Search results for: learning satisfaction

Authors: Aabha Chaubey, Bani Bhattacharya

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Evaluation of the quality of teaching in engineering education in India needs to be conducted on a continuous basis to achieve the best teaching quality in technical education. Quality teaching is an influential factor in technical education which impacts largely on learning outcomes of the students. Present study is not exclusively theory-driven, but it draws on various specific concepts and constructs in the domain of technical education. These include teaching and learning in higher education, teacher effectiveness, and teacher evaluation and performance management in higher education. Student Evaluation of Education Quality (SEEQ) was proposed as one of the evaluation instruments of the quality teaching in engineering education. SEEQ is one of the popular and standard instrument widely utilized all over the world and bears the validity and reliability in educational world. The present study was designed to evaluate the teaching quality through SEEQ in the context of technical education in India, including its validity and reliability based on the collected data. The multiple dimensionality of SEEQ that is present in every teaching and learning process made it quite suitable to collect the feedback of students regarding the quality of instructions and instructor. The SEEQ comprises of 9 original constructs i.e.; learning value, teacher enthusiasm, organization, group interaction, and individual rapport, breadth of coverage, assessment, assignments and overall rating of particular course and instructor with total of 33 items. In the present study, a total of 350 samples comprising first year undergraduate students from Indian Institute of Technology, Kharagpur (IIT, Kharagpur, India) were included for the evaluation of the importance of SEEQ. They belonged to four different courses of different streams of engineering studies. The above studies depicted the validity and reliability of SEEQ was based upon the collected data. This further needs Confirmatory Factor Analysis (CFA) and Analysis of Moment structure (AMOS) for various scaled instrument like SEEQ Cronbach’s alpha which are associated with SPSS for the examination of the internal consistency. The evaluation of the effectiveness of SEEQ in CFA is implemented on the basis of fit indices such as CMIN/df, CFI, GFI, AGFI and RMSEA readings. The major findings of this study showed the fitness indices such as ChiSq = 993.664,df = 390,ChiSq/df = 2.548,GFI = 0.782,AGFI = 0.736,CFI = 0.848,RMSEA = 0.062,TLI = 0.945,RMR = 0.029,PCLOSE = 0.006. The final analysis of the fit indices presented positive construct validity and stability, on the other hand a higher reliability was also depicted which indicated towards internal consistency. Thus, the study suggests the effectivity of SEEQ as the indicator of the quality evaluation instrument in teaching-learning process in engineering education in India. Therefore, it is expected that with the continuation of this research in engineering education there remains a possibility towards the betterment of the quality of the technical education in India. It is also expected that this study will provide an empirical and theoretical logic towards locating a construct or factor related to teaching, which has the greatest impact on teaching and learning process in a particular course or stream in engineering education.

Keywords: confirmatory factor analysis, engineering education, SEEQ, teaching and learning process

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Authors: Yiannis Koumpouros, Effie Papageorgiou, Alexandra Karavasili, Foteini Koureta

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The objective of the present study was to develop a scale called PYTHEIA. The PYTHEIA is a self-reported measure for the assessment of rehabilitation and assistive robotics and other assistive technology devices. The development of PYTHEIA faced the absence of a valid instrument that can be used to evaluate the assistive robotic devices both as a whole, as well as any of their individual components or functionalities implemented. According to the results presented, PYTHEIA is a valid and reliable scale able to be applied to different target groups for the subjective evaluation of various assistive technology devices.

Keywords: rehabilitation, assistive technology, assistive robots, rehabilitation robots, scale, psychometric test, assessment, validation, user satisfaction

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Authors: Lee Bih Ni, Zulfhikar Rabe, Nurul Asyikin Hassan

