Search results for: learning table

Authors: Olanike O. Akinwale, Isiaka S. Adedoyin

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Revenue is a fund realized by the government to meet both current and capital expenditures. The study found out the various ways through which local governments in Nigeria generate revenue or obtain funds and determined whether the people of Boripe local government are paying tax as at when due and also evaluated how the revenue generated is being used by the local government. During the course of this study, research questionnaires were drafted and distributed to respondents in the local government secretariat who supplied the information needed to carry out the research work. Data were collected by using simple random sampling technique where members of the population have been given equal chance of being picked as a member of the sample. Data were analysed using chart table; the chart analyzed the figure of the past two years revenue and expenditure of the local government. It was deduced from the result that revenue generated but this was not up to what one expected for this local government to finance the projected expenditure when the size was considered, its location as well as its natural endowment of this local government. This was due to lack of cooperation of the people and staffs within the local government in the local government jurisdiction as well as fraudulent activities the revenue collectors engaged in. Revenue generation is a fuel for development in any organization whether public or private. The ability of revenue drive of Boripe was not strong enough since the targeted revenue from taxation was not enough to meet the projected expenditure for a particular year as in 2016, the difference was carried forward to the next year.

Keywords: appraisal, expenditure, local government, questionnaire, revenue

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Authors: Andrew Kipp

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Student absenteeism is harmful to both the school and the absentee student. One approach to improving student absenteeism is targeting contextual factors within the students’ learning environment. However, contemporary literature has not taken an ecological agency approach to understanding student absenteeism. Ecological agency is a theoretical framework that magnifies the interplay between the environment and the actions of people within the environment. To elaborate, the person’s personal history and aspirations and the environmental conditions provide potential outlets or restrictions to their intended action. The framework provides the unique perspective of understanding absentee students’ decision-making through the affordances and constraints found in their learning environment. To that effect, the study was guided by the question, “Why do absentee students decide to engage in absenteeism in a suburban Kansas school?” A case study methodology was used to answer the research question. Four suburban, Kansas high school absentee students in the 2020-2021 school year were selected for the study. The fall 2020 semester was in a remote learning setting, and the spring 2021 semester was in an in-person learning setting. The study captured their decision-making with respect to school attendance throughsemi-structured interviews, prolonged observations, drawings, and concept maps. The data was analyzed through thematic analysis. The findings revealed that peer socialization opportunities, methods of instruction, shifts in cultural beliefs due to COVID-19, manifestations of anxiety and lack of space to escape their anxiety, social media bullying, and the inability to receive academic tutoring motivated the participants’ daily decision to either attend or miss school. The findings provided a basis to improve several institutional and classroom practices. These practices included more student-led instruction and less teacher-led instruction in both in-person and remote learning environments, promoting socialization through classroom collaboration and clubs based on emerging student interests, reducing instances of bullying through prosocial education, safe spaces for students to escape the classroom to manage their anxiety, and more opportunities for one-on-one tutoring to improve grades. The study provides an example of using the ecological agency approach to better understand the personal and environmental factors that lead to absenteeism. The study also informs educational policies and classroom practices to better promote student attendance. Further research should investigate other school contexts using the ecological agency theoretical framework to better understand the influence of the school environment on student absenteeism.

Keywords: student absenteeism, ecological agency, classroom practices, educational policy, student decision-making

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Authors: Vishnu Goyal, Basant Agarwal

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Sentiment analysis research provides methods for identifying the people’s opinion written in blogs, reviews, social networking websites etc. Sentiment analysis is to understand what opinion people have about any given entity, object or thing. Sentiment analysis research can be broadly categorised into three types of approaches i.e. semantic orientation, machine learning and lexicon based approaches. Feature selection methods improve the performance of the machine learning algorithms by eliminating the irrelevant features. Information gain feature selection method has been considered best method for sentiment analysis; however, it has the drawback of selection of threshold. Therefore, in this paper, we propose a hybrid feature selection methods comprising of information gain and proposed feature selection method. Initially, features are selected using Information Gain (IG) and further more noisy features are eliminated using the proposed feature selection method. Experimental results show the efficiency of the proposed feature selection methods.

