In recent years, there has been evidence of a growing interest on the part of universities to know in advance the academic performance of their students and allow them to establish timely strategies to avoid desertion and failure. One of the biggest challenges to predicting student performance is presented in the course “Programming Fundamentals” of Computer Science, Software Engineering, and Information Systems Engineering careers in Peruvian universities for high student dropout rates. The objective of this research was to explore the efficiency of Long-Short Term Memory Networks (LSTM) in the field of Educational Data Mining (EDM) to predict the academic performance of students during the seventh, eighth, twelfth, and sixteenth weeks of the academic semester, which allowed us to identify students at risk of failing the course. This research compares several predictive models, such as Deep Neural Network (DNN), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Support Vector Classifier (SVM), and K-Nearest Neighbor (KNN). A major challenge machine learning algorithms face is a class imbalance in a dataset, resulting in over-fitting to the available data and, consequently, low accuracy. We use Generative Adversarial Networks (GAN) and Synthetic Minority Over-sampling Technique (SMOTE) to balance the data needed in our proposal. From the experimental results based on accuracy, precision, recall, and F1-Score, the superiority of our model is verified concerning a better classification, with 98.3% accuracy in week 8 using LSTM-GAN, followed by DNN-GAN with 98.1% accuracy.

Despite global interest in active learning methodologies, limited empirical evidence exists on their implementation in developing countries, particularly regarding their relationship with teaching competencies. This study explores the association between teaching competencies and the application of active learning methodologies in a Peruvian public university. We hypothesized a strong positive correlation between specific teaching competencies–planning, pedagogical practice, and assessment–and the effective use of student-centered, personalized, and globally integrated methodologies. Using a non-experimental, cross-sectional correlational design, data were collected from 167 education students through validated questionnaires. Statistical analyses included descriptive measures, Spearman correlations, cluster analysis, and multiple correspondence analysis. Findings revealed a strong correlation (\(r = 0.862\)) between teaching competencies and active methodology implementation. Three teacher profiles–high-performing, moderate, and developing–were identified, each demonstrating distinct patterns of competency and methodology use. The analysis also uncovered strong associations between pedagogical practice and learning personalization, but weaker integration between student-centered strategies and global-intercultural dimensions. These results emphasize the multidimensional nature of effective teaching and the need for targeted professional development. While the study confirms the relevance of integrated teaching models, limitations related to sample size, context specificity, and self-reported data suggest cautious generalization. Future research should adopt mixed-method, longitudinal designs across diverse institutional contexts.

In this study, different strategies used to count vehicles and people in different image areas at a street intersection were analyzed to obtain counts at appropriate times suitable for real-time control of a traffic light. To achieve this, video recordings of cameras placed at the intersection were used to test and verify image processing algorithms and deep learning using the YOLOv3 network implemented on a 4 GB RAM Jetson Nano card. We counted the vehicles and people that stopped and crossed the polygons to delimit the different areas of interest, with a maximum error of ±2 in the validation tests for all cases. In addition, as a strategy, we combined the images from both cameras into a single one, thereby allowing us to make a single detection and subsequently determine if they are inside or outside the polygons used in separating the areas of interest with the respective counts. Furthermore, this enabled us to obtain information on vehicles and people stopped and crossing in a time of 0.73 s on average. Hence, it was established that the inclusion of the control algorithm is appropriate for real-time control of traffic lights.