Impact of intelligent tutoring on emotion and academic performance of systems engineering students at the national university of central Peru
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper investigates the impact and implementation of Intelligent Tutoring Systems (ITS) on enhancing educational outcomes for engineering students at the Universidad Nacional del Centro del Perú. The model emphasizes the role of ITS in improving academic achievement, student satisfaction, and engagement, considering critical dimensions like emotional attitude, cognitive receptivity, and reflective strategy. Using SmartPLS for data analysis and an application developed in Flutter, the study demonstrates that ITS can positively influence student emotion and performance. Reliability metrics confirm robustness, with Cronbach's alpha values between 0.76 and 0.876 and AVE scores above 0.7. Predictive power is supported by R-squared values of 0.746 for student emotion and 0.723 for ITS impact on academic performance. Path coefficients underscore significant relationships, such as ITS influence on emotional engagement (0.549) and academic satisfaction (0.384). Findings suggest that integrating ITS with emotional and cognitive dimensions can foster higher academic satisfaction and achievement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it