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Perceptions of teaching and learning automata theory in a college-level computer science course.

机译:在大学水平的计算机科学课程中教授自动机理论的知识。

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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. (Abstract shortened by UMI.)
机译:本文确定并描述了学生和教师的看法,这些看法有助于在竞争激烈的大学水平计算机科学课程中有效地教授和学习自动机理论。有效的教学是创造适当的学习环境以提供有效学习的能力。我们将有效学习定义为学生达到教师设定​​的学习目标的能力,并通过学习课程来证明这一点,同时报告良好的学习经验。我们通过在德克萨斯大学奥斯汀分校的自动机理论(CS 341)的两个部分(118名学生)进行了详细的定性案例研究,由Lily Quilt博士进行了教学。由于自动机理论具有固定的课程设置,因此许多课程和教科书都同意自动机理论所包含的内容,不同之处在于单个课程涵盖的深度和材料的数量,因此案例研究将得出可概括的发现。自动机理论在计算机科学课程中尤其成问题,因为在上这门课程之前,学生没有抽象思维的经验,无法理解该理论的相关性,并且喜欢具有更具体活动(例如编程)的课程。由于动力对学生的学习至关重要,因此这对任何自动机理论讲师都构成了特殊的挑战。通过学生调查,讲师访谈,课堂观察,材料和课程成绩分析,我们试图了解学生的看法,讲师对学生的期望以及这些看法在结构和指导方面如何发挥作用。我们的目标是提出建议,以使课程设计更好,从而提高自动机理论的学生成功率。我们创建了独特的理论基础,即教学实证主义,可以在此基础上学习大学课程。教育学的实证主义指出,通过检查教师和学生对教与学的理解,可以改进课程。这些改进最终可以开发出“最佳实践”教学环境。在严格的建构主义学习理论下,这种观点是不可能的,因为没有办法以“最佳”的方式来教导一群人。使用此理论基础,我们检查了从CS 341收集的数据。(摘要由UMI缩短。)

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