In essence, doctoral students who opt for an academic career path will have to become instructors. Nonetheless, research indicates that most PhD students have greater research experience than teaching experience. Furthermore, they may have taught through graduate teaching assistantships, which may or may not have included teaching-related training. If the doctoral students are not completely aware of all the different aspects that come with teaching, it can be challenging if they decide to become faculty members. The purpose of this study is to examine the factors influencing engineering doctoral students’ perceptions on their preparedness to teaching courses when they start their academic careers.A survey instrument was distributed digitally to students across 16 R1 universities across the United States (resulting n = 285). Three sections comprised the survey instrument: demographic data, free response questions, and Likert scale questions. The Likert scale questions evaluate the participants’ confidence or preparedness in areas of teaching such as the teaching and learning process (9 items); course design and delivery (8 items); creating a dynamic classroom (9 items); harnessing the power of technology (6 items); collaborative learning (6 items); and effective assessment (8 items). The data was collected in fall 2023. Exploratory factor analysis (EFA) was conducted to validate the factor structure. EFA revealed six factors, five factors were same as hypothesized (the teaching and learning process, course design and delivery, creating a dynamic classroom, collaborative learning, and effective assessment) and one new factor (ethical practices). The factor loadings for the final factors ranged from 0.42 to 0.99, and the internal consistency reliability (Cronbach’s α) for the six factors ranged from 0.77 to 0.86, indicating high reliability.In this study, the t-test, one-way ANOVA, and multiple regression analyses were conducted. Gender identity of engineering doctoral students significantly influenced only one factor (ethical practices). Men in comparison with other gender identities reported higher self-efficacy. None of the factors were significantly influenced by the engineering doctoral students’ perceptions on their preparedness to teach based on their first language. All six factors were influenced by the engineering doctoral students’ choice of career path. Overall, doctoral students interested in academia reported higher self-efficacy in the teaching-learning process in comparison with industry career path and other career paths. The multiple regression analysis revealed that career path (interested in academia), teaching assistant experience, and teaching experience, positive influence engineering doctoral students’ perceptions on their preparedness to teach.
Augmented Reality technology is increasingly being integrated into various fields, including surgery, to enhance intraoperative guidance and training. In the surgical domain, performing a successful surgery relies heavily on the expertise of the surgeon to navigate to the region of interest while avoiding critical structures. This study aims to develop and evaluate an Augmented Reality tool specifically designed for training in laparoscopic and minimally invasive surgeries, addressing the challenge of navigating to regions of interest skillfully. The procedure involved outlining the working principles for the Augmented Reality training tool, especially for minimally invasive surgeries. It was followed by a two-phase user study with a questionnaire to gather perspectives from clinical researchers, surgical trainees, and surgeon experts. Phase 1 of the study, involving a total of 36 participants, overlooked the perceived advantages of using Augmented Reality in surgeries and their present exposure to such tools. Phase 2, which involved 14 participants, specifically looked at the advantages of an augmented reality-based training tool and assessed its requirements. Subjective measures showed a positive expectant outlook among all subgroups to implement this tool in surgeries and surgical training. This is further validated by a significant p-value of less than 0.05 and chi-square value greater than 9.48 along with average Likert scale response being more than 4 in all the Likert-type of questions. Qualitative feedback from participants emphasized the tool's value in enhancing their training experience and providing real-time intraoperative support. In Phase 2 of the study, 78.6% of participants favored the semi-transparent color-coded edge-highlighted augmentation method, with 64.2% preferring Occlusion AR for vascular surgery training. A Chi-square test (p=0.0041) revealed statistically significant variability in responses, with 57.1% of participants rating the AR training tool's usefulness in future practice at 4, indicating broad agreement on its potential. Participants highlighted three main reasons for considering the AR tool most useful for training: exposure to complex or abnormal anatomy cases (71.4%), prevention of procedure-related complications (64.3%), and personalized preoperative planning based on patient variations (64.3%).The implications of these findings suggest that AR technology holds significant promise for improving surgical training and outcomes. Future work will focus on developing a robust AR training tool using the feedback and performing objective assessments with a mixed-participant trial group.
