Search results for: learning science

Authors: Eyibo Ebengeobong Eddie, Halil Zafer Alibaba

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The neglect of classroom acoustics is dominant in most educational facilities, meanwhile, hearing and listening is the learning process in this kind of facilities. A classroom should therefore be an environment that encourages listening, without an obstacles to understanding what is being taught. Although different studies have shown teachers to complain that noise is the everyday factor that causes stress in classroom, the capacity of individuals to understand speech is further affected by Echoes, Reverberation, and room modes. It is therefore necessary for classrooms to have an ideal acoustics to aid the intelligibility of students in the learning process. The influence of these acoustical parameters on learning and teaching in schools needs to be further researched upon to enhance the teaching and learning capacity of both teacher and student. For this reason, there is a strong need to provide and collect data to analyse and define the suitable quality of classrooms needed for a learning environment. Research has shown that acoustical problems are still experienced in both newer and older schools. However, recently, principle of acoustics has been analysed and room acoustics can now be measured with various technologies and sound systems to improve and solve the problem of acoustics in classrooms. These acoustic solutions, materials, construction methods and integration processes would be discussed in this paper.

Keywords: classroom, acoustics, materials, integration, speech intelligibility

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Authors: Mimoun Melliti

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In the wake of the global pandemic, educational systems worldwide faced unprecedented challenges and had to swiftly adapt to new conditions. This necessitated a fundamental shift in assessment processes, as traditional in-person exams became impractical. The present paper aims to investigate how educational systems have adapted to the new conditions imposed by the outbreak of the pandemic. This paper serves as a case study documenting the various decisions, conditions, experiments, and outcomes associated with transitioning the assessment processes of a higher education institution to a fully online format. The participants of this study consisted of 4666 students from health, engineering, science, and humanities disciplines, who were enrolled in general English (Eng101/104) and English for specific purposes (Eng102/113) courses at a preparatory year institution in Saudi Arabia. The findings of this study indicate that online assessment can be effectively implemented given the fulfillment of specific requirements. These prerequisites encompass the presence of competent staff, administrative flexibility, and the availability of necessary infrastructure and technological support. The significance of this case study lies in its comprehensive description of the various steps and measures undertaken to adapt to the "new normal" situation. Furthermore, it evaluates the impact of these measures and offers detailed recommendations for potential similar future scenarios.

Keywords: hybrid learning, testing, adaptive teaching, EFL

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Authors: Alona Faktor

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In this work, we present a NN-based computational model that can perform attention shifts according to high-level instruction. The instruction specifies the type of attentional shift using explicit geometrical relation. The instruction also can be of cognitive nature, specifying more complex human-human interaction or human-object interaction, or object-object interaction. Applying this approach sequentially allows obtaining a structural description of an image. A novel data-set of interacting humans and objects is constructed using a computer graphics engine. Using this data, we perform systematic research of relational segmentation shifts.

Keywords: cognitive science, attentin, deep learning, generalization

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Authors: Kai Qian, Lixin Tao

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Mobile devices such as smartphones are getting more and more popular in our daily lives. The security vulnerability and threat attacks become a very emerging and important research and education topic in computing security discipline. There is a need to have an innovative mobile security hands-on laboratory to provide students with real world relevant mobile threat analysis and protection experience. This paper presents an authentic teaching and learning mobile security approach with smartphone devices which covers most important mobile threats in most aspects of mobile security. Each lab focuses on one type of mobile threats, such as mobile messaging threat, and conveys the threat analysis and protection in multiple ways, including lectures and tutorials, multimedia or app-based demonstration for threats analysis, and mobile app development for threat protections. This authentic learning approach is affordable and easily-adoptable which immerse students in a real world relevant learning environment with real devices. This approach can also be applied to many other mobile related courses such as mobile Java programming, database, network, and any security relevant courses so that can learn concepts and principles better with the hands-on authentic learning experience.

