Search results for: students’ learning achievements

Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Yusef Hedjazi, Saeedeh Nazari Nooghabi

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New information and communication technologies (ICT) as one of the main needs of Faculty members in the process of teaching and learning has used in Irans higher education system since 2000.The main purpose of this study is to investigate the role of information and communication technologies (ICT) in teaching performance of Agricultural and Natural Resources Faculties at University of Tehran. The statistical population of the study consisted of all 250 faculties in Agriculture and Natural Resources Colleges and a questionnaire was used to collect data. The reliability of the questionnaire was confirmed by computing of Cronbachs Alpha coefficient at greater than .72. The study showed a significant relationship between agricultural Faculty members teaching performance and competency in using ICT. The results of the regression analysis also explained 51.7% of the variance, teaching performance. The six independent variables that accounted for the explained variance were experience in using educational websites or software, use of educational multimedia (e.g. film and CD, etc), making a presentation using PowerPoint, familiarity with online education websites, using News group to discuss on educational subjects with colleagues and students, and using Electronic communication (messengers) to solve studentsproblems.

Keywords: information and communication technologies, agricultural and natural resources, faculties, teaching performance

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Hesham Shaalan

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Senior design projects teach students many important skills. One of the major goals is to prepare students to apply effective problem-solving techniques to a problem that represents a real-world situation. This includes the ability to define the problem, compare alternative solutions, identify the best solution, and design the system. This paper describes the design of a shipboard power plant as a senior project in the Marine Engineering program at the U.S. Merchant Marine Academy. The design project was supervised by faculty members who guided a multidisciplinary group of seniors. The research project was sponsored by the Office of Naval Research. Each group of seniors focused on one of the main design aspects of the project, including the electric power system, nuclear power plant, ship hull design, and economics.

Keywords: senior design project, shipboard power system, engineering education, marine engineering

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Authors: Asma Ben Abdallah

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Language has a powerful influence over people and their actions. The language of advertising has a very great impact on the consumer. It makes use of different features from the linguistic continuum. The present paper attempts to apply the theories of stylistics to the analysis of advertising texts. In order to decipher the stylistic features of the advertising discourse, 30 advertising text samples designed by MA Business students have been selected. These samples have been analyzed at the level of design and content. The study brings insights into the use of stylistic devices in advertising, and it reveals that both linguistic and non-linguistic features of advertisements are frequently employed to develop a well-thought-out design and content. The practical significance of the study is to highlight the specificities of the advertising genre so that people interested in the language of advertising (Business students and ESP teachers) will have a better understanding of the nature of the language used and the techniques of writing and designing ads. Similarly, those working in the advertising sphere (ad designers) will appreciate the specificities of the advertising discourse.

Keywords: the language of advertising, advertising discourse, ad design, stylistic features

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Authors: Martina Maria Keitsch, Sangeeta Singh

