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Digital Learning, Social Science, and Life-course Studies
Published by WISE Pendidikan Indonesia
ISSN : -     EISSN : 31098096     DOI : https://doi.org/10.70211/disolife
Core Subject : Humanities, Education, Social,
Humanities Education Environmental Science Social Sciences
Digital Learning, Social Science, and Life-course Studies (DiSoLife) is a peer-reviewed scientific journal dedicated to publishing high-quality research in the fields of digital learning, social sciences, and life-course studies. The journal provides an open-access platform, ensuring all articles are freely available online without requiring a subscription. The DiSoLife journal is published twice a year, with the primary goal of disseminating innovative research findings to educators, researchers, and practitioners working in digital education, social sciences, and life-course studies. The journal explores how digital technologies are transforming educational practices, influencing social dynamics, and shaping life trajectories, offering insights into both theoretical perspectives and applied research.
Arjuna Subject : Ilmu Sosial - Life-span and Life-course Studies
Articles 22 Documents
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Economic Knowledge and Financial Management Practices: Self-Efficacy and AI-Supported Learning Engagement Suchi Fitri Yani; Anna Kotova
Digital Learning, Social Science, and Life-course Studies Vol. 2 No. 1 (2026): June
Publisher : WISE Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70211/disolife.v2i1.765

Abstract

Economic knowledge is widely regarded as a foundation for responsible financial behaviour, yet conceptual understanding does not necessarily translate into routine financial practice. This study examined whether economic concept knowledge was associated with everyday financial management practices among 312 undergraduate students in an AI-supported learning context, while evaluating financial self-efficacy as an indirect pathway and AI-supported learning engagement as a boundary condition. A quantitative predictive-correlational design was analysed using multiple regression, percentile-bootstrap indirect-effect estimation, and interaction analysis. Economic concept knowledge was positively associated with financial management practices (β = .435, p < .001), with the baseline model explaining 19.1% of outcome variance. A positive indirect association through financial self-efficacy was supported (indirect β = .115, 95% CI [.069, .172]), while the direct knowledge association remained significant. AI-supported learning engagement modestly strengthened the knowledge-practice association (β = .094, p = .047), and the final model explained 29.0% of variance. The findings are consistent with an account in which conceptual knowledge is more likely to be reflected in everyday practice when learners also report confidence to act and engage with AI-supported learning in an active, evaluative manner. The results motivate further longitudinal and experimental testing of financial-learning designs that combine conceptual reasoning, self-efficacy, and accountable AI use.
Explainable AI, Academic Confidence, and Decision-Making ‎Quality ‎in Physics Learning Nur Hidayah; Sodikin Sodikin
Digital Learning, Social Science, and Life-course Studies Vol. 2 No. 1 (2026): June
Publisher : WISE Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70211/disolife.v2i1.777

Abstract

Opaque artificial intelligence (AI) recommendations may undermine learners' ability to evaluate feedback and make informed decisions. This study examined whether an explainable artificial intelligence (XAI) learning system was associated with higher academic confidence and decision-making quality than a comparable AI system without explanatory output during undergraduate physics learning. A quantitative quasi-experimental pretest-posttest control-group design involved 120 students (mean age = 20.3 years, SD = 1.4; 54.2% female), with 60 students analyzed in each condition across eight weeks and treatment implemented through three intact courses. Academic confidence was measured using an 18-item study-adapted Academic Confidence Scale-Short Form (alpha = .87), whereas decision-making quality was assessed using a 16-item Learning Task Decision-Making Inventory (alpha = .83). ANCOVA controlling for corresponding pretest scores showed higher posttest academic confidence in the XAI condition, F(1, 117) = 52.34, p < .001, partial eta squared = .309, and higher decision-making quality, F(1, 117) = 38.71, p < .001, partial eta squared = .249. The results support an association between the explanatory-interface condition and both learner outcomes, but they do not establish confidence calibration, deliberative processing, or the proposed transparency mechanism. Interpretation is constrained by course-level allocation across only three clusters, unmodeled course dependence, a single-institution sample, study-adapted questionnaire outcomes, and the absence of a manipulation check or explanation-fidelity evidence.

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