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The Ministry of Education Malaysia dynamically and drastically made the subject of History mandatory to be in force in 2013. This is in recognition of the nation's heritage and treasures in maintaining true facts and information for future generations of the State. History reveals the civilization of a nation and the fact of national cultural heritage. Civilization needs to be preserved as a legacy of sovereign heritage. Today's generation is the catalyst for future heirs who will support the principle and direction of the country. In line with the National Education Philosophy that aims to shape the potential development of individuals holistically and uniquely in order to produce a balanced and harmonious student in terms of intellectual, spiritual, emotional and physical. Hence, understanding the importance of studying the history subject as a pillar of identity and the history of nationhood is to be a priority in the pursuit of knowledge and empowering the spirit of statehood that is nurtured through continuous learning at school. Judging from the aspect of teacher leadership role in integrating history in a combined way based on Teacher Education Philosophy. It empowers the teaching profession towards the teacher to support noble character. It also supports progressive and scientific views. Teachers are willing to uphold the State's aspirations and celebrate the country's cultural heritage. They guarantee individual development and maintain a united, democratic, progressive and disciplined society. Teacher's role as a change and leadership agent in education begins in the classroom through formal or informal educational processes. This situation is expanded in schools, communities and countries. The focus of this paper is on the role of teacher leadership influencing the effectiveness of teaching and learning history in the classroom environment. Leadership guides to teachers' perceptions on the role of teacher leadership, teaching leadership, and the teacher leadership role and effective teacher leadership role. Discussions give emphasis on aspects of factors affecting the classroom environment, forming the classroom agenda, effective classroom implementation methods, suitable climate for historical learning and teacher challenges in implicating the effectiveness of teaching and learning processes.

Keywords: teacher leadership, leadership lessons, effective classroom, effective teacher

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Authors: Omneya Messallam

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Technology has been developed dramatically in most of the educational disciplines. For instance, digital rendering subject, which is being taught in both Interior and Architecture fields, is witnessing almost annually updated software versions. A lot of students and educators argued that there will be no need for manual rendering techniques to be learned. Therefore, the Interior Design Visual Presentation 1 course (ID133) has been chosen from the first level of the Interior Design (ID) undergraduate program, as it has been taught for six years continually. This time frame will facilitate sound observation and critical analysis of the use of appropriate teaching methodologies. Furthermore, the researcher believes in the high value of the manual rendering techniques. The course objectives are: to define the basic visual rendering principles, to recall theories and uses of various types of colours and hatches, to raise the learners’ awareness of the value of studying manual render techniques, and to prepare them to present their work professionally. The students are female Arab learners aged between 17 and 20. At the outset of the course, the majority of them demonstrated negative attitude, lacking both motivation and confidence in manual rendering skills. This paper is a reflective appraisal of deploying two student-centred teaching pedagogies which are: Project-based learning (PBL) and Outcome-based education (OBE) on ID133 students. This research aims of developing some teaching strategies to enhance the quality of teaching in this given course over an academic semester. The outcome of this research emphasized the positive influence of applying such educational methods on improving the quality of students’ manual rendering skills in terms of: materials, textiles, textures, lighting, and shade and shadow. Furthermore, it greatly motivated the students and raised the awareness of the importance of learning the manual rendering techniques.

Keywords: project-based learning, outcome-based education, visual presentation, manual render, personal competences

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Authors: XuKe Cao

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This paper reviews the current state of research on low-resource languages in the field of speech recognition, focusing on the challenges faced by low-resource language speech recognition, including the scarcity of data resources, the lack of linguistic resources, and the diversity of dialects and accents. The article reviews recent progress in low-resource language speech recognition, including techniques such as data augmentation, end to-end models, transfer learning, and multi-task learning. Based on the challenges currently faced, the paper also provides an outlook on future research directions. Through these studies, it is expected that the performance of speech recognition for low resource languages can be improved, promoting the widespread application and adoption of related technologies.

Keywords: low-resource languages, speech recognition, data augmentation techniques, NLP

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Authors: L. Bih Ni, D. Norizah Ag Kiflee, T. Choon Keong, R. Talip, S. Singh Bikar Singh, M. Noor Mad Japuni, R. Talin

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The purpose of this study is to determine the effectiveness of video clips to enhance students' achievement and motivation towards learning and facilitating of history. We use narrative literature studies to illustrate the current state of the two art and science in focused areas of inquiry. We used experimental method. The experimental method is a systematic scientific research method in which the researchers manipulate one or more variables to control and measure any changes in other variables. For this purpose, two experimental groups have been designed: one experimental and one groups consisting of 30 lower secondary students. The session is given to the first batch using a computer presentation program that uses video clips to be considered as experimental group, while the second group is assigned as the same class using traditional methods using dialogue and discussion techniques that are considered a control group. Both groups are subject to pre and post-trial in matters that are handled by the class. The findings show that the results of the pre-test analysis did not show statistically significant differences, which in turn proved the equality of the two groups. Meanwhile, post-test analysis results show that there was a statistically significant difference between the experimental group and the control group at an importance level of 0.05 for the benefit of the experimental group.

