Explicit Scaffolding for Word Learning in Context through Multimedia Word Annotation

The computer-assisted learning system will automatically annotate a large number of words and consider the context in which it appears, and so will be able to create different annotations for the same word based on context. Using algorithms, the system will determine which words are likely unknown to the reader and automatically generate appropriate pictures, pronunciation keys, and dictionary entries. The system continually assesses the interaction with the student and modifies its instruction as the reader engages it (and improves). The feedback will be individualized to each student by considering his/her background characteristics, language level (e.g., as assessed by performance on the California Standards Test), and native language. Individual differences in student vocabulary knowledge will be registered in particular lexicons, allowing the personalized system to target unknown words for each student rather than using the broadband approach typically found in traditional instruction. Teachers will be integrally involved in the instruction and development of the annotation. They, along with parents, will actively contribute to "social annotation" through Web 2.0 techniques to further improve the quality of future annotations. The system will generate a report to the teacher to summarize how well the whole class, and how well each individual student is doing, thus providing instructional feedback for all aspects of the curriculum.