The material taught in a Formal Languages course is mathematical in nature and requires students to practice proofs and algorithms to understand the content. Traditional Formal Languages textbooks are heavy on prose, and homework typically consists of solving many paper exercises. Some instructors make use of finite state machine simulators like the JFLAP package. JFLAP helps students by allowing them to build models and apply various algorithms on these models, which improves student interaction with the studied material. However, students still need to read a significant amount of text and practice problems by hand to achieve understanding. Inspired by the principles of the Programmed Instruction (PI) teaching method, we seek to develop a new Formal Languages eTextbook capable of conveying these concepts more intuitively. The PI approach has students read a little, ideally a sentence or a paragraph, and then answer a question or complete an exercise related to that information. Based on the question response, students can continue to other information frames or retry to solve the exercise. Our goal is to present all algorithms using algorithm visualizations and produce proficiency exercises to let students demonstrate understanding. To evaluate the pedagogical effectiveness of our new eTextbook, we conduct time and performance evaluations across two offerings of the course CS4114 Formal Languages and Automata. In time evaluation, the time spent by students looking at instructional content with text and visualizations versus with PI frames is compared to determine levels of student engagement. In performance evaluation, students grades are compared to assess learning gains with text and paper exercises only, with text, visualizations with exercises, and with PI frames. / Doctor of Philosophy / Theory textbooks in computer science are hard to read and understand. Traditionally, instructors use books that are heavy on mathematical prose and paper exercises. Sometimes, instructors use simulators to allow students to create, simulate, and test models. Previously, we found that students tend to skip reading the text presented in the books. This leads to less understanding of the topics taught in the course. To increase student engagement, we developed a new eTextbook for the Formal Languages course. We used pedagogy based on Programmed Instruction, presenting the content in the form of short bits of prose followed by the related question. If students can solve the question correctly, this means that they understood the content and are ready to move forward. To help both instructors and students, we developed a new Formal Languages simulator named OpenFLAP. OpenFLAP allows instructors to create many exercises, and OpenFLAP can grade these exercises automatically.

When students use an online eTextbook with content and interactive graded exercises, they often display aspects of two types of behavior: credit-seeking, and knowledge-seeking. Any given student might behave to some degree in either way in a given assignment. In this work, we look at multiple aspects of detecting the degree to which either behavior is taking place, and investigate relationships to student performance. In particular, we focus on an eTextbook used for teaching Formal Languages, an advanced computer science course. This eTextbook is using Programmed Instruction (PI) framesets to deliver the material. We take two approaches to analyze session interactions in order to detect credit-seeking incidents. We first start with a coarse-grained approach by presenting an unsupervised model that clusters the behavior in the work sessions based on the sequence of different interactions that happens during them. Then we perform a fine-grained analysis where we consider the type of each question in the frameset, which can be a multi-choice, single-choice, or T/F question. We show that credit-seeking behavior is negatively affecting the learning outcome of the students. We also find that the type of the PI frame is a key factor in drawing students more into the credit-seeking behavior to finish the PI framesets quickly. We implement three machine learning models that predict students' midterm and overall semester grades based on their amount of credit-seeking behavior on the PI framesets. Finally, we provide a semisupervised learning model to aid in the work session labeling process. / Master of Science / Students frequently exhibit features of two types of behavior when using an online eTextbook with content and interactive graded exercises: credit-seeking and knowledge-seeking. When solving homework or studying a material, students can behave in either manner to some extent. In this research, we study links between student performance and different elements of recognizing the degree to which either behavior is occurring. We concentrate on an eTextbook used to teach an advanced computer science course, Formal Languages and Automata, using a teaching paradigm called Programmed Instruction (PI). In order to detect credit-seeking instances, we use two ways to study students' behavior in the Programmed Instruction sessions. We begin with a coarse-grained approach by building a model that can categorize work sessions into two groups based on the interactions that occur throughout them. Then we do a fine-grained analysis in which we analyze the question types in the framesets and their effect on the students' behavior. We show that credit-seeking behavior has a negative effect on students' learning outcomes. We discovered that the PI frame type is an important factor in enticing students to engage in credit-seeking behavior in an attempt to finish PI framesets fast. Finally, we present three predictive models that can forecast the students' midterm and total semester grades based on their credit-seeking behavior on the Programmed Instruction framesets.