This study compared several machine learning algorithms to predict student programming learning performance in an online learning environment. It used Explainable Machine Learning (EML) techniques to enhance interpretability. A range of algorithms, including Random Forest, Extra Trees, CatBoost, XGBoost, Naive Bayes, and KNearest Neighbors (KNN), were compared, with Extra Trees delivering the best results. Distinct from other EDM research mainly focused on predictive efficiency, we contributed by using the EML technique of SHapley Additive Explanations (SHAP), rooted in the Game Theory framework, to enhance model interpretability at both global and individual levels. At the global level, summary plots showed overall feature impacts, bar plots quantified the average effect of each feature, and dependence plots highlighted specific relationships. At the individual level, force plots identified critical features for individual predictions, decision plots traced the cumulative impact of features from the base value to the final output, and waterfall plots provided a breakdown of predictions. This study contributes to EDM by offering accurate predictive models and detailed interpretability, helping educational stakeholders make data-informed decisions to improve student outcomes.
The Ministry of Education is the New Zealand Government's lead advisor on the New Zealand education system and plays a significant role in shaping an education system that delivers equitable and excellent outcomes for all learners. To this end, they provide a wide range of services, resources, guidance and advice to the education sector, families, and learners.In addition to these core functions, the Ministry also supports a number of software applications that are essential for school leaders and staff, education providers, and sector suppliers. The core functionality of these applications is to provide a secure communication platform for government education institutions and education providers to conduct daily operations, communicate with each other, manage, and deliver educational content. The main thrust of this paper is to analyse data pertaining to a selection of these applications. By understanding how these applications are used and by whom, the Ministry can optimise their performance and ensure they meet the needs of their users.There are two key research questions this paper aims to answer: (1) Are there any observable seasonal usage patterns? (2) Which key applications have the potential to find wider applicability outside of New Zealand? The seasonal usage patterns are useful for managing and adjusting server requirements, ensuring that resources are allocated efficiently and effectively throughout the year.To address these questions, the paper analyses user access data to various applications to gain insights into usage trends and behaviours. The analysis shows that most applications have user usage patterns that are consistent with New Zealand's seasonal trends, particularly in line with the seasonal pattern of New Zealand's education operations. This finding highlights the predictable nature of application usage, which can be leveraged to improve service delivery and resource allocation.Additionally, our insights are useful to other countries and regions that aim to build similar applications to support their own education sectors. Other education systems could leverage the insights gained from our usage patterns and consider the applicability of our applications in their systems.
In engineering program tenure, most students get an opportunity to intern in a company and experience the professional life before they actually earn a degree and be employed. This practice helps them to be prepared with the professional etiquettes and helps them bridge the academic and professional lifecycle. The purpose of this work is two-fold. The work attempts to understand how students perceive the relevance of their academic coursework to their internship experiences and the recommendations that can be made to optimize the course structure design and learning outcomes to align with the key elements of the internships. The research questions are formulated on the grounds of identified purpose. With a pragmatic philosophical assumption and reflective practitioner model as conceptual framework, qualitative research method was adapted. With two cycles of coding process: descriptive, vivo and focused coding were used for data analysis. Data was collected using semi-structured interviews and survey forms. Self-selection was used for sampling and a total of 29 participants were part of the study. Students who participated in the interview were final year students of [name removed]. Themes were generated using the process that can guide the institutes to re-write the learning outcomes. Gaps were identified in the process and an analysis was carried out on how students find industry different from academics. The work brings out the differences in academic and professional settings in identified five themes. While the academics need to draft learning outcomes with professional objectives starting from lower semesters, industries need to collaborate in designing them as well. The work presents five themes on the areas of process of learning, skill set, technology and application of core courses in professional practices.
