Search results for: Early Childhood Learning

Authors: Irina Rönnqvist

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The world is constantly changing; therefore previous views or cultural patterns and programs formed by the “old world” cannot be suitable for solving actual problems. Indeed, reformation of an education system is unlikely to be effective without understanding of the processes that emerge in the field of employment. There is a problem in overcoming of the negative trends that determine imbalance of needs of the qualified work force and preparation of professionals by an education system. At the contemporary stage of economics the processes occurring in the field of labor and employment reproduce the picture of economic development of the country that cannot be imagined without the factor of labor mobility (e.g. migration). On the one hand, adult education has a significant impact on multifaceted development of economy. On the other hand, Sweden has one of the world's most generous asylum reception systems and the most liberal labor migration policy among the OECD countries. This effect affects the increased productivity. The focus of this essay is on problems of education and employment concerning social inclusion of migrants in working life in Sweden.

Keywords: migration, adaptation, formal learning, informal learning, Sweden

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Authors: Ashish James, Anusha James

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The impressive predictive performance of deep learning techniques on a wide range of tasks has led to its widespread use. Estimating the confidence of these predictions is paramount for improving the safety and reliability of such systems. However, the uncertainty estimates provided by neural networks (NNs) tend to be overconfident and unreasonable. Ensemble of NNs typically produce good predictions but uncertainty estimates tend to be inconsistent. Inspired by these, this paper presents a framework that can quantitatively estimate the uncertainties by leveraging the advances in transfer learning through slight modification to the existing training pipelines. This promising algorithm is developed with an intention of deployment in real world problems which already boast a good predictive performance by reusing those pretrained models. The idea is to capture the behavior of the trained NNs for the base task by augmenting it with the uncertainty estimates from a supplementary network. A series of experiments with known and unknown distributions show that the proposed approach produces well calibrated uncertainty estimates with high quality predictions.

Keywords: uncertainty estimation, neural networks, transfer learning, regression

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Authors: Yonatan Sverdlov, Shimon Ullman

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Artificial neural networks encounter a notable challenge known as continual learning, which involves acquiring knowledge of multiple tasks over an extended period. This challenge arises due to the tendency of previously learned weights to be adjusted to suit the objectives of new tasks, resulting in a phenomenon called catastrophic forgetting. Most approaches to this problem seek a balance between maximizing performance on the new tasks and minimizing the forgetting of previous tasks. In contrast, our approach attempts to maximize the performance of the new task, while ensuring zero forgetting. This is accomplished through the introduction of task-specific modulation parameters for each task, and only these parameters are learned for the new task, after a set of initial tasks have been learned. Through comprehensive experimental evaluations, our model demonstrates superior performance in acquiring and retaining novel tasks that pose difficulties for other multi-task models. This emphasizes the efficacy of our approach in preventing catastrophic forgetting while accommodating the acquisition of new tasks.

Keywords: continual learning, life-long learning, neural analogies, adaptive modulation

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Francisco A. Ribeiro Da Costa

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Who is responsible for teaching architecture; consider various ways to participate in learning, manipulating various pedagogical tools to streamline the creative process. The Architectural Design Studio (ADS) should become a holistic, systemic process responding to the complexity of our world. This essay corresponds to a deep reflection developed by the author on the teaching of architecture. The outcomes achieved are the corollary of experimentation; discussion and application of pedagogical methods that allowed consolidate the creativity applied by students. The purpose is to show the conjectures that have been considered effective in creating an intellectual environment that nurtures the subject of Architectural Design Studio (ADS), as an operational synthesis in the final stage of the degree. These assumptions, which are part of the proposed model, displaying theories and teaching methodologies that try to respect the learning process based on student learning styles Kolb, ensuring their latent specificities and formulating the structure of the ASD discipline. In addition, the assessing methods are proposed, which consider the architectural Design Studio as an operational synthesis in the teaching of architecture.

Keywords: teaching-learning, architectural design studio, architecture, education

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Authors: Munirah Alaboudi

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This paper discusses a research project carried out with 12 first-year graduate students enrolled in research methods course prior to undertaking a graduate thesis during the academic year 2019. The research was designed for the objective of creating research methods course structure that embraces an individualized and activity-based approach to learning in a highly engaging group environment. This approach targeted innovating the traditional research methods lecture-based, theoretical format where students reported less engagement and limited learning. This study utilized action research methodology in developing a different approach to research methods course instruction where student performance indicators and feedback were periodically collected to assess the new teaching method. Student learning was achieved through utilizing the flipped classroom approach where students learned the material at home and classroom activities were designed to implement and experiment with the newly acquired information, with the guidance of the course instructor. Student learning in class was practiced through a series of activities based on different research methods. With the goal of encouraging student engagement, a wide range of activities was utilized including workshops, role play, mind-mapping, presentations, peer evaluations. Data was collected through an open-ended qualitative questionnaire to establish whether students were engaged in the material they were learning, and to what degree were they engaged, and to test their mastery level of the concepts discussed. Analysis of the data presented positive results as around 91% of the students reported feeling more engaged with the active learning experience and learning research by “actually doing research, not just reading about it”. The students expressed feeling invested in the process of their learning as they saw their research “gradually come to life” through peer learning and practice during workshops. Based on the results of this study, the research methods course structure was successfully remodeled and continues to be delivered.

