Search results for: word learning

Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

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Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

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Authors: Hoora Dehghani, Meisam Shahbazi, Reza Zare

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During the previous decade, a significant evolution has occurred in the number of private educational centers and, accordingly, the increase in the number of providers and students of these centers around the world. The number of language teaching institutes in Iran that are considered private educational sectors is also growing exponentially as the request for learning foreign languages has extremely increased in recent years. This fact caused competition among the institutions in improving better services tailored to the students’ demands to win the competition. Along with the growth in the industry of education, higher education institutes should apply the marketing-related concepts and view students as customers because students’ outlooks are similar to consumers with education. Studying the influential factors in the selection of an institute has multiple benefits. Firstly, it acknowledges the institutions of the students’ choice factors. Secondly, the institutions use the obtained information to improve their marketing methods. It also helps institutions know students’ outlooks that can be applied to expand the student know-how. Moreover, it provides practical evidence for educational centers to plan useful amenities and programs, and use efficient policies to cater to the market, and also helps them execute the methods that increase students’ feeling of contentment and assurance. Thus, this study explored the influencing factors in the selection of a language learning institute by language learners and examined and compared the importance among the varying age groups and genders. In the first phase of the study, the researchers selected 15 language learners as representative cases within the specified age ranges and genders purposefully and interviewed them to explore the comprising elements in their language institute selection process and analyzed the results qualitatively. In the second phase, the researchers identified elements as specified items of a questionnaire, and 1000 English learners across varying educational contexts rated them. The TOPSIS method was used to analyze the data quantitatively by representing the level of importance of the items for the participants generally and specifically in each subcategory; genders and age groups. The results indicated that the educational quality, teaching method, duration of training course, establishing need-oriented courses, and easy access were the most important elements. On the other hand, offering training in different languages, the specialized education of only one language, the uniform and appropriate appearance of office staff, having native professors to the language of instruction, applying Computer or online tests instead of the usual paper tests respectively as the least important choice factors in selecting a language institute. Besides, some comparisons among different groups’ ratings of choice factors were made, which revealed the differences among different groups' priorities in choosing a language institute.

Keywords: choice factors, EFL institute selection, english learning, need analysis, TOPSIS

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Authors: Karwan K. Abdalrahman

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Due to the impacts of the internet and social media, new words and expressions enter the Kurdish language, and a number of familiarized words get new meanings. The case is especially true when the technique of transliteration is taken into consideration. Through transliteration, a number of selected words widely used on social media are entering the Kurdish media discourse. In addition, a selected number of Kurdish words get new cultural and psychological meanings. The significance of this study is to delve into the process of word formation in the Kurdish language and explore how new words and expressions are formed by social media users and got public recognition. First, the study investigates the English words that enter the Kurdish language through different social media platforms. All of these words are transliterated and are used in spoken and written discourses. Second, there are a specific number of Kurdish words that got new meanings in social media. As for these words, there are psychological and cultural factors that make people use these expressions for specific political reasons. It can be argued that they have an indirect political message along with their new linguistic usages. This is a qualitative study analyzing video content that was published in the last two years on social media platforms, including Facebook and YouTube. The collected data was analyzed based on the themes discussed above. The findings of the research can be summarized as follows: the widely used transliterated words have entered both the spoken and written discourses. Authors in online and offline newspapers, TV presenters, literary writers, columnists are using these new expressions in their writings. As for the Kurdish words with new meanings, they are also widely used for psychological, cultural, and political reasons.

Keywords: Kurdish language, social media, new meanings, transliteration, vocabulary

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Authors: Mei Xuan Tan, Eng Ying Bong

