Academic Technology Research Articles

Engineering Academics and Students’ Views on the Phases of Teaching and Learning of Engineering Mathematics in an Antenna Design Course

An antenna design course, naturally, aims to teach students to produce an antenna device, which is a vital tool in technological development. Typically, this course is taught and designed in an abstract manner, which may be an inappropriate approach to teaching it. This approach could be due to engineering academics and students’ poor understanding of how to model mathematics concepts. As a result, antennae experts suggest that antenna technology is a difficult device when it comes to mathematical descriptions, which has resulted in a sudden loss of interest among students in a school of engineering. Thus, this paper reports on the four phases of teaching antenna design using mathematical modelling approaches, with the specific aim of resolving the difficulties encountered in teaching an antenna design course. In achieving this teaching, the study explains the approaches and impact of teaching Mathematical Modelling (MM) in antenna design among engineering academics. In this study, the sample population included one engineering academic and four engineering students. The data were collected using a qualitative research approach, utilising an interview guide and an open-ended questionnaire. Finally, the results of this study confirmed the four major phases of teaching antenna design using engineering mathematics.

Teacher Empathy in an engineering classroom: Exploring the growth in perception of Teacher Empathy through the journey of three engineering faculty members

AbstractBackgroundIn higher education, Teacher Empathy is a term that refers to the empathetic skills of teachers. Researchers in other disciplines have shown that Teacher Empathy reduces teacher burnout and improves teacher satisfaction and student performance. There is little research on Teacher Empathy within engineering education.PurposeIn our current study, we explored the potential changes in perception of Teacher Empathy among three engineering faculty members as they utilized empathetic actions while teaching second‐year engineering courses. Understanding the experiences of faculty members will help us to implement and further explore Teacher Empathy in engineering education.MethodTeacher Action Research methodology helped us provide agency to our three participants and research with them instead of on them. We used a combination of values coding and simultaneous coding for data analysis.FindingsImproved confidence in their empathetic skills was observed for two participants as they showed positive evolution of their perception about Teacher Empathy. The other participant increased the number of empathetic approaches he used in his classroom.ConclusionReduced teacher burnout, improved teacher satisfaction, and better student performance were some of the major benefits of Teacher Empathy that emerged. The difference in the evolution of each participant's perception about Teacher Empathy indicated that the personality of a faculty member has an influence on implementation and success of Teacher Empathy.

Identity Trajectories of Faculty Members through Interdisciplinary STEAM Collaboration Paired with Public Communication

Faculty members in science, technology, engineering, and mathematics (STEM) fields are accustomed to presenting their research findings through journal publications, conference presentations, textbooks, and other academic mediums. However, the audience for these traditional forms of communication are other researchers, which raises concerns about how science research and knowledge are communicated to audiences who have less expertise on these topics. We sought to understand how faculty members develop their identities through collaborative professional development opportunities aimed at growing communication skills to communicate with audiences less familiar with research through interdisciplinary science, technology, engineering, arts, and mathematics (STEAM) activities. We conducted a qualitative, longitudinal study with sixteen STEAM faculty members to explore their identity trajectories as their interdisciplinary cohorts participated in various collaborations to engage with public audiences about their research. Through our analysis, we found that each faculty member’s dominant identity played a significant role in their identity trajectory through their professional development. We observed a significant growth in faculty members’ communication skills, such as learning new presentation techniques to engage others in their research areas of expertise and in their understanding of interdisciplinary STEAM collaboration. Our results provide insights into the identity trajectories of faculty members and how their identity development through these interdisciplinary STEAM collaborations will impact their formal education roles as researchers and teachers moving forward.

An Uneasy Peace: How STEM Progressive, Traditionalist, and Bridging Faculty Understand Campus Conflicts over Diversity, Anti-Racism, and Free Expression

In recent years an uneasy peace has descended in U.S. academe between those who feel research universities have done too little to advance the representation of minority groups and women and those who feel that the administrative policies developed to improve representation can and sometimes do come into conflict with core intellectual commitments of universities. Using quantitative and qualitative evidence from interviews with 47 natural sciences, engineering, and mathematics faculty members at a U.S. research university, the paper examines the background characteristics of three sets of protagonists - academic progressives, academic traditionalists, and those whose views bridge the divide - and the way respondents discussed and justified their viewpoints. The paper draws on the theory of strategic action fields to illuminate the structure and dynamics of the conflict and suggests modifications to the theory that would improve its explanatory power for this case.

