Natural Language Interfaces for Educational Technology

1. Natural Language Interfaces for Educational Technology Investigators: Barbara Di Eugenio (Computer Science) Prime Grant Support: ONR, NSF • Study the effectiveness of different paradigms for Educational Technology (ET): tutoring versus peer learning. Use ET to support Computer Science education. • Can ET be made more effective by providing natural dialogue between ET systems and students? • If yes, what features of natural dialogue engender the most learning? • Collect natural dialogues between humans (tutor helping student solve problem, two students solving problems together) • Domain: introductory Computer Science • Mine the dialogues for features thought to correlate with learning, using machine learning techniques • Build computational models for those features • Implement models in dialogue interfaces • Run systematic evaluation with students: compare at least two versions of ET system, one with full dialogue model, one without, or with simplified interface • Tutoring paradigm: • a) Developed 5 versions of iList, tutoring system that helps students with linked lists • b) iList1 through 5 evaluated with more than 200 students • c) iList5 is indistinguishable from expert tutor in learning effects • Peer learning paradigm: • a) Developed KSC-PaL, novel ET system that behaves like schoolmate (linked list domain) • b) Under evaluation