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There has been a growing recognition that sustainable development needs to consider economic, social and environmental aspects including gender. In Nepal, the majority of the population lives in rural areas, and many households do not have access to electricity. In rural areas, the difficulty of accessing energy is becoming one of the greatest constraints for improving living conditions. This is particularly true for women and children, who spent much time for collecting firewood and cooking and thus are often deprived of time for education, political- and business activities. The poster introduces an education and research project financed by the Norwegian Government. The project runs from 2015-2020 and is a collaboration between the Norwegian University of Science (NTNU) and Technology Institute of Engineering (IOE), Tribhuvan University. It has the title Master program and Research in Energy for Sustainable Social Development Energy for Sustainable Social Development (MSESSD). The project addresses engineering and architecture students and comprises several integral activities towards gender mainstreaming. The following activities are conducted; 1. Creating academic opportunities, 2. Updating administrative personnel on strategies to effectively include gender issues, 3. Integrating female and male stakeholders in the design process, 4. Sensitizing female and male students for gender issues in energy systems. The project aims to enable students to design end-user-friendly solutions which can, for example, save time that can be used to generate and enhance income. Relating to gender mainstreaming, design concepts focus on smaller-scale technologies, which female stakeholders can take control of and manage themselves. Creating academic opportunities, we have a 30% female students’ rate in each master student batch in the program with the goal to educate qualified female personnel for academia and policy-making/government. This is a very ambitious target in a Nepalese context. The rate of female students, who completed the MSc program at IOE between 1998 and January 2015 is 10% out of 180 students in total. For recruiting, female students were contacted personally and encouraged to apply for the program. Further, we have established a Master course in gender mainstreaming and energy. On an administrative level, NTNU has hosted a training program for IOE on gender-mainstreaming information and -strategies for academic education. Integrating female and male stakeholders, local women groups such as, e.g., mothers group are actively included in research and education for example in planning, decision-making, and management to establish clean energy solutions. The project meets women’s needs not just practically by providing better technology, but also strategically by providing solutions that enhance their social and economic decision-making authority. Sensitizing the students for gender issues in energy systems, the project makes it mandatory to discuss gender mainstreaming based on the case studies in the Master thesis. All activities will be discussed in detail comprising an overview of MSESSD, the gender mainstreaming master course contents’, and case studies where energy solutions were co-designed with men and women as lead-users and/or entrepreneurs. The goal is to motivate educators to develop similar forms of transnational gender collaboration.

Keywords: knowledge generation on gender mainstreaming, sensitizing students, stakeholder inclusion, education strategies for design and architecture in gender mainstreaming, facilitation for cooperation

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Authors: Paulina Pietrzyk-Kowalec

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This paper presents the phenomenon of multilingualism becoming the norm rather than the exception in the European Union. It also seeks to describe the correlation between the command of foreign languages and employability. It is evident that the challenges of today's societies when it comes to employability and to the reality of the current labor market are more and more diversified. Thus, it is one of the crucial tasks of higher education to prepare its students to face this kind of complexity, understand its nuances, and have the capacity to adapt effectively to situations that are common in corporations based in the countries belonging to the EU. From this point of view, the assessment of the impact that the command of foreign languages of European university students could have on the numerous business sectors becomes vital. It also involves raising awareness of future professionals to make them understand the importance of mastering communicative skills in foreign languages that will meet the requirements of students' prospective employers. The direct connection between higher education institutions and the world of business also allows companies to realize that they should rethink their recruitment and human resources procedures in order to take into account the importance of foreign languages. This article focuses on the objective of the multilingualism policy developed by the European Commission, which is to enable young people to master at least two foreign languages, which is crucial in their future careers. The article puts emphasis on the existence of a crucial connection between the research conducted in higher education institutions and the business sector in order to reduce current qualification gaps.

Keywords: cross-cultural communication, employability, human resources, language attitudes, multilingualism

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

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Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

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Authors: Elham Abbas Aljaaly, Nahlaa Abdulwahab Khalifa

