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Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses

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Title: Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses
Language: English
Authors: Smith, Vernon C., Lange, Adam, Huston, Daniel R.
Source: Journal of Asynchronous Learning Networks. Jun 2012 16(3):51-61.
Availability: Sloan Consortium. P.O. Box 1238, Newburyport, MA 01950. e-mail: publisher@sloanconsortium.org; Web site: http://sloanconsortium.org/publications/jaln_main
Peer Reviewed: Y
Page Count: 11
Publication Date: 2012
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Two Year Colleges
Descriptors: Academic Achievement, At Risk Students, Prediction, Community Colleges, Online Courses, Two Year College Students, Predictive Measurement, Predictor Variables, Models, Case Studies, School Holding Power, College Freshmen, Accounting, Decision Making, Data, Data Analysis, Computer Software, Computer Managed Instruction, Educational Technology, Computer System Design, Databases, Decision Support Systems, Distance Education, Web Based Instruction, College Instruction, Integrated Learning Systems
Geographic Terms: Arizona, Indiana
ISSN: 1939-5256
Abstract: Community colleges continue to experience growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging existing student information, higher education institutions can build statistical models, or learning analytics, to forecast student outcomes. This is a case study from a community college utilizing learning analytics and the development of predictive models to identify at-risk students based on dozens of key variables. (Contains 4 tables and 3 figures.)
Abstractor: As Provided
Number of References: 17
Entry Date: 2012
Access URL: https://sloanconsortium.org/jaln/v16n3/predictive-modeling-forecast-student-outcomes-and-drive-effective-interventions-online-co
Accession Number: EJ982673
Database: ERIC
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  Data: Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Asynchronous+Learning+Networks%22"><i>Journal of Asynchronous Learning Networks</i></searchLink>. Jun 2012 16(3):51-61.
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  Data: Sloan Consortium. P.O. Box 1238, Newburyport, MA 01950. e-mail: publisher@sloanconsortium.org; Web site: http://sloanconsortium.org/publications/jaln_main
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  Data: <searchLink fieldCode="DE" term="%22Arizona%22">Arizona</searchLink><br /><searchLink fieldCode="DE" term="%22Indiana%22">Indiana</searchLink>
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  Data: Community colleges continue to experience growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging existing student information, higher education institutions can build statistical models, or learning analytics, to forecast student outcomes. This is a case study from a community college utilizing learning analytics and the development of predictive models to identify at-risk students based on dozens of key variables. (Contains 4 tables and 3 figures.)
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RecordInfo BibRecord:
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 51
    Subjects:
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: At Risk Students
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Community Colleges
        Type: general
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: Two Year College Students
        Type: general
      – SubjectFull: Predictive Measurement
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Case Studies
        Type: general
      – SubjectFull: School Holding Power
        Type: general
      – SubjectFull: College Freshmen
        Type: general
      – SubjectFull: Accounting
        Type: general
      – SubjectFull: Decision Making
        Type: general
      – SubjectFull: Data
        Type: general
      – SubjectFull: Data Analysis
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      – SubjectFull: Computer Software
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      – SubjectFull: Computer Managed Instruction
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      – SubjectFull: Computer System Design
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      – SubjectFull: Databases
        Type: general
      – SubjectFull: Decision Support Systems
        Type: general
      – SubjectFull: Distance Education
        Type: general
      – SubjectFull: Web Based Instruction
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      – SubjectFull: College Instruction
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      – SubjectFull: Integrated Learning Systems
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      – SubjectFull: Arizona
        Type: general
      – SubjectFull: Indiana
        Type: general
    Titles:
      – TitleFull: Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses
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