This paper presents the development and implementation of a Virtual High Voltage Laboratory (VHVL) designed to simulate real-world high voltage testing environments for educational and research purposes. Traditional high voltage laboratories are essential for testing and understanding electrical components and systems under high voltage conditions but involve significant safety risks and substantial financial investment. The VHVL provides a cost-effective and safe alternative by utilizing advanced simulation technologies to replicate high voltage phenomena and testing scenarios. It integrates sophisticated modeling techniques and real-time data processing to accurately mimic the physical and electrical properties of high voltage systems. The virtual lab includes interactive modules that allow users to conduct a range of experiments, such as dielectric breakdown testing, insulation performance analysis, and corona discharge studies, in a controlled and risk-free environment. Built on a modular framework, the system supports scalability and adaptability, enabling continuous updates and the incorporation of new testing protocols and standards. Validation tests comparing the VHVL with traditional high voltage laboratories demonstrated its high fidelity and reliability. This paper discusses the design principles, technical challenges, and educational benefits of the VHVL, highlighting its potential to enhance engineering education, promote safer testing practices, and reduce the costs associated with high voltage experimentation. The findings suggest that the VHVL can significantly advance electrical engineering education and research, providing a versatile tool for both students and professionals in the field. These results are based on an evaluation of the lab's effectiveness in helping students understand the subject matter. Surveys indicate a significant improvement in quiz scores after using the VHVL, demonstrating its educational benefits and impact on learning outcomes.
We introduce the project Artificial Intelligence based Robotics (AiRobo), a partnership of universities from France, Germany, Greece, Hungary, and Romania. AiRobo’s aim is to significantly raise the level of competence and skills of academic staff in the field of artificial intelligence (AI) based robotics, thereby also increasing the attractivity and reputation of the related departments at the partner universities. We further address the European priority in national contexts regarding inclusion in higher education, covering social inclusion and outreach to people with less opportunities, e.g. people with disabilities or with migrant background. The project partners will collaboratively write a comprehensive book on AI-based robotics, provide tools and video tutorials to support the construction and analysis of robotic systems, develop robotic applications in various fields of high interest to the industry, implement trainings for academic staff of the partners, organize an international summer school and an international conference, and disseminate the project results through various channels.The outcomes of the project will include the training of 25 academic staff from partner universities in teaching AI-based robotic courses, a comprehensive set of teaching materials including the above-mentioned book, 7 robotic applications in different fields, tools and video tutorials, as well as scientific publications.
This paper explores the perceptions of students reading for Industrial Masters and Industrial Doctorate degrees regarding how practice-based research differs from academic research, the writing challenges they faced and strategies they adopted to overcome them, and their evaluation of their improvement in writing. These work-study students were working on research projects determined by their companies, in collaboration with the university. The students were enrolled in a 12-week mandatory pass/fail no credit Technical Writing and Communication module. Data was collected via students’ submissions in the LMS and an end-of-module online survey with the writing faculty taking on the role of participant-observer. The findings show that most students were not aware about the differences between practice-based research which is more succinct and template-based, and academic research which involves critique of literature. Furthermore, students mainly faced challenges in situating their problem statement in the context of past literature and determining the level of detail and appropriate methods to explain and justify their data collection and analysis procedures. Through self-analysis of journal articles, feedback from the writing faculty and a desire to make their writing relevant to their work, students learned what qualified as ‘academic’ in research-based writing and found their voice and identity as practitioner-researchers. This epistemological shift from practitioner to practitioner-researcher, however, could be seen more clearly only in the last two weeks of the trimester. Referencing more journal articles and consultations with the faculty often highlighted that academic writing, though succinct, required literaturesupported substantiation. The lack of a template to guide their writing might have hindered students’ ability to write coherently earlier in the writing process. The students were submitting their drafts for the introduction, literature review and methodology sections in separate documents rather than developing their ideas in the same document. The findings in this study reflect that it is important to clarify early to the students what qualifies as ‘academic’. Furthermore, faculty could impress on students that the skills of questioning claims and evidence-based substantiation are valuable transferable skills to enhance the relevance of the professional doctorate to their work, industry and themselves.
