Project-based learning (PBL) has gained traction in higher education as an effective pedagogical approach that promotes active learning, critical thinking, and the practical application of knowledge. Unlike traditional lecture-based methods, PBL engages students in collaborative projects that simulate real-world challenges, thereby enhancing their problem-solving skills. However, a notable gap exists in understanding how these theoretical benefits translate into actual student experiences. Therefore, this study aims to investigate undergraduate students’ perspectives and experiences in project-based learning. By understanding students’ perspectives and experiences, this research highlights the benefits, challenges, and overall effectiveness of PBL. A qualitative research design was employed, involving in-depth, semi-structured interviews with 30 undergraduate students. The students were exposed to project-based learning in research methodology and software development courses. The interviews were conducted in person and via video conferencing, ensuring a comfortable environment for participants to share their insights. The data collected from the interviews were transcribed verbatim and analyzed using thematic analysis to identify recurring themes and patterns. The thematic analysis revealed several key themes: the importance of consistent attendance due to the tailored and sequential nature of PBL; the facilitation of deeper understanding and the practical application of theoretical concepts; difficulties in adapting to PBL, especially when transitioning from traditional teaching methods; benefits and logistical challenges of collaboration; the crucial role of the iterative process of assessment and feedback in mastering concepts; and the fostering of autonomy and the development of essential skills such as time management and problem-solving.

Higher education has witnessed a massive transformation due to the advent of generative artificial intelligence (AI) technologies such as ChatGPT. In essence, AI has transformed various aspects of society as a whole. Despite the growing interest in applying AI tools such as ChatGPT in higher education, there is limited understanding of lecturers’ perceptions regarding their use in teaching and learning contexts. Studies reported in the literature have not comprehensively explored lecturers’ attitudes towards AI adoption, particularly in terms of its impact on classroom activities, assessment, and feedback. This study aims to fill this gap by qualitatively studying lecturers’ perspectives. The findings reveal that the advent of AI was met with mixed feelings among lecturers. Some lecturers embraced AI technologies and developed mechanisms for utilizing them in the classroom, while others resisted the change. This research is significant, as it can inform best practices and guide future implementation strategies of technologies in education.

The rapid increase in educational data from diverse sources such as learning management systems and assessment records necessitates the application of advanced analytical techniques to identify at-risk students and address persistent issues like dropout rates and academic underperformance. However, many existing models struggle with generalizability and fail to effectively manage data challenges such as class imbalance and missing data, leading to suboptimal predictive performance. This study proposes a hybrid deep learning model combining convolutional neural networks (CNN) and long short-term memory (LSTM) networks to improve the accuracy of student academic performance prediction and enable timely educational interventions. To improve the performance of the model, we incorporate feature selection techniques and optimization strategies to enhance reliability. We also address common preprocessing challenges such as missing data and data imbalance. The proposed model was evaluated on two benchmark datasets to ensure model generalization capability. The hybrid model achieved predictive accuracies of 98.93% and 98.82% on the two datasets, respectively, outperforming traditional machine learning models and standalone deep learning approaches across key performance metrics including accuracy, precision, recall, and F-score.

Educational data mining (EDM) is instrumental in the early detection of students at risk of academic underperformance, enabling timely and targeted interventions. Given that many undergraduate students face challenges leading to high failure and dropout rates, utilizing EDM to analyze student data becomes crucial. By predicting academic success and identifying at-risk individuals, EDM provides a data-driven approach to enhance student performance. However, accurately predicting student performance is challenging, as it depends on multiple factors, including academic history, behavioral patterns, and health-related metrics. This study aims to bridge this gap by proposing a deep learning model to predict student academic performance with greater accuracy. The approach combines a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) network to enhance predictive capabilities. To improve the model’s performance, we address key data preprocessing challenges, including handling missing data, addressing class imbalance, and selecting relevant features. Additionally, we incorporate optimization techniques to fine-tune hyperparameters to determine the best model architecture. Using key performance metrics such as accuracy, precision, recall, and F-score, our experimental results show that our proposed model achieves improved prediction accuracy of 97.48%, 90.90%, and 95.97% across the three datasets.

Internet of Things (IoT) networks are constantly exposed to various security challenges and vulnerabilities, including manipulative data injections and cyberattacks. Traditional security measures are often inadequate, overburdened, and unreliable in adapting to the heterogeneous yet diverse nature of IoT networks. This emphasizes the need for intelligent and effective methodologies. In recent times, deep learning models have been extensively used to monitor and detect intrusions in complex applications. The models can effectively learn and understand the dynamic characteristics of voluminous IoT datasets to prompt efficient decision-making predictions. This study proposes a hybrid Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) algorithm to enhance intrusion detection in IoT environments. The proposed CNN-GRU model is validated using two benchmark datasets: the IoTID20 and BoT-IoT intrusion detection datasets. The proposed model incorporates an effective technique to handle the class imbalance issues that are peculiar to voluminous datasets. The results demonstrate superior accuracy, precision, recall, F1-score, and area under the curve, with a reduced false positive rate compared to similar models in the literature. Specifically, the proposed CNN–GRU achieved up to 99.83% and 99.01% accuracy, surpassing baseline models by a margin of 2–3% across both datasets. These findings highlight the model’s potential for real-time cybersecurity applications in IoT networks and general industrial control systems.

Integrating large language models (LLMs) into higher education, particularly in programming education, reshapes how students interact with learning materials and develop coding skills. However, while the general utility of LLMs like ChatGPT, Gemini, and Claude has been acknowledged, a critical gap exists in understanding how specific prompting strategies influence student learning outcomes. This issue is significant in the context of programming education, where problem-solving, critical thinking, and conceptual understanding are essential yet complex cognitive skills. Although prior research has classified prompting behaviors, it has largely failed to assess their impact on actual learning. To address this gap, we explored how IT students employ various prompting strategies when engaging with LLMs during programming tasks. A mixed-methods approach was adopted, primarily qualitative and supported by basic quantitative analysis, to examine 842 prompts generated by 140 students across four core software development modules. The results revealed five dominant prompting strategies, which varied significantly in how they facilitated learning. Our findings suggest that prompting strategies significantly shape how students interact with LLMs and influence the depth of their learning.