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.
The dominant population in engineering classrooms today is Generation Z students who are highly digitally fluent, collaborative in learning styles, and interactive in their learning activities. Traditional pedagogies of lectures often fail to keep them engaged and develop higher-order thinking, while inquiry-based learning has emerged as a promising alternative. Despite the growing interest in IBL, limited empirical research has investigated how curiosity and critical thinking together influence students' perceived effectiveness of IBL in engineering education. This quantitative study used a survey approach with 500 engineering students in the age group of 18-21 years, using a 26-item Likert-scaled instrument that measured curiosity, critical thinking, IBL effectiveness, and learning preferences. The scales demonstrated strong internal consistency through Cronbach's alpha values which ranged between 0.88 and 0.95. The correlation and regression analyses revealed curiosity and critical thinking as statistically significant predictors of IBL effectiveness with r = 0.799 and 0.755, respectively, jointly explaining 64 percent of the variance, while experience with IBL accounted for an insignificant amount of variation. Student engagement and perceived learning effectiveness stem from their natural curiosity and reasoning abilities instead of their knowledge of the method. The research suggests that engineering programs should develop Generation Z students' cognitive skills through inquiry-based teaching methods which combine technology and reflection because these students need these competencies to succeed in our fast-paced world.
Formative assessment (FA) is a cornerstone of effective pedagogy, intended to monitor student learning and provide ongoing feedback. However, its successful implementation in engineering education hinges significantly on student perception and engagement. This paper presents a comprehensive study on the perceptions of undergraduate engineering students towards various formative assessment techniques. It investigates how students perceive the implementation of these methods, the challenges they face, and their preferences for feedback. A mixed-methods approach, employing surveys and focus group discussion, was used to gather data from 100 undergraduate Electronics and Telecommunication Engineering students. The results indicate a generally positive perception of FA, particularly its role in clarifying complex concepts and preparing for summative evaluations. However, significant challenges were identified, including inconsistent implementation by faculty, feedback that is often delayed or lacks actionable details, and increased workload. The study reveals a disconnect between the intended purpose of FA and its practical execution from the students’ viewpoint. Based on these findings, the paper proposes a strategic pathway for educators and institutions to enhance the effectiveness of formative assessment, fostering a more interactive and supportive learning environment in engineering disciplines. This roadmap emphasises faculty training, integration of technology for timely feedback, and co-creation of assessment strategies with students.
In India’s rapidly evolving educational landscape, Alumni networks present a transformative yet underutilized and untapped resource for the academic growth of the institute and its societal impact. This paper explores the strategic engagement of alumni to address the challenges in Indian Higher education, like employability gaps, skill gaps, research funding, and commercialization, and sustainable funding models for innovation and entrepreneurship. In addition, the paper also presents an analysis of premier institutes like IITs and NITs and private universities and demonstrates that active alumni engagement contributes to 25-30% higher employability, 40% industry-academic collaboration, and significant improvements in Institute rankings. The paper proposes a 3C framework:
Academic libraries are evolving from traditional repositories of information to dynamic spaces that promote innovation, cooperative work, and hands-on learning. This paper presents a case study of transforming an institute library into an Innovation Hub by creating a Common Component Studio (CCS) — an equipment-lending facility or hardware components library integrated with the institute’s library Online Public Access Catalogue (OPAC). The CCS offers over 1200+ prototyping components and tools for home lending, enabling students to engage in Project-Based Learning (PjBL) and Problem-Based Learning (PBL) without the delays and costs associated with sourcing hardware. The study outlines the proposed CCS framework for PjBL and PBL, which impacts Iteration Frequency (IF), Project Completion Rate (PCR), Innovation Quality Score (IQS), and Interdisciplinary Participation (IP) for hands-on learning and rapid prototyping in project-based learning. The results demonstrate that library-led equipment lending can significantly enhance the learning experience and position the CCS as a central hub for lending hardware components in institutional innovation ecosystems.