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Academic discussion during and after instructional teaching is an integral part of learning. Such discussion between the instructor and student or peer-to-peer discussion can be in several different forms. It could be face-to-face discussion, via email and use of online discussion forum. In this study, the effectiveness of using WhatsApp for academic discussion for a first year half-credit Chemistry course was examined. This study was run over two years with two different batches of students. Participation in the study was voluntary and student volunteers were recruited within the first week of the term. The activity in the WhatsApp group was monitored by two instructors teaching the course. At the end of the course, the students participated in an online survey to evaluate their experience of using WhatsApp for academic discussion. There were a total of 26 questions. The survey had a total of 4 sections with regards to the use of WhatsApp for academic discussion: 1) Familiarity with WhatsApp, 2) Effectiveness of using WhatsApp for discussion, 3) Challenges and 4) Overall experience. The main purpose of using an IM platform for academic discussion was to encourage after-class discussion amongst the students. 32% of the participants had used other online platform, such as Piazza and forums in Learning Management System (LMS), for after-class academic discussion with their instructors and peers. This was a low percentage considering that some courses use such online platform as their main forum amongst instructors and students. At the end of our study, over 83% of the participants felt that WhatsApp was a more effective platform compared to other online forum. One interesting finding was the effect of WhatsApp discussion on face-to-face interaction with instructors. 28% of the students agreed that the use of WhatsApp as a discussion forum had encouraged them to approach their instructors during or after class. 51% of students answered neutral. This could be interpreted that the use of WhatsApp had not affected the frequent (or lack of) face-to-face interaction with their instructors. A second survey question, similar but phrased differently from the first, was also asked to evaluate the aspect of face-to-face interaction with instructors. 34% disagreed that the use of WhatsApp had reduced the frequency of face-to-face interaction. This could imply that the frequency remained the same or might have increased. The 38% who agreed to a decrease in face-to-face interaction have either asked the questions in WhatsApp or had their questions answered by a query from another student in the group chat. These outcomes suggested that the use of technology aided and complemented face-to-face interaction between instructors and students. The study also looked at the challenges of using WhatsApp for academic discussion. Some challenges included difficulty in referring back to previous discussion and students finding some discussions irrelevant to them. In conclusion, the use of IM platform for academic discussion was desirable for the students, but it should not be the only channel as face-to-face consultation and online forum for lengthy discussion are still important for after-class learning of students.

Keywords: chemistry, pedogogy, technological tools, undergraduate

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Authors: Nasir Sulman

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In Pakistan, the inclusion of persons with disabilities in higher education institutions has significantly been increased with every passing year and anyone can observe a sizeable number of these students in each faculty. The study executes to conduct a baseline survey for measuring faculty understanding about the special needs, experiences of students with disabilities and support provided by university administration in order to teach these students effectively. The researcher has used mixed methods and the University of Karachi was selected through non-probability-based sampling method. This university is one of the largest universities in Pakistan where more than 40,000 students have been enrolled. Data was gathered through a questionnaire and focused group discussion from three stakeholders including students with disabilities, faculty members and members of the university administration. The key findings show that students with disabilities experience a number of problems related to accommodating their special needs. However, the most encouraging factors identified are the attitude, support, and motivation they received from various faculty members and university administration. On the basis of the findings of the study the researcher has prepared a faculty guidebook and established a ‘Model Learning Assistance Centre for Students with Disabilities’ in the Department of Special Education, University of Karachi. Both these efforts will be helpful for improving the support services for students with disabilities to strengthen the existing laws, policies, and practices in institutions of higher education.

Keywords: persons with disabilities, higher education, learning assistance center, faculty guidebook

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Authors: Colleen McMillan, Alice Schmidt-Hanbidge, Beth Archer-Kuhn, Heather Boynton, Judith Hughes

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It is well known that social work students enter the profession with higher scores of adverse childhood experiences (ACE). Add to that the fact that COVID-19 has forced higher education institutions to shift to online teaching and learning, where students, faculty and field educators in social work education have reported increased stressors as well as posing challenges in developing relationships with students and being able to identify mental health challenges including those related to trauma. This multi-institutional project included three Canadian post-secondary institutions at five sites (the University of Waterloo, the University of Calgary and the University of Manitoba) and partners; Desire To Learn (D2L), The Centre for Teaching Excellence at the University of Waterloo and the Taylor Institute for Teaching and Learning. A sequential mixed method research design was used. Survey data was collected from students, faculty and field education staff from the 3 universities using the Qualtrics Insight Platform, followed by virtual focus group data with students to provide greater clarity to the quantitative data. Survey data was analyzed using SPSS software, while focus group data was transcribed verbatim and organized with N-Vivo 12. Thematic analysis used line-by-line coding and constant comparative methods within and across focus groups. The following three objectives of the study were achieved: 1) Establish a Canadian baseline on trauma informed pedagogy and student experiences of trauma informed teaching in the online higher education environment during a pandemic; 2) Identify and document educator and student experiences of online learning regarding the ability to process trauma experiences; and, 3) Transfer the findings into a trauma informed pedagogical model for Social Work as a first step toward developing a universal trauma informed teaching model. The trauma informed pedagogy model would be presented in relation to the study findings.

Keywords: trauma informed pedagogy, higher education, social work, mental health

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Authors: Saurabh Farkya, Govinda Surampudi

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Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique.

