Search results for: learning science

Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Chad Goldsworthy, B. Rajeswari Matam

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The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.

Keywords: convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation

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Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Olaf Visker, Arnout De Vries, Lambert Schomaker

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Predictive policing refers to the usage of analytical techniques to identify potential criminal activity. It has been widely implemented by various police departments. Being a relatively new area of research, there are, to the author’s knowledge, no absolute tried, and true methods and they still exhibit a variety of potential problems. One of those problems is closely related to the lack of understanding of how acting on these prediction influence crime itself. The goal of law enforcement is ultimately crime reduction. As such, a policy needs to be established that best facilitates this goal. This research aims to find such a policy by using adversarial agent-based modeling in combination with modern reinforcement learning techniques. It is presented here that a baseline model for both law enforcement and criminal agents and compare their performance to their respective reinforcement models. The experiments show that our smart law enforcement model is capable of reducing crime by making more deliberate choices regarding the locations of potential criminal activity. Furthermore, it is shown that the smart criminal model presents behavior consistent with popular crime theories and outperforms the baseline model in terms of crimes committed and time to capture. It does, however, still suffer from the difficulties of capturing long term rewards and learning how to handle multiple opposing goals.

Keywords: adversarial, agent based modelling, predictive policing, reinforcement learning

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Authors: Kanu Priya Mohan

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Innovation in education is one of the essential pathways to achieve both educational, and development goals in today’s dynamically changing world. Over the last decade, coaching and mentoring have been applied in the field of education as positive intervention techniques for fostering teaching and learning reforms in the developed countries. The context of this research was Thailand’s educational reform process, wherein a project on coaching and mentoring (C&M) was launched in 2014. The C&M project endeavored to support the professional development of the school teachers in the various provinces of Thailand, and to also enable them to apply C&M for teaching innovative instructional techniques. This research aimed to empirically investigate the learning outcomes for the master trainers, who trained for coaching and mentoring as the first step in the process to train the school teachers. A mixed method study was used for evaluating the learning outcomes of training in terms of cognitive- behavioral-affective dimensions. In the first part of the research a quantitative research design was incorporated to evaluate the effects of learner characteristics and instructional techniques, on the learning outcomes. In the second phase, a qualitative method of in-depth interviews was used to find details about the training outcomes, as well as the perceived barriers and enablers of the training process. Sample size constraints were there, yet these exploratory results, integrated from both methods indicated the significance of evaluating training outcomes from the three dimensions, and the perceived role of other factors in the training. Findings are discussed in terms of their implications for the training of C&M, and also their impact in fostering positive education through innovative educational techniques in the developing countries.

Keywords: cognitive-behavioral-affective learning outcomes, mixed method research, teachers in Thailand, training evaluation

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Nik Siti Hanifah Nik Ahmad

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This research discuss about teaching cupping therapy or hijama by using an online 3D Virtual Learning Environment. The experimental platform was using of flash and Second Life as 2D and 3D comparison. 81 samples have been used in three experiments with 21 in the first and 30 in each second and third. The design of the presentation was tested in five categories such as effectiveness, ease of use, efficacy, aesthetic and users’ satisfaction. The results from three experiments had shown promising outcome for usage of the technique to be implement in teaching Cupping Therapy as well as other alternative or conventional medicine knowledge especially for training.

Keywords: medical and health, cupping therapy or hijama, second life, online 3D VLE, virtual worlds

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Authors: Abdi Omar Shuriye

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A social business is a business designed to address socio-economic problems to enhance the welfare of the communities involved. Lately, social business, with its focus on innovative ideas, is capturing the interest of educational institutions, governments, and non-governmental organizations. Social business uses a business model to achieve a social goal, and in the last few decades, the idea of imbuing social business into the education system of higher learning institutions has spurred much excitement. This is due to the belief that it will lead to job creation and increased social resilience. One of the higher learning institutions which have invested immensely in the idea is Albukhari International University; it is a private education institution, on a state-of-the-art campus, providing an advantageous learning ecosystem. The niche area of this institution is social business, and it graduates job creators, not job seekers; this Malaysian institution is unique and one of its kind. The objective of this paper is to develop a work plan, direction, and milestone as well as the focus area for the infusion of social business into higher learning institutions with special reference to Al-Bukhari International University. The purpose is to develop a prototype and model full-scale to enable higher learning education institutions to construct the desired curriculum fermented with social business. With this model, major predicaments faced by these institutions could be overcome. The paper sets forth an educational plan and will spell out the basic tenets of social business, focusing on the nature and implementational aspects of the curriculum. It will also evaluate the mechanisms applied by these educational institutions. Currently, since research in this area remains scarce, institutions adopt the process of experimenting with various methods to find the best way to reach the desired result on the matter. The author is of the opinion that social business in education is the main tool to educate holistic future leaders; hence educational institutions should inspire students in the classroom to start up their own businesses by adopting creative and proactive teaching methods. This proposed model is a contribution in that direction.