Keywords: feature selection, sentiment analysis, hybrid feature selection

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Authors: Ahlam Elkilani, Bayan Elsheikh Ali, Rasha Abu Romman, Amjed Al-mousa, Belal Sababha

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The amount of energy the world uses doubles every 20 years. Green homes play an important role in reducing the residential energy demand. This paper presents a platform that is intended to learn the behavior of home residents and build a profile about their habits and actions. The proposed resident aware home controller intervenes in the operation of home appliances in order to save energy without compromising the convenience of the residents. The presented platform can be used to simulate the actions and movements happening inside a home. The paper includes several optimization techniques that are meant to save energy in the home. In addition, several test scenarios are presented that show how the controller works. Moreover, this paper shows the computed actual savings when each of the presented techniques is implemented in a typical home. The test scenarios have validated that the techniques developed are capable of effectively saving energy at homes.

Keywords: green home, resident aware, resident profile, activity learning, machine learning

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Authors: Joko Nurkamto

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A lesson plan is a part of the planning phase in a learning and teaching system framing the scenario of pedagogical activities in the classroom. It informs a decision on what to teach and how to landscape classroom interaction. Regardless of these benefits, the writer has witnessed the fact that lesson plans are viewed merely as a teaching document. Therefore, this paper will explore teachers’ beliefs and practices in designing lesson plans. It focuses primarily on how both teachers and students negotiate lesson plans in which the students are deemed to be the agents of instructional innovations. Additionally, the paper will talk about how such lesson plans are enacted. To investigate these issues, document analysis, in-depth interviews, participant classroom observation, and focus group discussion will be deployed as data collection methods in this explorative case study. The benefits of the paper are to show different roles of lesson plans and to discover different ways to design and enact such plans from a socio-interactional perspective.

Keywords: instructional innovation, learning and teaching system, lesson plan, pedagogical activities, teachers' beliefs and practices

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Authors: Jingwen Ou

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Writing for academic purposes in a second or foreign language poses a significant challenge for non-native speakers, particularly at the tertiary level, where English academic writing for L2 students is often hindered by difficulties in academic discourse, including vocabulary, academic register, and organization. The past two decades have witnessed a rising popularity in the application of the data-driven learning (DDL) approach in EAP writing instruction. In light of such a trend, this study aims to enhance the integration of DDL into English for academic purposes (EAP) writing classrooms by investigating the perception of Chinese college students regarding the use of corpus tools for improving EAP writing. Additionally, the research explores their corpus consultation behaviors during training to provide insights into corpus-assisted EAP instruction for DDL practitioners. Given the uprising popularity of DDL, this research aims to investigate Chinese university students’ use of corpus tools with three main foci: 1) the influence of corpus tools on learning behaviours, 2) the influence of corpus tools on students’ academic writing performance outcomes, and 3) students’ perceptions and potential perceptional changes towards the use of such tools. Three corpus tools, CQPWeb, Sketch Engine, and LancsBox X, are selected for investigation due to the scarcity of empirical research on patterns of learners’ engagement with a combination of multiple corpora. The research adopts a pre-test / post-test design for the evaluation of students’ academic writing performance before and after the intervention. Twenty participants will be divided into two groups: an intervention and a non-intervention group. Three corpus training workshops will be delivered at the beginning, middle, and end of a semester. An online survey and three separate focus group interviews are designed to investigate students’ perceptions of the use of corpus tools for improving academic writing skills, particularly the rhetorical functions in different essay sections. Insights from students’ consultation sessions indicated difficulties with DDL practice, including insufficiency of time to complete all tasks, struggle with technical set-up, unfamiliarity with the DDL approach and difficulty with some advanced corpus functions. Findings from the main study aim to provide pedagogical insights and training resources for EAP practitioners and learners.

Keywords: corpus linguistics, data-driven learning, English for academic purposes, tertiary education in China

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Authors: Omid Noroozi, Martin Mulder

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The motivational potential of educational games is undeniable especially for teaching topics and skills that are difficult to deal with in traditional educational situations such as argumentation competence. Willingness to argue has an association with student epistemic beliefs, which can influence whether, and the way in which students engage in argumentative discourse activities and critical discussion. The goal of this study was to explore how undergraduate students engage with argumentative discourse activities which have been designed to intensify debate, and whether epistemic beliefs are significant to the outcomes. A pre-test, post-test design was used with students who were assigned to groups of four. They were asked to argue a controversial topic with the aim of exploring various perspectives, and the 'pros and cons' on the topic of 'Genetically Modified Organisms (GMOs)'. The results show that the game facilitated argumentative discourse and a willingness to argue and challenged peers, regardless of students’ epistemic beliefs. Furthermore, the game was evaluated positively in terms of students’ motivation and satisfaction with the learning experience.