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The active method of disease prevention is seen as the most affordable and sustainable action to deal with risks of non-communicable diseases such as obesity. This eight-week project aimed to pilot the feasibility and acceptability of a school-based programme, which is proposed to prevent and modify overweight status and possible related risk factors among student girls 'at the intermediate level' in Jeddah city. The programme was conducted through comprehensible approach targeting physical environment and school policies (nutritional/exercise/behavioural approach). The programme was designed to cultivate the personal and environmental awareness in schools for girls. This was applied by promoting healthy eating and physical activity through policies, physical education, healthier options for school canteens, and the creation of school health teams. The prevention programme was applied on 68 students (who agreed to participate) from grades 7th, 8th and 9th. A pre and post assessment questionnaire was employed on 66 students. The questionnaires were designed to obtain information on students' knowledge about health, nutrition and physical activity. Survey questions included information about nutrients, food consumption patterns, food intake and lifestyle. Physical education included training sessions for new opportunities for physical activities to be performed during school or after school hours. A running competition 'to enhance students’ performance for physical activities' was also conducted during the school visit. A visit to the school canteen was conducted to check, observe, record and assess all available food/beverage items and meals. The assessment method was a subjective method for the type of food/beverages if high in saturated fat, salt and sugar (HFSS) or non-HFSS. The school canteen administrators were encouraged to provide healthy food/beverage items and a sample healthy canteen was provided for implementation. Two healthy options were introduced to the school canteen. A follow up for students’ preferences for the introduced options and the purchasing power were assessed. Thirty-eight percent of young girls (n=26) were not participating in any form of physical activities inside or outside school. Skipping breakfast was stated by 42% (n=28) of students with no daily consumption (19%, n=13) for fruit/vegetables. Significant changes were noticed in students’ (n=66) overall responses to the pre and post questions (P value=.001). All students had participated in the conducted running competition sessions and reported satisfaction and enjoyment about the sessions. No absence was reported by the research team for attending physical education and activity sessions throughout the delivered programme. The purchasing power of the introduced healthy options of 'Salad and oatmeal' was increased to 18% in 8 weeks at the school canteen, and slightly affected the purchase for other less healthy options. The piloted programme indorsed better health and nutrition knowledge, healthy eating and lifestyle attitude, which could help young girls to obtain sustainable changes. It is expected that the outcomes of the programme will be a cornerstone for the futuristic national study that will assist policy makers and participants to build a knowledgeable health promotion scenario and make sure that school students have access to healthy foods, physical exercise and healthy lifestyle.

Keywords: adolescents, diet, exercise, behaviours, overweight/obesity, prevention-intervention programme, Saudi Arabia, schoolgirls

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Authors: Parisa Javid

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In architecture, daylight contributes to humans' mental and physical well-being and reduces the consumption of fossil fuels. Accordingly, Iran's rich architecture has valuable achievements and experiences that should be recognized and introduced to the Iranian and international architecture communities. There are many ways to reduce energy consumption in buildings, but electricity accounts for a large part of that consumption. Lighting up spaces with natural light is a significant factor in reducing energy consumption and preventing electricity dissipation. Aside from being expensive, electric lighting systems cause excessive heat and physical injury (eyes). This study is based on library records and documents. Modern lighting systems are used to reduce energy consumption in the interior of a building to allow for optimal transmission of natural light. It discusses how to use natural light in architecture and the benefits of natural light in buildings. Solar energy can be used more efficiently, and electrical power can be saved in residential, administrative, commercial, and educational buildings by using new methods such as light tubes and mirror directors. Modern lighting systems, natural light, and reduced energy consumption are keywords for these systems, which quickly return their investment.

Keywords: modern lighting systems, natural light, reduced energy consumption

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Authors: Christina Zourna, Ioanna Papavassiliou-Alexiou

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The purpose of this multi-phased action research PhD study in Greece was to investigate if and how Drama in Education (DiE) – used as an innovative group counselling method – may have positive effects on secondary education students’career self-efficacy and career decision-making skills development. Using both quantitative and qualitative research tools, high quality data were gathered at various stages of the research and were analysed through multivariate methods and open-source computer aided data analysis software such as R Studio, QualCoder, and SPSS packages. After a five-month-long educational intervention based on DiE method, it was found that 9th, 10th, and 11th gradersameliorated their self-efficacy and learned the process of making an informed career decision – through targeted information gathering about themselves and possible study paths – thus, developing career problem-solving and career management skills. Gender differences were non statistically important, while differences in grades showed a minor influence on some of the measured factorssuch as general career indecisiveness and self-evaluation. Students in the 11th grade scored significantly higher than younger students in the career self-efficacy scale and have stronger faith in their abilities e.g., choosing general over vocational school and major study orientation. The study has shown that DiE can be effective in group career guidance, especially concerning the pillars of self-awareness, self-efficacy, and career decision-making processes.