Experiential learning in the area of Electronics and Communication Engineering Education provides a hands-on approach that fosters active engagement and practical skill development among students. This TLP methodology emphasizes learning through practical implementation of realworld applications, enabling students to bridge the gap between theoretical knowledge and application. In this work, the experiential learning methodology adopted for the third-semester Electronics and Communication Engineering students for the core subject Analog Electronics is detailed. The pre-requisite for this course is Basic Electronics in which students were trained to design and analyze the basic circuits with certain exposure to practical learning. Students pursuing Electronics and Communication engineering often encounter various challenges in comprehending the subject matter. Analog electronics involves abstract concepts such as amplifiers, filters, oscillators and semiconductor devices, which can be challenging for them to grasp without hands-on experience. The theoretical foundation of Analog electronics often involves complex mathematical analysis, including differential equations, Fourier analysis, and Laplace transforms, which can be daunting for students. Students may struggle to understand the practical relevance and real-world applications of theory concepts, leading to a lack of motivation and engagement. Troubleshooting and debugging analog circuits require a deep understanding of the underlying principles and students may face difficulties in identifying and rectifying errors. Addressing these challenges through experiential learning and practical implementation can greatly enhance students' understanding of the fundamental course Analog electronics.
The surge in the e-learning market has compelled the need for intelligent predictive models to minimize the gap between performance analysis and personalized learning systems to improve student performance. With the wide growth of the e-learning market, there is a great demand for intelligent predictive models to bridge the gap between performance analysis and personalized learning systems to boost student performance. Yet, traditional prediction methods are not effective due to issues like class imbalance, outliers, irrelevant features, and limited model interpretability. This research introduces an advanced framework to predict student academic performance based on an educational data mining dataset, Students' Academic Performance Dataset (xAPI-Edu-Data), comprising 480 instances, 16 attributes, and multi-variate integer and categorical features of the e-learning environment and educational data mining. The proposed framework involves class imbalance handling using Synthetic Minority Oversampling Technique (SMOTE), outlier removal using Interquartile Range (IQR), feature scaling and data standardization using Z-score normalization. A novel hybrid feature selection method TRIFEX is proposed to select the most influencing features to the student performance by combining ANOVA F-statistics, Recursive Feature Elimination (RFE) and Lasso regularization. The Logistic Regression, Decision Tree, and K-Nearest Neighbor (KNN) classifiers are used in the study. The hyperparameter optimization is done using Randomized Search CV, Grid Search CV, and Optuna to increase the efficiency and generalization power of the model. In addition, a voting, based ensemble model is created to fuse the virtues of individual classifiers for good prediction. Experimental results have shown that the proposed ensemble model is more accurate with 98.99% accuracy, 99.00% F1-score and 0.1005 RMSE as compared to the conventional predictive models. The results suggest that the suggested method has substantial potential to increase the accuracy of prediction, explainability of features and individualized learning support in contemporary e-learning environments.
Excellence in research and innovation are stated national objectives under the New Education Policy (NEP) of India. International institutional ranking frameworks such as QS and THE assign significant weightage to research and development. Similar benchmarks are adopted by the National Institutional Ranking Framework (NIRF) and accreditation agencies such as NAAC, NBA etc. This has placed the onus of producing numeric research outcomes on all institutions alike. While this has succeeded in sprucing up the number of publications or patents filed in the short term, there is a need to examine whether this has led to broad basing of the research and innovation culture across the ecosystem. This paper therefore examines the state of the research and innovation ecosystem in private and autonomous STEM institutions in India by determining the attitude and perception of faculty members towards research and innovation. We find that faculty members are largely ill-equipped and ill-supported to produce quality research and innovation outcomes while the pressure to produce research is causing short-cuts to be adopted in the broader ecosystem. Finally, qualitative analysis uncovers insights leading to suggested corrective interventions needed to establish a culture of research and innovation in HEIs which may empower and enable the faculty members.
This paper investigates the complex relationship between engineering students’ attitudes toward AI-integrated English language learning and their academic outcomes. As AI tools increasingly reshape educational paradigms, understanding student dispositions is crucial for effective pedagogical integration. Conducted in the Indian higher education context, the research employs a sequential mixed-methods design, beginning with a quantitative survey of 250 engineering students, followed by semi-structured interviews with 25 students and 15 English language educators. Quantitative findings reveal a significant paradox. While students report high perceived utility of AI and strongly value human instruction, a moderate negative correlation (r=−0.42, p<.001) exists between an attitude of over-reliance and academic performance. Furthermore, regression analysis shows that students’ confidence in detecting AI errors is more strongly predicted by their technical discipline than their demonstrated language proficiency, indicating a critical overconfidence dilemma. The qualitative data substantiates these findings, with educators expressing alarm over skill atrophy and the uncritical acceptance of AI-generated “hallucinations”, a tendency students confirmed. The study argues that for engineering students, this overconfidence, born from their technical identity, creates a significant blind spot, leading them to outsource foundational critical thinking and writing skills. While AI offers powerful tools, its unmanaged integration risks promoting intellectual passivity and undermining long-term communicative competence. The study concludes by recommending the integration of AI within structured pedagogical frameworks that balance technological support with active, teacher-led development of critical evaluation skills. For engineering educators, the findings highlight the need to embed structured AI literacy, critical evaluation of machine-generated text, and teacher-guided verification practices into ESL coursework to prevent skill atrophy and promote long-term communicative competence.
