Search results for: learning effect

Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Nguyen J. M., Lucas G., Brunner M., Ruan S., Antonioli D.

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Deep learning based on convolutional neural networks (CNN) is a very powerful technique for classifying information from an image. We propose a new method, DeepNic, to transform each variable of a tabular dataset into an image where each pixel represents a set of conditions that allow the variable to make an error-free prediction. The contrast of each pixel is proportional to its prediction performance and the color of each pixel corresponds to a sub-family of NICs. NICs are probabilities that depend on the number of inputs to each neuron and the range of coefficients of the inputs. Each variable can therefore be expressed as a function of a matrix of 2 vectors corresponding to an image whose pixels express predictive capabilities. Our objective is to transform each variable of tabular data into images into an image that can be analysed by CNNs, unlike other methods which use all the variables to construct an image. We analyse the NIC information of each variable and express it as a function of the number of neurons and the range of coefficients used. The predictive value and the category of the NIC are expressed by the contrast and the color of the pixel. We have developed a pipeline to implement this technology and have successfully applied it to genomic expressions on an Affymetrix chip.

Keywords: tabular data, deep learning, perfect trees, NICS

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Authors: Martha Karunakar

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There exists a wide divide between the urban and the rural students in a vast country like India. This dichotomy is seen in the resources available to them, like the learning facilities, the infra-structure, the learning ambience and meeting of their basic needs of food, clothing and shelter. This paper discusses the effect of English language teaching as a Bridge course on a select batch of rural students at an Engineering college in Chennai, one of the four Metros of India. The study aims to understand how the teacher input and the teacher- peer-student interaction facilitates the acquisition of the basic structures of the English language to a group that is minimally exposed to the language. The objective in conducting the Bridge Course is to integrate these rural students into the mainstream and empower them in terms of English speaking ability; to enable them to comprehend their respective engineering classes where the medium of instruction is English and also to be able to interact with their urban peers. This program is conducted prior to the start of a regular academic session to equip them face the rigors of engineering education. The study is placed within the framework of Interaction theory in second language acquisition. The study evaluates the impact of linking theory and practice by implementing meaningful interaction not only within classrooms but also in the common areas. By providing intensive comprehensible input, it is anticipated that participant’s level of English language improves. The teaching methods and classroom activities included individual and group participation, encompassing all the four skills of listening, speaking, reading and writing (LSRW). The diagnostic tests that were administered before the commencement of the course and the exit test after the completion were used to record the impact of the training.

Keywords: comprehensible input, interaction, rural students, teaching English

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Authors: Kevin R. Wilson, Roger Mantie

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Community arts-based learning and participatory education are pedagogical techniques that serve to be advantageous for students, curriculum development, and local communities. Using an interpretive approach to examine the significance of this arts-informed research in relation to the eight ‘design imperatives’ proposed as the new model for measuring quality in scholarship for Arizona State University as ‘A New American University’, the purpose of this study was to investigate personal, social, and cultural benefits resulting from student engagement in interdisciplinary community-based projects. Students from a graduate level music education class at the ASU Tempe campus (n=7) teamed with students from an undergraduate level community development class at the ASU Downtown Phoenix campus (n=14) to plan, facilitate, and evaluate seven community-based projects in several locations around the Phoenix-metro area. Data was collected using photo evidence, student reports, and evaluative measures designed by the students. The effectiveness of each project was measured in terms of their ability to meet the eight design imperatives to: 1) leverage place; 2) transform society; 3) value entrepreneurship; 4) conduct use-inspired research; 5) enable student success; 6) fuse intellectual disciplines; 7) be socially embedded; and 8) engage globally. Results indicated that this community arts-based project sufficiently captured the essence of each of these eight imperatives. Implications for how the nature of this interdisciplinary initiative allowed for the eight imperatives to manifest are provided, and project success is expounded upon in relation to utility of each imperative. Discussion is also given for how this type of service learning project formatted within the ‘New American University’ model for measuring quality in academia can be a beneficial pedagogical tool in higher education.