Keywords: career decision-making skills, career self-efficacy, CDDQ scale, CDMSE-SF scale, drama in education method

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Authors: Seyyed Kavous Abbasi

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Technical and professional branch is a subcategory of high school educational system. It deals with the programs which have been designed for the promotion of applied science and necessary skill and growth of potential talents in students. The purpose of performance of this branch is preparing of preponderance of in police in different section of industries and service. The aim of this research is the survey of group relation family, economic, educational and individual factors and the student's tendency toward technical professional courses. The method of the study is descriptive survey. 195 subjects were chosen randomly from all the male and female students of technical and professional school in Izeh. Instrument for this research was research-made questionnaire consisting of 22 questions on the base of likers spectrum. The reliability of this questionnaire has been estimated 0.8. Analyses of research data has been performed in two levels of descriptive and inferential statistics. Analyses of data has shown that the family factors with average of 3.12, individual factors 3.95, economic factors 3.92 and educational factors 3.57 more than middle level have more effects , in comparison with the factor of group relation with average of 2.79 less than average level in tendency the technical and professional course . Comparison of effective factors in tendency to technical and professional course has shown that individual factors had the most effects and the group relation factors had the least effects. Comparison between male and female subject's ideas showed that there is a different between their ideas about economics and family factors.

Keywords: high school, relation family, individual factors, analysis interest

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Authors: Saeed Eskandari, Seyed Rasoul Mehdikhani

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Radiation sources have been used in many industries, such as gamma sources in medical imaging. These waves have destructive effects on humans and the environment. It is very important to detect and find the source of these waves because these sources cannot be seen by the eye. A portable robot has been designed and built with the purpose of revealing radiation sources that are able to scan the place from 5 to 20 meters away and shows the location of the sources according to the intensity of the waves on a two-dimensional digital image. The operation of the robot is done by measuring the pixels separately. By increasing the image measurement resolution, we will have a more accurate scan of the environment, and more points will be detected. But this causes a lot of time to be spent on scanning. In this paper, to overcome this challenge, we designed a method that can optimize this time. In this method, a small number of important points of the environment are measured. Hence the remaining pixels are predicted and estimated by regression algorithms in machine learning. The research method is based on comparing the actual values of all pixels. These steps have been repeated with several other radiation sources. The obtained results of the study show that the values estimated by the regression method are very close to the real values.

Keywords: regression, machine learning, scan radiation, robot

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Authors: Ahmad Saleem

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Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

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Authors: D. Lalitha Rani, K. Sankar Rao

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MSMEs, due to their unique characteristics, are found to have inherent capabilities to undertake technological and non-technological innovations successfully across industries and nations. While there is considerable empirical evidence to throw light on SME innovation contributions in the context of developed countries, there is hardly any evidence to reveal how innovative SMEs are in rapidly industrializing economies like India. Indian MSMEs are largely incremental innovators, prompted by their customers and involved in product and/or process innovations. But majority carried out innovations with internal efforts only whereas the minority which obtained external support, had better technical strength, indulged in more frequent and both product & process innovations. Such MSMEs achieved better innovation performance as well as better economic performance. Some of them internationalized themselves in the process. However such achievements are “an oasis” in the vast Indian SME sector. How to promote (i) innovations, (ii) quality of innovations and (iii) patenting culture among the SMEs is a challenge for Indian Policy Makers. However this paper examines what are the innovation practices which are being carried out in this sector and identified the barriers for innovations in this sector and concludes with proposing some policy recommendations for promoting innovations in MSME sector in India.

Keywords: MSMEs, incremental innovators, policies, non-technological innovations

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Ava Dodd, Jonathan Adams

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The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: Edward Sarich, Jack Ryan

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In contrast to learning new vocabulary incidentally in one’s first language, foreign language vocabulary is often acquired purposefully, because a lack of natural exposure requires it to be studied in an artificial environment. It follows then that foreign language vocabulary may be more efficiently acquired if it is purpose-driven, or linked to a clear and desirable outcome. The research described in this paper relates to the early stages of what is seen as a long-term effort to measure the effectiveness of a methodology for purpose-driven foreign language vocabulary instruction, specifically by analyzing whether directed studying from high-frequency vocabulary lists leads to an improvement in Test of English for International Communication (TOEIC) scores. The research was carried out in two sections of a first-year university English composition class at a small university in Japan. The results seem to indicate that purposeful study from relevant high-frequency vocabulary lists can contribute to raising TOEIC scores and that the test preparation methodology used in this study was thought by students to be beneficial in helping them to prepare to take this high-stakes test.

