Understanding Student Procrastination via Mixture Models
Saved in:
| Title: | Understanding Student Procrastination via Mixture Models |
|---|---|
| Language: | English |
| Authors: | Park, Jihyun, Yu, Renzhe, Rodriguez, Fernando, Baker, Rachel, Smyth, Padhraic, Warschauer, Mark |
| Source: | International Educational Data Mining Society. 2018. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2018 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | 1535300 1320527 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Time Management, Online Courses, Electronic Learning, Probability, Learning Activities, Models, Student Attitudes, Student Behavior, Undergraduate Students |
| Abstract: | Time management is crucial to success in online courses in which students can schedule their learning on a flexible basis. Procrastination is largely viewed as a failure of time management and has been linked to poorer outcomes for students. Past research has quantified the extent of students' procrastination by defining single measures directly from raw logs of student activity. In this work, we use a probabilistic mixture model to allow different types of behavioral patterns to naturally emerge from clickstream data and analyze the resulting patterns in the context of procrastination. Moreover, we extend our analysis to include measures of student regularity--how consistent the procrastinating behaviors are--and construct a composite Time Management Score ("TM"). Our results show that mixture modeling is able to unveil latent types of behavior, each of which is associated with a level of procrastination and its regularity. Overall, students identified as non-procrastinators tend to perform significantly better. Within non-procrastinators, higher levels of regularity signify better performance, while this may be the opposite for procrastinators. [For the full proceedings, see ED593090.] |
| Abstractor: | As Provided |
| Entry Date: | 2019 |
| Accession Number: | ED593094 |
| Database: | ERIC |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED593094 Name: ERIC Full Text Category: fullText Text: Full Text from ERIC |
|---|---|
| Header | DbId: eric DbLabel: ERIC An: ED593094 AccessLevel: 3 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Understanding Student Procrastination via Mixture Models – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Park%2C+Jihyun%22">Park, Jihyun</searchLink><br /><searchLink fieldCode="AR" term="%22Yu%2C+Renzhe%22">Yu, Renzhe</searchLink><br /><searchLink fieldCode="AR" term="%22Rodriguez%2C+Fernando%22">Rodriguez, Fernando</searchLink><br /><searchLink fieldCode="AR" term="%22Baker%2C+Rachel%22">Baker, Rachel</searchLink><br /><searchLink fieldCode="AR" term="%22Smyth%2C+Padhraic%22">Smyth, Padhraic</searchLink><br /><searchLink fieldCode="AR" term="%22Warschauer%2C+Mark%22">Warschauer, Mark</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>. 2018. – Name: Avail Label: Availability Group: Avail Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 11 – Name: DatePubCY Label: Publication Date Group: Date Data: 2018 – Name: SourceSuprt Label: Sponsoring Agency Group: SrcSuprt Data: National Science Foundation (NSF) – Name: NumberContract Label: Contract Number Group: NumCntrct Data: 1535300<br />1320527 – 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="%22Time+Management%22">Time Management</searchLink><br /><searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Probability%22">Probability</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Activities%22">Learning Activities</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Time management is crucial to success in online courses in which students can schedule their learning on a flexible basis. Procrastination is largely viewed as a failure of time management and has been linked to poorer outcomes for students. Past research has quantified the extent of students' procrastination by defining single measures directly from raw logs of student activity. In this work, we use a probabilistic mixture model to allow different types of behavioral patterns to naturally emerge from clickstream data and analyze the resulting patterns in the context of procrastination. Moreover, we extend our analysis to include measures of student regularity--how consistent the procrastinating behaviors are--and construct a composite Time Management Score ("TM"). Our results show that mixture modeling is able to unveil latent types of behavior, each of which is associated with a level of procrastination and its regularity. Overall, students identified as non-procrastinators tend to perform significantly better. Within non-procrastinators, higher levels of regularity signify better performance, while this may be the opposite for procrastinators. [For the full proceedings, see ED593090.] – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2019 – Name: AN Label: Accession Number Group: ID Data: ED593094 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED593094 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 11 Subjects: – SubjectFull: Time Management Type: general – SubjectFull: Online Courses Type: general – SubjectFull: Electronic Learning Type: general – SubjectFull: Probability Type: general – SubjectFull: Learning Activities Type: general – SubjectFull: Models Type: general – SubjectFull: Student Attitudes Type: general – SubjectFull: Student Behavior Type: general – SubjectFull: Undergraduate Students Type: general Titles: – TitleFull: Understanding Student Procrastination via Mixture Models Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Park, Jihyun – PersonEntity: Name: NameFull: Yu, Renzhe – PersonEntity: Name: NameFull: Rodriguez, Fernando – PersonEntity: Name: NameFull: Baker, Rachel – PersonEntity: Name: NameFull: Smyth, Padhraic – PersonEntity: Name: NameFull: Warschauer, Mark IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Type: published Y: 2018 Titles: – TitleFull: International Educational Data Mining Society Type: main |
| ResultId | 1 |