This study investigates the application of speaker diarization methodologies within educational contexts, addressing the unique linguistic and environmental challenges present in Indian learning environments. Despite the frequent switching between English and local languages, current state-of-the-art models often fail to accurately process these multilingual interactions. To bridge this gap, we propose integrating the diarization outputs of Pyannote.audio with the transcription capabilities of OpenAI’s Whisper model, alongside a custom Voice Activity Detection (VAD) and embedding clustering pipeline. Using a dataset of 137 recordings from an online Human-Computer Interaction (HCI) course and an augmented reality (AR) classroom, our findings highlight the enhanced performance of the proposed methodologies. The Pyannote-Whisper pipeline achieved the lowest Diarization Error Rate (DER) of 0.26, outperforming other models, including commercial applications such as Deepgram and Otter. The custom VAD + Embedding Clustering Pipeline also demonstrated robustness in multilingual contexts, showcasing its potential for accurate diarization without relying on transcription. Fine-tuning and hyperparameter optimization were performed, yet did not significantly improve upon the proposed models, indicating the robustness of the initial approach. This study underscores the potential for adapting existing models to improve speaker diarization in multilingual educational settings, providing a significant step towards more robust analysis of collaborative learning interactions.
Mastering SQL is a key data science competence. While most large language models are able to translate natural language queries to SQL, their ability to tutor learners and authentically assess student assignments are at the least fragile. In this paper, we introduce ExplainS as an experimental prototype. In this web-based system, we augment Gemini with abstract syntax tree (AST) to enhance Gemini’s semantic analysis power to be able to assist and tutor students better. This edition of ExplainS provides a collection of exercises with varying difficulty levels, covering core SQL concepts. Users interact with a dynamic schema display, and their queries are validated against carefully crafted solutions. To provide context-aware personalized feedback, ExplainS leverages Gemini and the SQLglot library to analyze query AST differences between user queries and correct solutions, pinpointing the root cause of errors. This emerging research is part of a wider Data Science effort, and in this paper, we only focus on the meaningful feedback generation component of the ExplainS system.
Buildings are crucial infrastructures that significantly impact our quality of life. In the past decade, there has been a wide use of smart building technologies, including the Internet of Things, Artificial Intelligence, Cloud/Edge Computing, and Big Data, to integrate different systems within the buildings, improve user comfort and productivity, and reduce costs, energy consumption, and carbon emissions. Due to the interdisciplinary nature of Smart buildings, limited courses focus on the interaction of different technologies. Additionally, these courses usually do not provide practical learning opportunities, leading to ineffective learning experiences. This paper presents our work on developing the course structure for an introductory course on smart buildings that not only introduces different technologies but also incorporates practical learning. In particular, we have developed an Edge AI platform and leveraged it to design a practical learning assessment component for effective learning of smart building technologies. The efficacy of the developed Edge AI platform and its impact on the learning experience has been evaluated using comprehensive student feedback. Our evaluation shows positive student feedback and improvement in learning compared to traditional programming assignments. Finally, based on our experience, we have also discussed several lessons learned that can be used to improve practical learning in the future.
The demand for hardware-oriented skills among the students of Software Engineering (SE) has experienced significant growth due to recent breakthroughs in cutting-edge technologies such as Internet of Things (IoT), semiconductor technologies, and immersive technologies. However, the traditional teaching approaches rely more on programming concepts, algorithm development, software design, and testing rather than instilling hardware-oriented concepts such as embedded systems in students. Research plays a crucial role in the process of product design and development, encompassing both software and hardware components, with the aim of effectively addressing user-centric needs. Thus, the introduction of research concepts in undergraduate programs becomes more pivotal in today’s learning context and makes students employable. This paper focuses on the relevance of research-integrated project-based learning for hardware-intensive courses in an engineering course. This experiential study focused on embedded system development SIT210 being taught to SE students with the added objective of inculcating higher-order thinking skills among engineering graduates. The research quotient was gradually introduced and incorporated in a systematic manner over a span of three years, and this paper presents a reflective analysis of the experiential journey. The integrated approach revealed an improved degree of engagement and learning among students. In addition, a research quotient paves the way for further studies, research options, and cultivates their critical thinking enabling them to explore alternative solutions to meet the needs of the community.
