For many pre-health and graduate programs, organic chemistry is often the most difficult prerequisite course that students will take. To alleviate this difficulty, an intelligent tutoring system was developed to provide valuable feedback to practice problems within organic chemistry. This paper focuses on the design and use of an intelligent input parser for nomenclature questions within this system. Students in Dr. Gould's Fall 2014 organic chemistry class used this system and their data was collected to analyze the effectiveness of the input parser. Overall the students' feedback was optimistic and there was a positive relationship between test scores and student use of the system.

The growing use of Learning Management Systems (LMS) in classrooms has enabled a great amount of data to be collected about the study behavior of students. Previously, research has been conducted to interpret the collected LMS usage data in order to find the most effective study habits for students. Professors can then use the interpretations to predict which students will perform well and which student will perform poorly in the rest of the course, allowing the professor to better provide assistance to students in need. However, these research attempts have largely analyzed metrics that are specific to certain graphical interfaces, ways of answering questions, or specific pages on an LMS. As a result, the analysis is only relevant to classrooms that use the specific LMS being analyzed.

For this thesis, behavior metrics obtained by the Organic Practice Environment (OPE) LMS at Arizona State University were compared to student performance in Dr. Ian Gould’s Organic Chemistry I course. Each metric gathered was generic enough to be potentially used by any LMS, allowing the results to be relevant to a larger amount of classrooms. By using a combination of bivariate correlation analysis, group mean comparisons, linear regression model generation, and outlier analysis, the metrics that correlate best to exam performance were identified. The results indicate that the total usage of the LMS, amount of cramming done before exams, correctness of the responses submitted, and duration of the responses submitted all demonstrate a strong correlation with exam scores.