Keywords: Video clips, Learning and Facilitation, Achievement, Motivation

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Authors: Mukadder Baran, Mustafa Sözbilir

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The purpose of this paper is to investigate the applicabillity of the Context- and Problem-Based Learning (CPBL) in general chemistry course to the subject of “Thermodynamics” but also the influence of CPBL on students’ achievement, retention of knowledge, their interest, attitudes, motivation and problem-solving skills. The study group included 13 freshman students who were selected with the sampling method appropriate to the purpose among those taking the course of General Chemistry within the Program of Medical Laboratory Techniques at Hakkari University. The application was carried out in the Spring Term of the academic year of 2012-2013. As the data collection tool, Lesson Observation form were used. In the light of the observations held, it was revealed that CPBL increased the students’ intragroup and intergroup communication skills as well as their self-confidence and developed their skills in time management, presentation, reporting, and technology use; and that they were able to relate chemistry to daily life. Depending on these findings, it could be suggested that the area of use of CPBL be widened; that seminars related to constructive methods be organized for teachers. In this way, it is believed that students will not be passive in the group any longer. In addition, it was concluded that in order to avoid the negative effects of the socio-cultural structure on the education system, research should be conducted in places where there is socio-cultural obstacles, and appropriate solutions should be suggested and put into practice.

Keywords: chemistry, education, science, context-based learning

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Authors: Naziema Begum Jappie

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In higher education, particularly within South African universities engaged in regional and global collaborations, the integration of intercultural competencies into teaching, learning, and assessment is essential for student success. Intercultural competencies and the digital platform are intwined in the fabric of teaching, learning, and assessments for student success in higher education. These are integral to virtual learning and exchange within higher education, which are expected to develop these competencies. However, this is not always the case because these are not always explicitly integrated into the academic agenda. Despite the prevalence of international students and exchange programmes, there is often a lack of deliberate integration of these competencies into academic agendas, even for South African students from different cultural, ethnic and language groups. This research addresses this gap by examining the impact of infusing intercultural activities into both face-to-face and digital learning platforms. Adopting an intersectional perspective, the study recognizes how social identities interact to shape individuals' self-perceptions and experiences in a university. Methodologically, this study employs a mixed-methods approach, combining quantitative surveys and qualitative interviews to assess the effectiveness of integrating intercultural competencies into digital platforms. Surveys administered to students and faculty measure changes in intercultural skills and attitudes before and after the implementation of targeted interventions. In-depth interviews with participants will provide further insights into the qualitative aspects of these changes, including their experiences and perceptions of the integration process. The research evaluates whether the strategic integration of intercultural competencies into digital platforms enhances students' intercultural skills and social justice awareness. The findings provide valuable insights for higher education academics and internationalization practitioners seeking to develop effective strategies for cultivating intercultural competencies among students.

Keywords: digital platform, higher education, intercultural competencies, interventions

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Authors: Rosana Marar, Edward Jaser

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This paper presents a software solution to enhance the content and presentation of graphic design and animation related textbooks. Using augmented and virtual reality concepts, a mobile application is developed to improve the static material found in books. This allows users to interact with animated examples and tutorials using their mobile phones and stereoscopic 3D viewers which will enhance information delivery. The application is tested on Google Cardboard with visual content in 3D space. Evaluation of the proposed application demonstrates that it improved the readability of static content and provided new experiences to the reader.