Keywords: research methods, higher education instruction, flipped classroom, graduate education

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Authors: Aishi Kundu, Elhan Raza

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"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

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Authors: Renga Rao Krishnamoorthy, Raihan Tahir

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The quality of classroom learning and teaching delivery has been and will continue to be debated at various levels worldwide. The regional cooperation programme to improve the quality and labour market orientation of the Technical and Vocational Education and Training (RECOTVET), ‘Deutsche Gesellschaft für Internationale Zusammenarbeit’ (GIZ), in line with the sustainable development goals (SDG), has taken the initiative in the development of quality TVET in the ASEAN region by developing the Quality Toolbox for Better TVET Delivery (Quality Toolbox). This initiative aims to provide quick and practical materials to trainers, instructors, and personnel involved in education and training at an institute to shape the quality of classroom learning and teaching. The Quality Toolbox for Better TVET Delivery was developed in three stages: literature review and development, validation, and finalization. Thematic areas in the Quality Toolbox were derived from collective input of concerns and challenges raised from experts’ workshops through moderated sessions involving representatives of TVET institutes from 9 ASEAN Member States (AMS). The sessions were facilitated by professional moderators and international experts. TVET practitioners representing AMS further analysed and discussed the structure of the Quality Toolbox and content of thematic areas and outlined a set of specific requirements and recommendations. The application exercise of the Quality Toolbox was carried out by TVET institutes among ASM. Experience sharing sessions from participating ASEAN countries were conducted virtually. The findings revealed that TVET institutes use two types of approaches in shaping the quality of learning and teaching, which is ascribed to inductive or deductive, shaping of quality in learning and teaching is a non-linear process and finally, Q-tools can be adopted and adapted to shape the quality of learning and teaching at TVET institutes in the following: improvement of the institutional quality, improvement of teaching quality and improvement on the organisation of learning and teaching for students and trainers. The Quality Toolbox has good potential to be used at education and training institutes to shape quality in learning and teaching.

Keywords: AMS, GIZ, RECOTVET, quality tools

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Authors: Mostafa ‘Neo’ Mohsenvand, Mohammad Rasool Izadi, Pattie Maes

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EEG-based biometrics (user identification) has been explored on small datasets of no more than 157 subjects. Here we show that the accuracy of modern supervised methods falls rapidly as the number of users increases to a few thousand. Moreover, supervised methods require a large amount of labeled data for training which limits their applications in real-world scenarios where acquiring data for training should not take more than a few minutes. We show that using contrastive learning for pre-training, it is possible to maintain high accuracy on a dataset of 2130 subjects while only using a fraction of labels. We compare 5 different self-supervised tasks for pre-training of the encoder where our proposed method achieves the accuracy of 96.4%, improving the baseline supervised models by 22.75% and the competing self-supervised model by 3.93%. We also study the effects of the length of the signal and the number of channels on the accuracy of the user-identification models. Our results reveal that signals from temporal and frontal channels contain more identifying features compared to other channels.

Keywords: brainprint, contrastive learning, electroencephalo-gram, self-supervised learning, user identification

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Authors: Cristina-Corina Bențea, Teodora-Mihaela Iconomescu, Laurențiu-Gabriel Talaghir, Claudiu Mereuță, Anamaria Berdilă

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The study aims to examine the relationships between motor abilities, self-evaluation of athletic competence, and demographic characteristics in a sample of late-childhood participants. Defined as physical elements that enable the movements, motor skills are classified according to movement precision as gross and fine motor skills. Across their development, children enhance their ability to coordinate the limbs to produce different actions. In educational settings, they perform various instructional activities that involve the improvement of their athletic prowess and are taught how to strengthen their gross and fine motor abilities. Also, in relation to their activities, children tend to evaluate themselves differently across the various domains of their life. Starting from childhood, athletic competence is one of the area-specific evaluations of competence that refers to one’s ability to do well at sports, including outdoor games. Method: The sample consisted of fifty-eight primary school children, thirty girls, and twenty-eight boys, with ages between 8-10 years. The Bruininks-Oseretsky test of motor proficiency was used to assess both gross and fine motor skills in eight specific areas (fine motor precision, fine motor integration, manual dexterity, bilateral coordination, balance, running speed and agility, upper-limb coordination, strength). Athletic competence self-perceived was assessed with one of the six subscales of the Self-Perception Profile for Children. Results: Were examined both the relationships between each motor skills scale and subscales and between motor skills and general self-perceived athletic competence. Results indicated correlations between the athletic competence and four motor skills subscales depending on the gender and age of the children. The findings of the study were discussed related to the possibility to improve children's physical proficiency in educational settings according to the level of self-perceived athletic competence.