Keywords: Character Recognition (OCR), Text to Speech (TTS), Support Vector Machines (SVM), Library of Support Vector Machines (LIBSVM)

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Authors: Miguel Angel Garcia-Ruiz, Pedro Cesar Santana-Mancilla, Laura Sanely Gaytan-Lugo

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This paper describes the design and application of a short in-class project conducted in Algoma University’s Human-Computer Interaction (HCI) course taught at the Bachelor of Computer Science. The project was based on the Maker Movement (people using and reusing electronic components and everyday materials to tinker with technology and make interactive applications), where students applied low-cost and easy-to-use electronic components, the Arduino Uno microcontroller board, software tools, and everyday objects. Students collaborated in small teams by completing hands-on activities with them, making an interactive walking cane for blind people. At the end of the course, students filled out a Technology Acceptance Model version 2 (TAM2) questionnaire where they evaluated microcontroller boards’ applications in HCI classes. We also asked them about applying the Maker Movement in HCI classes. Results showed overall students’ positive opinions and response about using microcontroller boards in HCI classes. We strongly suggest that every HCI course should include practical activities related to tinkering with technology such as applying microcontroller boards, where students actively and constructively participate in teams for achieving learning objectives.

Keywords: maker movement, microcontrollers, learning, projects, course, technology acceptance

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Authors: Madhukar Kasarla, Sumit Fogla, Kiran Panuganti, Gaurav Jain, Abhijit Ramanujam, Astha Jain, Shashank Kraleti, Sharat Musham, Arun Chaudhury

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Patients have often chosen to travel for care — making pilgrimages to academic meccas and state-of-the-art hospitals for sophisticated surgery. This culture is still persistent in the landscape of US healthcare, with hundred thousand of visitors coming to the shores of United States to seek the high quality of medical care. One of the major challenges in this form of medical tourism has been the language barrier. Thus, an Iraqi patient, with immediate needs of communicating the healthcare needs to the treating team in the hospital, may face huge barrier in effective patient-doctor communication, delaying care and even at times reducing the quality. To circumvent these challenges, we are proposing the use of a state-of-the-art tool, Talk2All, which can translate nearly one hundred international languages (and even sign language) in real time. The tool is an easy to download app and highly user friendly. It builds on machine learning principles to decode different languages in real time. We suggest that the use of Talk2All will tremendously enhance communication in the hospital setting, effectively breaking the language barrier. We propose that vigorous incorporation of Talk2All shall overcome practical challenges in international medical and surgical tourism.

Keywords: language translation, communication, machine learning, medical tourism

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Authors: Pratishtha Singh

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Gender-based violence (GBV) or gendered violence refers to violence inflicted on a person because of their gender. Majority of men who perpetrate gender-based violence, first do so during their teenage years. Further, the first sexual experience of most girls is coerced. In order to reduce the widespread occurrence of GBV, it is vital to intervene and reach people, especially boys, when their attitudes and beliefs about sexuality and gender are developing. This study aims to understand GBV through an adolescent lens, focusing on their knowledge, attitudes and experiences regarding gendered abuse. This is a cross-sectional, qualitative study. The respondents are Delhi based students in grades 11th and 12th, recruited via snowball sampling. Sixteen in-depth, telephonic interviews were carried out in the month of April, 2020. The data was transcribed verbatim into MS Word and qualitative coding was undertaken in Atlas.ti 8. Twelve out of sixteen respondents admitted experiencing sexual GBV. Out of these, a little more than half of the victims reported it to somebody. Thematic analysis revealed key themes of: (i) Introduction and reinforcement of a patriarchal structure (ii) Violence in teen dating (iii) Acceptability and normalization of violence and (iv) Justice System. Findings reflect a process wherein GBV becomes an intricate part of adolescents’ lives. Participants showed a moderately well-informed understanding of gendered abuse whereas attitudes reflected a complex combination of internalized patriarchy and a desire to bring positive societal reform. The results of this study highlight a need for health promoting, gender-equitable interventions.

Keywords: adolescents, gender, health, violence

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Authors: Liz Carmem Silva-Pereira, Raffael Alencar Mesquita Rodrigues, Francisco Helton Mendes Barbosa, Emerson de Freitas Ferreira

