A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare

A lesson learned during the pandemic is that social distancing saves lives. As it was shown recently, the healthcare industry is structured in a way that cannot protect medical staff from possible infectious diseases, such as COVID-19. Today's healthcare services seem anachronistic and not convenient for both doctors and patients. Although there have been several advances in recent years, especially in developed countries, the need for a holistic change is imperative. Evidently, future technologies should be introduced in the health sector, where Virtual Reality, Augmented Reality, Artificial Intelligence, and Tactile Internet can have vast applications. Thus, the healthcare industry could take advantage of the great evolution of pervasive computing. In this paper, we point out the challenges from the current visualization techniques and present a novel visualization technique assisted with haptics which is enhanced with artificial intelligent algorithms in order to offer remote patient examination and treatment through robotics. Such an approach provides a more detailed method of medical image data visualization and eliminates the possibility of diseases spreading, while reducing the workload of the medical staff. © 2023 by the authors.

Developing Novel Learning Spaces Through Social Media Channels for Sustainable CAD Engineering Education

This study investigates learners' viewing behaviors and engagement patterns through educational video analytics. Based on the performance of 42 instructional videos aiming to provide asynchronous help in both online and traditional Engineering laboratories in Higher Education, a comparative analysis has been performed. Data from YouTube channel's reports have been collected and processed in three time periods: the first semester of the academic year 2019–2020 and 2020–2021 in strictly remote teaching environments, and the second semester of 2021–2022 in traditional and hybrid learning modes. Even though instructor-generated educational videos have been a common tool for asynchronous support in online learning spaces, an evaluation by the available social media channels analytics has not been performed yet in an adequate level for leading to results. The most important outcome of this research is that YouTube analytics of the educational videos have shown that the social media channel can perform under different learning environments, with the same efficiency, proving the long-term viability of this construct of the learning strategy. Instructors and stake holders may profit from this study for future course planning in Engineering online and hybrid learning environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Hybrid Machine Learning Model for Grade Prediction in Online Engineering Education

Imposed and exclusively online learning, caused by COVID-19, revealed research challenges, e.g. curricula reformation and data collection. With this pool of data, this research explores grade prediction in an engineering module. A hybrid model was constructed, based on 35 variables, filtered out of statistical analysis and shown to be strongly correlated to students' academic performance. The hybrid model initially involves a Generalized Linear Model. Its errors are used as an extra dependent variable, incorporated to an artificial neural network. The architecture of the neural network can be described by the sizes of the: input layer (36), hidden layer (1), output layer (1). Since new factors are revealed to affect students' academic achievements, the model was trained in the 70% of participants to forecast the grade of the remaining 30%. The model has therefore been divided into three subsets, with a training set of 70% of the sample and one hidden layer predicting the test set (15%) and the validation set (15%). Finally, the model has yielded an R-2 of one. This suggests that the modeling framework effectively links the predictors with the grade (dependent variable) with absolute fitting success.

Analysis of Engineering Student Data in Online Higher Education during the COVID-19 Pandemic

—The COVID-19 pandemic has challenged many educational institutions around the world in 2020 and 2021 as traditional education has been interrupted to prevent the spread of the virus. This forced the transition from traditional education to fully distance learning environments for all levels of education. The widespread adoption of distance learning has led instructors to form new digital learning environments and methods. In response to this unexpected situation, data regarding engineering students and their interaction with the learning environment was accumulated and processed, generating a matrix of 129 × 165 variables. The motivation for this research is to identify new variables that impact student performance during the disorientation of the educational process due to the COVID-19 pandemic. Statistical analysis was performed and discussed in this paper including correlation analysis, factor analysis, and clustering. Reliability analysis was also performed and ANOVA (analysis of variance) was applied to clusters. The novelty of this work is to use student performance data and statistical analysis of online surveys to reveal patterns that can help reduce dropout rates and transform the educational process, under extenuating and imposed distance learning circumstances. A major finding is that by applying innovative teaching methods, thereby meeting the challenge of an imposed distance learning environment, students’ spatial conceptions improve, overcoming the absence of a physical learning space. Deep insights for individual students were discovered, as well as significant relationships between students’ transition from secondary to higher education and their understanding of geometric features. Evidence of the effectiveness of the online learning framework that was integrated showed that it positively influenced students’ learning styles. © 2021 Kassel University Press GmbH. All rights reserved.