Keywords: animation, augmented reality, google cardboard, interactive media, technology enhanced learning, virtual reality

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Authors: Fatma Hsain Ali Suliman

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Personality plays an important role in acquiring a second language. For second language learners to make maximum progress with their own learning styles, their individual differences must be recognized and attended to. Personality is considered to be a pattern of unique characteristics that give a person’s behavior a kind of consistency and individuality. Therefore, the enclosed study, which is entitled “The Role of Personality in Second language Acquisition: Extroversion and Introversion”, tends to shed light on the relationship between learners’ personalities and second language acquisition process. In other words, it aims at drawing attention to how individual differences of students as being extroverts or introverts could affect the language acquisition process. As a literature review, this paper discusses the results of some studies concerning this issue as well as the point views of researchers and scholars who have focused on the effect of extrovert and introvert personality on acquiring a second language. To accomplish the goals of this study, which is divided into 5 chapters including introduction, review of related literature, research method and design, results and discussions and conclusions and recommendations, 20 students of English Department, Faculty of Arts, Misurata University, Libya were handed out a questionnaire to figure out the effect of their personalities on the learning process. Finally, to be more sure about the role of personality in a second language acquisition process, the same students who were given the questionnaire were observed in their ESL classes.

Keywords: second language acquisition, personality, extroversion, introversion, individual differences, language learning strategy, personality factors, psycho linguistics

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Authors: Silvia Franco, Marcos Sarasola

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The Faculty of Business Administration at the Catholic University of Uruguay is performing an important educational innovation, unique in the country. In preparing future professionals in companies, teamwork competence is very important. However, there is no often a systematic and specific training in the acquisition of this competence in undergraduate students. For this reason, we have designed and implemented an educational innovation through an intervention method to improve teamwork competence for undergraduate students of business studies. Students’ teams are integrated according to the complementary roles of Belbin; changes in teamwork competence during training period are measured with CCSAC tool; classroom methodology in the prio-border teamwork by Team-Based Learning. Methodology also integrates coaching and support team performance during the first two semesters.

Keywords: business students, teamwork, learning, competences

Procedia PDF Downloads 366

Authors: P. Kooche Baghy, S. Eskandari, E.javanmard

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Neural network has been used as a predictive means of cold rolling force in this dissertation. Thus, imposed average force on rollers as a mere input and five pertaining parameters to its as a outputs are regarded. According to our study, feed-forward multilayer perceptron network has been selected. Besides, Bayesian algorithm based on the feed-forward back propagation method has been selected due to noisy data. Further, 470 out of 585 all tests were used for network learning and others (115 tests) were considered as assessment criteria. Eventually, by 30 times running the MATLAB software, mean error was obtained 3.84 percent as a criteria of network learning. As a consequence, this the mentioned error on par with other approaches such as numerical and empirical methods is acceptable admittedly.

Keywords: artificial neural network, Bayesian, cold rolling, force evaluation

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Authors: Qisheng Hu, Jianglei Han, Yue Yang, My Hoa Ha

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When a company is considering purchasing software for commercial use, contract risk assessment is critical to identify risks to mitigate the potential adverse business impact, e.g., security, financial and regulatory risks. Contract risk assessment requires reviewers with specialized knowledge and time to evaluate the legal documents manually. Specifically, validating contracts for a software vendor requires the following steps: manual screening, interpreting legal documents, and extracting risk-prone segments. To automate the process, we proposed a framework to assist legal contract document risk identification, leveraging pre-trained deep learning models and natural language processing techniques. Given a set of pre-defined risk evaluation problems, our framework utilizes the pre-trained transformer-based models for question-answering to identify risk-prone sections in a contract. Furthermore, the question-answering model encodes the concatenated question-contract text and predicts the start and end position for clause extraction. Due to the limited labelled dataset for training, we leveraged transfer learning by fine-tuning the models with the CUAD dataset to enhance the model. On a dataset comprising 287 contract documents and 2000 labelled samples, our best model achieved an F1 score of 0.687.

Keywords: contract risk assessment, NLP, transfer learning, question answering

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Authors: Motonobu Hattori

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In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudo patterns. Because, temporal sequence learning is more important than static pattern learning in the real world, in this study, we improve our conventional dual-network memory model so that it can deal with temporal sequences without catastrophic forgetting. The computer simulation results show the effectiveness of the proposed dual-network memory model.