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Due to the Covid-19 pandemic declared in March 2020 by the World Health Organization, the way of social coexistence across the planet was affected, especially in educational processes, from the implementation of the remote modality as a teaching strategy. This teaching-learning modality caused a change in the routine and learning of basic education students, which resulted in serious consequences for the return to face-to-face teaching in 2021. 2022, at the Federal Institute of Education, Science and Technology of Pará (IFPA) – Campus Paragominas had their training process severely affected, having studied the initial half of their training in the remote modality, which compromised the carrying out of practical classes, technical visits and field classes, essential for the student formation on the environmental technician. With the objective of promoting the recomposition of these students' learning after returning to the face-to-face modality, an educational strategy was developed in the last period of the course. As teaching methodologies were used for research as an educational principle, the integrative project and the parallel recovery action applied jointly, aiming at recomposing the basic knowledge of the natural sciences, together with the technical knowledge of the environmental area applied to the course. The project assisted 58 finalist students of the environmental technical course. A research instrument was elaborated with parameters of evaluation of the environmental quality for study in 19 collection points, in the Uraim River urban hydrographic basin, in the Paragominas City – Pará – Brazilian Amazon. Students were separated into groups under the professors' and laboratory assistants’ orientation, and in the field, they observed and evaluated the places' environmental conditions and collected physical data and water samples, which were taken to the chemistry and biology laboratories at Campus Paragominas for further analysis. With the results obtained, each group prepared a technical report on the environmental conditions of each evaluated point. This work methodology enabled the practical application of theoretical knowledge received in various disciplines during the remote teaching modality, contemplating the integration of knowledge, people, skills, and abilities for the best technical training of finalist students. At the activity end, the satisfaction of the involved students in the project was evaluated, through a form, with the signing of the informed consent term, using the Likert scale as an evaluation parameter. The results obtained in the satisfaction survey were: on the use of research projects within the disciplines attended, 82% of satisfaction was obtained; regarding the revision of contents in the execution of the project, 84% of satisfaction was obtained; regarding the acquired field experience, 76.9% of satisfaction was obtained, regarding the laboratory experience, 86.2% of satisfaction was obtained, and regarding the use of this methodology as parallel recovery, 71.8% was obtained of satisfaction. In addition to the excellent performance of students in acquiring knowledge, it was possible to remedy the deficiencies caused by the absence of practical classes, technical visits, and field classes, which occurred during the execution of the remote teaching modality, fulfilling the desired educational recomposition.

Keywords: integrative project, parallel recovery, research as an educational principle, teaching-learning

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Authors: G. Espinosa Garza, I. Loera, N. Antonyan

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In the last decades, teaching in particular engineering subjects is going through a significative problem. A quick evaluation of the entrepreneurial surroundings makes it more difficult for students to identify the course contents with real situations related with their future professions. Proposing teaching through challenges or problem-based projects, and real-life situations is turning into an important challenge for any university-level educator. The objective of this work is to present the educational experience and the investigation results taken through the Project Viability course, done by a group of professors and students from the Technologic of Monterrey. Currently, in Mexico, the orange peels are considered a dispose and they are not being utilized as an alternative to create subproducts. However, there is a great opportunity in its use as a raw material with the goal to originate the waste from the local citric firms or business. The project challenge consisted in the development of edible products from the orange peel with the intention to generate new healthy products. With this project, apart from the obtainment of the original results, the accomplishment consisted in creating a learning atmosphere, where students together with the professors were able to plan, evaluate, and implement the project related with the creative, innovative, and sustainable processes with the goal to apply it in the development of local solutions. In the present article, the pedagogic methodologies that allowed to carry out this project will be discussed.

Keywords: engineering subjects, learning project, orange peel, sustainable process

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Authors: Vaidehi Murarka, Sneha Mehta, Dishant Upadhyay

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Our project topic is “Alphabet Recognition using pixel probability distribution”. The project uses techniques of Image Processing and Machine Learning in Computer Vision. Alphabet recognition is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files etc. Alphabet Recognition based OCR application is sometimes used in signature recognition which is used in bank and other high security buildings. One of the popular mobile applications includes reading a visiting card and directly storing it to the contacts. OCR's are known to be used in radar systems for reading speeders license plates and lots of other things. The implementation of our project has been done using Visual Studio and Open CV (Open Source Computer Vision). Our algorithm is based on Neural Networks (machine learning). The project was implemented in three modules: (1) Training: This module aims “Database Generation”. Database was generated using two methods: (a) Run-time generation included database generation at compilation time using inbuilt fonts of OpenCV library. Human intervention is not necessary for generating this database. (b) Contour–detection: ‘jpeg’ template containing different fonts of an alphabet is converted to the weighted matrix using specialized functions (contour detection and blob detection) of OpenCV. The main advantage of this type of database generation is that the algorithm becomes self-learning and the final database requires little memory to be stored (119kb precisely). (2) Preprocessing: Input image is pre-processed using image processing concepts such as adaptive thresholding, binarizing, dilating etc. and is made ready for segmentation. “Segmentation” includes extraction of lines, words, and letters from the processed text image. (3) Testing and prediction: The extracted letters are classified and predicted using the neural networks algorithm. The algorithm recognizes an alphabet based on certain mathematical parameters calculated using the database and weight matrix of the segmented image.

