AIML (Artificial Intelligence Markup Language) for Online Chinese Language Learning Assessment.

Abstract

Are there relationships between Chinese language learners' asked questions while they are talking with a virtual robot and their Chinese language proficiency as well as other relevant information such as their psychology, their self-confidence, their interested complementary, supplementary information etc.? How to use them to correctly detect learners' language proficiency and all related information if the relationships do exist? The thesis is going to identify these relationships; test these relationships; find the causation of these relationships; explore the implementation of using these relationships. The researcher used a hybrid of quantitative, qualitative, case study and classical 'positivistic' design with which 30 Chinese language learners were selected as micro cases and the relationships between their asked questions while talking with a virtual robot and their Chinese proficiency as well as their psychology, their self-confidence, their interested complementary, supplementary information etc are identified, tested, their implementation explored. So we may use Chinese language learner's asked questions while they have a free conversation with a virtual robot to correctly detect their Chinese proficiency level, their psychology, their self-confidence, their interested complementary, supplementary information etc. according to the test of the researcher's sample statistics. This is efficient, because learners asked questions are the questions they really want to know from their free will without extra time required for a formal survey and assessment.