Keywords: corpus vocabulary, language asssessment, second language vocabulary acquisition, TOEIC test preparation

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Authors: Yingying Ding

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This study presents a methodology of specialized translation with the objective of helping teachers to improve the strategies in teaching translation. In order to allow students to acquire skills to translate specialized texts, they need to become familiar with the semantic and syntactic features of source texts and target texts. The aim of our study is to use a corpus-based approach in the teaching of specialized translation between Chinese and Italian. This study proposes to construct a specialized Chinese - Italian comparable corpus that consists of 50 economic contracts from the domain of food. With the help of AntConc, we propose to compile a comparable corpus in for translation teaching purposes. This paper attempts to provide insight into how teachers could benefit from comparable corpus in the teaching of specialized translation from Italian into Chinese and through some examples of passive sentences how students could learn to apply different strategies for translating appropriately the voice.

Keywords: contrastive studies, specialised translation, corpus-based approach, teaching

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Authors: Colleen Cleveland, W. Adam Baldowski

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In the dynamic realm of online education, the integration of games and entertainment has emerged as a powerful strategy to captivate learners, drive active participation, and cultivate meaningful learning experiences. This abstract presents an overview of the upcoming conference, "Game On," dedicated to exploring the transformative impact of gamification, interactive simulations, and multimedia content in the digital learning landscape. Introduction: The conference aims to blur the traditional boundaries between education and entertainment, inspiring learners of diverse ages and backgrounds to actively engage in their online learning journeys. By leveraging the captivating elements of games and entertainment, educators can enhance motivation, retention, and deep understanding among virtual classroom participants. Conference Highlights: Commencing with an exploration of theoretical foundations drawing from educational psychology, instructional design, and the latest pedagogical research, participants will gain valuable insights into the ways gamified elements elevate the quality of online education. Attendees can expect interactive sessions, workshops, and case studies showcasing best practices and innovative strategies, including game-based assessments and virtual reality simulations. Inclusivity and Diversity: The conference places a strong emphasis on inclusivity, accessibility, and diversity in the integration of games and entertainment for educational purposes. Discussions will revolve around accommodating diverse learning styles, overcoming potential challenges, and ensuring equitable access to engaging educational content for all learners. Educational Transformation: Educators, instructional designers, and e-learning professionals attending "Game On" will acquire practical techniques to elevate the quality of their online courses. The conference promises a stimulating and informative exploration of blending education with entertainment, unlocking the untapped potential of games and entertainment in online education. Conclusion: "Game On" invites participants to embark on a journey that transforms online education by harnessing the power of entertainment. This event promises to be a cornerstone in the evolution of virtual learning, offering valuable insights for those seeking to create a more engaging and effective online educational experience. Join us as we explore new horizons, pushing the boundaries of online education through the fusion of games and entertainment.

Keywords: online education, games, entertainment, psychology, therapy, pop culture

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Authors: Inezia Aurelia, Soebowo Musa

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Many firms still fail to develop organizational agility. There are more than 40% of organizations think that they are low/not agile in facing market change. Organizational culture plays an important role in developing the organizations to be adaptive in order to manage the VUCA effectively. This study examines the relationships of organizational culture towards participative leadership, employee satisfaction, employee work motivation, organizational learning, and absorptive capacity in developing organizational agility in managing the VUCA environment. 263 employees located from international chemical-based company offices across the globe who have worked for more than three years were the respondents in this study. This study showed that organizational clan culture promotes the development of participative leadership, which it has an empowering effect on people in the organization resulting in employee satisfaction. The study also confirms the role of organizational culture in creating organizational behavior within the organization that fosters organizational learning, absorptive capacity, and organizational agility, while the study also found that the relationship between participative leadership and employee work motivation is not significant.