Keywords: social business, curriculum, skills, university

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Authors: Davoud Masoumi, Maryam Bourbour

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By characterizing children’s actions in the scaffolding process, which is often undermined and ignored in the studies reviewed, this study aimed to examine children’s different (re)actions in relation to the teachers’ actions in a context where digital technologies are used. Over five months, 22 children aged 4-6 with five preschool teachers were video observed. The study brought in rich details of the children’s actions in relation to the teacher’s actions in the scaffolding process. The findings of the study reveal thirteen (re)actions, including Giving short response; Explaining; Participating in the activities; Examining; Smiling and laughing; Pointing and showing; Working together; Challenging each other; Problem-solving skills; Developing vocabulary; Choosing the activity; Expressing of the emotions; and Identifying the similarities and differences. Our findings expanded and deepened the understanding of the scaffolding process, which can contribute to the notion of scaffolding and help us to gain further understanding about scaffolding of children’s learning. Characterizing the children’s (re)action in relation to teacher’s scaffolding actions further can contribute to ongoing discussions about how teachers can scaffold children’s learning using digital technologies in the learning process.

Keywords: children’ (re)actions, scaffolding process, technologies, preschools

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Authors: Daniel Bulanda, Janusz A. Starzyk, Adrian Horzyk

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The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms.

Keywords: characteristic points, electrocardiogram, ECG, machine learning, signal compression

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Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom

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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.

Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN

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Authors: Ahmed Abdulla Ashlam, Atta Badii, Frederic Stahl

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In recent years, SQL injection attacks have been identified as being prevalent against web applications. They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into "SQLi" or "Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method Web-App auto-generated twin data structure replication. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as "Non-SQLi" has been developed. A special login form has been developed with a special instance of data validation; this verification process secures the web application from its early stages. The system has been tested and validated, up to 99% of SQLi attacks have been prevented.

Keywords: SQL injection, attacks, web application, accuracy, database

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Authors: Betty Kibirige

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Students entering the University of Zululand (UNIZULU) Science Faculty mostly come with skills that allow them to prepare for exams and pass them in order to satisfy requirements for entry into a tertiary Institution. Some students hail from deep rural schools with limited facilities, while others come from well-resourced schools. Personal experience has shown that it may take a student the whole time at a tertiary institution following the same skills as those acquired in high school as a sure means of entering the next level in their development, namely a postgraduate program. While it is apparent that at this point in human history, it is totally impossible to teach all the possible content in any one subject, many academics approach teaching and learning from the traditional point of view. It therefore became apparent to explore ways of developing a graduate that will be able to approach life with skills that allows them to navigate knowledge by applying conceptual and critical thinking skills. Recently, the Science Faculty at the University of Zululand introduced two Engineering programs. In an endeavour to approach the development of the Engineering graduate in this institution to be able to tackle problem-solving in the present-day excessive information availability, it became necessary to study and review approaches used by various academics in order to settle for a possible best approach to the challenge at hand. This paper focuses on the development of a deep rural child in a graduate with conceptual and critical thinking skills as major attributes possessed upon graduation. For this purpose, various approaches were studied. A combination of these approaches was repackaged to form an approach that may appear novel to UNIZULU and the rural child, especially for the Engineering discipline. The approach was checked by offering quiz questions to students participating in an engineering module, observing test scores in the targeted module and make comparative studies. Test results are discussed in the article. It was concluded that students’ graduate attributes could be tailored subconsciously to indeed include conceptual and critical thinking skills, but through more than one approach depending mainly on the student's high school background.