Research on the application value of Multimedia-Based virtual reality technology in drama education activities

The research and moral use of academic technology focuses on developing, implementing, and overseeing the use of suitable technical resources and procedures to enhance learning and achievement. Multimedia has found its position in some form as an educational technology platform in the contemporary environment of academic universities. The use of virtual reality software as an intellectual tool and learning provider allows students to perform cognitive rehabilitation of preexisting information frameworks. People are paying more and more attention to how preschoolers' holistic skills develop as education reform progresses. Drama education is incorporated into the school curriculum to enhance young children's artistic, intellectual, and linguistic skills. Therefore, this study aims to examine the potential of multimedia-based virtual reality technology (MVRT) in drama education. The participants in this study were students from different universities in China. Students were exposed to multimedia-based virtual reality technology, and its efficacy was assessed using a statistical analytic approach called Analysis of variance (ANOVA). Drama understanding rate, educational improvement ratio, teaching quality rate, student achievement ratio, computation time, and parental support rate are among the performance metrics used to assess performance. Multimedia-based virtual reality technology (MVRT) for drama education showed outstanding success, with a 98% improvement ratio in educational outcomes and higher teaching quality. Students exhibited improved performance, supported by solid parental approval, demonstrating the effectiveness of MVRT in enhancing educational experiences.

Bio‐inspired human network diagnostics: Ecological modularity and nestedness as quantitative indicators of human engineered network function

AbstractAnalyzing interactions between actors from a systems perspective yields valuable information about the overall system's form and function. When this is coupled with ecological modeling and analysis techniques, biological inspiration can also be applied to these systems. The diagnostic value of three metrics frequently used to study mutualistic biological ecosystems (nestedness, modularity, and connectance) is shown here using academic engineering makerspaces. Engineering students get hands‐on usage experience with tools for personal, class, and competition‐based projects in these spaces. COVID‐19 provides a unique study of university makerspaces, enabling the analysis of makerspace health through the known disturbance and resultant regulatory changes (implementation and return to normal operations). Nestedness, modularity, and connectance are shown to provide information on space functioning in a way that enables them to serve as heuristic diagnostics tools for system conditions. The makerspaces at two large R1 universities are analyzed across multiple semesters by modeling them as bipartite student‐tool interaction networks. The results visualize the predictive ability of these metrics, finding that the makerspaces tended to be structurally nested in any one semester, however when compared to a “normal” semester the restrictions are reflected via a higher modularity. The makerspace network case studies provide insight into the use and value of quantitative ecosystem structure and function indicators for monitoring similar human‐engineered interaction networks that are normally only tracked qualitatively.

The relationship between personal and professional goals and emotional state in academia: a study on unethical use of artificial intelligence.

Artificial Intelligence (AI) is a concept that has been a subfield of computer science since the 1950s. In recent years, with its growing development power, AI technologies have made significant progress and are now being used in many fields. Like in all areas, the use of AI technologies in academia has provided convenience to academics while also bringing ethical debates. In the literature part of the study, concepts such as AI, academia, academics and academic progress, ethics, ethical theories, academic ethics, and emotional states have been thoroughly examined and defined. In this study, starting from AI and scientific ethics, ethical issues arising from emotional states in academic research have been identified, and concrete solutions to these ethical issues have been proposed. The aim is to discuss the views of academics in order to determine what types of scientific ethical violations and prevention methods are involved. In this context, the semi-structured interview technique, which is one of the qualitative research methods, was preferred as the method. In the study, in-depth semi-structured interviews were conducted with 4 ethics experts and 4 psychology experts selected through snowball sampling technique. The data obtained through semi-structured in-depth interviews will be analyzed using content analysis. Within the context of the literature review and interviews: Ethics is based on the foundation of acting correctly. In this context, scientific ethics can be summarized as acting truthfully and honestly, not distorting data, and not trying to progress unfairly. The use of AI in academia is becoming increasingly widespread. From a positive perspective, this usage significantly contributes to making studies more practical. However, it can lead to problems such as unfair authorship, devaluation of human authorship, and incorrect data. The connection between academics' professional advancement goals and emotional states becomes prominent in this context. The potential of AI to facilitate progression can lead to unethical use. To prevent such situations, it is recommended to organize training sessions to increase professional awareness, internalize ethics personally, establish ethical committees specific to the field of AI, conduct more effective audits by academic publication and promotion committees, and implement specific regulations for AI. Finally, for future academic studies, it is suggested that the usage of AI in academic research be measured and evaluated by ethics experts. For psychologists, conducting surveys with academics to explore how they use AI in the context of their emotional states and professional advancement goals is recommended.

