Content Assessment in Intelligent Computer-aided Language Learning: Meaning Error Diagnosis for English as a Second Language Dissertation

Language practice that includes meaningful interaction is a critical component of many current language teaching theories. At the same time, existing research on intelligent computer-aided language learning (ICALL) systems has focused primarily on providing practice with grammatical forms. For most ICALL systems, content assessment of a learner response is limited to comparing it with a target response through string matching. But this severely restricts exercises that can be offered because expected responses must be tightly controlled. Yet the task-based activities that language instructors typically use in real language-learning settings require both form and content assessment in answer evaluation. Thus, there is a real need for ICALL systems that provide accurate content assessment beyond string matching. This thesis addresses that need by taking an empirically driven approach to the exploration of content assessment. This work argues that while some meaningful activities are too unrestricted for ICALL systems to provide effective content assessment, where the line should be drawn on a spectrum of language exercises is an open question. At one extreme of the spectrum, there are tightly restricted exercises requiring minimal analysis in order to assess content. At the other extreme are unrestricted exercises requiring extensive form and content analysis to assess content. While full natural language understanding is beyond the scope of current technology, this thesis explores activities in the space between spectrum extremes. The primary source of material for this exploration into ICALL content assessment is a corpus of language learner data collected for that purpose. The corpus is comprised exclusively of responses to short-answer reading comprehension questions by intermediate English language learners. Responses to these questions are ideal for developing and testing an