Penetration testing reports document cybersecurity vulnerabilities discovered and recommendations to remediate them. Thus, the reports need to be written clearly and structured for replication of test results, decision-making and action by a company’s senior management, IT management and IT technical staff. This study aims to explore students’ decision to use or not use genAI tools by examining their perceptions on using genAI tools, reasons for the use or non-use of genAI to revise their drafts for clarity and conciseness, cohesion and coherence, relevance to the audience, and professionalism and tone, their learning outcomes and the usefulness of the workshop. The students participated in a three-hour workshop embedded in a regular module delivered via zoom. During the workshop, the students were given the option to use or not use any genAI tools to help them perform the revision tasks in the workshop. The students performed the revision tasks in lab group teams and uploaded their revised drafts to the discussion forum in the learning management system for further feedback by the faculty. There was no advance support to students regarding any genAI tool as the aim of the study was to explore the above-mentioned aspects of their current use or non-use of genAI tools. They responded to an online survey at the end of the workshop. The findings revealed that the students were positive about the benefits of using genAI in helping them revise their penetration testing reports. This awareness, however, did not sway some students who chose not to use genAI tools as they wanted to understand and perform the task using their own thinking process. The genAI users mainly used ChatGPT and Copilot to perform the revision tasks. They found these tools helpful in assisting them to generate ideas, structure the report and perform the four revision tasks. Furthermore, they used these tools even though they were aware that moderate levels of editing on genAI’s output were required. The findings also showed that both groups of students mentioned similar learning outcomes from the workshop. However, the emphasis in the learning outcomes mentioned differed for both groups. This reflects that the critical elements in writing pedagogy with or without genAI tools might be students’ content and language proficiencies besides the ownership of their writing process.
In this study, we propose PeerSynergy, an innovative application that harnesses the power of AI by transforming direct answers into a more dynamic and interactive learning experience. The study aims to explore an innovative type of artificial intelligence (AI) education technology to avoid students’ passive reliance on AI, which can erode their cognitive reflection and critical thinking skills. Inspired by the Peer Instruction methodology proposed by Prof. Eric Mazur, our solution transforms AI-generated direct answers into guided, step-by-step conversations that encourage active student participation and the development of critical thinking skills. Through engineering AI prompts, PeerSynergy simulates a peer with a comparable level of knowledge, fostering a more collaborative and fairer debate in the learning process. This approach ensures that students are not merely passive recipients of massive information but are actively engaged in a dynamic educational dialogue.
In traditional network experiments, students gathered in classrooms and used specialized equipment to construct networks. However, there were significant financial costs associated with providing these classrooms and equipment. With the advancements in virtual machine technology and improvements in computing performance, it has become possible to simulate networks using virtual machines as network devices on computers. Consequently, e-learning systems have been developed, allowing network experiments to be conducted by operating virtual machines from network-connected computers.The author proposed LiNeS Cloud, a web-based exercise system for network security training, utilizing the virtual machine User-mode Linux. LiNeS Cloud aims to provide intuitive and seamless operation with responsive performance. The prototype of LiNeS Cloud was evaluated in a laboratory setting and received positive feedback regarding usability, with no issues pointed out regarding responsiveness.The author conducted seven 90-minute classes using LiNeS Cloud for network security training with third-year students enrolled in the network course of the Department of Information Engineering at the university where the author is employed. The learning effectiveness of this exercise was measured through self-assessment questionnaires completed by the students. The responses from the pre- and post-exercise questionnaires indicated a significant learning effect resulting from the exercises.
