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Course Recommendation for University Environments

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Bibliographic Details
Title: Course Recommendation for University Environments
Language: English
Authors: Ma, Boxuan, Taniguchi, Yuta, Konomi, Shin'ichi
Source: International Educational Data Mining Society. 2020.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Peer Reviewed: Y
Page Count: 7
Publication Date: 2020
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Course Selection (Students), College Students, Online Courses, Student Attitudes, Decision Making, Student Interests, Grade Prediction, Mathematics
Abstract: Recommending courses to students is a fundamental and also challenging issue in the traditional university environment. Not exactly like course recommendation in MOOCs, the selection and recommendation for higher education is a non-trivial task as it depends on many factors that students need to consider. Although many studies on this topic have been proposed, most of them only focus either on historical course enrollment data or on models of predicting course outcomes to give recommendation results, regardless of multiple reasons behind course selection behavior. To address such a challenge, we first conduct a survey to show the underlying characteristic of the course selection of university students. According to the survey results, we propose a hybrid course recommendation framework based on multiple features. Our experimental result illustrates that our method outperforms other approaches. Also, our framework is easier to interpret, scrutinize, and explain than conventional black-box methods for course recommendation. [For the full proceedings, see ED607784.]
Abstractor: As Provided
Entry Date: 2020
Accession Number: ED607802
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED607802
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PubType: Conference
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IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Course Recommendation for University Environments
– Name: Language
  Label: Language
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  Data: English
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Ma%2C+Boxuan%22">Ma, Boxuan</searchLink><br /><searchLink fieldCode="AR" term="%22Taniguchi%2C+Yuta%22">Taniguchi, Yuta</searchLink><br /><searchLink fieldCode="AR" term="%22Konomi%2C+Shin'ichi%22">Konomi, Shin'ichi</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2020.
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  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
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  Data: Y
– Name: Pages
  Label: Page Count
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  Data: 7
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2020
– Name: TypeDocument
  Label: Document Type
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  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
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  Data: <searchLink fieldCode="DE" term="%22Course+Selection+%28Students%29%22">Course Selection (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Interests%22">Student Interests</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+Prediction%22">Grade Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics%22">Mathematics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Recommending courses to students is a fundamental and also challenging issue in the traditional university environment. Not exactly like course recommendation in MOOCs, the selection and recommendation for higher education is a non-trivial task as it depends on many factors that students need to consider. Although many studies on this topic have been proposed, most of them only focus either on historical course enrollment data or on models of predicting course outcomes to give recommendation results, regardless of multiple reasons behind course selection behavior. To address such a challenge, we first conduct a survey to show the underlying characteristic of the course selection of university students. According to the survey results, we propose a hybrid course recommendation framework based on multiple features. Our experimental result illustrates that our method outperforms other approaches. Also, our framework is easier to interpret, scrutinize, and explain than conventional black-box methods for course recommendation. [For the full proceedings, see ED607784.]
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  Label: Abstractor
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  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2020
– Name: AN
  Label: Accession Number
  Group: ID
  Data: ED607802
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED607802
RecordInfo BibRecord:
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 7
    Subjects:
      – SubjectFull: Course Selection (Students)
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Online Courses
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Decision Making
        Type: general
      – SubjectFull: Student Interests
        Type: general
      – SubjectFull: Grade Prediction
        Type: general
      – SubjectFull: Mathematics
        Type: general
    Titles:
      – TitleFull: Course Recommendation for University Environments
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          Name:
            NameFull: Ma, Boxuan
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            NameFull: Taniguchi, Yuta
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            NameFull: Konomi, Shin'ichi
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              Type: published
              Y: 2020
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            – TitleFull: International Educational Data Mining Society
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