Keywords: contour-detection, neural networks, pre-processing, recognition coefficient, runtime-template generation, segmentation, weight matrix

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Authors: Xinghang Lv, Tao Peng, Jia Chen, Junping Liu, Xinrong Hu, Ruhan He, Minghua Jiang, Wenli Cao

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As one of the most dangerous vulnerabilities, effective detection of buffer overflow vulnerabilities is extremely necessary. Traditional detection methods are not accurate enough and consume more resources to meet complex and enormous code environment at present. In order to resolve the above problems, we propose the method for Buffer overflow detection based on Abstract syntax tree, Control flow graph, and Data dependency graph (BodeACD) in C/C++ programs with source code. Firstly, BodeACD constructs the function samples of buffer overflow that are available on Github, then represents them as code representation sequences, which fuse control flow, data dependency, and syntax structure of source code to reduce information loss during code representation. Finally, BodeACD learns vulnerability patterns for vulnerability detection through deep learning. The results of the experiments show that BodeACD has increased the precision and recall by 6.3% and 8.5% respectively compared with the latest methods, which can effectively improve vulnerability detection and reduce False-positive rate and False-negative rate.

Keywords: vulnerability detection, abstract syntax tree, control flow graph, data dependency graph, code representation, deep learning

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Authors: T. S. Terence Ma, Irene T. Ho

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Conducting adequate assessment of the challenges students with ASD (Autism Spectrum Disorder) face and the support they need is imperative for promoting their school adjustment. Students with ASD often show deficits in communication, social interaction, emotional regulation, and self-management in learning. While targeting these areas in intervention is often helpful, we argue that not enough attention has been paid to weak self-regulation being a key factor underlying their manifest difficulty in all these areas. Self-regulation refers to one’s ability to moderate their behavioral or affective responses without assistance from others. Especially for students with high functioning autism, who often show problems not so much in acquiring the needed skills but rather in applying those skills appropriately in everyday problem-solving, self-regulation becomes a key to successful adjustment in daily life. Therefore, a greater understanding of the construct of self-regulation, its relationship with other daily skills, and its role in school functioning for students with ASD would generate insights on how students’ school adjustment could be promoted more effectively. There were two focuses in this study. Firstly, we examined the extent to which self-regulation is a distinct construct that is differentiable from other daily skills and the most salient indicators of this construct. Then we tested a model of relationships between self-regulation and other daily school skills as well as their relative and combined effects on school adjustment. A total of 1,345 Grade1 to Grade 6 students with ASD attending mainstream schools in Hong Kong participated in the research. In the first stage of the study, teachers filled out a questionnaire consisting of 136 items assessing a wide range of student skills in social, emotional and learning areas. Results from exploratory factor analysis (EFA) with 673 participants and subsequent confirmatory factor analysis (CFA) with another group of 672 participants showed that there were five distinct factors of school skills, namely (1) communication skills, (2) pro-social behavior, (3) emotional skills, (4) learning management, and (5) self-regulation. Five scales representing these skill dimensions were generated. In the second stage of the study, a model postulating the mediating role of self-regulation for the effects of the other four types of skills on school adjustment was tested with structural equation modeling (SEM). School adjustment was defined in terms of the extent to which the student is accepted well in school, with high engagement in school life and self-esteem as well as good interpersonal relationships. A 5-item scale was used to assess these aspects of school adjustment. Results showed that communication skills, pro-social behavior, emotional skills and learning management had significant effects on school adjustment only indirectly through self-regulation, and their total effects were found to be not high. The results indicate that support rendered to students with ASD focusing only on the training of well-defined skills is not adequate for promoting their inclusion in school. More attention should be paid to the training of self-management with an emphasis on the application of skills backed by self-regulation. Also, other non-skill factors are important in promoting inclusive education.

Keywords: autism, assessment, factor analysis, self-regulation, school adjustment

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Authors: Mohosina Jabin Toma, Gerald Tembrevilla, Marina Milner-Bolotin