Keywords: argumentation, attitudinal change, epistemic beliefs, dialogue, digital game objectives and theoretical

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Authors: Angela Mathias Kavishe

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Sexual harassment directed at women is reported in many societies, including in Tanzania. The advent of ICT technology, especially in universities, seems to aggravate the situation by extending harassment to cyberspace in various forms, including cyberstalking. Evidence shows that online violence is more dangerous than physical one due to the ability to access multiple private information, attack many victims, mask the perpetrator's identity, suspend the threat for a long time and spread over time and space. The study aimed to measure the magnitude of cyber harassment in Tanzanian higher learning institutions and to assess institutional sensitivity to ICT-mediated gender-based violence. It was carried out in 4 higher learning institutions in Tanzania: Mwalimu Nyerere Memorial Academy and Institute of Finance Management in Dar es Salaam and SAUT, and the University of Dodoma, where a survey questionnaire was distributed to 400 students and 40 key informants were interviewed. It was found that in each institution, the majority of female students experienced online harassment on social media perpetrated by ex-partners, male students, and university male teaching staff. The perpetrators compelled the female students to post nude pictures, have sexual relations with them, or utilize the posted private photographs to force female students to practice online or offline sexual relations. These threats seem to emanate from social-cultural beliefs about the subordinate position of women in society and that women's bodies are perceived as sex objects. It is therefore concluded that cyberspace provides an alternative space for perpetrators to exercise violence towards women.

Keywords: cyberstalking, embodiment, gender-based violence, internet

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Authors: David Crighton, Kelly Smith

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The ability to adopt creative pedagogical approaches is increasingly vital in today’s educational landscape. This study examines the institutional barriers that hinder educators, in the UK, from embracing such innovation, focusing specifically on the experiences and perspectives of educational leaders. Current literature primarily focuses on the challenges that academics and teachers encounter, particularly highlighting how management culture and audit processes negatively affect their ability to be creative in classrooms and lecture theatres. However, this focus leaves a gap in understanding management perspectives, which is crucial for providing a more holistic insight into the challenges encountered in educational settings. To explore this gap, we are conducting semi-structured interviews with senior leaders across various educational contexts, including universities, schools, and further education colleges. This qualitative methodology, combined with thematic analysis, aims to uncover the managerial, financial, and administrative pressures these leaders face in fostering creativity in teaching and supporting professional learning opportunities. Preliminary insights indicate that educational leaders face significant barriers, such as institutional policies, resource limitations, and external performance indicators. These challenges create a restrictive environment that stifles educators' creativity and innovation. Addressing these barriers is essential for empowering staff to adopt more creative pedagogical approaches, ultimately enhancing student engagement and learning outcomes. By alleviating these constraints, educational leaders can cultivate a culture that fosters creativity and flexibility in the classroom. These insights will inform practical recommendations to support institutional change and enhance professional learning opportunities, contributing to a more dynamic educational environment. In conclusion, this study offers a timely exploration of how leadership can influence the pedagogical landscape in a rapidly evolving educational context. The research seeks to highlight the crucial role that educational leaders play in shaping a culture of creativity and adaptability, ensuring that institutions are better equipped to respond to the challenges of contemporary education.

Keywords: educational leadership, professional learning, creative pedagogy, marketisation

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Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

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Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

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Authors: Hediye Saglam

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This research has been carried out to bring in the 6 acquisitions in the 2014 Preschool Teaching Programme of the Turkish Ministry of Education through the method of analogy. This research is practiced based on the experimental pattern with pre-test and final test controlling groups. The working group of the study covers the group between 5-6 ages. The study takes 5 weeks including the 2 weeks spent for pre-test and the final test. It is conducted with the preschool teacher who gives the lesson along with the researcher in the in-class and out-of-class rainbow experiments of the students for 5 weeks. 'One Sample T Test' is used for the evaluation of the pre-test and final test. SPSS 17 programme is applied for the analysis of the data. Results: As an outcome of the study it is observed that analogy method affects children’s learning of the rainbow. For this very reason teachers should receive inservice training for different methods and techniques like analogy. This method should be included in preschool education programme and should be applied by teachers more often.

