Abstracts:Modern day organizations face a continuous challenge in ensuring that their employees are cognizant with malware and cyber attacks, since it has the potential to cause financial, legal, and reputational damage to them. Current awareness training exists in a multitude of forms to equip employees and organizations to protect themselves against malware and cyber attacks. This article proposes a more realistic and interactive approach to malware training through a simulated ransomware infection presented as a game, both for employees and students in cyber security domain. The proposed mechanism was tested by individuals within cyber industries and students and demonstrated at events within the South West of England to an audience of prospective employees and industry experts, who found the training beneficial and insightful into how malware can be avoided and identified. Overall, results from the development of the tool indicate that the ability to identify malicious files increased in the range of 12%–55%, with respondents generally agreeing the tool was useful for increasing learning capacity. External results from unstructured interviews appear to illustrate that individuals displayed a heightened awareness post-training. External surveys with undergraduate students studying cyber and computer science indicate 100% of students believe the training would be useful for some form of training, with 86% evaluating the training would be suitable for both unsupervised and supervised malware training. Language analysis revealed highly positive vocabulary in free-text questions from multiple year groups, most highly in second and third year cyber security cohorts.

Abstracts:We have introduced a novel approach to competency-based education in mechatronics from the undergraduate to the postgraduate level. What distinguishes this approach is the integration of modeling and control of sampled systems right from the beginning of the undergraduate education. It is achieved by changing the structure of the first-semester Calculus course to focus on discrete-time systems: emphasizing numerical differentiation and integration and difference equations. The curriculum is enriched by interdisciplinary homework project assignments that, given in subsequent courses throughout the education, are tied to the same quarter-vehicle model but vary in theoretical complexity. It demonstrates multiple dimensions of the same engineering problem, leading to a deeper understanding. Based on the discrete-time modeling studied in Calculus, students can solve the problem at a basic level and verify the results with measurements. Later, they can compare these solutions with those obtained using more advanced tools. This approach creates a synergy between different subjects ranging from the basics to the advanced control theory. This article focuses primarily on the mathematical toolkit that facilitates the achievement of our didactic goals.

Abstracts:Contribution: This study demonstrates the effectiveness of a dedicated soft skills (SSs) course in an electrical engineering (EE) undergraduate program, showing improvements in students’ appreciation and satisfaction of expressing most of these skills.Background: SSs, encompassing interpersonal and social competencies, are important for career success in engineering. However, these skills are often overlooked or only indirectly addressed in EE curricula. This study addresses the need for intentional SSs development in EE education, with potential implications for engineering programs worldwide.Research Questions: How does a dedicated SSs course affect EE students’ perception of the importance of these skills?Does such a course improve students’ satisfaction with their ability to express these skills?Methodology: A dedicated SSs course was designed and implemented for undergraduate EE students. The course explicitly focused on developing teamwork, time management, written and oral communication, and implicitly addressed several other skills. Students’ perceptions of the importance of SSs and their satisfaction with expressing these skills were assessed at the beginning and end of the course using a questionnaire.Findings: While students recognized the importance of SSs before the course, their appreciation for these skills’ contribution to job-seeking and career success increased after completing the course. More significantly, students reported higher levels of satisfaction in expressing several of the targeted skills by the end of the course. These results, though limited to a single course at one institution, suggest the value of incorporating dedicated SSs development into EE curricula.