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Science education in Bangladesh is losing its appeal at an alarming rate due to the lack of science laboratory equipment, excessive teacher-student ratio, and outdated teaching strategies. Research-based educational technologies aim to address some of the problems faced by teachers who have limited access to laboratory resources, like many Bangladeshi teachers. Physics Education Technology (PhET) research team has been developing science and mathematics interactive simulations to help students develop deeper conceptual understanding. Still, PhET simulations are rarely used in Bangladesh. The purpose of this study is to explore Bangladeshi teachers’ challenges in learning to implement PhET-enhanced pedagogies and examine teachers’ views on PhET’s pedagogical opportunities in secondary science education. Since it is a new technology for Bangladesh, seven workshops on PhET were conducted in Dhaka city for 129 in-service and pre-service teachers in the winter of 2023 prior to data collection. This study followed an explanatory mixed method approach that included a pre-and post-workshop survey and five semi-structured interviews. Teachers participated in the workshops voluntarily and shared their experiences at the end. Teachers’ challenges were also identified from workshop discussions and observations. The interviews took place three to four weeks after the workshop and shed light on teachers’ experiences of using PhET in actual classroom settings. The results suggest that teachers had difficulty handling new technology; hence, they recommended preparing a booklet and Bengali YouTube videos on PhET to assist them in overcoming their struggles. Teachers also faced challenges in using any inquiry-based learning approach due to the content-loaded curriculum and exam-oriented education system, as well as limited experience with inquiry-based education. The short duration of classes makes it difficult for them to design PhET activities. Furthermore, considering limited access to computers and the internet in school, teachers think PhET simulations can bring positive changes if used in homework activities. Teachers also think they lack pedagogical skills and sound content knowledge to take full advantage of PhET. They highly appreciated the workshops and proposed that the government designs some teacher training modules on how to incorporate PhET simulations. Despite all the challenges, teachers believe PhET can enhance student learning, ensure student engagement and increase student interest in STEM Education. Considering the lack of science laboratory equipment, teachers recognized the potential of PhET as a supplement to hands-on activities for secondary science education in Bangladesh. They believed that if PhET develops more curriculum-relevant sims, it will bring revolutionary changes to how Bangladeshi students learn science. All the participating teachers in this study came from two organizations, and all the workshops took place in urban areas; therefore, the findings cannot be generalized to all secondary science teachers. A nationwide study is required to include teachers from diverse backgrounds. A further study can shed light on how building a professional learning community can lessen teachers’ challenges in incorporating PhET-enhanced pedagogy in their teaching.

Keywords: educational technology, inquiry-based learning, PhET interactive simulations, PhET-enhanced pedagogies, science education, science laboratory equipment, teacher professional development

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Authors: Lorna Martha Dreyer

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Consistent with the changing paradigm on the rights of people with disabilities and in pursuit of social justice, there is internationally an increase in students with disabilities enrolling at Higher Education Institutions (HEIs). This trend challenges HEI’s to transform and attain Education for All (EFA) as a global imperative. However, while physical and sensory disabilities are observable, students with specific learning disabilities (SLD) do not present with any visible indications and are often referred to as “hidden” or “invisible” disabilities. This qualitative case study aimed to illuminate the experiences of students with SLDs at a South African university. The research was, therefore, guided by Vygotsky’s social-cultural theory (SCT). This research was conducted within a basic qualitative research methodology embedded in an interpretive paradigm. Data was collected through an online background survey and semi-structured interviews. Thematic qualitative content analysis was used to analyse the collected data systematically. From a social justice perspective, the major findings suggest that there are several factors that impede equal education for students with SLDs at university. Most participants in this small-scale study experienced a lack of acknowledgment and support from lecturers. They reported valuing the support of family and friends more than that of lecturers. It is concluded that lecturers need to be reflective of their pedagogical practices if authentic inclusion is to be realised.

Keywords: higher education, inclusive education, pedagogy, social-cultural theory, specific learning disabilities

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Authors: Sharifah Mousli, Sona Taheri, Jiayuan He

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Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters.

Keywords: autism spectrum disorder, clustering, optimization, unsupervised machine learning

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Authors: Rashmi Motwani, Ritu Raj

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The emergence of online educational environments has changed the way adolescents learn, which has benefits and drawbacks for their development. This review has as its goal the examination of how personality traits and adolescents’ well-being are associated in the setting of online education. This review analyses the effects of a variety of personality traits on the mental, emotional, and social health of online school-going adolescents by looking at a wide range of previous research. This research explores the mechanisms that mediate or regulate the connection between one's personality traits and well-being in an online educational environment. The elements can be broken down into two categories: technological, like internet availability and digital literacy, and social, including social support, peer interaction, and teacher-student connections. To improve the well-being of adolescents in online learning environments, it is essential to understand factors that moderate the effects of interventions and support systems. This review concludes by emphasising the complex nature of the association between individual differences in personality and the success of online students aged 13 to 18. This review contributes to the development of evidence-based strategies for promoting positive mental health and overall well-being among adolescents engaged in online educational settings by shedding light on the impact of personality traits on various dimensions of well-being and by identifying the mediating or moderating factors. Educators, governments, and parents can use the findings of this review to create an online learning environment that is safe and well-being for adolescents.

