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With the advent of Massive Open Online Courses (MOOCs) educators have the opportunity to collect data from students and use it to derive insightful information about the students. Specifically, for programming based courses the ability to identify the specific areas or topics that need more attention from the students can

With the advent of Massive Open Online Courses (MOOCs) educators have the opportunity to collect data from students and use it to derive insightful information about the students. Specifically, for programming based courses the ability to identify the specific areas or topics that need more attention from the students can be of immense help. But the majority of traditional, non-virtual classes lack the ability to uncover such information that can serve as a feedback to the effectiveness of teaching. In majority of the schools paper exams and assignments provide the only form of assessment to measure the success of the students in achieving the course objectives. The overall grade obtained in paper exams and assignments need not present a complete picture of a student’s strengths and weaknesses. In part, this can be addressed by incorporating research-based technology into the classrooms to obtain real-time updates on students' progress. But introducing technology to provide real-time, class-wide engagement involves a considerable investment both academically and financially. This prevents the adoption of such technology thereby preventing the ideal, technology-enabled classrooms. With increasing class sizes, it is becoming impossible for teachers to keep a persistent track of their students progress and to provide personalized feedback. What if we can we provide technology support without adding more burden to the existing pedagogical approach? How can we enable semantic enrichment of exams that can translate to students' understanding of the topics taught in the class? Can we provide feedback to students that goes beyond only numbers and reveal areas that need their focus. In this research I focus on bringing the capability of conducting insightful analysis to paper exams with a less intrusive learning analytics approach that taps into the generic classrooms with minimum technology introduction. Specifically, the work focuses on automatic indexing of programming exam questions with ontological semantics. The thesis also focuses on designing and evaluating a novel semantic visual analytics suite for in-depth course monitoring. By visualizing the semantic information to illustrate the areas that need a student’s focus and enable teachers to visualize class level progress, the system provides a richer feedback to both sides for improvement.
ContributorsPandhalkudi Govindarajan, Sesha Kumar (Author) / Hsiao, I-Han (Thesis advisor) / Nelson, Brian (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Paper assessment remains to be an essential formal assessment method in today's classes. However, it is difficult to track student learning behavior on physical papers. This thesis presents a new educational technology—Web Programming Grading Assistant (WPGA). WPGA not only serves as a grading system but also a feedback delivery tool

Paper assessment remains to be an essential formal assessment method in today's classes. However, it is difficult to track student learning behavior on physical papers. This thesis presents a new educational technology—Web Programming Grading Assistant (WPGA). WPGA not only serves as a grading system but also a feedback delivery tool that connects paper-based assessments to digital space. I designed a classroom study and collected data from ASU computer science classes. I tracked and modeled students' reviewing and reflecting behaviors based on the use of WPGA. I analyzed students' reviewing efforts, in terms of frequency, timing, and the associations with their academic performances. Results showed that students put extra emphasis in reviewing prior to the exams and the efforts demonstrated the desire to review formal assessments regardless of if they were graded for academic performance or for attendance. In addition, all students paid more attention on reviewing quizzes and exams toward the end of semester.
ContributorsHuang, Po-Kai (Author) / Hsiao, I-Han (Thesis advisor) / Nelson, Brian (Committee member) / VanLehn, Kurt (Committee member) / Arizona State University (Publisher)
Created2017