Keywords: community arts-based learning, participatory education, pedagogy, service learning

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Authors: May Ling González-Ruiz, Ana Cristina Solís-Solís

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Everyday more people can thrive towards the dream of pursuing a university diploma. This can be more attainable for some than for others who may face different types of limitations. Even though not all limitations come from within the individual but most of the times they come from without it may include the environment, the support of the person’s family, the school – its infrastructure, administrative procedures, and attitudes. This is a qualitative type of research that is developed through a case study. It is based on the experiences of two students who are visually impaired and who have attended a public university in Costa Rica. We enquire about the experiences of these two students in the English as a Foreign Language courses at the university scenario. An in-depth analysis of their lived experiences is presented. Their values, attitudes, and expectations serve as the guiding elements for this research. Findings are presented in light of the Social Justice Approach to inclusive education. Some of the most salient aspects found have to do with the attitudes the students used to face challenges; others point at those elements that may have hindered the learning experience of the persons observed and to those that encouraged them to continue their journey and successfully achieve a diploma.

Keywords: inclusion, case study, visually impaired student, learning experience, social justice approach

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Authors: Tesfaye Sisay Dessalegn

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The long-term performance of concrete structures is affected by the properties and behavior of concrete at an early age. However, the fundamental mechanisms affecting the early-age behavior of concrete have not yet been fully studied. The effect of water temperature on concrete is not sufficiently studied, and at the same time, the majority of studies focused on the effect of mixing water temperature on the workability and mechanical properties of concrete. However, to the best of the authors' knowledge, the effect of mixing water temperatures on plastic shrinkage cracking of concrete has not been studied yet.

Keywords: water temperature, early age concrete strength, mechanical properties of concrete, strength

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Authors: Saeed Eskandari, Seyed Rasoul Mehdikhani

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Radiation sources have been used in many industries, such as gamma sources in medical imaging. These waves have destructive effects on humans and the environment. It is very important to detect and find the source of these waves because these sources cannot be seen by the eye. A portable robot has been designed and built with the purpose of revealing radiation sources that are able to scan the place from 5 to 20 meters away and shows the location of the sources according to the intensity of the waves on a two-dimensional digital image. The operation of the robot is done by measuring the pixels separately. By increasing the image measurement resolution, we will have a more accurate scan of the environment, and more points will be detected. But this causes a lot of time to be spent on scanning. In this paper, to overcome this challenge, we designed a method that can optimize this time. In this method, a small number of important points of the environment are measured. Hence the remaining pixels are predicted and estimated by regression algorithms in machine learning. The research method is based on comparing the actual values of all pixels. These steps have been repeated with several other radiation sources. The obtained results of the study show that the values estimated by the regression method are very close to the real values.

Keywords: regression, machine learning, scan radiation, robot

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Authors: I. A. Libata

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The present study was carried out to determine the attitudes of Secondary School Students towards Biology in Birnin Kebbi metropolis. The population of the study is 2680 SS 2 Secondary School Students in Birnin Kebbi metropolis. Proportionate random sampling was used in selecting the samples. Oppinnionnaire was the only instrument used in the study. The instrument was subjected to test-retest reliability. The reliability index of the instrument was 0.69. Overall scores of the Students were analyzed and a mean score was determined, the mean score of students was 85. There were no significant differences between the attitudes of male and female students. The results also revealed that there was significant difference between the attitude of science and art students. The results also revealed that there was significant difference between the attitude of public and private school students. The study also reveals that majority of students in Birnin Kebbi Metropolis have positive attitudes towards biology. Based on the findings of this study, the researcher recommended that teachers should motivate students, which they can do through their teaching styles and by showing them the relevance of the learning topics to their everyday lives. Government and the school management should create the learning environment that helps motivate students not only to come to classes but also want to learn and enjoy learning Biology.