Evaluating Remote Task Assignment of an Online Engineering Module through Data Mining in a Virtual Communication Platform Environment

E-learning has traditionally emphasised educational resources, web access, student participation, and social interaction. Novel virtual spaces, e-lectures, and digital laboratories have been developed with synchronous or asynchronous practices throughout the migration from face-to-face teaching modes to remote teaching during the pandemic restrictions. This research paper presents a case study concerning the evaluation of the online task assignment of students, using MS Teams as an electronic platform. MS Teams was evaluated to determine whether this communication platform for online lecture delivery and tasks’ assessments could be used to avoid potential problems caused during the teaching process. Students’ data were collected, and after filtering out significant information from the online questionnaires, a statistical analysis, containing a correlation and a reliability analysis, was conducted. The substantial impact of 37 variables was revealed. Cronbach’s alpha coefficient calculation revealed that 89% of the survey questions represented internally consistent and reliable variables, and for the sampling adequacy measure, Bartlett’s test was calculated at 0.816. On the basis of students’ diligence, interaction abilities, and knowledge embedding, two groups of learners were differentiated. The findings of this study shed light on the special features of fully online teaching specifically in terms of improving assessment through digital tools and merit further investigation in virtual and blended teaching spaces, with the goal of extracting outputs that will benefit the educational community. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The impact of different learning approaches based on ms teams and moodle on students' performance in an on-line mechanical cad module

During the Covid-19 pandemic, various on-line tools have been employed to support both synchronous and asynchronous education in universities. Several efforts have been made towards investigating the influence of various factors on students' academic performance in higher education. However, investigating the effectiveness of different learning methods during the pandemic is still an under-researched area. The goal of this study was to investigate if different teaching approaches, based on the usage of two distinct learning tools (MS Teams and Moodle) and a combination of those two, have an impact on students' academic performance in a higher education engineering module. In this study, an analysis of variance test has been performed to explore the factors affecting students' learning achievements. Hypothesis testing has also been conducted to examine whether different learning approaches have an impact on first-year students' academic performance in a mechanical engineering CAD module. A major conclusion is that even though the investigated teaching approaches may not have a direct impact on distinct variables, e.g. students' enjoyability and familiarisation of the learning platforms, they can still affect their performance. Also, the utilisation of a different learning tool used for asynchronous support (i.e. Moodle) may result in high levels of learners' dropout cases. © 2021 World Institute for Engineering and Technology Education. All rights reserved.

Data mining for improving online higher education amidst COVID-19 pandemic: A case study in the assessment of engineering students

Instructional materials, internet accessibility, student involvement and communication have always been integral characteristics of e-learning. During the transition from face-to-face to COVID-19 new online learning environments, the lectures and laboratories at universities have taken place either synchronously (using platforms, like MS Teams) or asynchronously (using platforms, like Moodle). In this study, a case study of a Greek university on the online assessment of learners is presented. As a testbed of this research, MS Teams was employed and tested as being a Learning Management System for evaluating a single platform use in order to avoid disruption of the educational procedure with concurrent LMS operations during the pandemic. A statistical analysis including a correlation analysis and a reliability analysis has been used to mine and filter data from online questionnaires. 37 variables were found to have a significant impact on the testing of tasks' assignment into a single platform that was used at the same time for synchronous lectures. The calculation of Cronbach's Alpha coefficient indicated that 89% of the survey questions have been found to be internally consistent and reliable variables and sampling adequacy measure (Bartlett's test) was determined to be good at 0.816. Two clusters of students have been differentiated based on the parameters of their diligence, communication abilities and level of knowledge embedding. A hierarchical cluster analysis has been performed extracting a dendrogram indicating 2 large clusters in the upper branch, three clusters in the lower branch and an ensuing lower branch containing five clusters. © 2021 The authors and IOS Press.