Keywords: graduate attributes, conceptual skills, critical thinking skills, traditional approach

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Authors: Geoffrey Sinha

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This paper is primarily addressed to teachers who stand on the threshold of bringing technology and new media into their classrooms. Technology and new media, such as smart phones and tablets have changed the face of communication in general and of language teaching more specifically. New media has widespread appeal among young people in particular, so it is in the teacher’s best interests to bring new media into their lessons. It is the author’s firm belief that technology will never replace the teacher, but it is without question that the twenty-first century teacher must employ technology and new media in some form, or run the risk of failure. The level that one chooses to incorporate new media within their class is entirely in their hands.

Keywords: new media, social media, technology, education, language learning

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Authors: William Xiu Shun Wong, Myungsu Lim, Yoonjin Hyun, Chen Liu, Seongi Choi, Dasom Kim, Kee-Young Kwahk, Namgyu Kim

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Recently, many users have begun to frequently share their opinions on diverse issues using various social media. Therefore, numerous governments have attempted to establish or improve national policies according to the public opinions captured from various social media. In this paper, we indicate several limitations of the traditional approaches to analyze public opinion on science and technology and provide an alternative methodology to overcome these limitations. First, we distinguish between the science and technology analysis phase and the social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we successively apply a start list and a stop list to acquire clarified and interesting results. Finally, to identify the most appropriate documents that fit with a given subject, we develop a new logical filter concept that consists of not only mere keywords but also a logical relationship among the keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discover core issues and public opinions from 1,700,886 documents comprising SNS, blogs, news, and discussions.

Keywords: big data, social network analysis, text mining, topic modeling

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Authors: Odigie Veronica

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The paper focused on the application of Information and Communication Technology (ICT) in effective service delivery in view of the ongoing COVID-19 experience. It adopted the exploratory research method with three research objectives captured. Consequently, the objectives were to ascertain the meaning of online education, understand the concept of COVID-19 and to determine the relevance of online education in effective service delivery in institutions of learning. It is evident from the findings that through ICT, online mode of learning can be adopted in schools which helps greatly in promoting continual education. Online mode of education is practiced online; it brings both the teacher and learners from different places together, without any physical boundary/contact (at least 75%); and has helped greatly in human development in countries where it has been practiced. It is also a welcome development owing to its many benefits such as exposure to digital learning, having access to works of great teachers and educationists such as Socrates, Plato, Dewey, R.S. Peters, J. J. Rosseau, Nnamdi Azikwe, Carol Gilligan, J. I. Omoregbe, Jane Roland Martin, Jean Piaget, among others; and the facilitation of uninterrupted learning for class promotion and graduation of students. Developing the learners all round is part of human development which helps in developing a nation. These and many more are some benefits online education offers which make ICT very relevant in our contemporary society

Keywords: online education, COVID-19 pandemic, effective service delivery, human development

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Authors: D. Kokt, T. F. Dreyer

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The main objective of the study was to design a mentoring programme aimed at developing the soft skills of mentors. The mentors are all employed by a multinational corporation. The company had a mentoring plan in place that did not yield the required results, especially related to the development of soft skills. This prompted the researchers to conduct an extensive literature review followed by a mixed methods approach to ascertain the best practices in developing the soft skills of mentors. The outcomes of the study led to the development of a structured mentoring programme using 25 modules to be completed by mentors. The design incorporated a blended modular approach using both face-to-face teaching and teaching supported by Information Communication Technology (ICT). Blended learning was ideal as the ICT component helped to minimise instructor-mentor physical contact as part of the health measures during the Covid-19 pandemic. The blended learning approach also allowed instructors and mentors an online or offline mode, so that mentors could have more time for creative and cooperative exercises. A range of delivery methodologies were spread out across the different modules to ensure mentor engagement and accelerate mentor development. This included concept development through in-person instructor-led training sessions, concept development through virtual instructor-led training sessions, simulations, case studies, e-learning, role plays, interactive learning using mentoring toolkits, and experiential learning through application. The mentor development journey included formal modular competency assessments. All modules contained post-competency assessment consisting of 10 questions (comprising of a combination of explanatory questions and multiple-choice questions) to ensure understanding and deal with identified competency gaps. The minimum pass mark for all modular competency assessments was 80%. Mentors were allowed to retake the assessment if they scored less than 80% until they demonstrated understanding at the required level.