Virtual design pedagogy: understanding the Metaverse and improving creative design skills using ZEPETO studio platform™

ABSTRACT This study aimed to develop an accessible and cost-effective fashion design course activity for Metaverse and explore its efficacy through student experiences. The study enables students to translate their ideas into virtual fashion items in ZEPETO Studio™. A total of 43 undergraduate students with no prior experience in fashion item creation on Metaverse platforms voluntarily participated in this study. The researchers administered a questionnaire including three sections: (1) general information, (2) the creation of fashion items with an avatar, and (3) the learning experience. The findings provide this activity catalyzes students to derive enjoyment and generate income as virtual design creators beyond fostering creativity. This study will bridge the gap between theoretical content and immersive technology in academia. In addition, this pedagogical research, framed in experiential learning theory, contributes to generating design course content on design and entrepreneurship, embracing emerging technology of these practices and initiatives for current and future generations.

Using collaborative autoethnography to investigate mentoring relationships for novice engineering education researchers

Abstract Background The National Science Foundation Research Initiation in Engineering Formation (RIEF) program aims to increase research capacity in the field by providing funding for technical engineering faculty to learn to conduct engineering education research through mentorship by an experienced social science researcher. We use collaborative autoethnography to study the tripartite RIEF mentoring relationship between Julie, an experienced engineering education researcher, and two novice education researchers who have backgrounds in biomedical engineering—Paul, a biomedical engineering faculty member and major professor to the second novice, Deepthi, a graduate student. We ground our work in the cognitive apprenticeship model and Eby and colleagues’ mentoring model. Results Using data from written reflections and interviews, we explored the role of instrumental and psychosocial supports in our mentoring relationship. In particular, we noted how elements of cognitive apprenticeship such as scaffolding and gradual fading of instrumental supports helped Paul and Deepthi learn qualitative research skills that differed drastically from their biomedical engineering research expertise. We initially conceptualized our tripartite relationship as one where Julie mentored Paul and Paul subsequently mentored Deepthi. Ultimately, we realized that this model was unrealistic because Paul did not yet possess the social science research expertise to mentor another novice. As a result, we changed our model so that Julie mentored both Paul and Deepthi directly. While our mentoring relationship was overall very positive, it has included many moments of miscommunication and misunderstanding. We draw on Lent and Lopez’s idea of relation-inferred self-efficacy to explain some of these missed opportunities for communication and understanding. Conclusions This paper contributes to the literature on engineering education capacity building by studying mentoring as a mechanism to support technically trained researchers in learning to conduct engineering education research. Our initial mentoring model failed to take into account how challenging it is for mentees to make the paradigm shift from technical engineering to social science research and how that would affect Paul’s ability to mentor Deepthi. Our experiences have implications for expanding research capacity because they raise practical and conceptual issues for experienced and novice engineering education researchers to consider as they form mentoring relationships.

Smart Campus and Student Learning Engagement

This study aims to assess the implementation of smart campus and the students' learning engagement at Zhongshan College, China. A well-structured questionnaire was developed, and information was collected from 277 students and 377 teachers. The results indicate that both groups of respondents highly agree on the construction levels of the smart campus in terms of security operations, academic technology assistance, public relation services, and stakeholders' experience. Furthermore, respondents emphasized that the level of smart campus construction affects students' learning engagement. Specifically, students' personalized learning engagement is affected by the level of smart campus construction in security operations and public relations services. And the degree of students' use of smart learning resources is significantly related to the smart campus construction levels in all four dimensions. This study fills research gaps and provides valuable guidance for the development of smart campuses.

SPARKing academic technologies across the valley of death.

SPARKing academic technologies across the valley of death.