Keywords: catastrophic forgetting, dual-network, temporal sequences, hippocampal

Procedia PDF Downloads 270

Authors: Afrin Jaman Bonny, Mehrin Jahan, Zannatul Ferdhoush, Mumenunnessa Keya, Md. Shihab Mahmud, Sharun Akter Khushbu, Sheak Rashed Haider Noori, Sheikh Abujar

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In 2020, the SARS-CoV-2 pandemic had led all the educational institutions to move to online learning platforms to ensure safety as well as the continuation of learning without any disruption to students’ academic life. But after the reopening of those educational institutions suddenly in Bangladesh, it became a vital demand to observe students take on this decision and how much they are comfortable with the new habits. When all educational institutions were ordered to re-open after more than a year, data was collected from students of all educational levels. A Google Form was used to conduct this online survey, and a total of 565 students participated without being pressured. The survey reveals the students' preferences for online and offline education systems, as well as their mental health at the time including their behavior to get back to offline classes depending on getting vaccinated or not. After evaluating the findings, it is clear that respondents' choices vary depending on gender and educational level, with female and male participants experiencing various mental health difficulties and attitudes toward returning to offline classes. As a result of this study, the student’s overall perspective on the sudden reopening of their educational institutions has been analyzed.

Keywords: covid-19 epidemic, educational proceeding, university students, school/college students, physical activity, online platforms, mental health, psychological distress

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

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Authors: Annette G. Walters Ph.D.

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With a global shortage of qualified educational professionals, school systems continue to struggle with adequate staffing. How might learning communities meet the needs of all students, in particular those with specialized needs. While the task may seem foreboding and certain factors may seem divergent, all are connected in the education of students. Special education has a significant impact on the teaching and learning experience of all students in an educational community. Even when there are concerted efforts at embracing learners with diverse aptitude and abilities, there are often many important local factors that are misaligned, overlooked, or misunderstood. Working with learners with diverse abilities, often requires intentional services and supports for students to achieve success. Developing and implementing specialized instruction requires a multifaceted approach to supports the entire learning community, which includes educational providers, learners, and families, all while being mindful of fiscal and natural resources. This research explores the implications and complexities of special education instruction and specializing instruction, as well as leading and teaching diverse learners. This work is separated into three sections: the state of special education, teaching and leading diverse learners, and developing educational competencies through collaborative engagement. This structured analysis extrapolates historical and current research on special education practices and the role of educators in ensuring diverse students meet success.

Keywords: - diverse learners, - special education, - modification and supports, - curriculum and instruction, - classroom management, - formal and informal assessments

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Amrane Meriem, Oukid Salyha

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The presence of defects or bugs in software can lead to costly setbacks, operational inefficiencies, and compromised user experiences. The integration of Machine Learning(ML) techniques has emerged to predict and preemptively address software defects. ML represents a proactive strategy aimed at identifying potential anomalies, errors, or vulnerabilities within code before they manifest as operational issues. By analyzing historical data, such as code changes, feature im- plementations, and defect occurrences. This en- ables development teams to anticipate and mitigate these issues, thus enhancing software quality, reducing maintenance costs, and ensuring smoother user interactions. In this work, we used a recommendation system to improve the performance of ML models in terms of predicting the code severity and effort estimation.

Keywords: software engineering, machine learning, bugs detection, effort estimation

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Authors: Mohammad Arabi

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The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Vishal D. Pandya

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A great deal of perceptible change has been taking place in the way our institutions of higher learning are being managed in India today. It is believed that managers, whose intuition proves to be accurate, often tend to be the most successful, and this is what makes them almost like entrepreneurs. A certain entrepreneurial spirit is what is expected and requires a degree of insight of the manager to be successful depending upon the situational and more importantly, the heterogeneity as well as the socio-cultural aspect. Teachers in Higher Education have to play multiple roles to make sure that the Learning-Teaching process becomes effective in the real sense of the term. This paper makes an effort to take a close look at that, especially in the context of the management of English language teaching in Higher Education and, therefore, focuses on the management of English language teaching in higher education by understanding target situation analyses at the socio-cultural level.