Keywords: mentor, mentee, soft skills, mentor development, blended learning, modular approach

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Authors: Kenneth Philip Jones

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This dissertation investigates the potential of developing educational-based video games to motivate and engage African American males. The study employed a qualitative methodological approach by investigating African American males who are avid video game players and are currently enrolled at a college or university. The participants were individually and collectively video and audio recorded during the interviews and observations. Situated Learning theory analyzed how motivation and engagement can transfer from a video game to an educational context. The research aims to address the disparities in our educational systems when it comes to providing a culture, climate, and atmosphere that will enable the academic development of African American males. The primary objective of the findings is based on the participants’ responses and the data collected to provide recommendations to educators and scholars on how to address the issues that have demoralized African American males in education and provide a platform that will allow for equality in educational development and advancement.

Keywords: video games, motivation, behavioral, learning transfer

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Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

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Authors: Revoti Prasad Bora, Nikita Katyal, Saurabh Yadav

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Pre-processing plays an important role in various image processing applications. Most of the time due to the similar nature of images, a particular pre-processing or a set of pre-processing steps are sufficient to produce the desired results. However, in the education domain, there is a wide variety of images in various aspects like images with line-based diagrams, chemical formulas, mathematical equations, etc. Hence a single pre-processing or a set of pre-processing steps may not yield good results. Therefore, a Deep Learning based approach for dynamically selecting a relevant pre-processing technique for each image is proposed. The proposed method works as a classifier to detect hidden patterns in the images and predicts the relevant pre-processing technique needed for the image. This approach experimented for an image similarity matching problem but it can be adapted to other use cases too. Experimental results showed significant improvement in average similarity ranking with the proposed method as opposed to static pre-processing techniques.

Keywords: deep-learning, classification, pre-processing, computer vision, image processing, educational data mining

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Authors: Mario Gianni, Manuel A. Ruiz Garcia, Fiora Pirri

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In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV.

Keywords: Dirichlet processes, gaussian mixture models, learning motion patterns, tracked robots for urban search and rescue

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Authors: Willy Mbenza, Sho Kenjiro

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Increasing efforts are directed towards a better understanding and foreknowledge of extreme precipitation likelihood, given the adverse effects associated with their occurrence. This knowledge plays a crucial role in long-term planning and the formulation of effective emergency response. However, predicting extreme events reliably presents a challenge to conventional empirical/statistics due to the involvement of numerous variables spanning different time and space scales. In the recent time, Machine Learning has emerged as a promising tool for predicting the dynamics of extreme precipitation. ML techniques enables the consideration of both local and regional physical variables that have a strong influence on the likelihood of extreme precipitation. These variables encompasses factors such as air temperature, soil moisture, specific humidity, aerosol concentration, among others. In this study, we develop an ML model that incorporates both local and regional variables while establishing a robust relationship between physical variables and precipitation during the downscaling process. Furthermore, the model provides valuable information on the frequency and duration of a given intensity of precipitation.

Keywords: machine learning (ML), predictions, rainfall events, regional variables

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Authors: Houda Zouar, Hanane Sarnou

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In the 21st, century the concept of learners' autonomy is crucial. The concept of self-regulated learning has come forward as a result of enabling learners to direct their learning with autonomy towards academic goals achievement. Academic self-regulation is defined as the process by which learners systematically plan, monitor and asses their learning to achieve their academic established goals. In the field of English as a foreign language, teachers emphasise the role of learners’ autonomy to foster the process of English language learning. Consequently, academic self-regulation is considered as a vehicle to enhance autonomy among English language learners. However, not all learners can be equally self-regulators if not well assisted, mainly those novice pupils of basic education. For this matter, understanding the role of teachers in fostering academic self- regulation must be among the preliminary objectives in searching and developing this area. The present research work targets the role of the Algerian middle school teachers in enhancing academic self-regulation and teaching pupils how to learn, besides their role as models in the trajectory of teaching their pupils to become self-regulators. Despite the considerable endeavours in the field of educational setting on Self-Regulated Learning, the literature of the Algerian context indicates confined endeavours to undertake and divulge this notion. To go deeper into this study, a mixed method approach was employed to confirm our hypothesis. For data collection, teachers were observed and addressed by a questionnaire on their role in enhancing academic self- regulation among their pupils. The result of the research indicates that the attempts of middle school Algerian teachers are implicit and limited. This study emphasises the need to prepare English language teachers with the necessary skills to promote autonomous and self-regulator English learners.