Keywords: gross motor skills, fine motor skills, athletic competence, self-evaluation, children, education

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Authors: Kemal Izci, Ahmet Oguz Akturk

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Learning is dependent on cognitive and physical differences as well as other differences such as ethnicity, language, and culture. Furthermore, these differences also influence how students show their learning. Assessment is an integral part of learning and teaching process and is essential for effective instruction. In order to provide effective instruction, teachers need to provide equal assessment opportunities for all students to see their learning difficulties and use them to modify instruction to aid learning. Successful assessment practices are dependent upon the knowledge and value of teachers. Therefore, in order to use assessment to assess and support diverse students learning, preservice and inservice teachers should hold an appropriate understanding of equitable assessment. In order to prepare teachers to help them support diverse student learning, as a first step, this study aims to explore how preservice teachers’ understand equitable assessment. 105 preservice science teachers studying at teacher preparation program in a large university located at Eastern part of Turkey participated in the current study. A questionnaire, preservice teachers’ reflection papers and interviews served as data sources for this study. All collected data qualitatively analyzed to develop themes that illustrate preservice science teachers’ understanding of equitable assessment. Results of the study showed that preservice teachers mostly emphasized fairness including fairness in grading and fairness in asking questions not out of covered concepts for equitable assessment. However, most of preservice teachers do not show an understanding of equity for providing equal opportunities for all students to display their understanding of related content. For some preservice teachers providing different opportunities (providing extra time for non-native speaking students) for some students seems to be unfair for other students and therefore, these kinds of refinements do not need to be used. The results of the study illustrated that preservice science teachers mostly understand equitable assessment as fairness and less highlight the role of using equitable assessment to support all student learning, which is more important in order to improve students’ achievement of science. Therefore, we recommend that more opportunities should be provided for preservice teachers engage in a more broad understanding of equitable assessment and learn how to use equitable assessment practices to aid and support all students learning trough classroom assessment.

Keywords: science teaching, equitable assessment, assessment literacy, preservice science teachers

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Authors: Heba Salem

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This paper aims to share the researcher’s experience of using e-Portfolios as an assessment tool to follow up on students’ learning experiences and performance throughout the semester. It also aims at highlighting the importance of students’ self-reflection in the process of language learning. The paper begins by introducing the advanced media course, with its focus on listening and speaking skills, and introduces the students’ profiles. Then it explains the students’ role in the e-portfolio process as they are given the option to choose a listening text they studied throughout the semester and to choose a recorded oral production of their collection of artifacts throughout the semester. Students showcase and reflect on their progress in both listening comprehension and speaking. According to the research, re-listening to work given to them and to their production is a means of reflecting on both their progress and achievement. And choosing the work students want to showcase is a means to promote independent learning as well as self-expression. Students are encouraged to go back to the class learning outcomes in the process of choosing the work. In their reflections, students express how they met the specific learning outcome. While giving their presentations, students expressed how useful the experience of returning and going over what they covered to select one and going over their production as well. They also expressed how beneficial it was to listen to themselves and literally see their progress in both listening comprehension and speaking. Students also reported that they grasped more details from the texts than they did when first having it as an assignment, which coincided with one of the class learning outcomes. They also expressed the fact that they had more confidence speaking as well as they were able to use a variety of vocabulary and idiomatic expressions that students have accumulated. For illustration, this paper includes practical samples of students’ tasks and instructions as well as samples of their reflections. The results of students’ reflections coincide with what the research confirms about the effectiveness of the e-portfolios as a means of assessment. The employment of e-Portfolios has two-folded benefits; students are able to measure the achievement of the targeted learning outcomes, and teachers receive constructive feedback on their teaching methods.

Keywords: e-portfolios, assessment, self assessment, listening and speaking progress, foreign language, reflection, learning out comes, sharing experience

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Authors: Azza Taha Zaki

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Technology-enhanced teaching has been in the limelight since the 90s when educators started investigating and experimenting with using computers in the classroom as a means of building 21^st. century skills and motivating students. The concept of technology-enhanced strategies in education is kaleidoscopic! It has meant different things to different educators. For the purpose of this paper, however, it will be used to refer to the diverse technology-based strategies used to support and enrich the flipped learning process, in the classroom and outside. The paper will investigate how technology is put in the service of teaching and learning to improve the students’ learning experience as manifested in students’ attendance and engagement, achievement rates and finally, students’ projects at the end of the semester. The results will be supported by a student survey about relevant specific aspects of their learning experience in the modules in the study.

