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dc.contributor.authorCho, Eunohen
dc.date.accessioned2022-06-22T08:00:39Zen
dc.date.available2022-06-22T08:00:39Zen
dc.date.issued2022-06-21en
dc.identifier.othervt_gsexam:35089en
dc.identifier.urihttp://hdl.handle.net/10919/110852en
dc.description.abstractComputer Science students need to understand the mechanism of programming systems that involve computation, automation, and information. Computer scientists need to know how to design and analyze a problem and solve it with an algorithm. We study students' behaviors in CS education to find out patterns of those who need help. Several behaviors are examined: Time Management, Incremental development, Self-checking, Persistence, and Planning. Help-seeking, when done correctly, is known as a good strategy related to self-regulated learning. This behavior includes online searching, coming to office hours for help from instructional staff, and asking instructors and peers publicly on online forums. Some of these sources of help can be tracked more easily than others. We present efforts to collect and analyze data related to the help-seeking behavior of students in a second-semester programming course. The goal of this work is to establish mechanisms that will permit us to collect sufficient data from a variety of sources so that we can determine what help-seeking behavior patterns are associated with successful course outcomes. Our current data collection efforts are tied in part to the effects of the COVID-19 pandemic, which caused courses to be taught online during our data collection period that normally would be taught face-to-face. Data includes logs of viewing or posting questions to the online forum system Piazza, office hour visit logs, Zoom logs, and grades from the Canvas LMS. We present initial analysis such as comparing course grades with the number of times students received help from instructional staff both in office hours and online forum Piazza.en
dc.format.mediumETDen
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectHelp-seeeking behavior of CS undergraduate studentsen
dc.titleRelationship Between Help-seeking Behaviour of CS Undergraduate Students and Academic Performanceen
dc.typeThesisen
dc.contributor.departmentComputer Scienceen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineComputer Science and Applicationsen
dc.contributor.committeechairShaffer, Clifford A.en
dc.contributor.committeememberEllis, Margaret O.'Neilen
dc.contributor.committeememberHooshangi, Saraen
dc.description.abstractgeneralComputer Science students need to understand the mechanism of programming systems that involve computation, automation, and information. Computer scientists need to know how to design and analyze a problem and solve it with an algorithm. We study students' behaviors in CS education to find out patterns of those who need help. Several behaviors are examined: Time Management, Incremental development, Self-checking, Persistence, and Planning. Help-seeking, when done correctly, is known as a good strategy related to self-regulated learning. This behavior includes online searching, coming to office hours for help from instructional staff, and asking instructors and peers publicly on online forums. Some of these sources of help can be tracked more easily than others. We present efforts to collect and analyze data related to the help-seeking behavior of students in a second-semester programming course. The goal of this work is to establish mechanisms that will permit us to collect sufficient data from a variety of sources so that we can determine what help-seeking behavior patterns are associated with successful course outcomes. Our current data collection efforts are tied in part to the effects of the COVID-19 pandemic, which caused courses to be taught online during our data collection period that normally would be taught face-to-face. Data includes logs of viewing or posting questions to the online forum system Piazza, office hour visit logs, Zoom logs, and grades from the Canvas LMS. We present initial analysis such as comparing course grades with the number of times students received help from instructional staff both in office hours and online forum Piazza.en


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