View in EDS

A Course Recommender System Built on Success to Support Students at Risk in Higher Education

Saved in:
Bibliographic Details
Title: A Course Recommender System Built on Success to Support Students at Risk in Higher Education
Language: English
Authors: Kerstin Wagner (ORCID 0000-0002-6182-2142), Agathe Merceron, Petra Sauer, Niels Pinkwart (ORCID 0000-0001-7076-9737)
Source: Journal of Educational Data Mining. 2024 16(1):330-364.
Availability: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
Peer Reviewed: Y
Page Count: 35
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: At Risk Students, Algorithms, Foreign Countries, Course Selection (Students), Dropout Prevention, Best Practices, Predictor Variables, College Students, Computer Software Evaluation, Information Technology, Data Use
Geographic Terms: Germany
ISSN: 2157-2100
Abstract: In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a set of courses that have been passed by the majority of successful neighbors, that is, students who graduated from the study program. In terms of the number of recommended courses, we found a discrepancy between the number of courses that struggling students are recommended to take and the actual number of courses they take. This indicates that there may be an alternative path that these students could consider. However, the recommended courses align well with the courses taken by students who successfully graduated. This suggests that even students who are performing well could still benefit from the course recommender system designed for at-risk students. In the present work, we investigate a second type of success--a specific minimum number of courses passed--and compare the results with our first approach from previous work. With the second type, the information about success might be already available after one semester instead of after graduation which allows faster growth of the database and faster response to curricular changes. The evaluation of three different study programs in terms of dropout risk reduction and recommendation quality suggests that course recommendations based on students passing at least three courses in the following semester can be an alternative to guide students on a successful path.
Abstractor: As Provided
Notes: https://kwbln.github.io/jedm23
Entry Date: 2024
Accession Number: EJ1431194
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=EJ1431194
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: EJ1431194
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Course Recommender System Built on Success to Support Students at Risk in Higher Education
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kerstin+Wagner%22">Kerstin Wagner</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6182-2142">0000-0002-6182-2142</externalLink>)<br /><searchLink fieldCode="AR" term="%22Agathe+Merceron%22">Agathe Merceron</searchLink><br /><searchLink fieldCode="AR" term="%22Petra+Sauer%22">Petra Sauer</searchLink><br /><searchLink fieldCode="AR" term="%22Niels+Pinkwart%22">Niels Pinkwart</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7076-9737">0000-0001-7076-9737</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Data+Mining%22"><i>Journal of Educational Data Mining</i></searchLink>. 2024 16(1):330-364.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 35
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<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="%22At+Risk+Students%22">At Risk Students</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Selection+%28Students%29%22">Course Selection (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Dropout+Prevention%22">Dropout Prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Best+Practices%22">Best Practices</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Software+Evaluation%22">Computer Software Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Technology%22">Information Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Use%22">Data Use</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink>
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2157-2100
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a set of courses that have been passed by the majority of successful neighbors, that is, students who graduated from the study program. In terms of the number of recommended courses, we found a discrepancy between the number of courses that struggling students are recommended to take and the actual number of courses they take. This indicates that there may be an alternative path that these students could consider. However, the recommended courses align well with the courses taken by students who successfully graduated. This suggests that even students who are performing well could still benefit from the course recommender system designed for at-risk students. In the present work, we investigate a second type of success--a specific minimum number of courses passed--and compare the results with our first approach from previous work. With the second type, the information about success might be already available after one semester instead of after graduation which allows faster growth of the database and faster response to curricular changes. The evaluation of three different study programs in terms of dropout risk reduction and recommendation quality suggests that course recommendations based on students passing at least three courses in the following semester can be an alternative to guide students on a successful path.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: Note
  Label: Notes
  Group: Note
  Data: https://kwbln.github.io/jedm23
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1431194
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1431194
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 35
        StartPage: 330
    Subjects:
      – SubjectFull: At Risk Students
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Course Selection (Students)
        Type: general
      – SubjectFull: Dropout Prevention
        Type: general
      – SubjectFull: Best Practices
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Computer Software Evaluation
        Type: general
      – SubjectFull: Information Technology
        Type: general
      – SubjectFull: Data Use
        Type: general
      – SubjectFull: Germany
        Type: general
    Titles:
      – TitleFull: A Course Recommender System Built on Success to Support Students at Risk in Higher Education
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kerstin Wagner
      – PersonEntity:
          Name:
            NameFull: Agathe Merceron
      – PersonEntity:
          Name:
            NameFull: Petra Sauer
      – PersonEntity:
          Name:
            NameFull: Niels Pinkwart
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 2157-2100
          Numbering:
            – Type: volume
              Value: 16
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Journal of Educational Data Mining
              Type: main
ResultId 1