Keywords: attendance, British University, Egypt, flipped, student achievement, student-centred, student engagement, students’ projects

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Authors: Suzanne Zazycki, Bamidele Osamika, Heather Craska, Kaelyn Conaway, Reena Murphy, Stephanie Spence

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Invasive Plant Species (IPS) are an important component of effective preservation and conservation of natural lands management. IPS are non-native plants which can aggressively encroach upon native species and pose a significant threat to the ecology, public health, and social welfare of a community. The presence of IPS in U.S. nature preserves has caused economic costs, which has estimated to exceed $26 billion a year. While different methods have been identified to control IPS, few methods have been recognized for early detection of IPS. This study examined identified methods for early detection of IPS in Hueston Woods State Nature Preserve. Mixed methods research design was adopted in this four-phased study. The first phase entailed data gathering, the phase described the characteristics and qualities of IPS and the importance of early detection (ED). The second phase explored ED methods, Geographic Information Systems (GIS) and Citizen Science were discovered as ED methods for IPS. The third phase of the study involved the creation of hotspot maps to identify likely areas for IPS growth. While the fourth phase involved testing and evaluating mobile applications that can support the efforts of citizen scientists in IPS detection. Literature reviews were conducted on IPS and ED methods, and four regional experts from ODNR and Miami University were interviewed. A questionnaire was used to gather information about ED methods used across the state. The findings revealed that geospatial methods, including Unmanned Aerial Vehicles (UAVs), Multispectral Satellites (MSS), and Normalized Difference Vegetation Index (NDVI), are not feasible for early detection of IPS, as they require GIS expertise, are still an emerging technology, and are not suitable for every habitat for the ED of IPS. Therefore, Other ED methods options were explored, which include predicting areas where IPS will grow, which can be done through monitoring areas that are like the species’ native habitat. Through literature review and interviews, IPS are known to grow in frequently disturbed areas such as along trails, shorelines, and streambanks. The research team called these areas “hotspots” and created maps of these hotspots specifically for HW NP to support and narrow the efforts of citizen scientists and staff in the ED of IPS. The results further showed that utilizing citizen scientists in the ED of IPS is feasible, especially through single day events or passive monitoring challenges. The study concluded that the creation of hotspot maps to direct the efforts of citizen scientists are effective for the early detection of IPS. Several recommendations were made, among which is the creation of hotspot maps to narrow the ED efforts as citizen scientists continues to work in the preserves and utilize citizen science volunteers to identify and record emerging IPS.

Keywords: early detection, hueston woods state nature preserve, invasive plant species, hotspots

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Authors: Mustafa M. Donma, Orkide Donma

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Obesity is a low-grade inflammatory state. Childhood obesity is a multisystem disease, which is associated with a number of complications as well as potentially negative consequences. Gender is an important universal risk factor for many diseases. Hematological indices differ significantly by gender. This should be considered during the evaluation of obese children. The aim of this study is to detect hematologic indices that differ by gender in morbid obese (MO) children. A total of 134 MO children took part in this study. The parents filled an informed consent form and the approval from the Ethics Committee of Namik Kemal University was obtained. Subjects were divided into two groups based on their genders (64 females aged 10.2±3.1 years and 70 males aged 9.8±2.2 years; p ≥ 0.05). Waist-to-hip as well as head-to-neck ratios and body mass index (BMI) values were calculated. The children, whose WHO BMI-for age and sex percentile values were > 99 percentile, were defined as MO. Hematological parameters [haemoglobin, hematocrit, erythrocyte count, mean corpuscular volume, mean corpuscular haemoglobin, mean corpuscular haemoglobin concentration, red blood cell distribution width, leukocyte count, neutrophil %, lymphocyte %, monocyte %, eosinophil %, basophil %, platelet count, platelet distribution width, mean platelet volume] were determined by the automatic hematology analyzer. SPSS was used for statistical analyses. P ≤ 0.05 was the degree for statistical significance. The groups included children having mean±SD value of BMI as 26.9±3.4 kg/m² for males and 27.7±4.4 kg/m² for females (p ≥ 0.05). There was no significant difference between ages of females and males (p ≥ 0.05). Males had significantly increased waist-to-hip ratios (0.95±0.08 vs 0.91±0.08; p=0.005) and mean corpuscular hemoglobin concentration values (33.6±0.92 vs 33.1±0.83; p=0.001) compared to those of females. Significantly elevated neutrophil (4.69±1.59 vs 4.02±1.42; p=0.011) and neutrophil-to-lymphocyte ratios (1.70±0.71 vs 1.39±0.48; p=0.004) were detected in females. There was no statistically significant difference between groups in terms of C-reactive protein values (p ≥ 0.05). Adipose tissue plays important roles during the development of obesity and associated diseases such as metabolic syndrom and cardiovascular diseases (CVDs). These diseases may cause changes in complete blood cell count parameters. These alterations are even more important during childhood. Significant gender effects on the changes of neutrophils, one of the white blood cell subsets, were observed. The findings of the study demonstrate the importance of considering gender in clinical studies. The males and females may have distinct leukocyte-trafficking profiles in inflammation. Female children had more circulating neutrophils, which may be the indicator of an increased risk of CVDs, than male children within this age range during the late stage of obesity. In recent years, females represent about half of deaths from CVDs; therefore, our findings may be the indicator of the increasing tendency of this risk in females starting from childhood.