Abstracts:Contribution: This study explores the direct relationships between campus environment, personality traits, engineering self-efficacy, academic performance, and psychological well-being of engineering undergraduates, and the indirect relationships between them with engineering self-efficacy as mediator which had not been studied previously in a developing country context. The findings of this research guide policy makers to improve engineering education while considering these factors. Background: Engineering education plays a significant role in enhancing the quality of engineers. Personality traits, campus environment, and engineering self-efficacy could potentially impact the academic performance and psychological well-being of engineering undergraduates. An indirect relationship exists between these dimensions with engineering self-efficacy acting as a mediator. Research Questions: 1) Do campus environment, personality traits, and engineering self-efficacy significantly impact the academic performance of engineering undergraduates? 2) Do campus environment, personality traits, and engineering self-efficacy significantly impact the psychological well-being of engineering undergraduates? and 3) Does engineering self-efficacy mediate the indirect relationships between campus environment, personality traits, academic performance, and psychological well-being of engineering undergraduates? Methodology: The research participants were 1005 engineering undergraduates from 16 universities of Pakistan. Structural equation modeling (SEM) was used to assess the hypothesized relationships between personality traits, campus environment, engineering self-efficacy, academic performance, and psychological well-being. Findings: Personality traits and engineering self-efficacy significantly impact academic performance. Campus environment does not impact academic performance. Campus environment, personality traits, and engineering self-efficacy significantly impact psychological well-being. Campus environment and personality traits significantly impact engineering self-efficacy. Engineering self-efficacy mediates the relationship between the variables being studied.

Abstracts:Contribution: This research explores the benefits and challenges of developing, deploying, and evaluating a large language model (LLM) chatbot, MoodleBot, in computer science classroom settings. It highlights the potential of integrating LLMs into LMSs like Moodle to support self-regulated learning (SRL) and help-seeking behavior. Background: Computer science educators face immense challenges incorporating novel tools into LMSs to create a supportive and engaging learning environment. MoodleBot addresses this challenge by offering an interactive platform for both students and teachers. Research Questions: Despite issues like bias, hallucinations, and teachers’ and educators’ resistance to embracing new (AI) technologies, this research investigates two questions: (RQ1) To what extent do students accept MoodleBot as a valuable tool for learning support? (RQ2) How accurately does MoodleBot churn out responses, and how congruent are these with the established course content? Methodology: This study reviews pedagogical literature on AI-driven chatbots and adopts the retrieval-augmented generation (RAG) approach for MoodleBot’s design and data processing. The technology acceptance model (TAM) evaluates user acceptance through constructs like perceived usefulness (PU) and Ease of Use. Forty-six students participated, with 30 completing the TAM questionnaire. Findings: LLM-based chatbots like MoodleBot can significantly improve the teaching and learning process. This study revealed a high accuracy rate (88%) in providing course-related assistance. Positive responses from students attest to the efficacy and applicability of AI-driven educational tools. These findings indicate that educational chatbots are suitable for integration into courses to improve personalized learning and reduce teacher administrative burden, although improvements in automated fact-checking are needed.

Abstracts:Purpose: The present study aimed to integrate artificial intelligence technology (ChatGPT) with an in-depth literature review to explore knowledge dimensions of professional teaching and information and communication technology (ICT) integration models in TVET, and utilize the Delphi technique and ChatGPT to examine, enhance, and validate a newly proposed model for professional teaching and ICT integration in TVET.Methods: Three rounds of the Delphi technique were applied to develop and validate this new model. Artificial intelligence tool (ChatGPT) with an in-depth literature review were used to explore knowledge dimensions for TVET education. In Round 1, ChatGPT was used to transform the technological pedagogical content knowledge (CK) model for TVET instruction. A new suggested model was developed called the contextual technology andrology/pedagogy entrepreneurship work CK (CTA/PEWCK) model. Ten experts from the TVET sector participated in Round 2, and 39 participated in Round 3 to validate the new suggested model.Findings: The findings revealed that the fifteen knowledge dimensions extracted from this new suggested model presented essential knowledge for TVET education.Conclusion: Applying the CTA/PEWCK model offers professional development opportunities for TVET teachers that focus on hands-on experiences to develop competencies for sustainable development (ESD), enabling an integrated approach to knowledge dimensions, procedures, and attitude.

