Data Angelus, et al
Description
This dataset includes primary data collected through a survey for a research study titled "The Effect of AI Usage Frequency, AI Dependency, and AI Learning Support Effectiveness on Accounting Students' Learning Experience." Data were gathered from 95 accounting students in Jakarta and South Tangerang, Indonesia, using a structured questionnaire. The survey was distributed via Google Forms through WhatsApp, Instagram, and LinkedIn. The dataset contains four variables measured on a 6-point Likert scale: (Y) Accounting Students' Learning Experience as the dependent variable, (X1) AI Usage Frequency, (X2) AI Dependency, and (X3) AI Learning Support Effectiveness as independent variables. Each independent variable includes six indicator items. Descriptive statistics show that AI usage frequency (mean = 29.11) is relatively high and consistent among respondents. In contrast, AI dependency (mean = 21.93) and AI learning support effectiveness (mean = 21.41) display moderate levels with more variation. Data were analyzed using SmartPLS for validity and reliability testing and SPSS for descriptive and regression analysis. The results indicate that AI Usage Frequency has a significant positive effect on learning experience (β = 0.724, p = 0.000). However, AI Dependency and AI Learning Support Effectiveness did not show significant direct effects. The overall model accounts for 58.5% of the variance in learning experience (Adjusted R² = 0.585). This dataset can be used for further research on mediation, moderation, or long-term effects of AI usage on student learning outcomes in higher education.
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Steps to reproduce
1. We created a structured questionnaire based on four areas: AI Usage Frequency (X1), AI Dependency (X2), AI Learning Support Effectiveness (X3), and Accounting Students' Learning Experience (Y). Each area had six indicator items measured on a 6-point Likert scale, where 1 means Strongly Disagree and 6 means Strongly Agree. 2. We distributed the questionnaire online using Google Forms through WhatsApp, Instagram, and LinkedIn, targeting accounting students in Jakarta and South Tangerang, Indonesia. 3. We collected data through purposive sampling. We received 95 valid responses from first-year to fourth-year accounting students. 4. We calculated descriptive statistics using SPSS, including the mean, standard deviation, minimum, and maximum values for each variable. 5. We tested validity and reliability using SmartPLS. This included checking outer loadings for convergent validity, HTMT, and the Fornell-Larcker criterion for discriminant validity. We also assessed Cronbach's Alpha, Composite Reliability, and AVE for construct reliability. 6. We conducted hypothesis testing using multiple regression analysis in SPSS. This involved t-tests for partial significance, F-tests for simultaneous significance, and Adjusted R² for the coefficient of determination.
Institutions
- Binus UniversityJakarta, Jakarta