Keywords: children, gender, morbid obesity, neutrophil-to-lymphocyte ratio

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Authors: Aneia Rajiel Busmente, Renato Gunio Jr., Jazin Mautante, Denise Joy Mendoza, Raymond Benedict Tagorio, Gabriel Uy, Natalie Quinn Valenzuela, Ma. Elayza Villa, Francine Yezha Vizcarra, Sofia Madelle Yapan, Eugene Kurt Yboa

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COVID-19’s rapid spread caused a dramatic change in the nation, especially the educational system. The Department of Education was forced to adopt a practical learning platform without neglecting health, a printed modular distance learning. The Philippines' K–12 curriculum includes Statistics and Probability as one of the key courses as it offers students the knowledge to evaluate and comprehend data. Due to student’s difficulty and lack of understanding of the concepts of Statistics and Probability in Normal Distribution. The Self-Learning Module in Statistics and Probability about the Normal Distribution created by the Department of Education has several problems, including many activities, unclear illustrations, and insufficient examples of concepts which enables learners to have a difficulty accomplishing the module. The purpose of this study is to determine the effectiveness of self-learning module on the academic performance of students in the subject Statistics and Probability, it will also explore students’ perception towards the quality of created Self-Learning Module in Statistics and Probability. Despite the availability of Self-Learning Modules in Statistics and Probability in the Philippines, there are still few literatures that discuss its effectiveness in improving the performance of Senior High School students in Statistics and Probability. In this study, a Self-Learning Module on Normal Distribution is evaluated using a quasi-experimental design. STEM students in Grade 11 from National University's Nazareth School will be the study's participants, chosen by purposive sampling. Google Forms will be utilized to find at least 100 STEM students in Grade 11. The research instrument consists of 20-item pre- and post-test to assess participants' knowledge and performance regarding Normal Distribution, and a Likert scale survey to evaluate how the students perceived the self-learning module. Pre-test, post-test, and Likert scale surveys will be utilized to gather data, with Jeffreys' Amazing Statistics Program (JASP) software being used for analysis.

Keywords: self-learning module, academic performance, statistics and probability, normal distribution

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Authors: Hesheng Wang, Haoyu Wang, Chungang Zhuang

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Estimating the 6D pose of objects is a core step for robot bin-picking tasks. The problem is that various objects are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a method to regress 6D poses by predicting three points for each object in the 3D point cloud through deep learning. To solve the ambiguity of symmetric pose, we propose a labeling method to help the network converge better. Based on the predicted pose, an iterative method is employed for pose optimization. In real-world experiments, our method outperforms the classical approach in both precision and recall.

Keywords: pose estimation, deep learning, point cloud, bin-picking, 3D computer vision

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: Cecile Garry, Katia Rovira, Julie Brisson

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Autism Spectrum Disorder (ASD) is defined by a qualitative and quantitative impairment of social interaction. Indeed early intervention programs, such as the Early Start Denver Model (ESDM), aimed at encouraging the development of social skills. In classroom, the children need to be socially engaged to learn. Early intervention programs can thus be implemented in kindergarten schools. In these schools, ASD children have more opportunities to interact with their peers or adults than in elementary schools. However, the preschool children with ASD are less socially engaged than their typically developing peers in the classroom. They initiate, respond and maintain less the social interactions. In addition, they produce more responses than initiations. When they interact, the non verbal communication is more used than verbal or symbolic communication forms and they are more engaged with adults than with peers. Nevertheless, communicative patterns may vary according to the clinical profiles of ASD children. Indeed, the ASD children with better cognitive skills interact more with their peers and use more symbolic communication than the ASD children with a low cognitive level. ASD children with the less severe symptoms use more the verbal communication than ASD children with the more severe symptoms. Small groups and structured activities encourage coordinated joint engagement episodes in ASD children. Our goal is to evaluate ASD children’s social engagement development in class, with their peers or adults, during dyadic or group activities. Participants were 19 preschool children with ASD aged from 3 to 6 years old that benefited of an early intervention in special kindergarten schools. Severity of ASD symptoms was measured with the CARS at the beginning of the follow-up. Classroom situations of interaction were recorded during 10 minutes (5 minutes of dyadic interaction and 5 minutes of a group activity), every 2 months, during 10 months. Social engagement behaviors of children, including initiations, responses and imitation, directed to a peer or an adult, were then coded. The Observer software (Noldus) that allows to annotate behaviors was the coding system used. A double coding was conducted and revealed a good inter judges fidelity. Results show that ASD children were more often and longer socially engaged in dyadic than in groups situations. They were also more engaged with adults than with peers. Children with the less severe symptoms of ASD were more socially engaged in groups situations than children with the more severe symptoms of ASD. Then, ASD children with the less severe symptoms of ASD were more engaged with their peers than ASD children with the more severe symptoms of ASD. However, the engagement frequency increased during the 10 month of follow-up but only for ASD children with the more severe symptoms at the beginning. To conclude, these results highlighted the necessity of individualizing early intervention programs according to the clinical profile of the child.