This research studies the application of anomaly detection algorithms to enhance early intervention techniques in the academic and emotional well-being of students. While previous research has focused on conventional classification models, our strategy employs machine learning methods to identify unusual patterns in student performance and behavior. Using an extensive dataset that includes cognitive and psychological factors, our approach shows promise in accurately detecting anomalies. The results suggest that machine learning can effectively address emotional and academic difficulties through proactive intervention strategies, supporting positive student outcomes. This study underscores the importance of early anomaly detection in enabling prompt and targeted interventions to improve student well-being.
In the contemporary landscape of Indian higher education, the incorporation of Artificial Intelligence (AI) of- fers both unparalleled possibilities and considerable educational challenges. A principal concern is the potential diminution of the essential ‘human touch’ which is foundational to effective teach- ing. This study addresses this issue by proposing a data-driven framework to identify distinct student archetypes within an AI- augmented learning environment. We leverage a comprehensive dataset comprising 638 undergraduate engineering students, which includes prior academic records, weekly performance metrics, and system interaction logs, upon which the K-Means clustering algorithm is applied. Our analysis successfully delin- eates four distinct student archetypes: the ‘High-Achieving and Consistent’, the ‘Diligent but Struggling’, the ‘Disengaged and At-Risk’, and the ‘Erratic Performer’. By characterizing these cohorts based on their academic, behavioral, and engagement patterns, we provide educators with actionable insights. These insights empower instructors to transcend monolithic teaching strategies and implement targeted, personalised interventions. Such a strategy ensures that while AI manages scalability, the educator’s role is amplified, enabling them to provide a more nuanced, empathetic, and effective human touch where it is most critically needed. This work proposes a symbiotic model wherein AI-driven analytics and human pedagogy converge to foster a more supportive and effective learning ecosystem.
This study explores how broadened computational thinking (CT) models can support and enhance students’ approaches to problem solving within problem-based learning (PBL) settings. To evaluate the proposed model, the researchers introduced a structured framework that included clear metacognitive prompts, guidance for collaborative work, and support for iterative design. This framework was implemented with 70 undergraduate engineering students participating in a six-week PBL cycle. Evidence gathered over three semesters showed notable gains in students’ CT performance, more balanced group participation, reduced unnecessary task switching, and smoother workflow patterns. Regression findings indicated that equitable involvement, idea generation, and overall PBL process efficiency were strong predictors of growth in CT scores. Students’ use of plan-monitor-evaluate (PME) strategies further suggested deeper metacognitive activity. Overall, the results show that extended CT scaffolding enables PBL groups to produce stronger and more numerous outcomes, while also refining the reasoning and teamwork practices required to achieve them. These insights can help educators design PBL environments that foster more effective thinking and reinforce students’ understanding of the value of their collaborative and reasoning processes.
The rapidly evolving technology and business landscape necessitates the development of critical systems thinking skills among graduates to tackle complex, real-world challenges. This quantitative longitudinal study, spanning two years, explores the effectiveness of on-campus tech internships in enhancing systems thinking skills among 30 selected STEM students. Guided by expert mentors in system design, the internships addressed authentic problems with clear deliverables, supported by training, resources, access to facilities, and incentives for attaining pre-defined outcomes. Critical systems thinking was assessed using two parallel versions of the Engineering Systems Thinking Assessment (ESTA) to avoid familiarity bias, complemented by project journal evaluations on a continuous basis. Statistical analyses, including paired t-tests and ANOVA, demonstrated a significant enhancement in ESTA scores (p < 0.001), alongside notable outcomes: qualitative improvements in placement quality, patents filed, and improved standing in external hackathons. These results highlight the potential of long-term tech internships as a viable strategy to bolster graduate attributes, at least in small cohorts to begin with.
