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Can the Paths of Successful Students Help Other Students with Their Course Enrollments?

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Title: Can the Paths of Successful Students Help Other Students with Their Course Enrollments?
Language: English
Authors: Wagner, Kerstin, Merceron, Agathe, Sauer, Petra, Pinkwart, Niels
Source: International Educational Data Mining Society. 2023.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Page Count: 12
Publication Date: 2023
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: College Freshmen, At Risk Students, Dropouts, Dropout Programs, Success, Academic Achievement, Course Selection (Students), Artificial Intelligence, Information Systems, Technology Uses in Education, Decision Support Systems, Low Achievement, Program Effectiveness, Incidence, Enrollment, Predictor Variables, Peer Influence
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 and which is based on the explainable k-nearest neighbor algorithm, recommends a set of courses that have been passed by the majority of the student's nearest neighbors who have completed their studies. The present evaluation is based on the data of students from three different study programs. One result is that the recommendations do lower the dropout risk. We also discovered that while the recommended courses differed from those taken by students who dropped out, they matched quite well with courses taken by students who completed the degree program. Although the course recommender system targets primarily students at risk, students doing well could use it. Furthermore, we found that the number of recommended courses for struggling students is less than the number of courses they actually enrolled in. This suggests that the recommendations given indicate a different and hopefully feasible path through the study program for students at risk of dropping out. [For the complete proceedings, see ED630829.]
Abstractor: As Provided
Entry Date: 2023
Accession Number: ED630847
Database: ERIC
FullText Text:
  Availability: 0
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  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED630847
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  Data: Can the Paths of Successful Students Help Other Students with Their Course Enrollments?
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  Data: <searchLink fieldCode="AR" term="%22Wagner%2C+Kerstin%22">Wagner, Kerstin</searchLink><br /><searchLink fieldCode="AR" term="%22Merceron%2C+Agathe%22">Merceron, Agathe</searchLink><br /><searchLink fieldCode="AR" term="%22Sauer%2C+Petra%22">Sauer, Petra</searchLink><br /><searchLink fieldCode="AR" term="%22Pinkwart%2C+Niels%22">Pinkwart, Niels</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Educational+Data+Mining+Society%22"><i>International Educational Data Mining Society</i></searchLink>. 2023.
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  Data: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
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  Data: <searchLink fieldCode="DE" term="%22College+Freshmen%22">College Freshmen</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Students%22">At Risk Students</searchLink><br /><searchLink fieldCode="DE" term="%22Dropouts%22">Dropouts</searchLink><br /><searchLink fieldCode="DE" term="%22Dropout+Programs%22">Dropout Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Success%22">Success</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Selection+%28Students%29%22">Course Selection (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Systems%22">Information Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Support+Systems%22">Decision Support Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Low+Achievement%22">Low Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Incidence%22">Incidence</searchLink><br /><searchLink fieldCode="DE" term="%22Enrollment%22">Enrollment</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Peer+Influence%22">Peer Influence</searchLink>
– 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 and which is based on the explainable k-nearest neighbor algorithm, recommends a set of courses that have been passed by the majority of the student's nearest neighbors who have completed their studies. The present evaluation is based on the data of students from three different study programs. One result is that the recommendations do lower the dropout risk. We also discovered that while the recommended courses differed from those taken by students who dropped out, they matched quite well with courses taken by students who completed the degree program. Although the course recommender system targets primarily students at risk, students doing well could use it. Furthermore, we found that the number of recommended courses for struggling students is less than the number of courses they actually enrolled in. This suggests that the recommendations given indicate a different and hopefully feasible path through the study program for students at risk of dropping out. [For the complete proceedings, see ED630829.]
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  Data: 2023
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  Data: ED630847
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED630847
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
    Subjects:
      – SubjectFull: College Freshmen
        Type: general
      – SubjectFull: At Risk Students
        Type: general
      – SubjectFull: Dropouts
        Type: general
      – SubjectFull: Dropout Programs
        Type: general
      – SubjectFull: Success
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Course Selection (Students)
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Information Systems
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Decision Support Systems
        Type: general
      – SubjectFull: Low Achievement
        Type: general
      – SubjectFull: Program Effectiveness
        Type: general
      – SubjectFull: Incidence
        Type: general
      – SubjectFull: Enrollment
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Peer Influence
        Type: general
    Titles:
      – TitleFull: Can the Paths of Successful Students Help Other Students with Their Course Enrollments?
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            NameFull: Wagner, Kerstin
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            NameFull: Merceron, Agathe
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            NameFull: Sauer, Petra
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            NameFull: Pinkwart, Niels
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              Type: published
              Y: 2023
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            – TitleFull: International Educational Data Mining Society
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