Keywords: absorptive capacity, employee satisfaction, employee work motivation, organizational agility, organizational culture, organizational learning, participative leadership

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Authors: Shaan Khosla, Jon Krohn

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In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

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Authors: Hirokatsu Kawashima

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In an attempt to elucidate relationships between listening to music and pronunciation learning, a classroom-based investigation was conducted with Japanese EFL learners (n=45). The subjects were instructed to listen to English songs they liked on YouTube, especially paying attention to phonologically similar vowel and consonant minimal pair words (e.g., live and leave). This kind of activity, which included taking notes, was regularly carried out in the classroom, and the same kind of task was given to the subjects as homework in order to reinforce the in-class activity. The duration of these activities was eight weeks, after which the program was evaluated on a 9-point scale (1: the lowest and 9: the highest) by learners’ self-evaluation. The main questions for this evaluation included 1) how good the learners had been at pronouncing vowel and consonant minimal pair words originally, 2) how often they had listened to songs good for pronouncing vowel and consonant minimal pair words, 3) how frequently they had moved their mouths to vowel and consonant minimal pair words of English songs, and 4) how much they thought the program would support and enhance their pronunciation learning of phonologically similar vowel and consonant minimal pair words. It has been found, for example, A) that the evaluation of this program is by no means low (Mean: 6.51 and SD: 1.23), suggesting that listening to music may support and enhance pronunciation learning, and B) that listening to consonant minimal pair words in English songs and moving the mouth to them are more related to the program’s evaluation (r =.69, p=.00 and r =.55, p=.00, respectively) than listening to vowel minimal pair words in English songs and moving the mouth to them (r =.45, p=.00 and r =.39, p=.01, respectively).

Keywords: minimal pair, music, pronunciation, song

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Authors: Elke Mackensen

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Systems Engineering plays a key role during industrial product development of complex technical systems. The need for systems engineers in industry is growing. However, there is a gap between the industrial need and the academic education. Normally the academic education is focused on the domain specific design, implementation and testing of technical systems. Necessary systems engineering expertise like knowledge about requirements analysis, product cost estimation, management or social skills are poorly taught. Thus, there is the need of new academic concepts for teaching systems engineering skills. This paper presents a project-orientated training concept to prepare students from different technical degree programs for systems engineering activities. The training concept has been initially implemented and applied in the industrial engineering master program of the University of Applied Sciences Offenburg.

Keywords: educational systems engineering training, requirements analysis, system modelling, SysML

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Victor Milanes, Martha Hubertz

Abstract:

In the wake of COVID-19, all aspects of life have been estranged, and humanity has been forced to shift toward a more technologically integrated mode of operation. Essential work such as Healthcare, business, and public policy are a few notable industries that were initially dependent upon face-to-face modality but have completely reimagined their operation style. Unique to these fields, education was particularly strained because academics, teachers, and professors alike were obligated to shift their curriculums online over the course of a few weeks while also maintaining the expectation that they were educating their students to a similar level accomplished pre-pandemic. This was notable as research indicates two key concepts: Students prefer face-to-face modality, and due to the disruption in academic continuity/style, there was a negative impact on student's overall education and performance. With these two principles in mind, this study aims to inquire what online strategies could be best employed by teachers to educate their students, as well as what strategies could be adopted in a multimodal setting if deemed necessary by the instructor or outside convoluting factors (Such as the case of COVID-19, or a personal matter that demands the teacher's attention away from the classroom). Strategies and methods will be cross-analyzed via a ranking system derived from various recognized teaching assessments, in which engagement, retention, flexibility, interest, and performance are specifically accounted for. We expect to see an emphasis on positive social pressure as a dominant factor in the improved propensity for education, as well as a preference for visual aids across platforms, as research indicates most individuals are visual learners.

