by providing a more equitable assessment tailored to the student's progress [60]. In short, fuzzy logic introduces a more flexible and adaptable approach to educational assessment, enabling a more comprehensive and nuanced analysis of student performance. [PERSON] and coworkers discussed its wide application in decision-making, classification, prediction, analysis, and evaluation of student performance [61]. This supports the idea of using a fuzzy logic approach as a powerful tool to overcome the limitations of conventional assessment methods in education, particularly when attempting to rate competencies. Particularly, the Integrated Instruction scheme, as described by [24], is a fuzzy logic-based approach that serves as an alternative to traditional Summative and Rubric-based assessments. This method explicitly outlines guidelines for conducting a learning activity and includes specific indicators with which to determine whether the activity has been completed. The indicators within Integrated Instruction are grouped into three main attributes, aligned with essential aspects of competence acquisition: Knowledge, Procedure, and Attitude. A fuzzy set represents each attribute. In this contribution, we presented the Systematic Task-Based Assessment Method, which is intended to be a generalized version of the Integrated Instruction scheme [24]. This offers a dual operational slant, including the conventional ST modulus and its Fuzzy Inference System-based counterpart, ST-FIS. The ST-FIS relies on indicators that could be aligned with competence acquisition according to the attributes contemplated in the Integrated Instruction method. Then, the operational foundation of the ST-FIS allows for the straightforward incorporation of linguistic typifiers into grading. Again, each one of these attributes is represented by a fuzzy set. A Fuzzy Inference System considers the valuations of these indicators as inputs and generates a precise evaluation output. According to the present analysis, we argue that through consolidation of the operational stage, the ST-FIS scheme could provide an effective means of assessing student-acquired competencies. Concerning the relationship between the accuracy and consistency of a grading method, it is necessary to point out that the presently offered \(CI_{M}\) index measures consistency by integrating complexity penalization and method stability. We observe that \(CI_{M}\) increases with \(CP_{M}\), indicating that more complex methods tend to show higher consistency. The ST-FIS method reported the highest \(CI_{ST-FIS}=1.91\), suggesting that despite its greater complexity, it maintains high coherence in evaluation. Conversely, the Summative method had the lowest \(CI_{S}=1.20\), implying that its complexity might not be sufficient to capture more nuanced evaluations. The Summative method attained the lowest \(CP_{M}\) value with \(CP_{S}=0.23\), indicating that it is the least complex, likely employing a more direct approach with fewer elements and operations. The ST-FIS scheme had the highest \(CP_{ST-FIS}=0.96\), meaning it is the most structured and detailed method in terms of elements and operations. The relationship between \(CP_{M}\) and \(CI_{M}\) that is implicitly enforced by our formulation suggests that methods with higher complexity tend to achieve better consistency values. Moreover, the ratio \(\beta_{M}\) assesses a method's ability to maintain a well-structured approach without being excessively penalized for its complexity. In that regard, the ST-FIS achieved the highest efficiency (\(\beta_{ST-FIS}=0.88\)), indicating that despite its relatively high complexity (only surpassed by the Rubric in the present evaluation), it remained efficient mainly due to its well-structured design that prompted noticeable accuracy.