Narrative Smoothing in the Wild: A Pack Based Approach to Co-Constructing Narratives for Analysis

This methodology paper introduces a collective, team-based approach to constructing narratives in narrative research. The goal of the larger study was to explore the pedagogical belief and practices of engineering faculty members. The newly formed team of researchers ranged from novices to experts in the field of qualitative research, and this space created a unique opportunity to reflect on and explore the co-construction of Cody’s narrative, the first narrative that the team constructed. The narrative was smoothed and constructed in a way that reduces some of the limitations inherent in narrative smoothing, through a deliberate and intentional negotiation process. We hope that this deeper exploration of our methods is helpful for other narrative researchers who are interested in team-based approaches to co-construction of narratives.

Potentials of ChatGPT in Computer Programming: Insights from Programming Instructors

Potentials of ChatGPT in Computer Programming: Insights from Programming Instructors

Extent of Online Platform Utilization of the School of Industrial Technology Faculty Members

This study delved into identifying the most frequently used online learning platform of ASCOT-School of Industrial Technology (SInT) along with their motivation for usage and challenges faced. The respondents were all 24 SInT faculty members, comprising 15 males and nine (9) females, respectively. The study used a survey questionnaire carried out through Google Forms. Results show that Google Meet is the most frequently used online learning platform by SInT faculty members, followed by Facebook (Messenger and Groups), Google Classroom, Zoom, and YouTube, respectively. Accessibility was revealed to be the most important reason considered in the use of online learning platforms, while issues with stable and limited network and internet connectivity are the most salient concerns. From the results of this study, it is recommended to: a) sustain plans and programs intended for connectivity assistance (financial or devise-wise) for students and faculty members; b) integrate efficient online measures in the transition to full face-to-face classes; c) strengthen locally developed (ASCOT or SInT-based) digital programs that would cater to the specific needs and challenges of the community; and d) equip faculty members with digital competency through skills training and workshops on computer and technology.

Maximizing Journal Article Impact: Strategies for Enhanced Visibility in Today's Academic Sphere

In the dynamic realm of academia, researchers face the dual challenge of generating groundbreaking insights and ensuring widespread visibility for their contributions. This article explores the evolving strategies employed by researchers to enhance the visibility of their journal articles in the changing landscape of academic technology. Online publishing platforms have transformed scholarly communication, democratizing knowledge through open-access journals, preprint servers, and institutional repositories. Beyond traditional metrics, we delve into innovative methods, collaboration, and technology-driven solutions that amplify the reach and impact of scholarly articles. Visibility extends beyond dissemination, encapsulating the art of captivating diverse audiences and transcending disciplinary boundaries. This research article illuminates the path towards heightened visibility, empowering researchers to contribute to the collective tapestry of knowledge through means such as Academia.edu, ISSUU, Scribd, ResearchGate, social media, Search Engine Optimization (SEO), and ORCID. Enhanced visibility offers multifaceted advantages, including increased citations, higher impact factors, knowledge dissemination, international collaboration, career advancement, public engagement, and job opportunities within the scholarly community. Researchers are equipped with the insights needed to thrive in the evolving landscape of journal article visibility in the digital cosmos. Keywords academic publishing; article visibility; journal impact; research visibility; article promotion; citation indexing; academic visibility strategies; research impact factors

The Alignment between International and National Academic Accreditations - An Application in Information Systems Bachelor Program at Kingdom of Saudi Arabia

This study proposes a method for consolidating various program accreditations into a unified accreditation process for a single academic program. The primary challenge lies in harmonizing the diverse requirements of multiple accreditations. In the context of academic technology programs in Saudi Arabia, this research focuses on the integration of NCAAA and ABET accreditations, with a specific application to the Information Systems at King Abdulaziz university. The core methodology employed in this study involves applying Bloom's taxonomy of learning to identify the synergies between the accreditation criteria of ABET and NCAAA. To ascertain the alignment of learning outcome measurement based on both accreditations, a questionnaire was administered to faculty members within the College of Computer Science and Information Technology at renowned universities in the Kingdom of Saudi Arabia. This article is intended for researchers specializing in the quality of education in computing and engineering, as well as professionals, developers, and officials with an interest in academic accreditations. Furthermore, this research seeks to establish a foundation for the seamless dissemination of educational content through websites and digital applications, adhering to the standards and principles of academic accreditation.