Keywords: attitudes, students, Birnin-Kebbi, metropolis

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Authors: Tatjana Stefanovic Stanojevic, Milica Tosic Radev, Aleksandra Bogdanovic

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Depression is considered to be a leading cause of death and disability in the female population, and that is the reason why understanding the dynamics of the onset of depressive symptomatology is important. A review of the literature indicates the relationship between depressive symptoms and insecure attachment patterns, but very few studies have examined the mechanism underlying this relation. The aim of the study was to examine the pathway from the preoccupied attachment pattern to depressive symptomatology, as well as to test the mediation effect of mentalization, social anxiety and rumination in this relationship using a serial mediation model. The research was carried out on a geographical cluster sample from the general population of Serbia included within the project ‘Indicators and models of family and work roles harmonization’ funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia. This research was carried out on a subsample of 791 working-age female adults from 37 urban and rural locations distributed through 20 administrative districts of Serbia. The respondents filled in a battery of instruments, including Relationship Questionnaire - Clinical Version (RQ - CV), The Mentalization Scale (MentS), Scale of Social Anxiety (SA), Patient Ruminative Thought Style Questionnaire (RTSQ), Health Questionnaire (PHQ-9). The results confirm our assumption that the total indirect effect of the preoccupied attachment pattern to depressive symptoms is significant across all mediators separately. More importantly, this effect is still present in a model with a sequential mediator relationship, where social anxiety, rumination, and mentalization were perceived as serial mediators of a relationship between preoccupied attachment and depressive symptoms (estimated indirect effect=0.004, boot-strapped 95% CI=0.002 to 0.007). Our findings suggest that there is a significant specific indirect effect of the preoccupied attachment pattern to depressive symptoms, occurring through mentalization, social anxiety and rumination, indicating that preoccupied attachment cause decrease of a self related mentalization, which in turn causes increasing of social anxiety and rumination, concluding in depressive symptoms as a final consequence. The finding that the path from the preoccupied attachment pattern to depressive symptoms is typical in women is understandable from the perspective of both evolutionary and culturally conditioned gender differences. The practical implications of the study are reflected in the recommendations for the prevention and forehand psychotherapy response among preoccupied women with depressive symptomatology. Treatment of this specific group of depressed patients should be focused on strengthening mentalization, learning to accept and to understand herself better, reducing anxiety in situations where mistakes are visible to others, and replacing the rumination strategy with more constructive coping strategies.

Keywords: preoccupied attachment, depression, serial mediation model, mentalization, rumination

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Authors: Ahmad Saleem

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Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

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Authors: Inkeri Jaaskelainen

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The aim of this study is to shed light on what is it like to study in apprenticeship training using intermediate (or even lower level) Finnish. The aim is to find out and describe these students' experiences and feelings while acquiring a profession in Finnish as it is important to understand how immigrant background adult learners learn and how their needs could be better taken into account. Many students choose apprenticeships and start vocational training while their language skills in Finnish are still very weak. At work, students should be able to simultaneously learn Finnish and do vocational studies in a noisy, demanding, and stressful environment. Learning and understanding new things is very challenging under these circumstances, and sometimes students get exhausted and experience a lot of stress - which makes learning even more difficult. Students are different from each other, and so are their ways to learn. Both duties at work and school assignments require reasonably good general language skills, and, especially at work, language skills are also a safety issue. The empirical target of this study is a group of students with an immigrant background who studied in various fields with intensive L2 support in 2016–2018 and who by now have completed a vocational upper secondary qualification. The interview material for this narrative study was collected from those who completed apprenticeship training in 2019–2020. The data collection methods used are a structured thematic interview, a questionnaire, and observational data. Interviewees with an immigrant background have an inconsistent cultural and educational background - some have completed an academic degree in their country of origin while others have learned to read and write only in Finland. The analysis of the material utilizes thematic analysis, which is used to examine learning and related experiences. Learning a language at work is very different from traditional classroom teaching. With evolving language skills, at an intermediate level at best, rushing and stressing makes it even more difficult to understand and increases the fear of failure. Constant noise, rapidly changing situations, and uncertainty undermine the learning and well-being of apprentices. According to preliminary results, apprenticeship training is well suited to the needs of an adult immigrant student. In apprenticeship training, students need a lot of support for learning and understanding a new communication and working culture. Stress can result in, e.g., fatigue, frustration, and difficulties in remembering and understanding. Apprenticeship training can be seen as a good path to working life. However, L2 support is a very important part of apprenticeship training, and it indeed helps students to believe that one day they will graduate and even get employed in their new country.