Keywords: Algeria, English as a foreign language, middle school, self-regulation, Teachers' role

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Authors: Shaheeb Abdul Azeez, Siong-Choy Chong

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Background: Talent retention is increasingly becoming a major challenge for many industries due to the high turnover rate. Public higher learning institutions in the Maldives have a similar situation with the turnover of their employees'. This paper is to identify whether Human Resource Management (HRM) practices have any impact on employee retention in public higher learning institutions in the Maldives. Purpose: This paper aims to identify the influence of HRM practices on employee retention in public higher learning institutions in the Maldives. A total of 15 variables used in this study; 11 HRM practices as independent variables (leadership, rewards, salary, employee participation, compensation, training and development, career development, recognition, appraisal system and supervisor support); job satisfaction and motivation as mediating variables; demographic profile as moderating variable and employee retention as dependent variable. Design/Methodology/Approach: A structured self-administered questionnaire was used for data collection. A total of 300 respondents were selected as the study sample, representing the academic and administrative from public higher learning institutions using a stratified random sampling method. AMOS was used to test the hypotheses constructed. Findings: The results suggest that there is no direct effect between the independent variable and dependent variable. Also, the study concludes that no moderate effects of demographic profile between independent and dependent variables. However, the mediating effects of job satisfaction and motivation in the relationship between HRM practices and employee retention were significant. Salary had a significant influence on job satisfaction, whilst both compensation and recognition have significant influence on motivation. Job satisfaction and motivation were also found to significantly influence employee retention. Research Limitations: The study consists of many variables more time consuming for the respondents to answer the questionnaire. The study is focussed only on public higher learning institutions in the Maldives due to no participation from the private sector higher learning institutions. Therefore, the researcher is unable to identify the actual situation of the higher learning industry in the Maldives. Originality/Value: To our best knowledge, no study has been conducted using the same framework throughout the world. This study is the initial study conducted in the Maldives in this study area and can be used as a baseline for future researches. But there are few types of research conducted on the same subject throughout the world. Some of them concluded with positive findings while others with negative findings. Also, they have used 4 to 7 HRM practices as their study framework.

Keywords: human resource management practices, employee retention, motivation, job satisfaction

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Authors: Mary Deyo, Zmara Harrison

Abstract:

This study reports the outcomes of an 8-week experiential learning program for 6 adults with Intellectual and Developmental Disabilities (IDD) at a day habilitation program. The intervention foci for this program include executive function, language learning in the domains of expressive, receptive, and pragmatic language, and quality of life. The interprofessional collaboration aimed at supporting adults with IDD to reach person-centered, functional goals across skill domains is critical. This study is a significant addition to the speech-language pathology literature in that it examines a therapy method that potentially meets this need while targeting domains within the speech-language pathology scope of practice. Communication therapy was provided during highly valued and meaningful hands-on learning experiences, referred to as the Garden Club, which incorporated all aspects of planting and caring for a garden as well as related journaling, sensory, cooking, art, and technology-based activities. Direct care staff and an undergraduate research assistant were trained by SLP to be impactful language guides during their interactions with participants in the Garden Club. SLP also provided direct therapy and modeling during Garden Club. Research methods used in this study included a mixed methods analysis of a literature review, a quasi-experimental implementation of communication therapy in the context of experiential learning activities, Quality of Life participant surveys, quantitative pre- post- data collection and linear mixed model analysis, qualitative data collection with qualitative content analysis and coding for themes. Outcomes indicated overall positive changes in expressive vocabulary, following multi-step directions, sequencing, problem-solving, planning, skills for building and maintaining meaningful social relationships, and participant perception of the Garden Project’s impact on their own quality of life. Implementation of this project also highlighted supports and barriers that must be taken into consideration when planning similar projects. Overall findings support the use of experiential learning projects in day habilitation programs for adults with IDD, as well as additional research to deepen understanding of best practices, supports, and barriers for implementation of experiential learning with this population. This research provides an important contribution to research in the fields of speech-language pathology and other professions serving adults with IDD by describing an interprofessional experiential learning program with positive outcomes for executive function, language learning, and quality of life.

