A New KE-Free Online ICALL System Featuring Error Contingent Feedback

As a first step to implementing a human language teacher, we have developed a new template-based online ICALL (intelligent computer assisted language learning) system capable of automatically diagnosing free-format translated inputs of learners, returning error contingent feedback. The system architecture adopted allows language teachers to build their expertise into the system by themselves without help from KEs (knowledge engineers), thus solving long-standing KE bottlenecks of conventional expert-systems-based ITS (intelligent tutoring system) or ICALL (Murray 1999). The core of the system comprises a unique FSA (finite state automaton)-based template knowledge base system, a robust and global HCS (heaviest common sequence)-based diagnostic engine, a POST(part-of speech-tagged) parser and related learners’ model and an easy-to use VTAT (visual template authoring tool). To simplify the authoring task of often quite complex template patterns, we have developed two sets of simpler rules; the first group of rules allows language teachers to manipulate with ease the complex sentence patterns by constructing a template-template representation from which many separate templates can be extracted, and the second group of buggy rules can be used to generate syntactic bugs for learners automatically by replacing part of the correct syntactic rules with plausible buggy rules. Using study participants ’ responses extracted into the system templates, we present some convincing experimental verifications that the diagnostic engine is capable of providing error-contingent feedback and diagnosis applicable to a wide range of learners having various educational backgrounds