Thesis

Intelligent Tutoring System for Learning English Articles

It is known that learning English articles (A, THE, none) is very difficult despite the level of experience the student has with the English language. This is especially true for Asian students with a native language such as Chinese, Japanese, or Korean. Both researchers and teachers acknowledge that one of the most difficult aspects in the English language to learn is the articles. This is true for English as a second language (ESL) and English as a foreign language (EFL) students’. Yoshii and Milne argue that the intent behind article usage has to be described using diagrams. They developed a non-intelligent Computer Assisted Language Learning (CALL) system to help ESL students learn and understand English articles through diagrams. The system is called DaRT, Diagrammatic Reasoning Tool.
 
 Our project enhanced DaRT by first simplifying the diagrams, and then making it an Intelligent Tutoring System, composed of the domain model, the student model, and the pedagogy component. The system creates exercises dynamically, stores student progress in the student model, provides appropriate hints using the misconception library, and decides the best course of action. 
 
 This thesis will first introduce the use of intent-diagram pairs, and then describe each component of the system. It will also give detailed plans for summative evaluation and suggestions for future tasks.

It is known that learning English articles (A, THE, none) is very difficult despite the level of experience the student has with the English language. This is especially true for Asian students with a native language such as Chinese, Japanese, or Korean. Both researchers and teachers acknowledge that one of the most difficult aspects in the English language to learn is the articles. This is true for English as a second language (ESL) and English as a foreign language (EFL) students’. Yoshii and Milne argue that the intent behind article usage has to be described using diagrams. They developed a non-intelligent Computer Assisted Language Learning (CALL) system to help ESL students learn and understand English articles through diagrams. The system is called DaRT, Diagrammatic Reasoning Tool. Our project enhanced DaRT by first simplifying the diagrams, and then making it an Intelligent Tutoring System, composed of the domain model, the student model, and the pedagogy component. The system creates exercises dynamically, stores student progress in the student model, provides appropriate hints using the misconception library, and decides the best course of action. This thesis will first introduce the use of intent-diagram pairs, and then describe each component of the system. It will also give detailed plans for summative evaluation and suggestions for future tasks.

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