Search results for: student-centered teaching and learning

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

Procedia PDF Downloads 287

Authors: Shereen Kamel

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This study explores the recent application of bilingual education in Egyptian public schools. It aims to provide an overall picture of bilingual education programs globally and examine its adequacy to the Egyptian social and cultural context. The study also assesses the current application process of teaching English as a Second Language in public schools from the early childhood education stage and onwards, instead of starting it from middle school; as a strategy that promotes English language proficiency and equity among students. The theoretical framework is based on Jim Cummins’ bilingual education theories and on recent trends adopting different developmental theories and perspectives, like Stephen Crashen’s theory of Second Language Acquisition that calls for communicative and meaningful interaction rather than memorization of grammatical rules. The question posed here is whether bilingual education, with its peculiar nature, could be a good chance to reach out to all Egyptian students and prepare them to become global citizens. In addition to this, a more specific question is related to the extent to which social and cultural variables can affect the young learners’ second language acquisition. This exploratory analytical study uses mixed-methods research design to examine the application of bilingual education in Egyptian public schools. The study uses a cluster sample of schools in Egypt from different social and cultural backgrounds to assess the determining variables. The qualitative emphasis is on interviewing teachers and reviewing students’ achievement documents. The quantitative aspect is based on observations of in-class activities through tally sheets and checklists. Having access to schools and documents is authorized by governmental and institutional research bodies. Data sources will comprise achievement records, students’ portfolios, parents’ feedback and teachers’ viewpoints. Triangulation and SPSS will be used for analysis. Based on the gathered data, new curricula have been assigned for elementary grades and teachers have been required to teach the newly developed materials all of a sudden without any prior training. Due to shortage in the teaching force, many assigned teachers have not been proficient in the English language. Hence, teachers’ incompetency and unpreparedness to teach this grade specific curriculum constitute a great challenge in the implementation phase. Nevertheless, the young learners themselves as well as their parents seem to be enthusiastic about the idea itself. According to the findings of this research study, teaching English as a Second Language to children in public schools can be applicable and is culturally relevant to the Egyptian context. However, there might be some social and cultural differences and constraints when it comes to application in addition to various aspects regarding teacher preparation. Therefore, a new mechanism should be incorporated to overcome these challenges for better results. Moreover, a new paradigm shift in these teacher development programs is direly needed. Furthermore, ongoing support and follow up are crucial to help both teachers and students realize the desired outcomes.

Keywords: bilingual education, communicative approach, early childhood education, language and culture, second language acquisition

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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: Somayeh Sharifi

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The purpose of this study is to investigate the effects of collaborative reflection and class observation on improving the quality of teacher training courses and the students reading comprehension skills. 13 inexperienced English teachers teaching elementary courses that were at the same level of proficiency were chosen. Thirteen participants were allocated in two groups, with 7 teachers in the experimental group and the other 6 teachers in the control group. Since two groups were not selected randomly, this study is a form of quasi-experimental research. In addition to a 3-day teacher training course for both groups, teachers in experimental group recorded and observed 20 sessions of their own classes and 30 sessions of experienced teachers’ class and participated in 12 meetings -3 month once a week- in which teachers shared any event that they found interesting during observations and their own teaching and compare it with strategies that they learned in teacher training courses. In contrast, the control group did not engage in any process of observation and collaboration. In order to test students' performance in English before and after the treatment, a Key English Test (KET) was employed to check students' reading skill. The result of the test shows that there is not a significant difference in mean of scores in KET pretest in and, since they are close to each other. However by considering mean and median of posttest in both classes we perceive that although both control and experimental group students' proficiency in English enhanced, there was a significant difference in experimental group students' final scores before and after treatment.

Keywords: collaborative reflection, reading comprehension, teacher training courses, key English test (KET)

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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: 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: Raquel Mateo Mendaza, Carmen Novo Urraca

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The current paper presents the elaboration of a corpus database that aligns two different corpora in order to simplify the search of information both for researchers and students of Old English. This database comprises the information contained in two main reference corpora, namely the Dictionary of Old English Corpus (DOEC), compiled at the University of Toronto, and the York-Toronto-Helsinki Parsed Corpus of Old English (YCOE). The first one provides information on all surviving texts written in the Old English language. The latter offers the syntactical and morphological annotation of several texts included in the DOEC. Although both corpora are closely related, as the YCOE includes the DOE source text identifier, the main problem detected is that there is not an alignment of texts that allows for the search of whole fragments to be further analysed in terms of morphology and syntax. The database proposed in this paper gathers all this information and presents it in a simple, more accessible, visual, and educational way. The alignment of fragments has been done in an automatized way. However, some problems have emerged during the creating process particularly related to the lack of correspondence in the division of fragments. For this reason, it has been necessary to revise the whole entries manually to obtain a truthful high-quality product and to carefully indicate the gaps encountered in these corpora. All in all, this database contains more than 60,000 entries corresponding with the DOE fragments annotated by the YCOE. The main strength of the resulting product is its research and teaching implications in the study of Old English. The use of this database will help researchers and students in the study of different aspects of the language, such as inflectional morphology, syntactic behaviour of given words, or translation studies, among others. By means of the search of words or fragments, the annotated information on morphology and syntax will be automatically displayed, automatizing, and speeding up the search of data.