Keywords: acquisitions of preschool education programme, analogy method, pre-test/final test, rainbow experiments

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Authors: Piedad Garrido, Fernando Repulles, Andy Bloor, Julio A. Sanguesa, Jesus Gallardo, Vicente Torres, Jesus Tramullas

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With the emergence and development of Information and Communications Technologies (ICTs), Higher Education is experiencing rapid changes, not only in its teaching strategies but also in student’s learning skills. However, we have noticed that students often have difficulty when seeking innovative, useful, and interesting learning resources for their work. This is due to the lack of supervision in the selection of good query tools. This paper presents AINA, an Information Retrieval (IR) computer system aimed at providing motivating and stimulating content to both students and teachers working on different areas and at different educational levels. In particular, our proposal consists of an open virtual resource environment oriented to the vast universe of Disney comics and cartoons. Our test suite includes Disney’s long and shorts films, and we have performed some activities based on the Just In Time Teaching (JiTT) methodology. More specifically, it has been tested by groups of university and secondary school students.

Keywords: information retrieval, animation, educational resources, JiTT

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Authors: Ioanna Taouki, Marie Lallier, David Soto

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Metacognition refers to the capacity to reflect upon our own cognitive processes. Although there is an ongoing discussion in the literature on the role of metacognition in learning and academic achievement, little is known about its neurodevelopmental trajectories in early childhood, when children begin to receive formal education in reading. Here, we evaluate the metacognitive ability, estimated under a recently developed Signal Detection Theory model, of a cohort of children aged between 6 and 7 (N=60), who performed three two-alternative-forced-choice tasks (two linguistic: lexical decision task, visual attention span task, and one non-linguistic: emotion recognition task) including trial-by-trial confidence judgements. Our study has three aims. First, we investigated how metacognitive ability (i.e., how confidence ratings track accuracy in the task) relates to performance in general standardized tasks related to students' reading and general cognitive abilities using Spearman's and Bayesian correlation analysis. Second, we assessed whether or not young children recruit common mechanisms supporting metacognition across the different task domains or whether there is evidence for domain-specific metacognition at this early stage of development. This was done by examining correlations in metacognitive measures across different task domains and evaluating cross-task covariance by applying a hierarchical Bayesian model. Third, using robust linear regression and Bayesian regression models, we assessed whether metacognitive ability in this early stage is related to the longitudinal learning of children in a linguistic and a non-linguistic task. Notably, we did not observe any association between students’ reading skills and metacognitive processing in this early stage of reading acquisition. Some evidence consistent with domain-general metacognition was found, with significant positive correlations between metacognitive efficiency between lexical and emotion recognition tasks and substantial covariance indicated by the Bayesian model. However, no reliable correlations were found between metacognitive performance in the visual attention span and the remaining tasks. Remarkably, metacognitive ability significantly predicted children's learning in linguistic and non-linguistic domains a year later. These results suggest that metacognitive skill may be dissociated to some extent from general (i.e., language and attention) abilities and further stress the importance of creating educational programs that foster students’ metacognitive ability as a tool for long term learning. More research is crucial to understand whether these programs can enhance metacognitive ability as a transferable skill across distinct domains or whether unique domains should be targeted separately.

Keywords: confidence ratings, development, metacognitive efficiency, reading acquisition

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Authors: Shima Nikanjam, Badiossadat Hassanpour, Adi Irfan Che Ani

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The design process in architecture education is based upon the Learning-by-Doing method, which leads students to understand how to design by practicing rather than studying. First-year design studios, as starting educational stage, provide integrated knowledge and skills of design for newly jointed architecture students. Within the basic design studio environment, students are guided to transfer their abstract thoughts into visual concrete decisions under the supervision of design educators for the first time. Therefore, introductory design studios have predominant impacts on students’ operational thinking and designing. Architectural design thinking is quite different from students’ educational backgrounds and learning habits. This educational challenge at basic design studios creates a severe need to study the reality of design education at foundation year and define appropriate educational methods with convenient project types with the intention of enhancing architecture education quality. Material for this study has been gathered through long-term direct observation at a first year second semester design studio at the faculty of architecture at EMU (known as FARC 102), fall and spring academic semester 2014-15. Distribution of a questionnaire among case study students and interviews with third and fourth design studio students who passed through the same methods of education in the past 2 years and conducting interviews with instructors are other methodologies used in this research. The results of this study reveal a risk of a mismatch between the implemented teaching method, project type and scale in this particular level and students’ learning styles. Although the existence of such risk due to varieties in students’ profiles could be expected to some extent, recommendations can support educators to reach maximum compatibility.