Keywords: management, language teaching, English language teaching, higher education

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Margit Kastner

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To overcome the negative consequences associated with large class sizes and to support students in developing the necessary competences (e.g., critical thinking, problem-solving, or team-work skills) a marketing course has been redesigned by implementing project-based learning with peer assessment (PBL&PA). This means that students can voluntarily take advantage of this supplementary offer and explore -in addition to attending the lecture where clicker questions are asked- a real-world problem, find a solution, and assess the results of peers while working in small collaborative groups. In order to handle this with little further effort, the process is technically supported by the university’s e-learning system in such a way that students upload their solution in form of an assignment which is then automatically distributed to peer groups who have to assess the work of three other groups. Finally, students’ work is graded automatically considering both, students’ contribution to the project and the conformity of the peer assessment. The purpose of this study is to evaluate students’ perception of PBL&PA using an online-questionnaire to collect the data. More specifically, it aims to discover students’ motivations for (not) working on a project and the benefits and problems students encounter. In addition to the survey, students’ performance was analyzed by comparing the final grades of those who participated in PBL&PA with those who did not participate. Among the 260 students who filled out the questionnaire, 47% participated in PBL&PA. Besides extrinsic motivations (bonus credits), students’ participation was often motivated by learning and social benefits. Reasons for not working on a project were connected to students’ organization and management of their studies (e.g., time constraints, no/wrong information) and teamwork concerns (e.g., missing engagement of peers, prior negative experiences). In addition, high workload and insufficient extrinsic motivation (bonus credits) were mentioned. With regards to benefits and problems students encountered during the project, students provided more positive than negative comments. Positive aspects most often stated were learning and social benefits while negative ones were mainly attached to the technical implementation. Interestingly, bonus credits were hardly named as a positive aspect meaning that intrinsic motivations have become more important when working on the project. Team aspects generated mixed feelings. In addition, students who voluntarily participated in PBL&PA were, in general, more active and utilized further course offers such as clicker questions. Examining students’ performance at the final exam revealed that students without participating in any of the offered active learning tasks performed poorest in the exam while students who used all activities were best. In conclusion, the goals of the implementation were met in terms of students’ perceived benefits and the positive impact on students’ exam performance. Since the comparison of the automatic grading with faculty grading showed valid results, it is possible to rely only on automatic grading in the future. That way, the additional workload for faculty will be within limits. Thus, the implementation of project-based learning with peer assessment can be recommended for large classes.

Keywords: automated grading, large classes, peer assessment, project-based learning

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Authors: Eva Laryea, Clement Yeboah Authors

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A pretest-posttest within subjects, experimental design was employed to examine the effects of a computerized basic statistics learning game on achievement and statistics-related anxiety of students enrolled in introductory graduate statistics course. Participants (N = 34) were graduate students in a variety of programs at state-funded research university in the Southeast United States. We analyzed pre-test posttest differences using paired samples t-tests for achievement and for statistics anxiety. The results of the t-test for knowledge in statistics were found to be statistically significant indicating significant mean gains for statistical knowledge as a function of the game-based intervention. Likewise, the results of the t-test for statistics-related anxiety were also statistically significant indicating a decrease in anxiety from pretest to posttest. The implications of the present study are significant for both teachers and students. For teachers, using computer games developed by the researchers can help to create a more dynamic and engaging classroom environment, as well as improve student learning outcomes. For students, playing these educational games can help to develop important skills such as problem solving, critical thinking, and collaboration. Students can develop interest in the subject matter and spend quality time to learn the course as they play the game without knowing that they are even learning the presupposed hard course. The future directions of the present study are promising, as technology continues to advance and become more widely available. Some potential future developments include the integration of virtual and augmented reality into educational games, the use of machine learning and artificial intelligence to create personalized learning experiences, and the development of new and innovative game-based assessment tools. It is also important to consider the ethical implications of computer game-based pedagogy, such as the potential for games to perpetuate harmful stereotypes and biases. As the field continues to evolve, it will be crucial to address these issues and work towards creating inclusive and equitable learning experiences for all students. This study has the potential to revolutionize the way basic statistics graduate students learn and offers exciting opportunities for future development and research. It is an important area of inquiry for educators, researchers, and policymakers, and will continue to be a dynamic and rapidly evolving field for years to come.

