Electronic Theses and Dissertations





Document Type


Degree Name

Master of Science


Computer Science

Committee Chair

Vasile Rus

Committee Member

Scott Fleming

Committee Member

Dipankar Dasgupta


The automatic assessment of student answers is one of the critical components of an Intelligent Tutoring System (ITS) because accurate assessment of student input is needed in order to provide effective feedback that leads to learning. But this is a very challenging task because it requires natural language understanding capabilities. The process requires various components, concepts identification, co-reference resolution, ellipsis handling etc. As part of this thesis, we thoroughly analyzed a set of student responses obtained from an experiment with the intelligent tutoring system DeepTutor in which college students interacted with the tutor to solve conceptual physics problems, designed an automatic answer assessment framework (DeepEval), and evaluated the framework after implementing several important components. To evaluate our system, we annotated 618 responses from 41 students for correctness. Our system performs better as compared to the typical similarity calculation method. We also discuss various issues in automatic answer evaluation.


Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.