LIFE: Lymba’s Immediate Feedback E-learning System

Providing fast feedback to students is critically important. Currently, feedback is provided by instructors manually, which is expensive and has limited bandwidth. This talk will show how semantic technology can be used to generate questions with correct answers and provide immediate feedback to students. The system starts by automatically creating an ontology containing the most relevant concepts from a textbook. The ontology provides a high-level domain overview and is a useful learning tool in itself. The system uses ontologies to select important concepts from a textbook and generate questions about them. Furthermore, student answers are automatically matched against correct answers using textual entailment techniques. The result is immediate feedback to students in terms of what they answered correctly and what they missed. For inaccurate answers the system suggests relevant textbook fragments, so that students can review and improve their knowledge.

Tatiana Erekhinskaya is a Research Scientist and Product Manager at Lymba Corporation. She received a PhD degree in Computer Science from the University of Texas at Dallas with a dissertation on probabilistic models for text understanding. Tatiana has been working in Natural Language Processing for more than 10 years. In her career, she acted as a technical leader on a broad range of projects that included misspelling-robust syntactic parsing for Russian, the first syntax-based opinion mining for Russian, and more recently semantics-driven projects for English in the medical domain, national security, and enterprise applications. One of her latest projects is knowledge extraction from Chinese texts. Her primary research areas are deep semantic processing and big data with a special emphasis on the medical domain.