Creating a metamodel for predicting learners satisfaction by utilizing an educational information system during COVID-19 pandemic

Faced with the disruption generated by the COVID-19 pandemic, the advent of enforced and exclusive online learning presented a challenging opportunity for researchers worldwide, to quickly adapt curricula to this new reality and gather electronic data by tracking students' satisfaction after attending online modules. Many researchers have looked into the subject of student satisfaction to discover if there is a link between personal satisfaction and academic achievement. Using a set of data, filtered out of a statistical analysis applied on an online survey, with 129 variables, this study investigates students' satisfaction prediction in a first-semester Mechanical Engineering CAD module combined with the evaluation and the effectiveness of specific curriculum reforms. A hybrid machine learning model that has been created, initially consists of a Generalized Linear Model (GLAR), based on critical variables that have been filtered out after a correlation analysis. Its fitting errors are utilized as an extra predictor, that is used as an input to an artificial neural network. The model has been trained using as a basis the 70% of the population (consisting of 165 observations) to predict the satisfaction of the remaining 30%. After several trials and gradual improvement, the metamodel's architecture is produced. The trained hybrid model's final form had a coefficient of determination equal to 1 (R = 1). This indicates that the data fitting method was successful in linking the independent variables with the dependent variable 100 percent of the time (satisfaction prediction). © 2021 The authors and IOS Press.

Development of an innovative learning methodology aiming to optimise learners spatial conception in an online mechanical CAD module during COVID-19 pandemic

The COVID-19 pandemic struck humanity in February 2020. Closures of educational institutions, worldwide, resulted to the creation of emergency remote teaching environments as a substitute to face to face learning. The disruption caused in the academic community has stimulated innovative learning methods within all levels of the educational sector. New parameters affecting knowledge transmission are getting involved while students follow courses apart on a common virtual learning environment. This research is based on a first-semester Mechanical Engineering CAD module in tertiary education. A learning strategy has been applied by reforming the traditional face-to-face leaning mode to a fully remote learning environment. The methods applied have been tested using statistical analysis and have shown to contribute significantly in students' spatial perception in 2-Dimentional Drawings. The outcomes of this research reveal a novel teaching strategy that improved students' academic achievements in CAD during the lockdown. Specific aspects can be considered sustainable on their return back to normality. © 2021 The authors and IOS Press.

Machine Learning and Statistical Analysis applied on Mechanical Engineering CAD course: A Case Study during ERTE Pahse in the Context of Higher Education

As the COVID-19 pandemic stroke the humanity on March 2020, the Higher Education Institutions had to confront rapidly the emergency to readapt the organization of their courses in order to respond to those extenuating circumstances.An Emergency Remote Teaching Environment (ERTE) [1] [10] was created by the teaching team, in order to get as close as a 6 months' pre-planned Intentional Online Teaching Environment (IOTE) module can get. To achieve the above, new technologies and features have been used for the online lectures;Social media were also used [7] in order to surpass the lack of the physical environment and face-to-face instruction.This study focuses on predicting the students' satisfaction on the ERTE, by defining constructs and variables that are taken into consideration at an online survey of evaluating students' satisfaction on IOTE. The students' satisfaction rate is evaluated, focusing on the transition difficulties from a face-to face Teaching environment (FFTE) to an ERTE.The present study consists of a comparative analysis, regarding the efficiency of online mechanical Computer Aided Design courses at the University of West Attica, Athens, Greece, filled in by the students attending the remote teaching-online class courses.A dataset has been generated, considering their attendance, behavioral and comprehension parameters described in a questionnaire examining 40 variables, grouped in 9 constructs.A statistical analysis takes place, including univariate analysis (bar charts, pie charts, and frequency tables), correlation analysis, clustering and principal component analysis.The outcomes of this research aim in establishing the sustainability of these new technology features. © 2020 IEEE.