Keywords: alignment, corpus database, morphosyntactic analysis, Old English

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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: Nhac Thanh Huong

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New teaching methods have led to the changes in the teachers’ roles in an English class, in which teachers’ error correction is an integral part. Language error and corrective feedback have been the interest of many researchers in foreign language teaching. However, the techniques and the effectiveness of teachers’ feedback have been a question of much controversy. This present case study has been carried out with a view to finding out the patterns of teachers’ corrective feedback and their impact on students’ uptake in English speaking lessons of legal English major students at Hanoi Law University. In order to achieve those aims, the study makes use of classroom observations as the main method of data collection to seeks answers to the two following questions: 1. What patterns of corrective feedback occur in English speaking lessons for second- year legal English major students in Hanoi Law University?; 2. To what extent does that corrective feedback lead to students’ uptake? The study provided some important findings, among which was a close relationship between corrective feedback and uptake. In particular, recast was the most commonly used feedback type, yet it was the least effective in terms of students’ uptake and repair, while the most successful feedback, namely meta-linguistic feedback, clarification requests and elicitation, which led to students’ generated repair, was used at a much lower rate by teachers. Furthermore, it revealed that different types of errors needed different types of feedback. Also, the use of feedback depended on the students’ English proficiency level. In the light of findings, a number of pedagogical implications have been drawn in the hope of enhancing the effectiveness of teachers’ corrective feedback to students’ uptake in foreign language acquisition process.

Keywords: corrective feedback, error, uptake, speaking English lesson

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Authors: Hsin-Jen Chen

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Primary schooling in Taiwan is in a process of radical restructuring during the decade. At the center of these restructuring is the position of the principal and questions to do with how principals, as school leaders, respond to radical change. Adopting a case-study approach, the study chose a middle Taiwanese primary school to investigate how the principal learned to be political. Using micropolitical leadership, the principal at the researched site successfully coped with internal change and external demands. On the whole, judging from the principal’s leadership style on the mediation between parents and teachers, as well as school-based curriculum development, it could be argued that the principal was on the stance of being a leader of the cultural transformation instead of cultural reproduction. In doing so, the qualitative evidence has indicated that the principal seemed to be successful in coping with the demands of rapid change. Continuing learning for leadership is the core of working as a principal.

Keywords: micropolitics, leadership, micropolitical leadership, learning for leadership

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Authors: Nidhal K. Azawi, John M. Gauch

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Colorectal cancer is one of the leading causes of cancer death in the US and the world, which is why millions of colonoscopy examinations are performed annually. Unfortunately, noise, specular highlights, and motion artifacts corrupt many images in a typical colonoscopy exam. The goal of our research is to produce automated techniques to detect and correct or remove these noninformative images from colonoscopy videos, so physicians can focus their attention on informative images. In this research, we first automatically extract features from images. Then we use machine learning and deep neural network to classify colonoscopy images as either informative or noninformative. Our results show that we achieve image classification accuracy between 92-98%. We also show how the removal of noninformative images together with image alignment can aid in the creation of image panoramas and other visualizations of colonoscopy images.

Keywords: colonoscopy classification, feature extraction, image alignment, machine learning

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Authors: Hans Schumann, Colin Domnauer, Louis Rosenberg

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In this study, machine learning techniques were deployed on real-time human swarm data to forecast the likelihood of outcomes for English Premier League matches in the 2020/21 season. These techniques included ensemble models in combination with neural networks and were tested against an industry standard of Vegas Oddsmakers. Predictions made from the collective intelligence of human swarm participants managed to achieve a positive return on investment over a full season on matches, empirically proving the usefulness of a new artificial intelligence valuing human instinct and intelligence.

