Aldo Ramírez-Arellano
Biographic Data
| ID | 118901 |
|---|---|
| NAME | Aldo Ramírez-Arellano |
| GIVEN NAMES | Aldo |
| FAMILY NAME | Ramírez-Arellano |
| SIGNATURE | RAMÍREZ-ARELLANO A |
| AFFILIATIONS | Instituto Politécnico Nacional |
| ORCID | 0000-0002-6782-9847 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Deng entropy and LSTM neural network to classify built cultural heritage with severe damage
An automatic approach for 3D recognition of structural state of uniquely built cultural heritage with severe and very severe damage is proposed. • Deng entropy and information dimension computed from voxel maps of the 3D reconstruction of built cultural heritage are exploited. • Deng entropy sequences are processed by bidirectional Long-Short Term Memory neural network yielding successful training and test results. • The approach is successfully …
Effects of Personality and Motivation on Students’ Academic Engagement and Metacognitive-Cognitive Strategies
Several investigations have focused on the relationship between personality and learning performance. However, the relationships among personality traits, motivation, emotional and behavioral engagement, and metacognitive-cognitive strategies remain unexplored. This research introduces a model based on a personality system set comprising metacognition, cognition, motivation, emotional and behavioral engagement. Five core education theories suppor…
The Influence of Motivation, Emotions, Cognition, and Metacognition on Students' Learning Performance
Previous research has proposed a conceptual model of the relationships between motivation, emotions, cognition, and metacognition related to students' learning performance and validated it in a blended learning context that combines traditional learning with the use of educational technology. This study examined these relationships to test the hypothesis that the levels of some factors in a face-to-face learning environment differ from those in a…
No prominent works on this page.
The Influence of Motivation, Emotions, Cognition, and Metacognition on Students' Learning Performance
Previous research has proposed a conceptual model of the relationships between motivation, emotions, cognition, and metacognition related to students' learning performance and validated it in a blended learning context that combines traditional learning with the use of educational technology. This study examined these relationships to test the hypothesis that the levels of some factors in a face-to-face learning environment differ from those in a…
Effects of Personality and Motivation on Students’ Academic Engagement and Metacognitive-Cognitive Strategies
Several investigations have focused on the relationship between personality and learning performance. However, the relationships among personality traits, motivation, emotional and behavioral engagement, and metacognitive-cognitive strategies remain unexplored. This research introduces a model based on a personality system set comprising metacognition, cognition, motivation, emotional and behavioral engagement. Five core education theories suppor…
Deng entropy and LSTM neural network to classify built cultural heritage with severe damage
An automatic approach for 3D recognition of structural state of uniquely built cultural heritage with severe and very severe damage is proposed. • Deng entropy and information dimension computed from voxel maps of the 3D reconstruction of built cultural heritage are exploited. • Deng entropy sequences are processed by bidirectional Long-Short Term Memory neural network yielding successful training and test results. • The approach is successfully …
Cognition (2 works) · Cognitive psychology (2 works) · Innovative Teaching and Learning Methods (2 works) · Mathematics education (2 works) · Metacognition (2 works) · Psychology (2 works) · Academic achievement (1 works) · Archaeology (1 works) · Artificial Intelligence (1 works) · Artificial neural network (1 works)