Keywords: architecture education, basic design studio, educational method, forms creation skill

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Authors: Zakaria Kharbouch, Mustapha Bouchaara, F. Elkouihen, A. Habbal, A. Ratnani, A. Faik

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Solid-state lithium-ion batteries have garnered increasing interest in modern energy research due to their potential for safer, more efficient, and sustainable energy storage systems. Among the critical components of these batteries, the electrolyte plays a pivotal role, with LLZO garnet-based electrolytes showing significant promise. Garnet materials offer intrinsic advantages such as high Li-ion conductivity, wide electrochemical stability, and excellent compatibility with lithium metal anodes. However, optimizing ionic conductivity in garnet structures poses a complex challenge, primarily due to the multitude of potential dopants that can be incorporated into the LLZO crystal lattice. The complexity of material design, influenced by numerous dopant options, requires a systematic method to find the most effective combinations. This study highlights the utility of machine learning (ML) techniques in the materials discovery process to navigate the complex range of factors in garnet-based electrolytes. Collaborators from the materials science and ML fields worked with a comprehensive dataset previously employed in a similar study and collected from various literature sources. This dataset served as the foundation for an extensive data refinement phase, where meticulous error identification, correction, outlier removal, and garnet-specific feature engineering were conducted. This rigorous process substantially improved the dataset's quality, ensuring it accurately captured the underlying physical and chemical principles governing garnet ionic conductivity. The data refinement effort resulted in a significant improvement in the predictive performance of the machine learning model. Originally starting at an accuracy of 0.32, the model underwent substantial refinement, ultimately achieving an accuracy of 0.88. This enhancement highlights the effectiveness of the interdisciplinary approach and underscores the substantial potential of machine learning techniques in materials science research.

Keywords: lithium batteries, all-solid-state batteries, machine learning, solid state electrolytes

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: C. Zimmermann, E. Lackner, M. Ebner

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Massive Open Online Courses (MOOCs) are increasingly popular learning hubs that are boasting considerable participant numbers, innovative technical features, and a multitude of instructional resources. Still, there is a high level of evidence showing that almost all MOOCs suffer from a declining frequency of participant activity and fairly low completion rates. In this paper, we would like to share the lessons learned in implementing several design patterns that have been suggested in order to foster participant activity. Our conclusions are based on experiences with the ‘Dr. Internet’ MOOC, which was created as an xMOOC to raise awareness for a more critical approach to online health information: participants had to diagnose medical case studies. There is a growing body of recommendations (based on Learning Analytics results from earlier xMOOCs) as to how the decline in participant activity can be alleviated. One promising focus in this regard is instructional design patterns, since they have a tremendous influence on the learner’s motivation, which in turn is a crucial trigger of learning processes. Since Medieval Age storytelling, micro-learning units and specific comprehensible, narrative structures were chosen to animate the audience to follow narration. Hence, MOOC participants are not likely to abandon a course or information channel when their curiosity is kept at a continuously high level. Critical aspects that warrant consideration in this regard include shorter course duration, a narrative structure with suspense peaks (according to the ‘storytelling’ approach), and a course schedule that is diversified and stimulating, yet easy to follow. All of these criteria have been observed within the design of the Dr. Internet MOOC: 1) the standard eight week course duration was shortened down to six weeks, 2) all six case studies had a special quiz format and a corresponding resolution video which was made available in the subsequent week, 3) two out of six case studies were split up in serial video sequences to be presented over the span of two weeks, and 4) the videos were generally scheduled in a less predictable sequence. However, the statistical results from the first run of the MOOC do not indicate any strong influences on the retention rate, so we conclude with some suggestions as to why this might be and what aspects need further consideration.

Keywords: case study, Dr. internet, experience, MOOCs, design patterns

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

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Authors: Claudia Martínez-Araneda, Mariella Gutiérrez, Pedro Gómez, Diego Maldonado, Alejandra Segura, Christian Vidal-Castro