Abstracts:Contribution: A remote laboratory system for induction motors was developed in this study. By creating an original simulated induction motor, the structure of the motor can be observed, and the current and magnetic flux can be measured safely.Background: Electrical machinery has little appeal to young engineers. Such machinery deals with invisible electromagnetic phenomena; thus, it is difficult for young engineers to understand the involved principles. The risks associated with high voltage and high-speed rotation are also considered reasons for this low interest.Intended Outcomes: The remote laboratory system enables remote learning even in educational institutions that do not have specialized simulated induction motors. In addition, it is possible to repeat experimental learning as required to ensure that the student has learned the content sufficiently.Application Design: This system is designed such that it can be used without teachers or teaching assistants support, and the number of controllable functions and operations increase gradually according to the learning content.Findings: The proposed remote laboratory system was evaluated experimentally with 46 student participants from Saga University and the Chiba Institute of Technology to confirm the usefulness of the system. Tests conducted before and after using the system confirmed that the participant’s understanding of induction motors improved. In addition, the results of a system usability scale evaluation confirmed that there were no problems with operation of the remote laboratory system.

Abstracts:Contributions: This article presents the results from a teaching innovation project based on the creation of educational videos by students and their assessment through blind peer review in the context of an electric circuit course. This article also analyses the activity’s impact on learning outcomes by comparing the results of participating students with nonparticipants, as well as with results from the previous years. The study includes surveys completed by students. Background: Electric circuit courses involve a cumulative learning process that advances throughout the course. Students who do not adhere to a regular study-homework routine often struggle to maximize the benefits of their class time and are more prone to test failures. Research Questions (RQs): RQ1) Can peer assessment be relied upon as a grading method in an electrical engineering course? RQ2) Is it possible to enhance students’ study routines and improve their results by incorporating assessment activities different from partial exams, such as creating educational videos and peer review assessments? Methodology: Students create videos, which are then submitted to the designated task through the Moodle workshop tool. Subsequently, peer reviews are conducted using a rubric form. The reliability of peer review is analysed by comparing the grades assigned by students with those assigned by teachers who are introduced as incognito reviewers. Findings: The evaluation system, relying on peer assessments, demonstrated fair reliability. Participants have substantially improved their academic performance while dedicating less time to preparing for the different evaluation tests.

Abstracts:In recent years, knowledge tracing (KT) within intelligent tutoring systems (ITSs) has seen rapid development. KT aims to assess a student’s knowledge state based on past performance and predict the correctness of the next question. Traditional KT often treats questions with different difficulty levels of the same concept as identical representations, limiting the effectiveness of question embedding. Additionally, higher-order semantic relationships between questions are overlooked. Graph models have been employed in KT to enhance question embedding representation, but they rarely consider the directed relationships between learning interactions. Therefore, this article introduces a novel approach, KT through Enhanced Questions and Directed Learning Interaction Based on multigraph embeddings in ITSs (MGEKT), to address these limitations. One channel enhances question embedding representation by capturing relationships between students, concepts, and questions. This channel defines two meta paths, facilitating the learning of high-order semantic relationships between questions. The other channel constructs a directed graph of learning interactions, leveraging graph attention convolution to illustrate their intricate relationships. A new gating mechanism is proposed to capture long-term dependencies and emphasize critical information when tracing students’ knowledge states. Notably, MGEKT employs reverse knowledge distillation, transferring knowledge from two small models (student models) to a large model (teacher model). This knowledge distillation enhances the model’s generalization performance and improves the perception of crucial information. In comparative evaluations across four datasets, MGEKT outperformed baselines, demonstrating its effectiveness in KT.

Abstracts:Contribution: This article suggests a novel method for diagnosing a learner’s cognitive proficiency using deep neural networks (DNNs) based on her answers to a series of questions. The outcome of the forecast can be used for adaptive assistance. Background: Often a learner spends considerable amounts of time in attempting questions on the concepts she has already mastered. Therefore, it is desirable to appropriately diagnose her cognitive proficiency and select the questions that can help improve preparedness. Research Question: Can the cognitive proficiency of a learner be progressively predicted when she attempts a series of questions? Methodology: A novel approach using DNNs to diagnose the learner’s proficiency after she attempts a set of questions is proposed in this article. Subsequently, to realize the effectiveness of the proposed prediction model, an algorithm is introduced that can select questions of required difficulty based on the predicted proficiency level. An appropriate question sequence can facilitate a learner’s faster attainment of the necessary competency level. Findings: The experimental results indicate that the proposed approach can predict the ability of learners with an accuracy of 91.21%. Moreover, the proposed technique outperforms the existing techniques by 33.19% on an average.