Keywords: artificial intelligence, data science, English Premier League, human swarming, machine learning, sports betting, swarm intelligence

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Authors: Juliana Osman, David Gilbert, Caroline Tan

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Innovation capability is recognized as a critical factor that contributes to promoting firm growth and wealth creation. While studies on innovation are in abundance, few empirical studies have been undertaken to examine the relationships of intellectual capital with innovation capability, and research investigating the combinations of strategic orientation dimensions is limited and virtually nothing in regard to the Malaysian context. This research investigates the impact of intellectual capital and three strategic orientations on the innovation capability and firm performance of Malaysian ICT SMEs. Data was collected from 213 firms relating to intellectual capital and the three strategic orientations; market orientation, learning orientation and technology orientation. Using partial least squares structural equation modelling (PLS-SEM) to analyse the data, results indicate that while market orientation has a direct negative relationship to firm performance, it is positively related to performance through the mediating effect of innovation capability. Learning orientation and technology orientation are mediated by innovation capability, while intellectual capital was found to be partially mediated by innovation capability. Findings indicate that firm performance is positively and significantly related to innovation capability and that market orientation, learning orientation, technology orientation and intellectual capital are all significant and positively related to innovation capability. The developed model indicates that Malaysian ICT SMEs would perform better with greater emphasis on developing innovation capability through enhancement of intellectual capital and the strategic orientations measured in this study.

Keywords: innovation capability, intellectual capital, strategic orientations, PLS-SEM

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Authors: Mirzaii Khadigeh

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Background and objectives: Sexual health education is an important part of health promotion services. The major role of sex education is on mothers’ shoulders. So, they have to be equipped with enough knowledge, attitude and self-efficacy for teens’ education. The present study aimed to determine the effect of team-learning and multimedia software on mothers’ knowledge, attitudes and self-efficacy in sexual health education to 12-14-year-old sons in Mashhad in 1395. Materials and methods: In this research, two experimental and one control group were employed using random sampling, which was done on 132 mothers of high school pupils. They were randomly assigned into experimental and control groups. The data were collected using demographic information and a researcher-constructed questionnaire to investigate the mothers’ knowledge, attitude, and self-efficacy and DASS21(The Depression, Anxiety and Stress Scale). They were run after confirming their reliability and validity. Intervention for the multimedia group was in the form of four CDs- each for 45 minutes- that were given to the mothers each week. At the end of the fourth week, a question-and-answer session was administered for 60 minutes. The team-learning group received intervention once a week (totally four weeks). Two weeks later, the data were collected and analyzed via Chi-square, Fisher, Kruskal-Wallis and ANOVA. Findings: Knowledge, attitude and self-efficacy of mothers in sexual health before the intervention did not have any significant differences (p >0.05). At the end of the study, the difference between the scores of the knowledge, attitude and self-efficacy in the three groups was meaningfully different (p < 0/001), but the difference between the two groups of multimedia and team-learning was not significant (p> 0.05 ). Discussion and conclusion: The result reported the efficacy of both team-leaning and multimedia software, which implies that the multimedia software training method was as effective as team-learning training one on the knowledge, attitude and self-efficacy of mothers. But, the multimedia training method is highly advised due to its availability, flexibility, and interest.

Keywords: training one on the knowledge, attitude, self-efficacy of mothers, boys

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Authors: Salome Schulze

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The delay of students in completing their dissertations is a worldwide problem. At the University of South Africa where this research was done, only about a third of the students complete their studies within the required period of time. This study explored the reasons why the students interrupted their studies, and why they resumed their research at a later stage. If this knowledge could be utilised to improve the throughput of doctoral students, it could have significant economic benefits for institutions of higher education while at the same time enhancing their academic prestige. To inform the investigation, attention was given to key theories concerning the learning of doctoral students, namely the situated learning theory, the social capital theory and the self-regulated learning theory, based on the social cognitive theory of learning. Ten students in the faculty of Education were purposefully selected on the grounds of their poor progress, or of having been in the system for too long. The collection of the data was in accordance with a Finnish study, since the two studies had the same aims, namely to investigate student engagement and disengagement. Graphic elicitation interviews, based on visualisations were considered appropriate to collect the data. This method could stimulate the reflection and recall of the participants’ ‘stories’ with very little input from the interviewer. The interviewees were requested to visualise, on paper, their journeys as doctoral students from the time when they first registered. They were to indicate the significant events that occurred and which facilitated their engagement or disengagement. In the interviews that followed, they were requested to elaborate on these motivating or challenging events by explaining when and why they occurred, and what prompted them to resume their studies. The interviews were tape-recorded and transcribed verbatim. Information-rich data were obtained containing visual metaphors. The data indicated that when the students suffered a period of disengagement, it was sometimes related to a lack of self-regulated learning, in particular, a lack of autonomy, and the inability to manage their time effectively. When the students felt isolated from the academic community of practice disengagement also occurred. This included poor guidance by their supervisors, which accordingly deprived them of significant social capital. The study also revealed that situational factors at home or at work were often the main reasons for the students’ procrastinating behaviour. The students, however, remained in the system. They were motivated towards a renewed engagement with their studies if they were self-regulated learners, and if they felt a connectedness with the academic community of practice because of positive relationships with their supervisors and of participation in the activities of the community (e.g., in workshops or conferences). In support of their learning, networking with significant others who were sources of information provided the students with the necessary social capital. Generally, institutions of higher education cannot address the students’ personal issues directly, but they can deal with key institutional factors in order to improve the throughput of doctoral students. It is also suggested that graphic elicitation interviews be used more often in social research that investigates the learning and development of the students.

