Social networking site use and self-reported academic performance: Moderating roles of motivation and self-regulated learning

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Type of the article: Research Article

The growing integration of social networking sites (SNSs) into higher education has fueled debate about their contribution to academic performance. This study examines how three forms of academic SNS use, interaction with friends, interaction with lecturers, and cooperative learning, affect self reported performance among 510 undergraduate students from public, private, and international universities in Hanoi, Vietnam, and tests the moderating roles of academic motivation and self regulated learning. Multiple regression and moderation analyses (Hayes’ PROCESS macro) were used.

Cooperative learning was the strongest predictor of performance (β = .479, p < .001), followed by interaction with lecturers (β = .253, p < .001) and interaction with friends (β = .184, p < .001), with the model explaining 63.8% of variance. Academic motivation and self regulated learning significantly strengthened the effect of friend interaction on performance but did not moderate the effects of lecturer interaction or cooperative learning. This indicates that structured, instructionally guided SNS activities produce stable academic benefits regardless of motivation or self regulation, while the value of informal peer interaction depends on these individual capacities. Performance and self regulated learning also differed significantly by gender, year of study, and university type, but not by discipline. Overall, results suggest that the academic effectiveness of SNSs depends on the structuredness of interaction and students’ motivational and self regulatory capacities, not merely on usage intensity. Academic performance was measured via self report rather than objective academic records.

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    • Figure 1. A conceptual model of the impact of SNSs on the performance of students
    • Figure 2. Revised conceptual model of SNS use and student academic performance
    • Table 1. Overview of the sample profile
    • Table 2. Discriminant validity: Fornell-Larcker criterion and HTMT ratios
    • Table 3. Reliability and validity indicators
    • Table 4. Mean differences in student performance and self-regulated learning across demographic groups
    • Table 5. Multiple regression analysis predicting student performance
    • Table 6. Summary of moderation analyses predicting Student Performance
    • Table A1. Measurements
    • Table A2. Factor loadings and item wording
    • Conceptualization
      Anh Thi Mai Nguyen, Tuan Thanh Nguyen
    • Data curation
      Anh Thi Mai Nguyen, Tuan Thanh Nguyen
    • Formal Analysis
      Anh Thi Mai Nguyen, Tuan Thanh Nguyen
    • Investigation
      Anh Thi Mai Nguyen, Tuan Thanh Nguyen
    • Methodology
      Anh Thi Mai Nguyen
    • Project administration
      Anh Thi Mai Nguyen
    • Supervision
      Anh Thi Mai Nguyen
    • Writing – original draft
      Anh Thi Mai Nguyen, Tuan Thanh Nguyen
    • Writing – review & editing
      Anh Thi Mai Nguyen
    • Validation
      Tuan Thanh Nguyen
    • Visualization
      Tuan Thanh Nguyen