Knowledge tracing (KT) is a method to evaluate a student's knowledge state (KS) based on their historical problem-solving records by predicting the next answer's binary correctness. Although widely applied to closed-ended questions, it lacks a detailed option tracing (OT) method for assessing multiple-choice questions (MCQs). This paper introduces a general OT method that can be seamlessly integrated into deep knowledge tracing (DKT) methods through data processing techniques and network output modules. Using a million-level assignment record of MCQs from a K-12 math learning platform, which includes two types of knowledge components (KCs), skill and misconception, we converted five different DKT models into deep option tracing (DOT) models. Performance metrics demonstrate that OT enhances KT performance and effectively identifies students' future option selection tendencies. Furthermore, using the best OT model, we extracted students' problem-solving sequence features and learning gains to analyze learning patterns. The results reveal that for beginners in middle school mathematics, consecutive errors in the same skill might lead to greater learning gains. Finally, we applied network analysis to reveal connections between skills based on students' error tendencies. Our work contributes to KT methods and related empirical findings in learning analytics (LA) for knowledge assessment.