Leveraging performance and feedback‐seeking indicators from a digital learning platform for early prediction of students' learning outcomes.

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 underachie...

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Publicado en:Journal of Computer Assisted Learning Vol. 40; no. 1; pp. 219 - 241
Autores principales: Ober, Teresa M., Cheng, Ying, Carter, Matthew F., Liu, Cheng
Formato: research tables/charts Journal Article
Publicado: Wiley-Blackwell Feb2024
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Feb2024
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      pub: Wiley-Blackwell
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        atl: Leveraging performance and feedback‐seeking indicators from a digital learning platform for early prediction of students' learning outcomes.
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          Ober, Teresa M.
          Cheng, Ying
          Carter, Matthew F.
          Liu, Cheng
        affil: Department of Psychology, University of Notre Dame, Dame Indiana, , USA
      sug:
        subj:
          Students, High School Psychosocial Factors
          Online Education
          Outcomes of Education
          Academic Performance
          Feedback Utilization
          Learning Methods
          Statistics Education
          Student Attitudes Evaluation
          Human
          Funding Source
          Prospective Studies
          Descriptive Statistics
          Sex Factors
          Race Factors
          Ethnic Groups
          United States
          Parents
          Educational Status
          Relative Risk
          Comparative Studies
          Female
          Male
          Adolescence
          Post Hoc Analysis
          Chi Square Test
          Adolescent: 13-18 years
          Female
          Male
      ab: 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; Mage = 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 (R2 = 0.314–0.412) and AP exam scores (κ = 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. Lay Description: What is already known about this topic: Students' early academic performance predicts end‐of‐year success.Students' self‐regulated learning behaviours are another predictor of academic success.Clickstream data have been used as an indicator of the application of self‐regulated learning strategies, particularly behaviours associated with seeking feedback.Indicators of feedback‐seeking behaviours reflected via clickstream data have been shown to predict student learning.Yet, how soon associations between early performance and such indicators of self‐regulated learning as feedback‐seeking are robust across different groups of learners, different learning circumstances, and different types of learning outcomes has yet to be more fully understood. What this paper adds: We investigated whether performance and feedback‐seeking behaviours predict end‐of‐year learning outcomes at different points during the academic year across multiple cohorts of learners (2017–2018, 2018–2019, and 2019–2020) and using two different learning outcomes (class grades, AP exam scores).One cohort was affected by COVID‐19 during the 2019–2020 academic year, allowing us to examine whether differences emerge amidst unprecedented circumstances.We used assignment scores and online feedback‐seeking to predict end‐of‐year learning outcomes.Assignment scores were positively associated with end‐of‐year grades.First assignment performance predicted AP exam scores.Use of feedback (an indication of self‐regulated learning behaviours) tended to predict learning outcomes, though not consistently across cohorts. Implications for practice and/or policy: The findings from this study could improve understanding of how digital learning platform measurements predict end‐of‐year learning outcomes though also highlight the importance of context differences.Such findings hold implications for early identification of at‐risk students and appreciation for differences between learners and contexts.These findings also contribute to a growing understanding of the impact of the COVID‐19 pandemic on student engagement and learning.
      pubtype: Academic Journal
      doctype:
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        tables/charts
        Journal Article
      ougenre: Article
    language: English
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