ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Deokgun Park


Evaluating and providing feedback to hundreds of free text assignments in an online environment is a challenging task for an instructor where he has to scan through essays to identify perspectives that are expected to appear in those essays. Reading large number of essays and then finding themes and providing customized feedback are time-consuming process. We have proposed a text analytics system named EssayIQ that aids course instructor in identifying assignment themes, providing theme presence statistics and giving feedback to learners. To the best of our knowledge, this is the first system that analyzes free text assignments in line with the instructor defined themes. Through our experiments on one online course, we have shown that model based on sentence-level semantic embedding outperforms word and phrase based embedding models. We have also shown that EssayIQ system can identify themes and can generate overall theme statistics for over hundred submissions within minutes with minimal theme knowledge intake by EssayIQ. The theme identification quality of EssayIQ system is also comparable with human/coach annotators. The code of this project is publicly available in github (https://github.com/Shadek07/EssayIQ).


Automated concept identification, Visual analytics, Yext analytics, Phrase2vec, Online learning, Word2vec, Universal sentence encoder, Reflection essay, Debriefing essay


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington