Modelling dynamical systems and predicting their responses are fundamental skills in mechanical engineering, yet they remain challenging due to the abstract nature of the underlying mathematics. In particular, convolution, central to the analysis of linear time-invariant (LTI) systems, is often introduced theoretically, limiting students' ability to develop physical intuition and practical understanding. This work presents an active learning framework for teaching continuous- and discrete-time convolution by linking differential equation-based modelling to system response prediction. The approach integrates visual animations, MATLAB-based computational activities, and a structured hands-on computational laboratory to support progressive learning. Students progress from interpreting basic signal interactions to applying convolution to predict dynamical system responses. A key contribution is the comparison between convolution-based solutions and numerical time integration using the Runge–Kutta method for a single-degree-of-freedom (SDOF) mechanical system. This dual perspective reinforces impulse response, superposition, and system modelling while demonstrating the equivalence between different solution strategies. Student assessment results indicate improved engagement and conceptual understanding. The proposed framework provides a coherent methodology connecting mathematical formulation, physical interpretation, and computational implementation, with its novelty lying in the structured integration of these complementary elements within a unified learning framework for engineering education.
The present work explores the effectiveness of video based Ansys tutorials as a complementary learning tool in undergraduate mechanical engineering education. A survey of 58 students was conducted at the University of Strathclyde across three levels of academic study. The findings indicate that self-paced video tutorials significantly enhanced the understanding of Finite Element Analysis and Computational Fluid Dynamics, particularly for beginners. Features noted of particular value include step-by-step explanations, and the ability to pause and replay content, which align with established principles of multimedia learning. The results also revealed that 55% of participants experienced cognitive load at least occasionally, and 47% of respondents expressed a desire for more interactive learning elements. These findings highlight the importance of careful instructional design and lead to practical recommendations for educators to develop modular, interactive video resources to better manage cognitive load and promote active learning. Overall, this research provides context-specific, empirical evidence to inform the integration of video tutorials within a blended learning framework that supports students in learning complex engineering simulation software.
Theory-intensive engineering courses face persistent challenges including obscure abstract concepts, disconnection between theory and practice, and insufficient experimental conditions. To address these deficiencies, this study developed and validated a pedagogical architecture integrating Outcome-Based Education (OBE), Project-Based Learning (PBL) and Artificial Intelligence Generated Content (AIGC), termed the OBE-AIGC-PBL framework. The architecture integrates a four-stage inquiry cycle and a four-layer AIGC cognitive scaffolding system to support systematic and deep learning. A quasi-experiment was conducted involving 66 undergraduate students from an Automotive Theory course, with 34 in the experimental group and 32 in the control group (traditional PBL without AIGC). Results indicate that the proposed pedagogical architecture significantly improved students’ comprehensive engineering competencies (Cohen's d = 0.96), particularly in simulation modeling capability (d = 1.13) and theoretical knowledge mastery (d = 1.02). The experimental group achieved a 22.1% higher score in comprehensive engineering design problems and completed 7 times more design iterations than the control group. Notably, Levene's test confirmed that the architecture significantly reduced score dispersion (SD = 7.8 vs 10.2, F(1,64) = 4.21, p = 0.044) and demonstrated favorable educational equity effects by narrowing achievement gaps among students. This study extends cognitive load theory and zone of proximal development theory into intelligent learning environments, repositioning AIGC as a core instructional element. The validated architecture provides a replicable, scalable, and theoretically grounded solution for improving teaching quality in theory-intensive engineering education.
In advanced undergraduate and graduate mechanical engineering courses on statistical thermodynamics, the one-dimensional Cartesian quantum harmonic oscillator (QHO) is a standard model used to calculate energy levels used in the vibrational partition functions of diatomic gases. However, when inquisitive students compare this model to the full radial Schrödinger equation, which allows for motion in three dimensions, they encounter a confusing discrepancy: the radial equation contains a first-derivative term, 2 r d R d r , which is absent in the Cartesian approximation. This leads to the pedagogical question: ”Where did the first derivative go?” To the best knowledge of the author, the answer to this question is not available in the literature. This paper provides engineering educators with a derivation to answer this question by using matched asymptotic expansions. We demonstrate that the Cartesian QHO equation corresponds to the leading-order inner-region problem of the full radial equation in the case where angular momentum effects are negligible. This approach provides a mathematical foundation for a core engineering thermodynamics concept and introduces students to perturbation methods. The results clarify a commonly overlooked theoretical issue and offer a framework for integrating perturbation methods into engineering curricula. This work strengthens the conceptual foundation of a core topic in statistical thermodynamics while enhancing its pedagogical presentation.
Introductory engineering undergraduate students frequently struggle to comprehend the fundamental theories of thermodynamics in thermal-fluid systems due to an inability to visualize abstract transfers of work and heat between a system and its surroundings. In contrast, students typically find greater success conceptualizing material in introductory solid mechanics courses where they can physically see, touch, and visualize dimensional changes to structural members under a load. To bridge this gap, this manuscript introduces foundational thermodynamic relations through the tangible evaluation of stress and strain in incompressible solids. This mechanical analysis is then extrapolated to compressible solid systems under hydrostatic pressure, demonstrating how normal stresses and volumetric strains directly map to pressure and volume changes. Showing that the resulting energy equations derived from solid systems are identical to those traditionally used in thermal-fluid systems provides students with a clear, solid-energy-based visual bridge that strengthens their conceptual understanding of engineering thermodynamics.
In machine tool training, students must understand machining procedures and safety precautions before operating the equipment. Therefore, we propose and evaluate a mixed reality (MR) lathe training system for pre-practice training. The system allows students to perform turning operations on virtual workpieces using the levers and handwheels of a physical, powered-down lathe and includes a warning mode for hazard-zone entry and an accident mode that reproduces virtual accidents. Sixteen undergraduate students enrolled in the Department of Mechanical Systems Engineering were divided into two groups and completed a preliminary operating session, two MR training sessions, and actual machining; the safety-feedback order was counterbalanced between groups. During spindle rotation in both training and actual machining, the participants moved to a safer standing position, farther from the chuck-side hazard zone, and the number of hazard-zone entry detection events decreased significantly between the first and second MR training sessions. The machining task success rates were 37.5%, 81.3%, and 100.0% in the first and second sessions and actual machining, respectively. These findings suggest that MR lathe training has the potential to support both machining task performance and the learning of safety-related behaviors as pre-practice instruction.
While both in-class and out-of-class assessments have identified advantages and disadvantages, there is no clear consensus in the literature regarding which format best supports student outcomes and engagement with content in a flipped classroom. To explore the impacts of quiz format in a flipped technical writing course, students were assigned to take quizzes covering pre-class work in either in-class (ICQ) or out-of-class (OCQ) format. Quiz scores, material retention, study habits, and student perspectives and behaviors were compared between groups to assess effects on student performance and course engagement. While quiz performance was slightly improved in the OCQ group, no statistically significant differences were detected between groups in performance on an end-of-semester retention assessment, suggesting that neither format compromised students’ ability to recall and apply their knowledge in new contexts. Students did report altered engagement with the course, however, with students in the ICQ group reporting a higher motivation to attend class than students in the OCQ group. In particular, female students and students from groups often underrepresented in engineering in the ICQ group reported increased attendance motivation compared to the OCQ group; this difference was not observed in their peers. In spite of this attendance motivation benefit, more students preferred the OCQ format to the ICQ format. This study did not detect statistically significant differences in material retention between quiz formats, so instructors may weigh student preferences for flexible assessment against the attendance motivation and engagement benefits reported by students who took in-class quizzes.
Low competency rates in practical machining courses remain a persistent challenge in manufacturing engineering education. This study applied the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology to a lathe machining course in which only 13.3% of 60 students initially met the competency standard, with deficits spanning dimensional accuracy, surface finish, machine setup, and safety practice. Following measurement system validation through Gage R&R analysis, root cause analysis traced poor performance to systemic instructional factors insufficient practice time, subjective grading, and the absence of standardized procedures rather than student background. Targeted interventions were implemented, including standardized work instructions for all ten lathe operations, competency-based progression requiring demonstrated proficiency before advancement, and weekly formative assessment. After the intervention, 91.7% of students achieved competency (χ 2 = 73.818, p < 0.001), with mean scores improving from 51.3 to 70.5 (t = −46.78, p < 0.001) and process capability rising from Cpk = −0.34 to 0.43. In practical terms, students machined workpieces with dimensional variation reduced by nearly two-thirds, selected cutting parameters deliberately rather than by guesswork, performed chuck centering and machine setup independently, and showed the greatest gains in traditionally difficult operations such as knurling, boring, and parting off. Statistical process control charts confirmed the improvements were sustained. The study provides a framework for applying industrial quality methodology to hands-on skills education.
Teaching Frequency Response Function (FRF)-based modal identification is a well-known challenge in structural dynamics courses: the topic is mathematically demanding, and its practical subtleties — such as signal noise effects, estimator selection, and stability diagram interpretation — are difficult to convey through lectures alone. This paper proposes a reproducible computational laboratory framework to support undergraduate and graduate courses in structural dynamics and vibrations. The framework uses SAP2000™ for finite element modeling of a four-story, three-dimensional portal frame, and MATLAB™ native functions (modalfrf, modalsd, modalfit) for FRF computation and modal parameter identification via the Least Squares Complex Exponential (LSCE) method. Broadband white-noise excitation was applied computationally, and additive Gaussian noise was introduced into the simulated response signals to reproduce a controlled measurement-noise scenario. A structured noise-sensitivity study, conducted at levels of 5% to 30% of the maximum response amplitude, allowed students to observe how signal contamination differentially affects natural frequency and damping estimation across vibration modes. Results show that natural frequencies exhibit low sensitivity to noise, whereas damping ratios are significantly more vulnerable, particularly in higher modes. A pilot implementation with seven graduate students showed high technical performance (mean score of 96.4%) and positive perceptions of the methodology (overall mean of 4.67/5). By treating noise as a pedagogical variable, the framework transforms an often black-box signal-processing procedure into an explicit learning experience. The complete computational code is available on GitHub, allowing students and instructors to reproduce the analyses and adapt the routines for educational or research purposes.
The implementation of the K-12 curriculum in the Philippines has resulted in a “strand mismatch,” creating a critical mathematics preparedness deficit among non-STEM students entering engineering programs. This study evaluates the efficacy of institutional supplemental courses—Advanced Algebra and Trigonometry—as a bridging mechanism to mitigate academic disparities and promote equity among Mechanical Engineering students. Employing a Mixed-Methods Explanatory Sequential design, the study utilized a quasi-experimental cohort comparison. A stratified random sample of 167 students (N = 167) was assessed using validated Student Perception Surveys and academic records. Data were analyzed using independent samples t-tests, Two-Way ANOVA, and thematic analysis of open-ended responses. Quantitative results indicated no statistically significant difference in perceived course usefulness between STEM and non-STEM cohorts (p > 0.05 , Cohen's d = 0.15 ), suggesting successful student assimilation. A significant interaction effect ( p = 0.032) revealed that the intervention functionally closed the performance gap in Calculus ( Δ < 0.07 on a 1.0–5.0 grading scale where 1.0 is excellent). However, a residual gap persisted in Physics for Engineers ( Δ = 0.15 ). Qualitatively, while students acknowledged the foundational value of the courses, they explicitly called for contextualized pedagogy that better links abstract mathematics to real-world engineering applications. The curricular intervention successfully promotes academic equity and resolves calculus deficiencies. However, to ensure holistic readiness, future pedagogical strategies must strengthen the conceptual transfer between abstract mathematics and applied physical sciences.
Formula Student (FS) is a major international engineering design competition that offers students a highly authentic, large-scale project experience. While prior research demonstrates that engineering design competitions enhance student learning, less is known about how students experience FS compared with curriculum-based project-based learning (PBL). This qualitative study draws on semi-structured interviews with ten current and former FS participants at a single university and employs thematic analysis to examine this relationship. Findings indicate that participants experienced FS as a qualitatively different form of engineering practice, with motivation emerging as the central mechanism underpinning engagement and learning. While initial participation was driven by intrinsic interest in motorsport, sustained engagement was supported by authenticity that encompassed personal meaning, professional context, real-world experience, collaborative community and impact. In contrast, curriculum-based projects were described as short-term, constrained, predominantly grade-driven, with authenticity limited to simulated professional contexts. The study contributes by demonstrating how authenticity operates as a mechanism for sustained motivation and offers implications for the design of more engaging and educationally powerful engineering curricula.
This study presents a novel approach to enhance the educational utility of small-scale wind tunnels through low-cost instrumentation and automation. A legacy Plint TE80 vertical smoke tunnel was repurposed using consumer-grade components to enable automated measurement of aerodynamic properties. Two experimental methodologies were implemented to calculate drag coefficients: the Wake Momentum Deficit Principle and the Surface Pressure Distribution Method, using a NACA0012 airfoil and a cylindrical model. The system achieved drag coefficient measurements within 2% of literature values, demonstrating its viability for educational use. Beyond technical validation, the project addresses broader educational challenges: limited access to high-cost aerodynamic facilities, especially in low-resource settings. By demonstrating that legacy equipment can be modernised affordably (less than 200 GBP) and effectively, this work supports the integration of hands-on experimentation into engineering curricula. This case study underscores the potential of accessible instrumentation to enrich fluid dynamics education and foster deeper student engagement with experimental methods.
This study evaluates an innovative teaching methodology that deviates from both traditional lectures and common flipped classroom models. While it shares the principle of shifting core content delivery to pre-class videos, its innovation lies in the in-person class. This class is preserved as a theoretical class, replacing slide presentations with live demonstrations, physical materials and fluid discussions that adapt to students’ questions. The foundational content is provided by videos professionally created and uploaded to the educational YouTube channel. A quasi-experimental study was conducted to quantify the impact of the methodology. Participants ( N = 13 for control, N = 10 for experimental) were students from the Bachelor's in Informatics and Computing Engineering, a cohort strategically chosen to ensure no prior knowledge of the topic. Results revealed a statistically significant improvement ( p -value = 0.011) in performance for the experimental group, with a two-way ANOVA confirming that “Teaching methodology” was the sole factor with a significant main effect. Additionally, two surveys were conducted with students from the Materials course in the bachelor's in Industrial Engineering and Management, who were taking one of the courses following the new methodology. One survey was applied at the end of the first part of the course and another during the final week of classes. This allowed the evaluation of student perceptions at different points in their learning journey, providing insight into how the methodology influenced engagement and comprehension over time. Qualitative data from 13 student interviews revealed that the methodology was widely valued and desired to be implemented in other courses. The interviews with professors confirmed the methodology's benefits, also revealing that the videos served as a crucial tool for their own class preparation. Although the current applications have been within engineering programs, the underlying principles of combining digital preparation with interactive and discussion-based theoretical sessions are broadly applicable across disciplines. Future work should explore this potential in varied educational contexts.
Mechanical engineering education increasingly requires instructional designs that prepare students to address complex real-world problems involving uncertainty, sustainability constraints, and sociotechnical design compromises. Within this context, complex thinking has been proposed as a formative framework to support integrative reasoning, system-level analysis, and informed decision-making beyond disciplinary boundaries. This study examines the implementation of complex thinking across two consecutive project-based courses: Integrative Workshop I (IW1) and Integrative Workshop II (IW2), within an undergraduate Mechanical Administrative Engineering program. A mixed-methods, descriptive–exploratory research design with sequential formative monitoring was employed. Data collection included Likert-type surveys administered at multiple stages, open-ended reflective responses, and analytical rubrics used to assess the technical performance and design quality of student-developed prototypes. Quantitative data were analyzed using descriptive statistics and dimension-level internal consistency measures, while qualitative data were examined through thematic coding and triangulated with performance-based evidence. Results indicate positive trends in system-level reasoning, multidisciplinary knowledge integration, reflective decision-making, and collaborative engineering practice. Comparisons between IW1 and IW2 reveal a progression toward more holistic and context-aware design approaches. Overall, the findings support the value of sequencing integrative, project-based courses for supporting the development of complex-thinking competencies in undergraduate engineering education.
MOOCs generate high volumes of interaction data of the learner at a massive level, yet the challenges of how to sustain learner engagement and reduce the incidence of dropout rates remain unresolved by the educators due to the varying learning behaviours and evolving interests of the learners. To counter this limitation, the study suggests a hybrid rule-based and machine-learning framework, which will allow to establish a dynamic user profile in MOOC platforms by integrating both clickstream analysis and clustering learner trajectories. The framework is informed by applying explicit pedagogical rules, which classify learners into three levels of engagement, performance and risk, and simultaneously, using HDBSCAN-based clustering of trajectories to demonstrate the implicit temporal behavioral modes in the sequential interaction data. The research conducts experiment on the Open University Learning Analytics Dataset (OULAD) that comprises interaction records of 32,593 students enrolled in 22 courses. The results demonstrated that the hybrid method could succeed in categorizing the learners into meaningful behavioral categories such as steady, irregular, late starter and dropout-prone without using previous clusters. Clustering encourages a Silhouette Score of 0.306, Davies-Bouldin Index of 0.559 and Calinski-Harabasz Index of 1912.55, which suggest high cluster separation and stability. Furthermore, the visualization and correlation analyses confirm that the engagement features are the most prominent in differentiating the learners. The offered system can offer an interpretable, scalable, and flexible answer to learner profiling that can be used to support personalized recommendations, early warning, and interventions in the environment of large-scale MOOCs.
Laboratory activities support conceptual understanding in engineering education, particularly in aerodynamics and computational fluid dynamics (CFD), where many flow phenomena are difficult to interpret using numerical results alone. This paper presents the development, implementation, and educational evaluation of a low-cost smoke flow visualisation system for the TecQuipment AF-100 subsonic wind tunnel. The system enables real-time observation of airflow and was integrated into laboratory and project-based learning within undergraduate aerodynamics teaching. The system was developed through an iterative process involving computer-aided design, CFD-informed refinement, prototyping, and experimental testing, and was applied across a range of educational case studies, including aerofoil testing and Formula Student vehicle aerodynamics. The educational impact was evaluated using an anonymous post-activity student survey comprising Likert-scale and open-ended questions. The results indicate positive student perceptions of improved conceptual understanding, stronger links between theory and experiment, and increased confidence in interpreting aerodynamic phenomena. As these findings are based on self-reported student perceptions, and indicative of the educational benefit of having smoke visualisation accompany quantitative wind tunnel measurements. Hence, smoke flow visualisation can support engagement and experimental learning in undergraduate aerodynamics laboratories.
Video-based instruction plays an increasingly prominent role in higher education, but little is known about how students actually engage with such materials across an academic cycle. This study analyses one year of learning analytics from an engineering YouTube channel integrated into university teaching to examine temporal and design-related patterns of student engagement. A time series analysis established how and when students interact with the channel. Dominant weekly (approx. 7-day) and shorter (approx. 3-day) viewing cycles were identified, with engagement intensifying sharply ahead of assessment periods. This temporal context motivated a closer look at which videos students return to. Principal Component Analysis (PCA) was applied to reduce dimensionality and create a composite behavioural performance score. This score served as the basis for a clustering analysis that identified three distinct video categories: high-retention/low-popularity, balanced, and high-popularity/low-retention videos. Video duration and playlist position emerged as key characteristics separating these clusters. Building on this, a predictive model was trained exclusively on video pre-upload features to forecast the composite score. Both Random Forest (RF) and Linear Regression (LR) models demonstrated strong predictive power on an unseen testing set, achieving R 2 values of 0.796 and 0.810, respectively. The results were further interpreted through Cognitive Load Theory and Mayer's Multimedia Learning principles. While findings are scoped to behavioural platform metrics and no direct learning outcome data were incorporated, the results provide practical guidance for future video design and identify the pre-upload characteristics that most influence how students interact with educational video content.
Renewable energy engineering education necessitates innovative pedagogical approaches to support interdisciplinary learning to accommodate diverse learners with technically complex curricula. However, empirical evidence concerning the influence of different pedagogies on heterogeneous learner groups in undergraduate renewable energy engineering courses is limited. A comparative pedagogical intervention was implemented with 124 final-year engineering students to assess learners’ performance across conventional, experiential, project-based and blended learning pedagogies. Students were categorized on the basis of their previous academic performance as advanced, medium and slow learners and categorized on the basis of locality as rural and urban learners. Moreover, cross-combination groups based on academic performance and locality are categorized. Repeated-measures ANOVA followed by Tukey's post hoc test was carried out to determine the significance between the different groups, with a minimum p value of 0.05 considered to indicate statistical significance. Project-based and blended pedagogical approaches demonstrated significantly higher learner performance than conventional and experiential approaches across most learner categories did (p < 0.05). Compared with rural learners, urban learners generally achieved higher scores, whereas medium and slow learners showed substantial improvement under learner-centered approaches. Effect size analysis revealed predominantly large pedagogical effects, particularly among urban slow learners (ηp 2 = 0.353), urban medium-sized learners (ηp 2 = 0.302) and slow learners overall (ηp 2 = 0.279). The findings demonstrate that learner-centered pedagogical approaches, particularly project-based and blended learning, positively influence academic performance in renewable energy engineering education. This study emphasizes the importance of embracing flexible and interactive teaching methods to effectively support diverse learner groups and improve overall learning results.
To address the growing need for interdisciplinary skills in biomedical engineering education, this paper introduces a longitudinal hands-on learning experience centering on Physics-Informed Neural Networks. We developed a two-stage pedagogical pathway following the same cohort of students from their second to their third year, designed to demystify the convergence of machine learning and computational mechanics. Initially, within a second-year continuum biomechanics course, students established the fundamentals of the methodology by solving the Fisher equation and a damped harmonic oscillator. This phase allowed learners to transition from conceptual understanding to the basic implementation of physical laws within deep learning frameworks. Subsequently, in the third year, an advanced activity focused on a mechanical problem of linear elasticity was introduced. In this phase, students were challenged to autonomously generate synthetic ”ground truth” data through Finite Element Method simulations using the open-source software FEBio. This activity served a dual pedagogical purpose: it reinforced the application of PINNs in solid mechanics and, simultaneously, empowered students to create and validate against finite element models. Quantitative analysis of pre- and post-intervention tests, evaluated via the Wilcoxon signed-rank test, shows a large effect size in knowledge acquisition regarding PINN fundamentals. Furthermore, technical assessment of the hybrid projects using a specific rubric reveals that students successfully navigated the interoperability between FEM and Deep Learning. We discuss the potential of this ”Gray-Box” educational model to modernize biomechanics instruction and its broader implications for other engineering disciplines, while acknowledging the limitations of a single-cohort design regarding causal inference and generalizability.
Creep testing is a fundamental pillar of materials engineering, critical for assessing the long-term mechanical behaviour of materials under constant load at elevated temperatures. This research aims to design and optimise a digital load application system for such machines. It will do so by leveraging Autodesk Inventor for Computer-Aided Design (CAD) modelling, ANSYS for finite element analysis (FEA), and MATLAB for the digital optimisation of the controls. The primary focus is on the design and control of the load application system. The study was conducted using aluminum alloys (e.g., 6061), mild steel (S355jr), and austenitic stainless steel (SS304). Testing conditions are confined to a range of 300°C to 600°C, and the system is designed and validated for tests lasting up to 100 h. The Simulink models for the three varied materials produced force and deformation responses that align with materials science principles. As expected, the control system had to exert the greatest effort to achieve the same level of deformation in stainless steel, followed by mild steel, and then aluminum. The discussion of the results confirms that the proposed digitally controlled creep testing system is a viable and superior alternative to traditional mechanical systems for educational laboratories.
