This paper introduces a Dialog-Based Computer-Assisted second-Language Learning (DB-CALL) system using semantic and grammar correctness evaluations and the results of its experiment. While the system dialogues with English learners about a given topic, it automatically evaluates the grammar and content properness of their English utterances, then gives corrective feedback on grammar and semantics. The system consists of a non-native optimized speech recognition module and a semantic/grammar correctness evaluation based tutoring module. The tutoring module decides to continue the dialogue or asks learners to try again by evaluating semantic correctness of their utterances, and also gives them turn-by-turn semantic and grammatical corrective feedback. The semantic correctness evaluation consists of a 2-classes classifier for the "pass or try again" and a 6-classes classifier for semantic corrective feedback, using the domain knowledge and language model. The grammatical correctness is evaluated by a hybrid grammatical error correction system composed of four approaches: a rule-based, a machine learning-based, an n-gram based, and an edit distance based approach. In the experiments, in which 30 subjects in a real environment took part, we acknowledged that the "pass or try again" evaluation has a success rate of 97.5%, the semantic feedback classification has a success rate of 87.8%, and the precision and recall for grammar error correction are 79.2% and 60.9%, respectively. [For full proceedings, see ED564162.]