Self-explanations (SE) are an effective method to promote learning because they can help students identify gaps and inconsistencies in their knowledge and revise their faulty mental models. Given this potential, it is beneficial for intelligent tutoring systems (ITS) to promote SEs and adaptively respond based on SE quality. We developed and evaluated classification models using combinations of SE content (e.g., inverse weighted word-overlap) and contextual cues (e.g., SE response time, topic being discussed). SEs were coded based on correctness and presence of different types of errors. We achieved some success at classifying SE quality using SE content and context. For correct vs. incorrect discrimination, context-based features were more effective, whereas content-based features were more effective when classifying different types of errors. Implications for automatic assessment of learner SEs by ITSs are discussed.