Keywords: apprenticeship training, vocational basic degree, Finnish learning, wee-being

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Authors: Assem Sallam, Aly Elzahaby, Ahmed Makled, Mohamed Khalil

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Change in design parameters of launch vehicle affects its overall flight path trajectory. In this paper, several design parameters are introduced to study their effect. Selected parameters are the launch vehicle mass, which is presented in the form of payload mass, the maximum allowable angle of attack the launch vehicle can withstand, the flight path angle that is predefined for the launch vehicle second stage, the required inclination and its effect on the launch azimuth and finally by changing the launch pad coordinate. Selected design parameters are studied for their effect on the variation of altitude, ground range, absolute velocity and the flight path angle. The study gives a general mean of adjusting the design parameters to reach the required launch vehicle performance.

Keywords: launch vehicle azimuth, launch vehicle trajectory, launch vehicle payload, launch pad location

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Authors: M. M. Fercan, A. S. Kipcak, O. Dere Ozdemir, M. B. Piskin, E. Moroydor Derun

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Coffee is a widely consumed beverage with many components such as caffeine, flavonoids, phenolic compounds, and minerals. Coffee consumption continues to increase due to its physiological effects, its pleasant taste, and aroma. Robusta and Arabica are two basic types of coffee beans. The coffee bean used for Turkish coffee is Arabica. There are many elements in the structure of coffee and have various effect on human health such as Sodium (Na), Boron (B), Magnesium (Mg) and Iron (Fe). In this study, the amounts of Mg, Na, Fe, and B contents in Turkish coffee are determined and effect of sugar addition is investigated for conscious consumption. The analysis of the contents of coffees was determined by using inductively coupled plasma optical emission spectrometry (ICP-OES). From the results of the experiments the Mg, Na, Fe and B contents of Turkish coffee after sugar addition were found as 19.83, 1.04, 0.02, 0.21 ppm, while without using sugar these concentrations were found 21.46, 0.81, 0.008 and 0.16 ppm. In addition, element contents were calculated for 1, 3 and 5 cups of coffee in order to investigate the health effects.

Keywords: health effect, ICP-OES, sugar, Turkish coffee

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Authors: Isari Pairoa, Proud Arunrangsiwed

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Celebrity and brand mascot endorsement have been explored for more than three decades. Both endorsers can effectively transfer their reputation to corporate image and can influence the customers to purchase the product. However, there was little known about the mediators between the level of endorsement and its effect on buying behavior. The objective of the current study is to identify the gab of the previous studies and to seek possible mediators. It was found that consumer’s memory and identification are the mediators, of source credibility and endorsement effect. A future study should confirm the model of endorsement, which was established in the current study.

Keywords: product endorsement, memory, identification theory, source credibility, unintentional effect

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Authors: A. Shebani, S. Iwnicki

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Understanding the nature of wheel and rail wear in the railway field is of fundamental importance to the safe and cost effective operation of the railways. Twin disc wear testing is used extensively for studying wear of wheel and rail materials. The University of Huddersfield twin disc rig was used in this paper to examine the effect of surface conditions on wheel and rail wear measurement under a range of wheel/rail contact conditions, with and without contaminants. This work focuses on an investigation of the effect of dry, wet, and lubricated conditions and the effect of contaminants such as sand on wheel and rail wear. The wheel and rail wear measurements were carried out by using a replica material and an optical profilometer that allows measurement of wear in difficult location with high accuracy. The results have demonstrated the rate at which both water and oil reduce wheel and rail wear. Scratches and other damage were seen on the wheel and rail surfaces after the addition of sand and consequently both wheel and rail wear damage rates increased under these conditions. This work introduced the replica material and an optical instrument as effective tools to study the effect of surface conditions on wheel and rail wear.

Keywords: railway wheel/rail wear, surface conditions, twin disc test rig, replica material, Alicona profilometer

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Authors: Rohan Roberts

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The purpose of this project was to collaborate with a group of high-functioning, more-able students (aged 15-18) to promote STEM Education and a love for science by bringing a cosmic perspective into the classroom and high school environment. This was done using 21st century science of learning, a focus on the latest research on Neuroeducation, and modern pedagogical methods based on Howard Gardner's theory of Multiple Intelligences, Bill Lucas’ theory of New Smarts, and Sir Ken Robinson’s recommendations on encouraging creativity. The result was an increased sense of passion, excitement, and wonder about science in general, and about the marvels of space and the universe in particular. In addition to numerous unique and innovative science-based initiatives, clubs, workshops, and science trips, this project also saw a marked rise in student-teacher collaboration in science learning and in student engagement with the general public through the press, social media, and community-based initiatives. This paper also outlines the practical impact that bringing a cosmic perspective into the classroom has had on the lives, interests, and future career prospects of the students involved in this endeavour.