Keywords: autism spectrum disorder, preschool children, developmental psychology, early interventions, social interactions

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Authors: Basman Abdul Jabbar

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The researchers aimed to improve the importance of the good posture without any divisions & deformities. The importance of research lied in the discovery posture deformities early so easily treated before its transformation into advanced abnormalities difficult to treat and may need surgical intervention. Research problem was noting that some previous studies were based on the discovery of posture deformities, which was dependent on the (self-evaluation) which this type did not have accuracy to discover deformities. The Samples were (500) schoolchildren aged (9-11 years, males) at Baghdad al Karak. They were students at primary schools. The measure included all posture deformities. The researcher used video camera to analyze the posture deformities according to biomechanical variables by Kinovea software for motion analysis. The researcher recommended the need to use accurate scientific methods for early detection of posture deformities in children which contribute to the prevention and reduction of distortions.

Keywords: biomechanics, children, deformities, posture

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Authors: Rajender Dahiya

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Investigation outcomes of major process incidents have been consistent for decades and validate that the causes and consequences are often identical. The debate remains as we continue to experience similar process incidents even with enormous development of new tools, technologies, industry standards, codes, regulations, and learning processes? The objective of this paper is to investigate the most common causes of major industrial incidents and reveal industry challenges and best practices to prevent such incidents. The author, in his current role, performs audits and inspections of a variety of high-hazard industries in North America, including petroleum refineries, chemicals, petrochemicals, manufacturing, etc. In this paper, he shares real life scenarios, examples, and case studies from high hazards operating facilities including key challenges and best practices. This case study will provide a clear understanding of the importance of near miss incident investigation. The incident was a Safe operating limit excursion. The case describes the deficiencies in management programs, the competency of employees, and the culture of the corporation that includes hazard identification and risk assessment, maintaining the integrity of safety-critical equipment, operating discipline, learning from process safety near misses, process safety competency, process safety culture, audits, and performance measurement. Failure to identify the hazards and manage the risks of highly hazardous materials and processes is one of the primary root-causes of an incident, and failure to learn from past incidents is the leading cause of the recurrence of incidents. Several investigations of major incidents discovered that each showed several warning signs before occurring, and most importantly, all were preventable. The author will discuss why preventable incidents were not prevented and review the mutual causes of learning failures from past major incidents. The leading causes of past incidents are summarized below. Management failure to identify the hazard and/or mitigate the risk of hazardous processes or materials. This process starts early in the project stage and continues throughout the life cycle of the facility. For example, a poorly done hazard study such as HAZID, PHA, or LOPA is one of the leading causes of the failure. If this step is performed correctly, then the next potential cause is. Management failure to maintain the integrity of safety critical systems and equipment. In most of the incidents, mechanical integrity of the critical equipment was not maintained, safety barriers were either bypassed, disabled, or not maintained. The third major cause is Management failure to learn and/or apply learning from the past incidents. There were several precursors before those incidents. These precursors were either ignored altogether or not taken seriously. This paper will conclude by sharing how a well-implemented operating management system, good process safety culture, and competent leaders and staff contributed to managing the risks to prevent major incidents.

Keywords: incident investigation, risk management, loss prevention, process safety, accident prevention

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Authors: Ehi E. Aimiuwu

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As of 2017, many researchers in educational journals are still wondering if students are effectively and efficiently engaged in active learning in the online learning environment. The goal of this qualitative single case study and narrative research is to explore if students are actively engaged in their online learning. Seven online students in the United States from LinkedIn and residencies were interviewed for this study. Eleven online learning techniques from research were used as a framework.  Data collection tools were used for the study that included a digital audiotape, observation sheet, interview protocol, transcription, and NVivo 12 Plus qualitative software.  Data analysis process, member checking, and key themes were used to reach saturation. About 85.7% of students preferred individual grading. About 71.4% of students valued professor’s interacting 2-3 times weekly, participating through posts and responses, having good internet access, and using email.  Also, about 57.1% said students log in 2-3 times weekly to daily, professor’s social presence helps, regular punctuality in work submission, and prefer assessments style of research, essay, and case study.  About 42.9% appreciated syllabus usefulness and professor’s expertise.

