Understanding Behavioral Patterns in Digital Skills Learning Through Advanced Deep Learning Techniques

Abstract

Gaining digital skills has become essential as digital technologies progressively permeate every aspect of knowledge and work, even for students studying non-technological subjects who frequently do not have formal exposure to computational tools and environments. Developing intelligent assistance systems tailored to different learner profiles requires an understanding of and ability to model their learning habits. To evaluate and predict the behavioral patterns of learners of digital skills, this paper proposes a hybrid deep learning framework that combines the benefits of recurrent neural networks (RNNs) for temporal sequence modeling and convolutional neural networks (CNNs) for static feature extraction. Digital education platforms offering skill-based instruction (e.g., data literacy, coding principles, digital collaboration) were used to collect a behavioral dataset of 2,000 students. Academic success, self-reported motivation, engagement metrics, and time and frequency-based indicators are some of the features. With a classification accuracy of 92.23%, the suggested a hybrid model significantly outperforms both independent architectures and standard machine learning benchmarks (such as logistic regression and SVM). According to behavioral data, studying at night, staying involved, and having a strong desire are all important indicators of success in online learning environments. The model demonstrates the viability of deep behavioral modeling for intelligent tutoring and adaptive feedback systems by enabling the generation of personalized, real-time learning implications. Particularly for disadvantaged or multidisciplinary learner groups, these results offer a methodological foundation for incorporating AI-driven learner modeling into scalable digital skills education systems.