Securing the e-learning platforms has become increasingly difficult as user activities grow more complex and cyber threats become more advanced. Conventional detection techniques often fail to capture subtle irregularities in behavior, which can result in a higher number of false alarms and reduced accuracy. To overcome these limitations, this study adopts an autoencoder model enhanced with an adaptive threshold mechanism. The model first learns the pattern of normal user activity and then identifies unusual behavior by measuring reconstruction errors—where typical patterns are reconstructed accurately, while anomalies produce larger deviations. Unlike fixed thresholds, the adaptive approach adjusts dynamically based on the distribution of these errors, allowing for more precise detection. The experimental findings show strong performance, achieving 99.35% accuracy, 98.84% precision, 100% recall, and an F1-score of 99.41%, clearly surpassing traditional methods. Overall, the results suggest that this approach is highly effective for detecting anomalies and can significantly strengthen security in modern, data-driven elearning environments.
The outlook towards Finance evolved significantly over the past few decades, moving from the assumptions of traditional theories like the Efficient Market theory, Capital Asset Pricing theory to more interdisciplinary approaches that integrate psychology and more recently on neuroscience. Traditional finance is built on the foundation that investors are rational decision-makers who seek to maximize utility with complete access to information. In reality however, real-world financial behaviour often deviates from these assumptions. This gap led to the emergence of behavioural finance, which incorporates psychological insights to explain how cognitive biases, emotions, and heuristics influence financial decisionmaking. The evolution did not stop there. With advancements in brain-imaging technologies, neuroscience has developed into a leading area to study neural mechanisms underlying financial decisions, popularly known as Neurofinance. By examining how different regions of the brain respond to risk, reward, and uncertainty, Neurofinance provides deeper insights into investor behaviour beyond observable actions. This conceptual study traces the intellectual journey from traditional finance to behavioural finance and finally to Neurofinance. The paper highlights how each stage has expanded the understanding of financial decision-making. More importantly, the study explores how these insights can be incorporated into financial education. Integrating behavioural and Neurofinance concepts into academic curricula can help students develop a realistic understanding of financial markets, recognize cognitive biases, and make more informed financial decisions. The study puts forth that incorporating interdisciplinary perspectives into finance education will not only improve financial literacy but also equip future professionals with better analytical and decision-making skills. Thus, the transition from traditional finance to Neurofinance represents not merely a theoretical evolution but also a significant opportunity for transforming financial education.
The 50:50 Theory-Lab Teaching Model is a successful model for providing Verilog hardware description language (HDL) course material to undergraduate engineering students, with a balanced approach to delivery through both theory (classroom) and lab (practical) experience. The model allows equal opportunities for students to gain both knowledge (theoretical) and hands-on experience (practical). The educational approach used by this model was tested with a group of sixty (60) undergraduate engineering students. The educational techniques associated with this model used the same tools used in the industry ( ModelSim, Xilinx ISE, and Vivado), which were evaluated using a rubric that was later expressed as quantitative data. Statistical methods performed on this quantitative data revealed that the students in the 50:50 Theory-Lab Teaching Model performed more accurately when simulating their designs, were more efficient when debugging their designs, and had a greater understanding of the concepts related to the implementation of Verilog HDL compared to students taught through traditional lecture methods (p < 0.05) with large effect sizes (Cohen's d > 0.80). Therefore, the educational approach developed in this project is a cost-effective and flexible way of delivering course content, thereby enabling students to develop technical competencies and analytical skills and demonstrate job readiness, which meets the objectives of the OBE Principles and NEP 2020 Guidelines. Compared to the baseline, the inputs to this model resulted in an increase of +22%, +24%, and +22% for simulation accuracy, debugging performance, and project evaluation scores, respectively (p-value < 0.01, Cohen’s d > 0.90). Furthermore, SPI improvements, 18.4%, SES efficiencies of 27%, and 21 Concept Retention Gain (CRG) support these findings.
In this paper, the redesign and evaluation of ARM Microcontroller and Embedded Systems Laboratory will be presented to overcome the weaknesses in the conventional assessment practices, which is based on memorization and writing of the record. A five-part format authentic assessment model was introduced using the CDIO (Conceive–Design–Implement-Operate) framework and the tasks included single-peripheral, multi-peripheral integration, hex-file reverse engineering, collaborative open-ended experimentation, and the university-required summative test. The results of 67 students who were working with the LPC1768 Cortex-M3 platform were compared using threshold-based and average-based attainment approaches. The findings show poor performance on foundational and multi-peripheral tasks (CO1, CO2), moderate on analytical reverse-engineering tasks (CO3) and high on collaborative and resource-rich tasks (CO4, CO5). The results indicate that the scaffolding of early conceptual and integrative abilities requires more strength, and that real and authentic, design-based assessments induce more learning than memory-driven assessments. The paper provides a replicable framework of improving embedded systems pedagogy by providing outcome-based, practical assessment techniques.
This study investigates the employability, willingness and professional skills of MBA students across particular districts of Maharashtra, focusing on how specific capabilities effect career outcomes. 163 students participated in a structured survey to collect information on their financial awareness, analytical skills, presentation abilities, digital literacy. Survey data examined using logistic regression, correlation and Chi-Square tests. Key forecasters of employability included financial and market literacy (β = 0.38, p = 0.002), willingness to relocate (β = 1.28, p = 0.008) and adoption of digital skills. A strong connotation was observed between job interest and relocation flexibility (χ² = 209.11, p < 0.001). Although most students showed high job interest (94.4%) and digital adaptableness (93.8%), important gaps were identified in analytical tool expertise (65.4% reported only basic SPSS skills), MS Office certification (50.6% lacked certification) and presentation skill (78.1% had fewer than three presentations). The results underscore the need for curriculum enhancement, with financial literacy modules, software-based analytical training, and structured communication practice. Recommendations highlight industry-integrated learning, certification programs, and competency-focused interventions to align management education with employer expectations.
