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Leveraging Performance and Feedback-Seeking Indicators from a Digital Learning Platform for Early Prediction of Students' Learning Outcomes

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Title: Leveraging Performance and Feedback-Seeking Indicators from a Digital Learning Platform for Early Prediction of Students' Learning Outcomes
Language: English
Authors: Teresa M. Ober (ORCID 0000-0001-9698-9543), Ying Cheng, Matthew F. Carter, Cheng Liu
Source: Journal of Computer Assisted Learning. 2024 40(1):219-240.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 22
Publication Date: 2024
Sponsoring Agency: Institute of Education Sciences (ED)
National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
Contract Number: R305A180269
1350787
Document Type: Journal Articles
Reports - Research
Education Level: High Schools
Secondary Education
Descriptors: Academic Achievement, Feedback (Response), Student Behavior, At Risk Students, Identification, Outcomes of Education, Student Educational Objectives, Electronic Learning, Advanced Placement Programs, Statistics Education, High School Students
DOI: 10.1111/jcal.12870
ISSN: 0266-4909
1365-2729
Abstract: Background: Students' tendencies to seek feedback are associated with improved learning. Yet, how soon this association becomes robust enough to make predictions about learning is not fully understood. Such knowledge has strong implications for early identification of students at-risk for underachievement via digital learning platforms. Objectives: We sought to understand how early in the academic year students' end-of-year learning outcomes could be predicted by their performance and feedback-seeking behaviours within a digital learning platform. We analysed data collected at different time points in the academic year and across different cohorts of students within the context of high school advanced placement (AP) Statistics courses. Methods: High school students enrolled in AP Statistics spanning three academic years between 2017 and 2020 (N = 726; M[subscript age] = 16.72 years) completed 3 or 4 homework assignments, each 2 and 3 months apart. Results and conclusions: Across the three cohorts, and even as early as the first assignment, a model consisting of demographic variables (gender, race/ethnicity, parental education), assignment performance, and interaction with the digital score report explained significant variation in students' final course grades (R[superscript 2] = 0.314-0.412) and AP exam scores (? = 0.583-0.689). Students' assignment performance was positively associated with end-of-year learning outcomes. Students who more frequently checked their digital score reports tended to receive better learning outcomes, though not consistently across cohorts. Implications: These findings further an understanding of how students' early performance and feedback-seeking behaviours within a digital learning platform predict end-of-year learning outcomes.
Abstractor: As Provided
Notes: https://osf.io/dnu32
IES Funded: Yes
Entry Date: 2024
Accession Number: EJ1407115
Database: ERIC
FullText Text:
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  Data: Leveraging Performance and Feedback-Seeking Indicators from a Digital Learning Platform for Early Prediction of Students' Learning Outcomes
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  Data: <searchLink fieldCode="AR" term="%22Teresa+M%2E+Ober%22">Teresa M. Ober</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9698-9543">0000-0001-9698-9543</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ying+Cheng%22">Ying Cheng</searchLink><br /><searchLink fieldCode="AR" term="%22Matthew+F%2E+Carter%22">Matthew F. Carter</searchLink><br /><searchLink fieldCode="AR" term="%22Cheng+Liu%22">Cheng Liu</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2024 40(1):219-240.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: Y
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  Data: 22
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  Data: 2024
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  Data: Institute of Education Sciences (ED)<br />National Science Foundation (NSF), Division of Research on Learning in Formal and Informal Settings (DRL)
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  Data: <searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Students%22">At Risk Students</searchLink><br /><searchLink fieldCode="DE" term="%22Identification%22">Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Outcomes+of+Education%22">Outcomes of Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Educational+Objectives%22">Student Educational Objectives</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Advanced+Placement+Programs%22">Advanced Placement Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics+Education%22">Statistics Education</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink>
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  Data: 10.1111/jcal.12870
– Name: ISSN
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Students' tendencies to seek feedback are associated with improved learning. Yet, how soon this association becomes robust enough to make predictions about learning is not fully understood. Such knowledge has strong implications for early identification of students at-risk for underachievement via digital learning platforms. Objectives: We sought to understand how early in the academic year students' end-of-year learning outcomes could be predicted by their performance and feedback-seeking behaviours within a digital learning platform. We analysed data collected at different time points in the academic year and across different cohorts of students within the context of high school advanced placement (AP) Statistics courses. Methods: High school students enrolled in AP Statistics spanning three academic years between 2017 and 2020 (N = 726; M[subscript age] = 16.72 years) completed 3 or 4 homework assignments, each 2 and 3 months apart. Results and conclusions: Across the three cohorts, and even as early as the first assignment, a model consisting of demographic variables (gender, race/ethnicity, parental education), assignment performance, and interaction with the digital score report explained significant variation in students' final course grades (R[superscript 2] = 0.314-0.412) and AP exam scores (? = 0.583-0.689). Students' assignment performance was positively associated with end-of-year learning outcomes. Students who more frequently checked their digital score reports tended to receive better learning outcomes, though not consistently across cohorts. Implications: These findings further an understanding of how students' early performance and feedback-seeking behaviours within a digital learning platform predict end-of-year learning outcomes.
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  Data: As Provided
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  Data: https://osf.io/dnu32
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  Data: 2024
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  Data: EJ1407115
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      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Feedback (Response)
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      – SubjectFull: Student Behavior
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      – SubjectFull: Statistics Education
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      – TitleFull: Leveraging Performance and Feedback-Seeking Indicators from a Digital Learning Platform for Early Prediction of Students' Learning Outcomes
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