Perceptions of teaching and learning automata theory in a college-level computer science course
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This dissertation identifies and describes student and instructor perceptions that contribute to effective teaching and learning of Automata Theory in a competitive college−level Computer Science program. Effective teaching is the ability to create an appropriate learning environment in order to provide effective learning. We define effective learning as the ability of a student to meet instructor set learning objectives, demonstrating this by passing the course, while reporting a good learning experience. We conducted our investigation through a detailed qualitative case study of two sections (118 students) of Automata Theory (CS 341) at The University of Texas at Austin taught by Dr. Lily Quilt. Because Automata Theory has a fixed curriculum in the sense that many curricula and textbooks agree on what Automata Theory contains, differences being depth and amount of material to cover in a single course, a case study would allow for generalizable findings. Automata Theory is especially problematic in a Computer Science curriculum since students are not experienced in abstract thinking before taking this course, fail to understand the relevance of the theory, and prefer classes with more concrete activities such as programming. This creates a special challenge for any instructor of Automata Theory as motivation becomes critical for student learning. Through the use of student surveys, instructor interviews, classroom observation, material and course grade analysis we sought to understand what students perceived, what instructors expected of students, and how those perceptions played out in the classroom in terms of structure and instruction. Our goal was to create suggestions that would lead to a better designed course and thus a higher student success rate in Automata Theory. We created a unique theoretical basis, pedagogical positivism, on which to study college−level courses. Pedagogical positivism states that through examining instructor and student perceptions of teaching and learning, improvements to a course are possible. These improvements can eventually develop a “best practice” instructional environment. This view is not possible under a strictly constructivist learning theory as there is no way to teach a group of individuals in a “best” way. Using this theoretical basis, we examined the gathered data from CS 341. Our classroom observations revealed several useful instructional techniques. First, an overview lecture should be given so that students have a schema by which to pigeonhole concepts during the semester. Second, using a course webpage to post solutions to homework helps reduce the time between which students complete an assignment and when they receive feedback. The interview data suggested that Dr. Quilt’s instructional strategies, thus perhaps other Automata Theory instructors’ strategies, were strongly influenced by how she learned the material. She covered roughly the same material in CS 341 that she was taught. Dr. Quilt also strongly believes that learning abstract material comes through practice, which is how she mastered the concepts. Student survey responses indicated a love/hate relationship with the topics in CS 341. Students disliked the abstractness of the material, but liked the challenge of solving problems. The responses also suggested that the material at the end of the course should be given more lecture time by covering finite state machines faster. Students expressed that finite state machines were easier to learn. Using end−of−course grades, we analyzed student performance by sub−dividing students into four categories: ultra, high, average, and low performers. Using these categories, we took a holistic view of the survey data to find specific characteristics that could predict performance. Attendance was important for success. We also found that past performance in prerequisite theory courses was a statistically significant indicator for success in CS 341. This work concludes with a summary of suggestions for Automata Theory instructors aimed at improving student learning experiences and success.