Keywords: doctoral students, engaging and disengaging experiences, graphic elicitation interviews, student procrastination

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Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

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Authors: Gaya Tridinanti

Abstract:

Enhanching English vocabulary in early childhood is the main problem often faced by teachers. Thus, the purpose of this study was to determine the enhancement of children’s English vocabulary acquisition by using digital storytelling. This type of research was an action research. It consisted of a series of four activities done in repeated cycles: planning, implementation, observation, and reflection. The subject of the study consisted of 30 students of B group (5-6 years old) attending Happy Kids Kindergarten Palembang, Indonesia. This research was conducted in three cycles. The methods used for data collection were observation and documentation. Descriptive qualitative and quantitative methods were also used to analyse the data. The research showed that the digital storytelling learning activities could enhance the children’s English vocabulary acquisition. It is based on the data in which the enhancement in pre-cycle was 37% and 51% in Cycle I. In Cycle II it was 71% and in Cycle III it was 89.3%. The results showed an enhancement of about 14% from the pre-cycle to Cycle I, 20% from Cycle I to Cycle II, and enhancement of about 18.3% from Cycle II to Cycle III. The conclusion of this study suggests that digital storytelling learning method could enhance the English vocabulary acquisition of B group children at the Happy Kids Kindergarten Palembang. Therefore, digital storytelling can be considered as an alternative to improve English language learning in the classroom.

Keywords: acquisition, enhancing, digital storytelling, English vocabulary

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Authors: Chien-kuo Chang, Bo-wei Wu, Yi-yun Tang, Min-chiu Wu

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This paper proposes a power equipment diagnosis system based on partial discharge (PD), which is characterized by increasing the readability of experimental data and the convenience of operation. This system integrates a variety of analysis programs of different data formats and different programming languages and then establishes a set of interfaces that can follow and expand the structure, which is also helpful for subsequent maintenance and innovation. This study shows a case of using the developed Convolutional Neural Networks (CNN) to integrate with this system, using the designed model architecture to simplify the complex training process. It is expected that the simplified training process can be used to establish an adaptive deep learning experimental structure. By selecting different test data for repeated training, the accuracy of the identification system can be enhanced. On this platform, the measurement status and partial discharge pattern of each equipment can be checked in real time, and the function of real-time identification can be set, and various training models can be used to carry out real-time partial discharge insulation defect identification and insulation state diagnosis. When the electric power equipment entering the dangerous period, replace equipment early to avoid unexpected electrical accidents.

Keywords: partial discharge, convolutional neural network, partial discharge analysis platform, adaptive deep learning

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Authors: Ana Mouta, Ana Paulino

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The following text presents the ik.model, a theoretical framework that guided the pedagogical implementation of meaningful educational technology-based projects in formal education worldwide. In this paper, we will focus on how this framework has enabled the development of media literacy projects for early childhood education during the last three years. The methodology that guided educators through the challenge of systematically merging analogic and digital means in dialogic high-quality opportunities of world exploration is explained throughout these lines. The effects of this methodology on early age media literacy development are considered. Also considered is the relevance of this skill in terms of post-contemporary challenges posed to learning.

Keywords: early learning, ik.model, media literacy, pedagogy

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Authors: Rik van Leeuwen, Ger Koole

Abstract:

Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

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Authors: Lifar M. S., Tereshchenko A. A., Bulgakov A. N., Guda S. A., Guda A. A., Soldatov A. V.

