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Description
Assemblers and compilers provide feedback to a programmer in the form of error messages. These error messages become input to the debugging model of the programmer. For the programmer to fix an error, they should first locate the error in the program, understand what is causing that error, and finally resolve that error. Error messages play an important role in all three stages of fixing of errors. This thesis studies the effects of error messages in the context of teaching programming. Given an error message, this work investigates how it effects student’s way of 1) understanding the error, and 2) fixing the error. As part of the study, three error message types were developed – Default, Link and Example, to better understand the effects of error messages. The Default type provides an assembler-centric single line error message, the Link type provides a program-centric detailed error description with a hyperlink for more information, and the Example type provides a program centric detailed error description with a relevant example. All these error message types were developed for assembly language programming. A think aloud programming exercise was conducted as part of the study to capture the student programmer’s knowledge model. Different codes were developed to analyze the data collected as part of think aloud exercise. After transcribing, coding, and analyzing the data, it was found that the Link type of error message helped to fix the error in less time and with fewer steps. Among the three types, the Link type of error message also resulted in a significantly higher ratio of correct to incorrect steps taken by the programmer to fix the error.
ContributorsBeejady Murthy Kadekar, Harsha Kadekar (Author) / Sohoni, Sohum (Thesis advisor) / Craig, Scotty D. (Committee member) / Jordan, Shawn S (Committee member) / Gary, Kevin A (Committee member) / Arizona State University (Publisher)
Created2017
Description
Software engineering education today is a technologically advanced and rapidly evolving discipline. Being a discipline where students not only design but also build new technology, it is important that they receive a hands on learning experience in the form of project based courses. To maximize the learning benefit, students must conduct project-based learning activities in a consistent rhythm, or cadence. Project-based courses that are augmented with a system of frequent, formative feedback helps students constantly evaluate their progress and leads them away from a deadline driven approach to learning.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
One aspect of this research is focused on evaluating the use of a tool that tracks student activity as a means of providing frequent, formative feedback. This thesis measures the impact of the tool on student compliance to the learning process. A personalized dashboard with quasi real time visual reports and notifications are provided to undergraduate and graduate software engineering students. The impact of these visual reports on compliance is measured using the log traces of dashboard activity and a survey instrument given multiple times during the course.
A second aspect of this research is the application of learning analytics to understand patterns of student compliance. This research employs unsupervised machine learning algorithms to identify unique patterns of student behavior observed in the context of a project-based course. Analyzing and labeling these unique patterns of behavior can help instructors understand typical student characteristics. Further, understanding these behavioral patterns can assist an instructor in making timely, targeted interventions. In this research, datasets comprising of student’s daily activity and graded scores from an under graduate software engineering course is utilized for the purpose of identifying unique patterns of student behavior.
ContributorsXavier, Suhas (Author) / Gary, Kevin A (Thesis advisor) / Bansal, Srividya K (Committee member) / Sohoni, Sohum (Committee member) / Arizona State University (Publisher)
Created2016