Keywords: pretest-posttest within subjects, experimental design, achievement, statistics-related anxiety

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Authors: A. Olivi, P. Reyes Cabrera

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Ageing and urbanization are two of the main trends that characterize the twenty-first century. Its trending is especially accelerated in the emerging countries of Asia and Latin America. Chile is one of the countries in the Latin American region, where the demographic transition to ageing is becoming increasingly visible. The challenges that the new demographic scenario poses to urban administrators call for searching innovative solutions to maximize the functional and psycho-social benefits derived from the relationship between older people and the environment in which they live. Although mobility is central to people's everyday practices and social relationships, it is not distributed equitably. On the contrary, it can be considered another factor of inequality in our cities. Older people are a particularly sensitive and vulnerable group to mobility. In this context, based on the ageing in place strategy and following the social innovation approach within a spatial context, the "Community Observatory of Territorial Information Control and Management" project aims at the collective search and validation of solutions for the satisfaction of mobility and accessibility specific needs of urban aged people. Specifically, the Observatory intends to: i) promote the direct participation of the aged population in order to generate relevant information on the territorial situation and the satisfaction of the mobility needs of this group; ii) co-create dynamic and efficient mechanisms for the reporting and updating of territorial information; iii) increase the capacity of the local administration to plan and manage solutions to environmental problems at the neighborhood scale. Based on a participatory mapping methodology and on the application of digital technology, the Observatory designed and developed, together with aged people, a crowdsourcing platform for smartphones, called DIMEapp, for reporting environmental problems affecting mobility and accessibility. DIMEapp has been tested at a prototype level in two neighborhoods of the city of Valparaiso. The results achieved in the testing phase have shown high potential in order to i) contribute to establishing coordination mechanisms with the local government and the local community; ii) improve a local governance system that guides and regulates the allocation of goods and services destined to solve those problems.

Keywords: accessibility, ageing, city, digital technology, local governance

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Authors: Christodoulakis Georgios, Kiourti Elisavet

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The Covid-19 pandemic has affected millions of people on a global scale, while lockdowns and quarantine measures were adopted periodically by a vast number of countries. These peculiar socio-historical conditions have led to the growth of participation in online environments. At the same time, the official educational bodies of many countries have been forced, for the first time at least for Greece and Cyprus, to switch to distance learning methods throughout the educational levels. However, this has not been done without issues, both in the technological and functional level, concerning the tools and the processes. Video games are the finest example of simulations of distance learning problem-solving environments. They incorporate different semiotic modes (e.g., a combination of image, sound, texts, gesture) while all this takes place in social and cultural constructed contexts. Players interact in the game environment in terms of spaces, objects, and actions in order to accomplish their goals, solve its problems, and win the game. In addition, players are engaging in layering literacies, which include combinations of independent and collaborative, digital and nondigital practices and spaces acting jointly to support meaning making, including interaction among and across texts and modalities (Abrams, 2017). From this point of view, players are engaged in collaborative, self-directed, and interest-based experiences by going back and forth and around gameplay. Within this context, this paper investigates the way Einstown, a greek serious video game, functions as an effective distance learning environment for teaching Greek as a second|foreign language to adults. The research methodology adopted is the case study approach using mixed methods. The participants were two adult women who are immigrants in Greece and who had zero gaming experience. The results of this research reveal that the videogame Einstown is, in fact, a digital environment of literacy through which the participants achieve active learning, cooperation, and engage in digital and non-digital literacy practices that result in improving the learning of specialized vocabulary presented throughout the gameplay.