Keywords: technological integration, multimodal teaching, education, student engagement

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Authors: Zihan Geng, Yifan Hou

Abstract:

Background: Loneliness is the feeling of being isolated, which is becoming increasingly common among adolescents. A cross-sectional study was performed to determine the association between loneliness and different demographics. Methods: To identify the presence of loneliness, the UCLA Loneliness Scale (Version 3) was employed. The "Questionnaire Star" in Chinese version, as the online survey on the official website, was used to distribute the self-rating questionnaires to the students in Beijing from Grade 7 to Grade 12. The questionnaire includes sociodemographic items and the UCLA Loneliness Scale. Results: Almost all of the participants exhibited “caseness” for loneliness, as defined by UCLA. Out of 266 questionnaires, 2.6% (7 in 266) students fulfilled the presence criteria for a low degree of loneliness. 29.7% (79 in 266) of adolescents met the criteria for a moderate degree of loneliness. Moreover, 62.8% (167 in 266) and 4.9% (13 in 266) of students fulfilled the presence criteria for a moderately high and high degree of loneliness, respectively. In the Pearson χ2 test, there were significant associations between loneliness and some demographic factors, including grade (P<0.001), the number of adults in the family (P=0.001), the evaluation of appearance (P=0.034), the evaluation of self-satisfaction (P<0.001), the love in family (P<0.001), academic performance (P=0.001) and emotional support from friends (P<0.001). In the multivariate logistic analysis, the number of adults (2 vs.≤1, OR=0.319, P=0.015), time spent on social media (≥4h vs. ≤1h, OR=4.862, P=0.029), emotional support of friends (more satisfied vs. dissatisfied, OR=0.363, P=0.027) were associated with loneliness. Conclusions: Our results suggest the relationship between loneliness and some sociodemographic factors, which raise the possibility to reduce the loneliness among adolescents. Therefore, the companionship of family, the encouragement from friends and regulating the time spent on social media may decrease the loneliness in adolescents.

Keywords: loneliness, adolescents, demographic factors, UCLA loneliness scale

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Authors: Ifeanyi Adigwe, Thomas Van der Walt

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Digital media has been integrated into the social and entertainment life of young children, and as such, the impact of digital media appears to affect young people of all ages and it is believed that this will continue to shape the world of young children. Since, technological advancement of digital media presents adolescents with diverse contexts, platforms and avenues to engage with digital media outside the home environment and from parents' supervision, a wide range of new challenges has further complicated the already difficult tasks for parents and altered the landscape of parenting. Despite the fact that adolescents now have access to a wide range of digital media technologies both at home and in the learning environment, parenting practices such as active, restrictive, co-use, participatory and technical mediations are important in mitigating of online risks adolescents may encounter as a result of digital media use. However, these mediation practices only focus on the home environment including digital media present in the home and may not necessarily transcend outside the home and other learning environments where adolescents use digital media for school work and other activities. This poses the question of who mediates adolescent's digital media use outside the home environment. The learning environment could be a ''loose platform'' where an adolescent can maximise digital media use considering the fact that there is no restriction in terms of content and time allotted to using digital media during school hours. That is to say that an adolescent can play the ''bad boy'' online in school because there is little or no restriction of digital media use and be exposed to online risks and play the ''good boy'' at home because of ''heavy'' parental mediation. This is the reason why parent mediation practices have been ineffective because a parent may not be able to track adolescents digital media use considering the diversity of contexts, platforms and avenues adolescents use digital media. This study argues that due to the diverse nature of digital media technology, parents may not be able to monitor the 'whereabouts' of their children in the digital space. This is because adolescent digital media usage may not only be confined to the home environment but other learning environments like schools. This calls for urgent attention on the part of teachers to understand the intricacies of how digital media continue to shape the world in which young children are developing and learning. It is, therefore, imperative for parents to liaise with the schools of their children to mediate digital media use during school hours. The implication of parents- teachers mediation practices are discussed. The article concludes by suggesting that third party mediation by teachers in schools and other learning environments should be encouraged and future research needs to consider the emergent strategy of teacher-children mediation approach and the implication for policy for both the home and learning environments.

Keywords: digital media, digital age, parent mediation, third party mediation

Procedia PDF Downloads 144

Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

Abstract:

The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

Procedia PDF Downloads 136