Keywords: cosmic perspective, gifted and talented, neuro-education, STEM education

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Ava Dodd, Jonathan Adams

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The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: Colleen Cleveland, W. Adam Baldowski

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In the dynamic realm of online education, the integration of games and entertainment has emerged as a powerful strategy to captivate learners, drive active participation, and cultivate meaningful learning experiences. This abstract presents an overview of the upcoming conference, "Game On," dedicated to exploring the transformative impact of gamification, interactive simulations, and multimedia content in the digital learning landscape. Introduction: The conference aims to blur the traditional boundaries between education and entertainment, inspiring learners of diverse ages and backgrounds to actively engage in their online learning journeys. By leveraging the captivating elements of games and entertainment, educators can enhance motivation, retention, and deep understanding among virtual classroom participants. Conference Highlights: Commencing with an exploration of theoretical foundations drawing from educational psychology, instructional design, and the latest pedagogical research, participants will gain valuable insights into the ways gamified elements elevate the quality of online education. Attendees can expect interactive sessions, workshops, and case studies showcasing best practices and innovative strategies, including game-based assessments and virtual reality simulations. Inclusivity and Diversity: The conference places a strong emphasis on inclusivity, accessibility, and diversity in the integration of games and entertainment for educational purposes. Discussions will revolve around accommodating diverse learning styles, overcoming potential challenges, and ensuring equitable access to engaging educational content for all learners. Educational Transformation: Educators, instructional designers, and e-learning professionals attending "Game On" will acquire practical techniques to elevate the quality of their online courses. The conference promises a stimulating and informative exploration of blending education with entertainment, unlocking the untapped potential of games and entertainment in online education. Conclusion: "Game On" invites participants to embark on a journey that transforms online education by harnessing the power of entertainment. This event promises to be a cornerstone in the evolution of virtual learning, offering valuable insights for those seeking to create a more engaging and effective online educational experience. Join us as we explore new horizons, pushing the boundaries of online education through the fusion of games and entertainment.

Keywords: online education, games, entertainment, psychology, therapy, pop culture

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Authors: Shaan Khosla, Jon Krohn

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In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

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Authors: Hirokatsu Kawashima

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In an attempt to elucidate relationships between listening to music and pronunciation learning, a classroom-based investigation was conducted with Japanese EFL learners (n=45). The subjects were instructed to listen to English songs they liked on YouTube, especially paying attention to phonologically similar vowel and consonant minimal pair words (e.g., live and leave). This kind of activity, which included taking notes, was regularly carried out in the classroom, and the same kind of task was given to the subjects as homework in order to reinforce the in-class activity. The duration of these activities was eight weeks, after which the program was evaluated on a 9-point scale (1: the lowest and 9: the highest) by learners’ self-evaluation. The main questions for this evaluation included 1) how good the learners had been at pronouncing vowel and consonant minimal pair words originally, 2) how often they had listened to songs good for pronouncing vowel and consonant minimal pair words, 3) how frequently they had moved their mouths to vowel and consonant minimal pair words of English songs, and 4) how much they thought the program would support and enhance their pronunciation learning of phonologically similar vowel and consonant minimal pair words. It has been found, for example, A) that the evaluation of this program is by no means low (Mean: 6.51 and SD: 1.23), suggesting that listening to music may support and enhance pronunciation learning, and B) that listening to consonant minimal pair words in English songs and moving the mouth to them are more related to the program’s evaluation (r =.69, p=.00 and r =.55, p=.00, respectively) than listening to vowel minimal pair words in English songs and moving the mouth to them (r =.45, p=.00 and r =.39, p=.01, respectively).