Self-Regulated Learning (SRL) is a multifaceted process encompassing cognitive, affective, metacognitive, and motivational components. Understanding the interplay of these components, particularly emotions, is crucial for fostering effective learning behaviors. This study addresses the challenge of recognizing Learning-Centered Emotions (LCEs) in educational settings by reannotating the DAiSEE dataset (a dataset for learning-centric emotion with Indian students) to provide frame-by-frame emotion labels suitable for static image analysis. Using this reannotated dataset, we trained and validated several state-of-the-art deep learning models and fine-tuned the Inception ResNet V2 model, demonstrating superior performance. The fine-tuned model was applied to the MEttLE (Modelling-based estimation learning environment) open-ended learning environment to analyze the interplay between detected academic emotions and cognitive processes among six engineering students. The results revealed significant differences in emotional distributions among high, medium, and low performers. High performers exhibited higher engagement levels, while low performers showed more frequent transitions into boredom. A Chi-square test of independence indicated a strong association between students’ performance levels and their emotional responses, with significant differences across cognitive processes based on their performance (high, medium, low) in the given task in the environment. Additionally, our findings emphasize the importance of personalized interventions and the potential for intelligent tutoring systems to adapt in real-time to the emotional states of learners.
The traditional experiment projects of circuits fundamentals and analog circuits are often isolated, focusing on individual knowledge points with little correlation between them. These experiments mainly involve circuit validation, lacking innovative project design. As a result, students often struggle to develop a meaningful understanding of the course's professional applications, as well as the practical use of knowledge and technology, even after completing these experiments. The electric trolley, a high-tech system integrating electronic circuits, sensors, and mechanical structures, serves as a comprehensive platform for environmental awareness, planning and decision-making, and autonomous driving. We exploit this system as a foundation to reform circuit and analog electronics courses through project-based and experimental teaching. First, aligned with the curriculum and knowledge framework of circuit fundamentals and analog circuit theory, we design an experimental project focusing on black-and-white lane detection for the electric trolley. Then, we develop a problem-oriented, step-by-step approach to guide the implementation of these experiments, progressively introducing more complex engineering challenges that reflect real-world design problems. Finally, we integrate experimental results with theoretical knowledge and align the experimental content with actual industry demands, fostering a seamless connection between education and industrial applications. Classroom surveys and assessment results indicate that the experimental content is both challenging and engaging for students.
The Feynman learning technique is an active learning strategy that helps learners simplify complex information through student-led teaching and discussion. In this paper, we present the development and usability testing of the Feynman Bot, which uses the Feynman technique to assist self-regulated learners who lack peer or instructor support. The Bot embodies the Feynman learning technique by encouraging learners to discuss their lecture material in a question-answer-driven discussion format. The Feynman Bot was developed using a large language model with Langchain in a Retrieval-Augmented-Generation framework to leverage the reasoning capability required to generate effective discussion-oriented questions. To test the Feynman bot, a controlled experiment was conducted over three days with fourteen participants. Formative and summative assessments were conducted, followed by a self-efficacy survey. We found that participants who used the Feynman Bot experienced higher learning gains than the Passive Learners' group. Moreover, Feynman Bot Learners' had a higher level of comfort with the subject after using the bot. We also found typing to be the preferred input modality method over speech, when interacting with the bot. The high learning gains and improved confidence with study material brought about by the Feynman Bot makes it a promising tool for self-regulated learners.
A multidisciplinary collaborative project-based learning approach is proposed to teach the Internet of Things with the aim of developing critical thinking, idea development, and intellectual design skills. In the real world, people from different disciplines need to work collaboratively in the new product development process. However, in the current curriculum and teaching environment, the collaboration part is missing. Although new product/innovation subjects are offered in engineering and business schools, they are run separately and only focus on one aspect of the process. Extensive research has demonstrated that working with peers from other disciplines enables students to gain different perspectives, helps them to examine their own values and pre-existing knowledge, and improves their active engagement. An interactive framework is designed which links students from different disciplines together to identify real world problems, develop their own ideas to solve them, develop both business and engineering aspects of their products, and demonstrate and present them using different medium. Student satisfaction surveys are conducted to evaluate the effectiveness of the framework. The innovative component attracted high students’ satisfaction from both engineering and business students and students rated their experiences positively. This study could help other institutions in the successful implementation of multidisciplinary project components in their higher education program.