Teaching in engineering education encompasses numerous curriculum factors and has become increasingly complex and challenging, particularly due to students’ short attention spans and difficulty in sustaining focus. This challenge is further intensified by the constant exposure to vast volumes of information in the digital era, making it harder to identify and retain meaningful learning content. This work proposes a systematic methodology that integrates and couples active learning tools through a coherent, symbiotic teaching–learning–evaluation process designed to foster a student-centred environment and nurture the joy of learning. The approach combines three complementary strategies—Think-Pair-Share (TPS), Concept Mapping, and Student Team Achievement Division (STAD)—and is implemented in a first-year engineering mathematics course. The lecture plan, learning activities, feedback mechanisms, and evaluation processes are developed at a micro-level to accommodate diverse learning styles and the psychological needs of contemporary learners. Quantitative and qualitative comparisons with the traditional teaching approach indicate notable improvements in self-confidence, critical thinking, innovation, and social interaction. Notably, end-semester examination scores increased by 28.49% compared to the traditional method, affirming the effectiveness of the integrated approach. Based on observations from a class of sixty students, statistical analysis performed using ANOVA software, results show that there is a positive improvement in the proposed methodology, but however reliability and consistency needs to be tested for heterogeneous range of students.
Preparing engineering graduates with practical machine learning skills is crucial in the current era of artificial intelligence. To enhance practical machine learning skills among electronics engineering students, Project -Based Learning (PBL) pedagogy was implemented. To evaluate the effectiveness of the PBL pedagogy, the proposed study investigated the outcomes and experiences of the stakeholders. The study focused on two research questions “What is the impact of transitioning from traditional approach to PBL approach on Machine Learning skills among undergraduate engineering students?” and “What are the perceived benefits and challenges of implementing Project -Based Learning (PBL) in a machine learning course, as experienced by both undergraduate engineering students and faculty?” In the study participants were purposely sampled and twelve students participated in the Focus Group Discussion (FGD) and three faculty participated in the semi -structured interview. The data from FGD and semi -structured interview was used to arrive at the perceived benefits and challenges faced by the faculty and students by implementing Project -Based Learning (PBL) in a machine learning course through thematic analysis of the data. Further, to perform impact analysis, students’ scores and attendance were considered from control group and experimental group to determine the impact of PBL pedagogy. Student data from two academic years was considered to perform impact analysis. The students in the academic years without PBL as pedagogy is considered as ‘control group’ and students with PBL as pedagogy is considered as ‘experimental group’. The proposed study is an empirical and quasi-experimental study with mixed-methods design aiming at measuring the impact of PBL initiative on students and faculty. For the quantitative analysis, the data from three academic years is considered while for the qualitative analysis, data was collected through semi structured interviews from three faculty members, common across the three course deliveries were considered while six students from two academic years of PBL implementation were purposively sampled to participate in the FGD. Quantitative analysis shows that the transition from traditional mode of delivery to PBL mode of delivery helped students to improve their machine learning skills and the impact was evident through changes in the mark’s distribution range (score band range). The initiative helped the students to translate the machine learning skills into publications. The initiative resulted in 70 Scopus indexed publications and one Q1 journal publication. Qualitative analysis of data from FGDs and interviews revealed the benefits and challenges experienced by students and teachers. The analysis highlighted the importance of processes, resources, scaffolding models, and the teacher's transition from lecturer to mentor within the PBL environment. Notably, the study found that course outcomes extended beyond the immediate course, impacting students' subsequent projects and career interests.
A novel undergraduate course, Professional Ethics and Sustainability in the Age of AI, bridges critical gaps in engineering education by combining experiential learning with outcome-based assessment. Developed at Pimpri Chinchwad College of Engineering (PCCOE), the curriculum employs four research-grounded activities: historical case analyses of ethical disasters, TARES Test evaluations of AI advertisements, governance quizzes on surveillance systems, and multi-stakeholder role-plays about algorithmic grading. Interim results from 46-60 participants demonstrate significant competencies development: 91.3% of students recognize AI's ethical influence, 95.7% show heightened emotional awareness, with strong performance in persuasion literacy (M=4.40/5) and governance knowledge (M=9.43/10). Structured assessments reveal 81.8% attainment in ethical reasoning and 80.5% in communication/governance skills, while qualitative analysis uncovers sophisticated engagement with fairness, transparency, and accountability principles.