Keywords: minimal pair, music, pronunciation, song

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Authors: Saifunnahar

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As a former English colony, the relationship of Bangladesh with the English language goes a long way back. English is taught as a compulsory subject in Bangladesh from an early age starting from grade 1 and continuing through the 12th, yet, students are not competent enough to communicate in English proficiently. To improve students’ English language competency, the Bangladesh Ministry of Education introduced communicative language teaching (CLT) methods in English classrooms in the 1990s. It has been decades since this effort was taken, but the students’ level of proficiency is still not satisfactory. The main reason behind this failure is that CLT-based teaching-learning methods have not been effectively implemented. Very little research has been conducted to address the issues English as a foreign language (EFL) classrooms are facing to carry out CLT methodologies in secondary schools (grades 6 to 10) in Bangladesh. Though the secondary level is crucial for students’ language learning and retention, EFL classrooms are marked with various issues that make teaching-learning harder for teachers and students. This study provides an overview of the status of CLT in EFL classrooms and the reasons behind failing to implement CLT in secondary schools in Bangladesh through an analysis of the qualitative data collected from different literature. Based on the findings, effective approaches have been recommended to employ CLT in EFL classrooms.

Keywords: Bangladesh, communicative language teaching, English as a foreign language, secondary schools, pedagogy

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Fatima Ghebghoub

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The solvent extraction of cobalt (II) from sulfate medium using di(2-ethylhexy1) phosphoric acid (D2EHPA, HL) at 25°C has been investigated. The influence of the following parameters was studied: the equilibrium pH, the concentration of the extractant and the nature of diluent. The effect of the diluent using polar and non-polar solvents in the extraction of nickel(II) is discussed. The extracted nickel (II species were found to be CoL2 in 1-octanol and methyl isobutyl ketone and CoL2.2HL in toluene, dichloromethane, chloroform, carbon tetrachloride and cyclohexane. The extraction constants are evaluated for the different diluents.

Keywords: liquid-liquid extraction, cobalt(II), di(2-ethylhexyl) phosphoric acid, diluent effect

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Authors: Aminath Waseela, Vinesh Chandra, Shaun Nykvist,

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The success of technology integration initiatives hinges on the knowledge and skills of teachers to effectively integrate technology in classroom teaching. Consequently, gaining an understanding of teachers' technology knowledge and its integration can provide useful insights on strategies that can be adopted to enhance teaching and learning, especially in developing country contexts where research is scant. This paper extends existing knowledge on teachers' use of technology by developing a conceptual framework that recognises how three key types of knowledge; content, pedagogy, technology, and their integration are at the crux of teachers' technology use while at the same time is amenable to empirical studies. Although the aforementioned knowledge is important for effective use of technology that can result in enhanced student engagement, literature on how this knowledge leads to effective technology use and enhanced student engagement is limited. Thus, this theoretical paper proposes a framework to explore teachers' knowledge through the lens of the Technological Pedagogical and Content Knowledge (TPACK); the integration of technology in classroom teaching through the Substitution Augmentation Modification and Redefinition (SAMR) model and how this affects students' learning through the Bloom's Digital Taxonomy (BDT) lens. Studies using this framework could inform the design of professional development to support teachers to develop skills for effective use of available technology that can enhance student learning engagement.

Keywords: information and communication technology, ICT, in-service training, small island developing states, SIDS, student engagement, technology integration, technology professional development training, technological pedagogical and content knowledge, TPACK

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Authors: Ifeanyi Adigwe, Thomas Van der Walt