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The purpose of this document is to showcase the preliminary work in developing an EduChatbot-type tool and measuring the effects of its use aimed at providing effective feedback to students in programming courses. This bot, hereinafter referred to as tutorBot+, was constructed based on chatGPT and is tasked with assisting and delivering timely positive feedback to students in the field of computer science at the Universidad Católica de Concepción. The proposed working method consists of four stages: (1) Immersion in the domain of Large Language Models (LLMs), (2) Development of the tutorBot+ prototype and integration, (3) Experiment design, and (4) Intervention. The first stage involves a literature review on the use of artificial intelligence in education and the evaluation of intelligent tutors, as well as research on types of feedback for learning and the domain of chatGPT. The second stage encompasses the development of tutorBot+, and the final stage involves a quasi-experimental study with students from the Programming and Database labs, where the learning outcome involves the development of computational thinking skills, enabling the use and measurement of the tool's effects. The preliminary results of this work are promising, as a functional chatBot prototype has been developed in both conversational and non-conversational versions integrated into an open-source online judge and programming contest platform system. There is also an exploration of the possibility of generating a custom model based on a pre-trained one tailored to the domain of programming. This includes the integration of the created tool and the design of the experiment to measure its utility.

Keywords: assessment, chatGPT, learning strategies, LLMs, timely feedback

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Yaxin Bi

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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Tracy Smith-Carrier

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Burgeoning research extols the benefits of innovative experiential learning activities to increase participants’ engagement, enhance their individual learning, and bridge the gap between theory and practice. This presentation discusses findings from a multiple case study on poverty simulation events conducted with two samples: undergraduate students and community participants. After exploring the nascent research on the benefits and limitations of poverty simulation activities, the study explores whether participating in a poverty simulation resulted in changes to participants’ beliefs about the causes and effects of poverty, as well as shifts in their attitudes and actions toward people experiencing poverty. For the purposes of triangulation, quantitative and qualitative data from a variety of sources were analyzed: participant feedback surveys, qualitative responses, and pre, post, and follow-up questionnaires. Findings show statistically significant results (p<.05) from both samples on cumulative scores of the modified Attitudes Toward Poverty Scale, indicating an improvement in participants’ attitudes toward poverty. Although generally positive about their experiences, participating in the simulation did not appear to have prompted participants to take specific actions to reduce poverty. Conclusions drawn from the research study suggest that poverty simulation planners should be wary of adopting scenarios that emphasize, or fail to adequately contextualize, behaviours or responses that might perpetuate individual explanations of poverty. Moreover, organizers must carefully consider how to ensure participants in their audience currently experiencing low-income do not become emotionally distressed, triggered or further marginalized in the process. While overall participants were positive about their experiences in the simulation, the events did not appear to have prompted them to action. Moving beyond the goal of increasing participants’ understandings of poverty, interventions that foster greater engagement in poverty issues over the long-term are necessary.

Keywords: empathy, experiential learning, poverty awareness, poverty simulation

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Authors: Igbozuruike Chigozie Jude, Agwu Agwu Ejem

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This study investigated that advertisement has an impact on audience retention on YouTube Comedy skits, which is the most watched content on YouTube in Lagos, Nigeria. The main objective was to determine if the advertisements affect the average number of times they spend watching YouTube comedy skits. The study was anchored on Festinger's (1952) cognitive dissonance theory. The research method for this exercise was a survey to get responses from people in Lagos state on how they react to the advertisements they face when watching YouTube comedy skits in Lagos state. The sample size derived from the Krejcie and Morgan (1970) Table was 384 YouTube users. The instrument that was used to gather data was a questionnaire. The findings showed that the adverts have far-reaching exposure by the target audience, but most of the audience perceived them to be intrusive. It was also found that there is not enough evidence to infer that advertisement is indeed impacting audience retention on YouTube comedy skits in Lagos, Nigeria. The reason is that, for a majority of the audience, adverts do not essentially affect their retention on those skits, but for a considerable percentage (34%), these adverts do break their concentration and affect how much time they end up spending on the YouTube comedy skits. It was recommended that, among others, there should be regular monitoring and adaptation of YouTube advertisements to the audience preferences and behaviors of the audience. Insights on changes or trends in audience preferences can be gained through surveys.

Keywords: advertisement, audience, YouTube, comedy skits, Lagos Nigeria

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Authors: Václav Maněna, Roman Dostál, Štěpán Hubálovský

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Social networks play an important role in the lives of children and young people. Along with the high penetration of mobile technologies such as smartphones and tablets among the younger generation, there is an increasing use of social networks already in elementary school. The paper presents the results of research, which was realized at schools in the Hradec Králové region. In this research, the authors focused on issues related to communications on social networks for children, teenagers and young people in the Czech Republic. This research was conducted at selected elementary, secondary and high schools using anonymous questionnaires. The results are evaluated and compared with the results of the research, which has been realized in 2008. The authors focused on the possibilities of using social networks in education. The paper presents the possibility of using the most popular social networks in education, with emphasis on increasing motivation for learning. The paper presents comparative analysis of social networks, with regard to the possibility of using in education as well.