Keywords: personality traits, adolescent, wellbeing, online education

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Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Oktawia Braniewicz

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The phenomenon of emigration and migration increasingly affects Poland's borders as well. For this reason, it is necessary to examine the level of protection of Human Rights in Polish Centres for Foreigners. The field study covered 11 centers for Foreigners in the provinces Kujawsko-Pomorskie Region, Lubelskie Region, Lodzkie Region, Mazowieckie Region and Podlaskie Region. Photographic documentation of living and social conditions, conversations with center employees and refugees allow to show a comprehensive picture of the situation prevailing in Centres for Foreigners. The object of reflection will be, in particular, the standards resulting from art. 8 and 13 of the Convention for the Protection of Human Rights and Fundamental Freedoms and article 2 of Protocol No. 1 to the Convention for the Protection of Human Rights and Fundamental Freedoms. The degree of realization of the right to education and the right to respect for family and private life will be shown. Issues related to learning the Polish language, access to a professional translator and psychological help will also be approximated. Learning Polish is not obligatory, which causes problems with assimilation and integration with other members of the new community. In centers for foreigners, there are no translators - a translator from an external company is rented if necessary. The waiting time for an interpreter makes the refugees feel anxious, unable to communicate with the employees of the centers (this is a situation in which the refugees do not know either English, Polish or Russian). Psychologist's help is available on designated days of the week. There is no separate specialist in child psychology, which is a serious problem.

Keywords: human rights, Polish centres, foreigners, fundamental freedoms

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Authors: Vathsala Kaluarachchi, Oshani Wimalarathna, Charith Vandebona, Gayani Chandrarathna, Lakmal Rupasinghe, Windhya Rankothge

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It is the fundamental function of a monitoring system to allow users to collect and process data. A worldwide threat, the corona outbreak has wreaked havoc in Sri Lanka, and the situation has gotten out of hand. Since the epidemic, the Sri Lankan government has been unable to establish a systematic system for monitoring corona patients and providing emergency care in the event of an outbreak. Most patients have been held at home because of the high number of patients reported in the nation, but they do not yet have access to a functioning medical system. It has resulted in an increase in the number of patients who have been left untreated because of a lack of medical care. The absence of competent medical monitoring is the biggest cause of mortality for many people nowadays, according to our survey. As a result, a smartphone app for analyzing the patient's state and determining whether they should be hospitalized will be developed. Using the data supplied, we are aiming to send an alarm letter or SMS to the hospital once the system recognizes them. Since we know what those patients need and when they need it, we will put up a desktop program at the hospital to monitor their progress. Deep learning, image processing and application development, natural language processing, and blockchain management are some of the components of the research solution. The purpose of this research paper is to introduce a mechanism to connect hospitals and patients even when they are physically apart. Further data security and user-friendliness are enhanced through blockchain and NLP.

Keywords: blockchain, deep learning, NLP, monitoring system

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Authors: Harvey Sanchez-Restrepo, Jorge Louca

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Equity and education are major focus of government policies around the world due to its relevance for addressing the sustainable development goals launched by Unesco. In this research, we developed a primary analysis of a data set of more than one hundred educational and non-educational factors associated with learning, coming from a census-based large-scale assessment carried on in Ecuador for 1.038.328 students, their families, teachers, and school directors, throughout 2014-2018. Each participating student was assessed by a standardized computer-based test. Learning outcomes were calibrated through item response theory with two-parameters logistic model for getting raw scores that were re-scaled and synthetized by a learning index (LI). Our objective was to develop a network for modelling educational deprivation and analyze the structure of inequality gaps, as well as their relationship with socioeconomic status, school financing, and student's ethnicity. Results from the model show that 348 270 students did not develop the minimum skills (prevalence rate=0.215) and that Afro-Ecuadorian, Montuvios and Indigenous students exhibited the highest prevalence with 0.312, 0.278 and 0.226, respectively. Regarding the socioeconomic status of students (SES), modularity class shows clearly that the system is out of equilibrium: the first decile (the poorest) exhibits a prevalence rate of 0.386 while rate for decile ten (the richest) is 0.080, showing an intense negative relationship between learning and SES given by R= –0.58 (p < 0.001). Another interesting and unexpected result is the average-weighted degree (426.9) for both private and public schools attending Afro-Ecuadorian students, groups that got the highest PageRank (0.426) and pointing out that they suffer the highest educational deprivation due to discrimination, even belonging to the richest decile. The model also found the factors which explain deprivation through the highest PageRank and the greatest degree of connectivity for the first decile, they are: financial bonus for attending school, computer access, internet access, number of children, living with at least one parent, books access, read books, phone access, time for homework, teachers arriving late, paid work, positive expectations about schooling, and mother education. These results provide very accurate and clear knowledge about the variables affecting poorest students and the inequalities that it produces, from which it might be defined needs profiles, as well as actions on the factors in which it is possible to influence. Finally, these results confirm that network analysis is fundamental for educational policy, especially linking reliable microdata with social macro-parameters because it allows us to infer how gaps in educational achievements are driven by students’ context at the time of assigning resources.