Keywords: second/foreign language, vocabulary acquisition, literacy, serious video games

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Authors: Raeed Alanazi

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Introduction: Nursing students have a crucial role to play in the inhibition of infectious diseases and, therefore, must be trained in infection control and prevention modules prior to entering clinical settings. Simulations have been found to have a positive impact on infection control skills and the use of standard precautions. Aim: The purpose of this study was to use the four sources of self-efficacy in explaining the level of clinical self-efficacy and adherence to infection control practices in Saudi nursing students during simulation practice. Method: A cross-sectional design with convenience sampling was used. This study was conducted in all Saudi nursing schools, with a total number of 197 students participated in this study. Three scales were used simulation self- efficacy Scale (SSES), the four sources of self-efficacy scale (SSES), and Compliance with Standard Precautions Scale (CSPS). Multiple linear regression was used to test the use of the four sources of self-efficacy (SSES) in explaining level of clinical self-efficacy and adherence to infection control in nursing students. Results: The vicarious experience subscale (p =.044) was statistically significant. The regression model indicated that for every one unit increase in vicarious experience (observation and reflection in simulation), the participants’ adherence to infection control increased by .13 units (β =.22, t = 2.03, p =.044). In addition, the regression model indicated that for every one unit increase in education level, the participants’ adherence to infection control increased by 1.82 units (beta=.34= 3.64, p <.001). Also, the mastery experience subscale (p <.001) and vicarious experience subscale (p = .020) were shared significant associations with clinical self-efficacy. Conclusion: The findings of this research support the idea that simulation-based learning can be a valuable teaching-learning method to help nursing students develop clinical competence, which is essential in providing quality and safe nursing care.

Keywords: simulation-based learning, clinical self-efficacy, infection control, nursing students

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Authors: Isabel Moscol, Carlos J. Díaz, Ciro Rodríguez

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Hip replacement becomes necessary when a person suffers severe pain or considerable functional limitations and the best option to enhance their quality of life is through the replacement of the damaged joint. One of the main components in femoral prostheses is the stem which distributes the loads from the joint to the proximal femur. To preserve more bone stock and avoid weakening of the diaphysis, a short starting stem was selected, generated from the intramedullary morphology of the patient's femur. It ensures the implantability of the design and leads to geometric delimitation for personalized optimization with machine learning (ML) and metaheuristic algorithms. The present study attempts to design a cementless short stem to make the strain deviation before and after implantation close to zero, promoting its fixation and durability. Regression models developed to estimate the percentage change of maximum principal stresses were used as objective optimization functions by the metaheuristic algorithm. The latter evaluated different geometries of the short stem with the modification of certain parameters in oblique sections from the osteotomy plane. The optimized geometry reached a global stress shielding (SS) of 18.37% with a determination factor (R²) of 0.667. The predicted results favour implantability integration in the short stem optimization to effectively reduce SS in the proximal femur.

Keywords: machine learning techniques, metaheuristic algorithms, short-stem design, stress shielding, hip replacement

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Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

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To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

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Authors: Min-Feng Hsieh

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Nowadays, we are facing a highly competitive environment in which the situation for survival has come even more critical than ever before. The challenge we will be confronted with is no longer can be dealt with the single system of knowledge. The abilities we urgently need to acquire is something that can lead us to cross over the boundaries between different disciplines and take us to a neutral ground that gathers and integrates powers and intelligence that surrounds us. This paper aims at discussing how a trans-disciplinary design course organized by the College of Design at Chaoyang University can react to this modern challenge. By orchestrating an experimental course format and by developing a series of coaching strategies, a trans-disciplinary learning environment has been created and practiced in which students selected from five different departments, including Architecture, Interior Design, Visual Design, Industrial Design, Landscape and Urban Design, are encouraged to think outside their familiar knowledge pool and to learn with/from each other. In the course of implementing this program, a parallel research has been conducted alongside by adopting the theory and principles of Action Research which is a research methodology that can provide the course organizer emergent, responsive, action-oriented, participative and critically reflective insights for the immediate changes and amendments in order to improve the effect of teaching and learning experience. In the conclusion, how the learning and teaching experience of this trans-disciplinary design studio can offer us some observation that can help us reflect upon the constraints and division caused by the subject base curriculum will be pointed out. A series of concepts for course design and teaching strategies developed and implemented in this trans-disciplinary course are to be introduced as a way to promote learners’ self-motivated, collaborative, cross-disciplinary and student-centered learning skills. The outcome of this experimental course can exemplify an alternative approach that we could adopt in pursuing a remedy for dealing with the problematic issues of the current educational practice.

Keywords: course design, coaching strategy, subject base curriculum, trans-disciplinary

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