Keywords: class facilitation, course management, online teaching, online education, student engagement

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Authors: Shafaq Rubab

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One of the biggest reasons of dropouts from schools is lack of motivation and interest among the students, particularly in mathematics. Many developing countries are facing this problem and this issue is lowering the literacy rate in these developing countries. The best solution for increasing motivation level and interest among the students is using tablet game-based learning. However, a pedagogically sound game required a well-planned instructional design model to enhance learner’s attention and confidence otherwise effectiveness of the learning games suffers badly. This research aims to evaluate the impact of the pedagogically sound instructional design model on students’ motivation by using tablet game-based learning. This research was conducted among the out-of-school-students having an age range from 7 to 12 years and the sample size of two hundred students was purposively selected without any gender discrimination. Qualitative research was conducted by using a survey tool named Instructional Material Motivational Survey (IMMS) adapted from Keller Arcs model. A comparison of results from both groups’ i.e. experimental group and control group revealed that motivation level of the students taught by the game was higher than the students instructed by using conventional methodologies. Experimental group’s students were more attentive, confident and satisfied as compared to the control group’s students. This research work not only promoted the trend of digital game-based learning in developing countries but also supported that a pedagogically sound instructional design model utilized in an educational game can increase the motivation level of the students and can make the learning process a totally immersive and interactive fun loving activity.

Keywords: digital game-based learning, student’s motivation, instructional design model, learning process

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Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

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The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

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Authors: Guebli Abdelkader, Regiueg Madani, Sbaa Bouabdellah

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The goals of this study are to find ways to determine the value of dynamic priorities in motor learning between some basic skills in beginner’s basketball (U14), based on skills of shooting and defense against the shooter. Our role is to expose the statistical results in compare & correlation between samples of study in tests skills for the shooting and defense against the shooter. In order to achieve this objective, we have chosen 40 boys in middle school represented in four groups, two controls group’s (CS1, CS2) ,and two experimental groups (ES1: training on skill of shooting, skill of defense against the shooter, ES2: experimental group training on skill of defense against the shooter, skill of shooting). For the statistical analysis, we have chosen (F & T) tests for the statistical differences, and test (R) for the correlation analysis. Based on the analyses statistics, we confirm the importance of classifying priorities of basketball basic skills during the motor learning process. Admit that the benefits of experimental group training are to economics in the time needed for acquiring new motor kinetic skills in basketball. In the priority of ES2 as successful dynamic motor learning method to enhance the basic skills among beginner’s basketball.

Keywords: basic skills, basketball, motor learning, children

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Authors: Louis Okon Akpan

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National Open University of Nigeria (NOUN) is an open and distance learning institution whose aim is to provide education for all and also promote lifelong learning in Nigeria. Before now, student-centred learning was adopted. In recent times, online facilitation has been introduced. Therefore, the study explores ways in which students are satisfied with online facilitation provided by NOUN lecturers. A qualitative approach was adopted. The interpretive paradigm was employed as a lens to interpret narratives from the participants. In order to gather information for the study, a semi-structured interview was developed for sixteen participants who were purposively selected from eight facilities of the university. After data gathering from the field, it was subjected to transcription and coding. The emergence of themes from the coded data was analysed using thematic analysis. Findings indicated that students found online learning, recently introduced by the university management, extremely fulfilling and rewarding.

Keywords: online facilitation, lecturer, students’ satisfaction, National Open University of Nigeria

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Authors: Shanaka Gunasekara, Maleen Jayasuriya, Nalin Harischandra, Lilantha Samaranayake, Gamini Dissanayake

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One serious facet of the worsening Human-Elephant conflict (HEC) in nations such as Sri Lanka involves elephant-train collisions. Endangered Asian elephants are maimed or killed during such accidents, which also often result in orphaned or disabled elephants, contributing to the phenomenon of lone elephants. These lone elephants are found to be more likely to attack villages and showcase aggressive behaviour, which further exacerbates the overall HEC. Furthermore, Railway Services incur significant financial losses and disruptions to services annually due to such accidents. Most elephant-train collisions occur due to a lack of adequate reaction time. This is due to the significant stopping distance requirements of trains, as the full braking force needs to be avoided to minimise the risk of derailment. Thus, poor driver visibility at sharp turns, nighttime operation, and poor weather conditions are often contributing factors to this problem. Initial investigations also indicate that most collisions occur in localised “hotspots” where elephant pathways/corridors intersect with railway tracks that border grazing land and watering holes. Taking these factors into consideration, this work proposes the leveraging of recent developments in Convolutional Neural Network (CNN) technology to detect elephants using an RGB/infrared capable camera around known hotspots along the railway track. The CNN was trained using a curated dataset of elephants collected on field visits to elephant sanctuaries and wildlife parks in Sri Lanka. With this vision-based detection system at its core, a prototype unit of an early warning system was designed and tested. This weatherised and waterproofed unit consists of a Reolink security camera which provides a wide field of view and range, an Nvidia Jetson Xavier computing unit, a rechargeable battery, and a solar panel for self-sufficient functioning. The prototype unit was designed to be a low-cost, low-power and small footprint device that can be mounted on infrastructures such as poles or trees. If an elephant is detected, an early warning message is communicated to the train driver using the GSM network. A mobile app for this purpose was also designed to ensure that the warning is clearly communicated. A centralized control station manages and communicates all information through the train station network to ensure coordination among important stakeholders. Initial results indicate that detection accuracy is sufficient under varying lighting situations, provided comprehensive training datasets that represent a wide range of challenging conditions are available. The overall hardware prototype was shown to be robust and reliable. We envision a network of such units may help contribute to reducing the problem of elephant-train collisions and has the potential to act as an important surveillance mechanism in dealing with the broader issue of human-elephant conflicts.