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Digital media has been integrated into the social and entertainment life of young children, and as such, the impact of digital media appears to affect young people of all ages and it is believed that this will continue to shape the world of young children. Since, technological advancement of digital media presents adolescents with diverse contexts, platforms and avenues to engage with digital media outside the home environment and from parents' supervision, a wide range of new challenges has further complicated the already difficult tasks for parents and altered the landscape of parenting. Despite the fact that adolescents now have access to a wide range of digital media technologies both at home and in the learning environment, parenting practices such as active, restrictive, co-use, participatory and technical mediations are important in mitigating of online risks adolescents may encounter as a result of digital media use. However, these mediation practices only focus on the home environment including digital media present in the home and may not necessarily transcend outside the home and other learning environments where adolescents use digital media for school work and other activities. This poses the question of who mediates adolescent's digital media use outside the home environment. The learning environment could be a ''loose platform'' where an adolescent can maximise digital media use considering the fact that there is no restriction in terms of content and time allotted to using digital media during school hours. That is to say that an adolescent can play the ''bad boy'' online in school because there is little or no restriction of digital media use and be exposed to online risks and play the ''good boy'' at home because of ''heavy'' parental mediation. This is the reason why parent mediation practices have been ineffective because a parent may not be able to track adolescents digital media use considering the diversity of contexts, platforms and avenues adolescents use digital media. This study argues that due to the diverse nature of digital media technology, parents may not be able to monitor the 'whereabouts' of their children in the digital space. This is because adolescent digital media usage may not only be confined to the home environment but other learning environments like schools. This calls for urgent attention on the part of teachers to understand the intricacies of how digital media continue to shape the world in which young children are developing and learning. It is, therefore, imperative for parents to liaise with the schools of their children to mediate digital media use during school hours. The implication of parents- teachers mediation practices are discussed. The article concludes by suggesting that third party mediation by teachers in schools and other learning environments should be encouraged and future research needs to consider the emergent strategy of teacher-children mediation approach and the implication for policy for both the home and learning environments.

Keywords: digital media, digital age, parent mediation, third party mediation

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Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

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The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

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Authors: Ruth Nsibirano

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Online mode of delivery in higher institutions of learning, popularly known in some circles as e-Learning or distance education is a new phenomenon that is steadily taking root in African universities but specifically at Makerere University. For slightly over a decade, the Department of Open and Distance Learning has been offering the first generation mode of distance education. In this, learning and teaching experiences were based on the use of hard copy materials circulated through postal services in a rather correspondence mode. There were more challenges to this including high dropout rates, limited support to the learners and sustainability issues. Fortunately, the Department was supported by the Norwegian Government through a NORHED grant to “leapfrog” to the fifth generation of distance education that makes more use of educational technologies and tools. The capacity of faculty staff was gradually enhanced through a series of training to handle the upgraded structure of fifth generation distance education. The trained staff was then tasked to develop modules befitting an online delivery mode, for use on the program. This paper will present attitudes, experiences of the course writers with a view of sharing the good practices that enabled them leap from e-faculty trainees to distinct online course writers. This perspective will hopefully serve as building blocks to enhance the capacity of other upcoming distance education programs in low capacity universities and also promote the uptake of e-Education on the continent and beyond. Methodologically the findings were collected through individual interviews with the 30 course writers. In addition, semi structured questionnaires were designed to collect data on the profile, challenges and lessons from the writers. Findings show that the attitudes of course writers on project supported activities are so much tagged to the returns from their committed efforts. In conclusion, therefore, it is strategically useful to assess and selectively choose which individual to nominate for involvement at the initial stages.

Keywords: distance education, online course content, staff attitudes, best practices in online learning

Procedia PDF Downloads 253

Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

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With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: machine learning, imbalanced data, data mining, big data

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Authors: Adel Elomri

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The notion of carbon footprinting is ever more widespread as companies are becoming increasingly aware that tackling carbon emissions and being seen to do so is a key issue to face governments, customers and other stakeholders’ pressures towards delivering environmentally friendly services and activities. In this contest, many firms are taking self-initiatives to reduce their own carbon emissions while some other are constrained to obey to different regulations/policies (e.g. carbon tax or carbon Cap) designed by higher authorities targeting a low-carbon environment. Using buyer-vendor framework, this paper provides some insights on how effective are these self-initiatives and regulatory policies when only concerning firms at the individual level and not the whole supply chain they are part of. We show that when firms individually engage in reducing their direct carbon emissions either under self-initiatives or regulatory policy, an opposite expected outcome resulting in a higher global supply chain emission can occur. This effect is referred to as the carbon seesaw effect. Moreover, we show that coordinating or centralizing the supply chain -contrary to what one may think at first- is not often the appropriate solution to get rid of this effect.

Keywords: carbon emissions, supply chain coordination, EOQ, sustainable operations

Procedia PDF Downloads 319