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Metal nanoparticles are widely used as heterogeneous catalysts to activate adsorbed molecules and reduce the energy barrier of the reaction. Reaction product yield depends on the interplay between elementary processes - adsorption, activation, reaction, and desorption. These processes, in turn, depend on the inlet feed concentrations, temperature, and pressure. At stationary conditions, the active surface sites may be poisoned by reaction byproducts or blocked by thermodynamically adsorbed gaseous reagents. Thus, the yield of reaction products can significantly drop. On the contrary, the dynamic control accounts for the changes in the surface properties and adjusts reaction parameters accordingly. Therefore dynamic control may be more efficient than stationary control. In this work, a reinforcement learning algorithm has been applied to control the simulation of CO oxidation on a catalyst. The policy gradient algorithm is learned to maximize the CO₂ production rate based on the CO and O₂ flows at a given time step. Nonstationary solutions were found for the regime with surface deactivation. The maximal product yield was achieved for periodic variations of the gas flows, ensuring a balance between available adsorption sites and the concentration of activated intermediates. This methodology opens a perspective for the optimization of catalytic reactions under nonstationary conditions.

Keywords: artificial intelligence, catalyst, co oxidation, reinforcement learning, dynamic control

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Authors: Deborah L. Smith

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To review our program and determine the best redesign options, department members gathered feedback and input through focus groups, analysis of data, and a review of the current research to ensure that the changes proposed were not based solely on the state’s new professional standards. In designing course assignments and assessments, we listened to a variety of constituents, including students, other institutions of higher learning, MDE webinars, host teachers, literacy clinic personnel, and other disciplinary experts. As a result, we are designing a program that is more inclusive of a variety of field experiences for growth. We have determined ways to improve our program by connecting academic disciplinary knowledge, educational psychology, and community building both inside and outside the classroom for professional learning communities. The state’s release of new professional standards led my department members to question what is working and what needs improvement in our program. One aspect of our program that continues to be supported by research and data analysis is the function of supervised field experiences with meaningful feedback. We seek to expand in this area. Other data indicate that we have strengths in modeling a variety of approaches such as cooperative learning, discussions, literacy strategies, and workshops. In the new program, field assignments will be connected to multiple courses, and efforts to scaffold student learning to guide them toward best evidence-based practices will be continuous. Despite running a program that meets multiple sets of standards, there are areas of need that we directly address in our redesign proposal. Technology is ever-changing, so it’s inevitable that improving digital skills is a focus. In addition, scaffolding procedures for English Language Learners (ELL) or other students who struggle is imperative. Diversity, equity, and inclusion (DEI) has been an integral part of our curriculum, but the research indicates that more self-reflection and a deeper understanding of culturally relevant practices would help the program improve. Connections with professional learning communities will be expanded, as will leadership components, so that teacher candidates understand their role in changing the face of education. A pilot program will run in academic year 22/23, and additional data will be collected each semester through evaluations and continued program review.

Keywords: DEI, field experiences, program redesign, teacher preparation

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Authors: D. I. George Amalarethinam, A. Emima

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Educational Data Mining (EDM) is one of the newest topics to emerge in recent years, and it is concerned with developing methods for analyzing various types of data gathered from the educational circle. EDM methods and techniques with machine learning algorithms are used to extract meaningful and usable information from huge databases. For scientists and researchers, realistic applications of Machine Learning in the EDM sectors offer new frontiers and present new problems. One of the most important research areas in EDM is predicting student success. The prediction algorithms and techniques must be developed to forecast students' performance, which aids the tutor, institution to boost the level of student’s performance. This paper examines various classification techniques in prediction methods and data mining tools used in EDM.

Keywords: classification technique, data mining, EDM methods, prediction methods

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Authors: Syed Atif Hassan, Luv Mehta, Syed Asif Hassan

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Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.

Keywords: classifier, machine learning, music tracks, popularity, prediction

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Authors: Ken Hampshire, Thomas Mazzuchi, Shahram Sarkani

Abstract:

As a subset of metaheuristics, nature-inspired optimization algorithms such as particle swarm optimization (PSO) have shown promise both in solving intractable problems and in their extensibility to novel problem formulations due to their general approach requiring few assumptions. Unfortunately, single instantiations of algorithms require detailed tuning of parameters and cannot be proven to be best suited to a particular illustrative problem on account of the “no free lunch” (NFL) theorem. Using these algorithms in real-world problems requires exquisite knowledge of the many techniques and is not conducive to reconciling the various approaches to given classes of problems. This research aims to present a unified view of PSO-based approaches from the perspective of relevant systems engineering problems, with the express purpose of then eliciting the best solution for any problem formulation in an ensemble learning bucket of models approach. The central hypothesis of the research is that extending the PSO algorithms found in the literature to real-world optimization problems requires a general ensemble-based method for all problem formulations but a specific implementation and solution for any instance. The main results are a problem-based literature survey and a general method to find more globally optimal solutions for any systems engineering optimization problem.

Keywords: particle swarm optimization, nature-inspired optimization, metaheuristics, systems engineering, ensemble learning

Procedia PDF Downloads 73