Keywords: computer vision, deep learning, human-elephant conflict, wildlife early warning technology

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Authors: Felix E. Gbenoba, Opeyemi Dahunsi

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Self-instructional materials are at the heart of instructional delivery in Open and Distance Learning (ODL). The success of any ODL institution depends on the availability of instructional materials in quality and quantity. An ODL study material is expected to fully play the teacher plays in the face-to-face learning environment. In Nigeria, efforts to deliver ODL learning materials have been peculiarly challenging. Although researchers are unrelenting in hewing out ways to make ODL delivery in Africa generally and Nigeria in particular, meet the learners’ needs and acceptable global practices, the prospects of integrating instructional media into distance learning courses are largely unexplored. In the present study, we critically examine the prospects of integration of instructional media into ODL courses for pedagogic and other benefits it portends for delivery via the distance learning mode. Although efforts to integrate media in ODL have been recorded before now, the reality has not matched the expectation so far in Nigeria. This does not mean that the existing instructional materials have not produced any significant positive results in improving the overall learning (and teaching) experience in its institutions; it implies that increased integration as suggested here will further improve the experience as well as bring up the new challenges. Obstacles and problems of instructional materials and media development that could have affected the open educational resource initiatives are well established. The first aspect of this paper recalls the revolutionary strides that ODL brought to delivery of education in Nigeria particularly. The other aspect is on what instructional media are, their role, prospects and challenges for ODL in Nigeria; these are examined vis a vis the challenges of development, production and distribution of print instructional materials as the major format of instructional delivery at Nigeria’s only single mode ODL institution, NOUN. In the third aspect, we justify the need and benefits of integrating instructional media into the courses and make recommendations.

Keywords: instructional delivery, instructional media, ODL, media integration, Nigeria, self-instructional materials

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

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Authors: P. Maheshwari, M. Brindavan

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The present research aimed to study the level of socio-emotional skills and perceived self-efficacy of children with learning disability. The study further investigated the relationship between the levels of socio-emotional skills, perceived self-efficacy and academic achievement of children with learning disability. The sample comprised of 40 children with learning disability as their primary condition, belonging to middle or upper middle class, living with both the parents, residing in Mumbai. Purposive or Judgmental and snowball sampling technique was used to select the sample for the study. Proformas in the form of questionnaires were used to obtain the background information of the children with learning disability. A self-constructed Child’s Perceived Self-Efficacy Assessment Scale and Child’s Social and Emotional Skills Assessment Scale was used to measure the level of child’s perceived self-efficacy and their level of social and emotional skill respectively. Academic scores of the child were collected from the child’s parents or teachers and were converted into a percentage. The data was analyzed quantitatively using SPSS. Spearman rho or Pearson Product Moment correlation was used to ascertain the multiple relationships between child’s perceived self-efficacy, child’s social and emotional skills and child’s academic achievement. The findings revealed majority (27) of the children with learning disability perceived themselves having above average level of social and emotional skills while 13 out of 40 perceived their level of social and emotional skills at an average level. Domain wise analyses revealed that, in the domain of self- management (26) and relationship skills (22) more number of the children perceived themselves as having average or below average level of social and emotional skills indicating that they perceived themselves as having average or below average skills in regulating their emotions, thoughts, and behaviors effectively in different situations, establishing and maintaining healthy and rewarding relationships with diverse groups and individuals. With regard to perceived self-efficacy, the majority of the children with learning disability perceived themselves as having above average level of self-efficacy. Looking at the data domain wise it was found that, in the domains of self-regulated learning and emotional self-efficacy, 50% of the children perceived themselves at average or below average level, indicating that they perceived themselves as average on competencies like organizing academic activities, structuring environment to make it conducive for learning, expressing emotions in a socially acceptable manner. Further, the correlations were computed, and significant positive correlations were found between children’s social and emotional skills and academic achievement (r=.378, p < .01), and between children’s social and emotional skills and child’s perceived self-efficacy (r = .724, p < .01) and a positive significant correlation was also found between children’s perceived self-efficacy and academic achievement (r=.332, p < .05). Results of the study emphasize on planning intervention for children with learning disability focusing on improving self-management and relationship skills, self-regulated learning and emotional self-efficacy.

Keywords: learning disability, social and emotional skills, perceived self-efficacy, academic achievement

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