Who Should I Help Next? Simulation of Office Hours Queue Scheduling Strategy in a CS2 Course
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| Title: | Who Should I Help Next? Simulation of Office Hours Queue Scheduling Strategy in a CS2 Course |
|---|---|
| Language: | English |
| Authors: | Zhikai Gao, Gabriel Silva de Oliveira, Damilola Babalola, Collin Lynch, Sarah Heckman |
| Source: | International Educational Data Mining Society. 2024. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 7 |
| Publication Date: | 2024 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | 1821475 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Teacher Responsibility, Educational Strategies, Higher Education, Computer Science Education, Help Seeking, Problem Solving, Teacher Student Relationship, College Faculty, Working Hours, Serial Ordering, Simulation |
| Abstract: | Promptly and properly addressing students' help requests during office hours is a critical challenge for large CS courses. With a large amount of help requests, instructors often find themselves facing a long office hours queue and need to decide who to help next. Most instructors typically select the earliest arrival students (FCFS), while some instructors prioritize students who haven't been helped recently to ensure fairness. To better understand and quantify how those different strategies affect the queue and students' experience, we simulated the office hours queue with four different strategies under three different queue loads using the students' problem-solving behaviors as a guide. Our simulation results show that when the queue is relaxed, different strategies make no difference. When the queue is busy or normal, prioritizing students who haven't helped today is the best strategy. Moreover, we also discussed how to develop a strategy based on students' code commit status, and corresponding simulation results indicate those strategies have no impact on the queue. [For the complete proceedings, see ED675485.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED675557 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED675557 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
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| Items | – Name: Title Label: Title Group: Ti Data: Who Should I Help Next? Simulation of Office Hours Queue Scheduling Strategy in a CS2 Course – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhikai+Gao%22">Zhikai Gao</searchLink><br /><searchLink fieldCode="AR" term="%22Gabriel+Silva+de+Oliveira%22">Gabriel Silva de Oliveira</searchLink><br /><searchLink fieldCode="AR" term="%22Damilola+Babalola%22">Damilola Babalola</searchLink><br /><searchLink fieldCode="AR" term="%22Collin+Lynch%22">Collin Lynch</searchLink><br /><searchLink fieldCode="AR" term="%22Sarah+Heckman%22">Sarah Heckman</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2024. – Name: Avail Label: Availability Group: Avail Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 7 – Name: DatePubCY Label: Publication Date Group: Date Data: 2024 – Name: SourceSuprt Label: Sponsoring Agency Group: SrcSuprt Data: National Science Foundation (NSF) – Name: NumberContract Label: Contract Number Group: NumCntrct Data: 1821475 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Speeches/Meeting Papers<br />Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Teacher+Responsibility%22">Teacher Responsibility</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Strategies%22">Educational Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+Education%22">Computer Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Help+Seeking%22">Help Seeking</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Student+Relationship%22">Teacher Student Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22College+Faculty%22">College Faculty</searchLink><br /><searchLink fieldCode="DE" term="%22Working+Hours%22">Working Hours</searchLink><br /><searchLink fieldCode="DE" term="%22Serial+Ordering%22">Serial Ordering</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation%22">Simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Promptly and properly addressing students' help requests during office hours is a critical challenge for large CS courses. With a large amount of help requests, instructors often find themselves facing a long office hours queue and need to decide who to help next. Most instructors typically select the earliest arrival students (FCFS), while some instructors prioritize students who haven't been helped recently to ensure fairness. To better understand and quantify how those different strategies affect the queue and students' experience, we simulated the office hours queue with four different strategies under three different queue loads using the students' problem-solving behaviors as a guide. Our simulation results show that when the queue is relaxed, different strategies make no difference. When the queue is busy or normal, prioritizing students who haven't helped today is the best strategy. Moreover, we also discussed how to develop a strategy based on students' code commit status, and corresponding simulation results indicate those strategies have no impact on the queue. [For the complete proceedings, see ED675485.] – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: ED675557 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 7 Subjects: – SubjectFull: Teacher Responsibility Type: general – SubjectFull: Educational Strategies Type: general – SubjectFull: Higher Education Type: general – SubjectFull: Computer Science Education Type: general – SubjectFull: Help Seeking Type: general – SubjectFull: Problem Solving Type: general – SubjectFull: Teacher Student Relationship Type: general – SubjectFull: College Faculty Type: general – SubjectFull: Working Hours Type: general – SubjectFull: Serial Ordering Type: general – SubjectFull: Simulation Type: general Titles: – TitleFull: Who Should I Help Next? Simulation of Office Hours Queue Scheduling Strategy in a CS2 Course Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhikai Gao – PersonEntity: Name: NameFull: Gabriel Silva de Oliveira – PersonEntity: Name: NameFull: Damilola Babalola – PersonEntity: Name: NameFull: Collin Lynch – PersonEntity: Name: NameFull: Sarah Heckman IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Titles: – TitleFull: International Educational Data Mining Society Type: main |
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