Keywords: social networks, motivation, e-learning, mobile technology

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Gianni Jacucci

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Epistemology traditionally finds its place within human research philosophies and methodologies. Artificial intelligence methods pose challenges, particularly given the unresolved relationship between AI and pivotal concepts in social arenas such as hermeneutics and accountability. We begin by examining the essential criteria governing scientific rigor in the human sciences. We revisit the three foundational philosophies underpinning qualitative research methods: empiricism, hermeneutics, and phenomenology. We elucidate the distinct attributes, merits, and vulnerabilities inherent in the methodologies they inspire. The integration of AI, e.g., deep learning algorithms, sparks an interest in evaluating these criteria against the diverse forms of AI architectures. For instance, Interpreted AI could be viewed as a hermeneutic approach, relying on a priori interpretations, while straight AI may be perceived as a descriptive phenomenological approach, processing original and uncontaminated data. This paper serves as groundwork for such explorations, offering preliminary reflections to lay the foundation and outline the initial landscape.

Keywords: artificial intelligence, deep learning, epistemology, qualitative research, methodology, hermeneutics, accountability

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Authors: I. O. Oladipo, Olusegun Adewale Ajayi, Omoniyi Oladipupo Adigun

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Information Communication Technology (ICT) have the potential to contribute to different facets of educational development and effective learning; expanding access, promoting efficiency, improve the quality of learning, enhancing the quality of teaching and provide important mechanism for the economic crisis. Considering the prevalence of unemployment among the higher institution graduates in this nation, in which much seems not to have been achieved in this direction. In view of this, the purpose of this study is to create an awareness and enlightenment of ICT for empowerment opportunities after school. A self-developed modified 4-likert scale questionnaire was used for data collection among Colleges of Education, Physical and Health Education students in Oyo State. Inferential statistical analysis of chi-square set at 0.05 alpha levels was used to analyze the stated hypotheses. The study concludes that awareness and enlightenment of ICT significantly influence empowerment opportunities and recommended that college of education students should be encouraged on the application of ICT for job opportunity after school.

Keywords: employment, empowerment, information communication technology, physical education

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Authors: Praveen Huded, Suresh Dash

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Pile foundations are one of the most preferred deep foundation system for high rise or heavily loaded structures. In many instances, the failure of the pile founded structures in liquefiable soils had been observed even in many recent earthquakes. Recent centrifuge and shake table experiments on two layered soil system have credibly shown that failure of pile foundation can occur because of buckling, as the pile behaves as an unsupported slender structural element once the surrounding soil liquefies. However the buckling capacity depends on largely on the depth of soil liquefied and its residual strength. Hence it is essential to check the pile against the possible buckling failure. Beam on non-linear Winkler Foundation is one of the efficient method to model the pile-soil behavior in liquefiable soil. The pile-soil interaction is modelled through p-y springs, different author have proposed different types of p-y curves for the liquefiable soil. In the present paper the influence two such p-y curves on the buckling capacity of pile foundation is studied considering initial geometric and non-linear behavior of pile foundation. The proposed method is validated against experimental results. Significant difference in the buckling capacity is observed for the two p-y curves used in the analysis. A parametric study is conducted to understand the influence of pile diameter, pile flexural rigidity, different initial geometric imperfections, and different soil relative densities on buckling capacity of pile foundation.

Keywords: Pile foundation , Liquefaction, Buckling load, non-linear py curve, Opensees

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Authors: Umamaheswari Shanmugam, Silvia Ronchi, Radu Vornicu

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that are able to use the large amount and variety of data generated during healthcare services every day. As we read the news, over 500 machine learning or other artificial intelligence medical devices have now received FDA clearance or approval, the first ones even preceding the year 2000. One of the big advantages of these new technologies is the ability to get experience and knowledge from real-world use and to continuously improve their performance. Healthcare systems and institutions can have a great benefit because the use of advanced technologies improves the same time efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and also to protect patients’ safety. The evolution and the continuous improvement of software used in healthcare must take into consideration the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device approval, but they are necessary to ensure performance, quality, and safety, and at the same time, they can be a business opportunity if the manufacturer is able to define in advance the appropriate regulatory strategy. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems.

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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