Keywords: complex network, educational deprivation, evidence-based policy, large-scale assessments, policy informatics

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Authors: Anh Duong

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The three-phase reading lesson has been put forth around the world as the new and innovative framework which is corresponding to the learner-centered trend in English language teaching and learning. Among three stages, pre-reading attracts many teachers’ and researchers’ attention for its vital role in preparing students with knowledge and interest in reading class. The researcher’s desire to exemplify effectiveness of activities prior to text reading has provoked the current study. Three main aspects were investigated in this paper, i.e. teachers’ and student’s perception of pre-reading stage, teachers’ exploitation of pre-reading techniques and teachers’ recommendation of effective pre-reading activities. Aiming at pre-reading techniques for first-year students at English Department, this study involved 200 fresh-men and 10 teachers from Division 1 to participate in the questionnaire survey. Interviews with the teachers and classroom observation were employed as a tool to take an insight into the responses gained from the early instrument. After a detailed procedure of analyzing data, the researcher discovered that thanks to the participants’ acclamation of pre-reading stage, this phase was frequently conducted by the surveyed teachers. Despite the fact that pre-reading activities apparently put a hand in motivating students to read and creating a joyful learning atmosphere, they did not fulfill another function as supporting students’ reading comprehension. Therefore, a range of techniques and notices when preparing and conducting pre-reading phase was detected from the interviewed teachers. The findings assisted the researcher to propose some related pedagogical implications concerning teachers’ source of pre-reading techniques, variations of suggested activities and first-year reading syllabus.

Keywords: pre-reading stage, pre-reading techniques, teaching reading, language teaching

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Authors: Bastien Batardière, Joon Kwon

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For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

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Authors: Jiraporn Prommaha

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The purpose of this research is to study the development of Thailand Brand Identity and the perception of its image in order to find any guidelines for the identity development and the image perception of Thailand Brand in Health and Wellness Tourism. The paper is conducted through mixed methods research, both the qualitative and quantitative researches. The qualitative focuses on the in-depth interview of executive administrations from public and private sectors involved scholars and experts in identity and image issue, main 11 people. The quantitative research was done by the questionnaires to collect data from foreign tourists 800; Chinese tourists 400 and UK tourists 400. The technique used for this was the Exploratory Factor Analysis (EFA), this was to determine the relation between the structures of the variables by categorizing the variables into group by applying the Varimax rotation technique. This technique showed recognition the Thailand brand image related to the 2 countries, China and UK. The results found that guidelines for brand identity development and image perception of health and wellness tourism in Thailand; as following (1) Develop communication in order to understanding of the meaning of the word 'Health and beauty tourism' throughout the country, (2) Develop human resources as a national agenda, (3) Develop awareness rising in the conservation and preservation of natural resources of the country, (4) Develop the cooperation of all stakeholders in Health and Wellness Businesses, (5) Develop digital communication throughout the country and (6) Develop safety in Tourism.

Keywords: brand identity, image perception, nation branding, health and wellness tourism, mixed methods research

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Authors: Jui-Chien Hsieh

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Background:12-lead electrocardiography(ECG) is one of frequently-used tools to detect atrial fibrillation (AF), which might degenerate into life-threaten stroke, in clinical Practice. Based on this study, the AF detection by the clinically-used 12-lead ECG device has only 0.73~0.77 positive predictive value (ppv). Objective: It is on great demand to develop a new algorithm to improve the precision of AF detection using 12-lead ECG. Due to the progress on artificial intelligence (AI), we develop an ECG deep model that has the ability to recognize AF patterns and reduce false-positive errors. Methods: In this study, (1) 570-sample 12-lead ECG reports whose computer interpretation by the ECG device was AF were collected as the training dataset. The ECG reports were interpreted by 2 senior cardiologists, and confirmed that the precision of AF detection by the ECG device is 0.73.; (2) 88 12-lead ECG reports whose computer interpretation generated by the ECG device was AF were used as test dataset. Cardiologist confirmed that 68 cases of 88 reports were AF, and others were not AF. The precision of AF detection by ECG device is about 0.77; (3) A parallel 4-layer 1 dimensional convolutional neural network (CNN) was developed to identify AF based on limb-lead ECGs and chest-lead ECGs. Results: The results indicated that this model has better performance on AF detection than traditional computer interpretation of the ECG device in 88 test samples with 0.94 ppv, 0.98 sensitivity, 0.80 specificity. Conclusions: As compared to the clinical ECG device, this AI ECG model promotes the precision of AF detection from 0.77 to 0.94, and can generate impacts on clinical applications.

Keywords: 12-lead ECG, atrial fibrillation, deep learning, convolutional neural network

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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