Reflections on the Artificial Intelligence Transformation: Responsible Use and the Role of Institutional Research and Institutional Effectiveness Professionals

This article explores the potential impact of artificial intelligence (AI) and machine learning on higher education. It overviews current generative AI capabilities and argues for ethical frameworks to address issues such as bias. The article advocates for a multidisciplinary governance approach involving institutional stakeholders by examining past academic technology adoption. It highlights the strategic role institutional research (IR) and institutional effectiveness (IE) professionals can play in navigating AI complexities. This article provides specific suggestions for IR/IE professionals to embrace the role of AI ethicist: continuously developing AI literacy, ensuring ethical deployment, upholding privacy and confidentiality, mitigating bias, enforcing accountability, championing explainable AI, incorporating student perspectives, and developing institutional AI policies. The article concludes by asserting that IR/IE’s research expertise, ethical commitment, and belief in human judgment equip the field to adapt to and lead in the AI era. By taking an active role, IR/IE can shape the technology’s impact to benefit higher education.

Revolutionizing Education: The Transformative Power of AI Technologies in PR

In the coming years, Artificial Intelligence (AI) is set to make a profound impact on higher education, particularly within the field of Public Relations (PR). This surge in the adoption of AI technologies in academia is fuelled by compelling factors. AI is on the verge of transforming PR practices, elevating the efficiency and effectiveness of communication strategies. The data-driven prowess of AI equips PR professionals with enhanced capabilities to understand and engage with target audiences, tailor messages with precision, automate routine tasks, such as content creation, social media management, and data analysis, and liberate practitioners from administrative burdens. As educational institutions increasingly recognize the value of AI in PR, investments in its implementation are expected, heralding a paradigm shift in higher education communication and stakeholder engagement. The current article aims to delve into the European framework for AI applications in education, present the landscape of existing technology tools used in the communication area, and review recent publications describing the benefits and possible drawbacks of using generative AI technologies in the sphere of teaching.

Do faculty members matter in corporate entrepreneurship in Kuwaiti higher education institutions?

PurposeThe adoption of an entrepreneurial posture supports higher education institutions (HEIs) in their quest for growth. The present study examines the role faculty members play in adopting an entrepreneurial orientation (EO) in HEIs within the Kuwaiti academic context and aims to assess whether this orientation contributes to fostering corporate entrepreneurship in their institutions.Design/methodology/approachPrimary data were collected to study the relationship between faculty EO and the EO of their respective HEI. Empirical research was conducted based on a questionnaire completed by 341 engineering and business faculty members employed at Kuwaiti universities and colleges. The research model was tested and validated using structural equation modelling (SEM).FindingsThe results show a positive relationship between the faculty EO and corporate entrepreneurship in HEIs, which was negatively moderated by human resource management (HRM) practices. These findings emphasise the need for HEIs in Kuwait to evolve their HRM practices towards enhancing innovation, proactiveness and risk-taking amongst faculty.Originality/valueThis study highlights the strategic renewal perspective in HEI-EO and how faculty initiatives can support it.

An Ensemble Modeling Approach to Enhance Grade Prediction in Academic Engineering Programming Courses

Predicting the future academic grades of students can play a pivotal role in enhancing their performance in specific courses, consequently yielding a positive impact on their prospective academic, professional, and personal achievements, as well as on society at large. The field of programming is rapidly gaining prominence as an essential profession spanning multiple domains, marked by abundant opportunities and financial rewards. To cater to the diverse interests of students, the recommended curriculum structure for engineering programs in computing adeptly combines theoretical knowledge with practical programming skills. This approach ensures that students acquire a comprehensive understanding of programming courses, allowing them to choose the path that aligns best with their envisioned careers as programmers This research endeavors to introduce ensemble prediction techniques aimed at identifying students who exhibit the potential for advancement, or conversely, those who may not excel in four university-level programming courses. The outcomes of this study are presented alongside valuable performance assessment metrics for five ensemble methodologies, namely AdaBoost, Bagging, Random Forest, Stacking, and Voting. This evaluation employs a 10-fold cross-validation methodology and incorporates the Principal Component Analysis (PCA) for feature ranking. The results unequivocally demonstrate that both the Stacking and Random Forest ensemble approaches have attained the highest level of accuracy when applied to two distinct datasets.