{'fraction_non_alphanumeric': 0.025157232704402517, 'fraction_numerical': 0.0017152658662092624, 'mean_word_length': 5.118881118881119, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Natural Language Processing for Open Ended Questions in Mathematics within Intelligent Tutoring Systems

Intelligent tutoring systems continue to enable teachers insight into their students in an immediate fashion. With deep fine-grained data provided to the teachers, they can gain a deeper understanding of the student’s learning. While multiple systems exist, most are limited to specific, close-ended questions; these include questions with a set of known acceptable answers, such as solving for ‘x’ in an equation (i.e. in ‘x+4=6’, the clear answer would be 2). Questions of this variety are implemented within these systems and allot for timely feedback to the students. A system can easily decipher certain values to be incorrect answers and help can be offered to the student. While close-ended problems provide a wide range of insights into the student’s process, they are often unable to gain the deeper discernment of the student’s understanding. Open response questions elicit a greater scope of the student’s understanding. However, very few intelligent tutoring systems provide support to teachers and students for these types of questions. Within the few that can, they are not able to offer automation for the process. One of the greater appeals of computer-based systems is that they provide teachers automated grading and give students immediate feedback. It is therefore my goal to further the study and development of automated assessment and feedback tools to support open-ended problems within computer-based systems. Toward this goal, my focus of research is on the development and deployment of automatic grading models, exploration of fairness within such models, and expansion of existing systems to leverage this research.