The COVID-19 pandemic has resulted in an escalation in the demand for online learning, leading to the need for experts, such as experienced math teachers, to classify exercises. However, achieving accurate and effective classification may present challenges due to differing expert opinions and complex exercise concepts. To address this challenge, we propose the use of the Concept Identification Visualizer (CIV). The CIV tool assists experts who lack engineering programming knowledge in comprehending the exercises and evaluating student responses. The tool leverages Knowledge Tracing to extract relevant information from students' answers and presents this data in a visual format. By providing a more comprehensive understanding of the exercises, the experts enable more informed exercise classification based on student feedback and improve the overall effectiveness of online learning.