AI-Based and QR Code-Based Digital Attendance Systems as Predictors of Elementary School Teachers’ Work Discipline in Rural Indonesia
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Eka Prestianingsih
Achmad Buchori
https://orcid.org/0000-0002-0109-5254
Fakhruddin
Mohamad Ikram bin Zakaria
Abstract
This study aims to examine whether Artificial Intelligence (AI)-based digital attendance and QR Code-based digital attendance systems predict elementary school teachers’ self-reported work discipline in Kedu District, Temanggung Regency, Indonesia. A quantitative approach was employed using a cross-sectional explanatory survey design with an ex post facto method. The population consisted of 192 teachers from 26 public elementary schools. Using the Slovin formula with a 5% margin of error, 130 teachers were selected through proportional random sampling. Data were collected during the 2025/2026 academic year using a structured questionnaire measured on a five-point Likert scale. The instruments demonstrated high reliability, with Cronbach’s Alpha values ranging from 0.947 to 0.964. Data were analyzed using descriptive statistics and multiple linear regression analysis with IBM SPSS Statistics version 26. The findings indicate that AI-based digital attendance significantly predicts teachers’ self-reported work discipline (β = 0.216, p < .001). QR Code-based digital attendance also significantly predicts self-reported work discipline and shows a stronger predictive contribution (β = 0.599, p < .001). The regression model demonstrates that both digital attendance systems collectively explain 48.3% of the variance in teachers’ self-reported work discipline (R² = 0.483). These findings indicate that digital attendance systems are relevant administrative technologies associated with teacher discipline perceptions in rural elementary schools. However, because work discipline was measured through self-reported perceptions, the findings should be interpreted as predictive relationships rather than objective evidence of behavioral change. This study contributes to digital educational management research by integrating AI-based and QR Code-based attendance systems into a single predictive model and providing insights into technology-based administrative practices in rural school contexts.
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Copyright (c) 2026 Eka Prestianingsih, Achmad Buchori, Fakhruddin, Mohamad Ikram bin Zakaria
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