Keywords: experiential learning, adults, intellectual and developmental disabilities, expressive language, receptive language, pragmatic language, executive function, communication therapy, day habilitation, interprofessionalism, quality of life

Procedia PDF Downloads 127

Authors: Meera Rajeev Kumar

Abstract:

An overall social, emotional, and cultural development in a child is what a parent expects. There is no point in comparing the generations of 70’s or 80’s with that of the children of today as the trends are changing drastically. Technology has enabled them to become smart as well as over smart in one way or the other. Children today are quite ignorant of today’s values or ethics and are imbibing different cultures around them and ultimately confused on what to follow. The researcher has gained experience in transmitting or imparting the traditional culture through performing arts. It is understood that the children undergo a transformation from what they knew to what the truth is, through learning and experience. Through performing arts, the child develops an emotional, quick learning, abundant creativity, and ultimately self-realisation on what is right and wrong. The child also gains good organising skills, good decision making skills, therefore summing up to a holistic development. The sample study is 50, and a random sampling technique is adopted to differentiate between a normal child and a child learning an art. The study is conducted in Tamil Nadu, in India.

Keywords: creativity, cultural, emotional, empower

Procedia PDF Downloads 202

Authors: Chandrakala Varatharajoo, Adelina Binti Asmawi, Nabeel Abdallah Mohammad Abedalaziz

Abstract:

This study investigated the impact of inflectional and derivational morphemic analysis awareness on ESL secondary school students’ vocabulary learning strategy. The quasi-experimental study was conducted with 106 low proficiency secondary school students in two experimental groups (inflectional and derivational) and one control group. The students’ vocabulary acquisition was assessed through two measures: Morphemic Analysis Test and Vocabulary- Morphemic Test in the pretest and posttest before and after an intervention programme. Results of ANCOVA revealed that both the experimental groups achieved a significant score in Morphemic Analysis Test and Vocabulary-Morphemic Test. However, the inflectional group obtained a fairly higher score than the derivational group. Thus, the results indicated that ESL low proficiency secondary school students performed better on inflectional morphemic awareness as compared to derivatives. The results also showed that the awareness of inflectional morphology contributed more on the vocabulary acquisition. Importantly, learning inflectional morphology can help ESL low proficiency secondary school students to develop both morphemic awareness and vocabulary gain. Theoretically, these findings show that not all morphemes are equally useful to students for their language development. Practically, these findings indicate that morphological instruction should at least be included in remediation and instructional efforts with struggling learners across all grade levels, allowing them to focus on meaning within the word before they attempt the text in large for better comprehension. Also, by methodologically, by conducting individualized intervention and assessment this study provided fresh empirical evidence to support the existing literature on morphemic analysis awareness and vocabulary learning strategy. Thus, a major pedagogical implication of the study is that morphemic analysis awareness strategy is a definite boon for ESL secondary school students in learning English vocabulary.

Keywords: ESL, instruction, morphemic analysis, vocabulary

Procedia PDF Downloads 402

Authors: Elisabeth Desiana Mayasari, Laurensia Aptik Evanjeli, Brigitta Erlita Tri Anggadewi

Abstract:

The condition of the COVID-19 pandemic demands adaptation in various aspects of human life, including in the field of education. Teachers are expected to do distance learning or Learning From Home (LFH). The teacher said that he experienced stress, anxiety, feeling depressed, and afraid based on the interview. Learning adaptations and pandemic situations can impact the mental health of teachers, so the purpose of this study is to determine the mental health of teachers while teaching online. This research was conducted with a quantitative approach using a survey method. The subjects in this study were 330 elementary school teachers under the auspices of a foundation in Yogyakarta. Teachers' mental health was measured using the Indonesian version of The Mental Health Inventory (MHI-38), which has a reliability of 0.888. The results showed that the teachers generally had a good mental health condition marked by a lower negative aspect score than the positive aspect. In addition, the overall mental health aspect shows that some teachers have better mental health when compared to the average score, as well as higher positive aspect scores in all sub-aspects.

Keywords: mental health, teacher, COVID-19 pandemic, MHI-38

Procedia PDF Downloads 183