Keywords: mobile computing, Android, network, security, labware

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Authors: Mehrab Karimian

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The present research aimed to investigate the problems of teaching and learning English in middle schools in Esfahan, Iran. These problems are associated with the learner, teacher, textbook, syllabus, and language policy. The instrument used was a self-constructed likert scale questionnaire. All the variables had a hand in the problems among which textbook, syllabus and language policy had the most effect. Twenty five problems were distinguished among which some are as follows: students do not consider pair work important; most of the time, most teachers do not speak in English in the classroom; the textbook does not include CDs or cassettes, does not consists of all the English Skills; the syllabus does not include one or two projects for students apart from the midterm or final test, Language Policy being not completely familiar with the steps of EFL teaching, does not selecting the most qualified and proficient teachers in EFL teaching. It can be concluded that the language policy should take a practical step in reducing the problems by changing the textbooks and providing more teaching aids for the teachers.

Keywords: teaching and learning english, problems of teaching and learning english, middle school, Iran

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Authors: Martina Holenko Dlab, Natasa Hoic-Bozic

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This paper presents an original approach to student and group activity level assessment that relies on certainty factors theory. Activity level is used to represent quantity and continuity of student’s contributions in individual and collaborative e‑learning activities (e‑tivities) and is calculated to assist teachers in assessing quantitative aspects of student's achievements. Calculated activity levels are also used to raise awareness and provide recommendations during the learning process. The proposed approach was implemented within the educational recommender system ELARS and validated using data obtained from e‑tivity realized during a blended learning course. The results showed that the proposed approach can be used to estimate activity level in the context of e-tivities realized using Web 2.0 tools as well as to facilitate the assessment of quantitative aspect of students’ participation in e‑tivities.

Keywords: assessment, ELARS, e-learning, recommender systems, student model

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Authors: Tian Luo, Wing Mui Winnie So

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Gaining career awareness in STEM is an important educational outcome in STEM education. While many studies focused on students’ understanding of scientists and engineers, very few studies explore students’ perceptions of technologists as a group of STEM professionals. In this study, 300 valid surveys which include drawing task and follow-up questions about technologist were collected from 4th to 6th grade students. The results showed that 75.1% of the students draw a technologist as a male and 19.3% draw a technologist as a female. Most students believe that technologists use math, science or engineering in their work and can name a few categories of technologists. The drawings also showed that students tend to present technologists as people who work with a computer.

Keywords: STEM (Science, Technology, Engineering, and Mathematics) education, elementary students, technologist, STEM careers

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Authors: Priyadarshini Kiran, Gulshan Kumar

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: Social media are becoming the most important tools for interaction among learners, pedagogues and parents where everybody can share, exchange, comment, discuss and create information and knowledge in a collaborative way. The present case study attempts to highlight the role of social media (WhatsApp) in nurturing learning habits in toddlers with the help of parents in primary education. The Case study is based on primary data collected from a primary school situated in a small town in the northern state of Uttar Pradesh, India. In research methodology, survey and structured interviews have been used as a tool collected from parents and pedagogues. The findings Suggest: - To nurture learning habits in toddlers, parents and pedagogues use social media site (WhatsApp) in real-time and that too is convenient and handy; - Skill enhancement on the part of Pedagogues as a result of employing innovative teaching-learning techniques; - Social media sites serve as a social connectivity tool to ward off negativity and monotony on the part of parents and pedagogues in the wake of COVID- 19

Keywords: innovative teaching-learning techniques, pedagogues, social media, nurture, toddlers

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Authors: Aanuoluwapo Olusola Adesanya, Adesina Joseph

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This paper examined the class-size and instructional materials as correlates of pupils learning and academic achievement in primary school. The population of the study comprised 198 primary school pupils in three selected schools in Ogun State, Nigeria. Data were collected through questionnaire and were analysed with the use of multiple regression and ANOVA to analysed the correlation between class-size, instructional materials (independent variables) and learning achievement (dependent variable). The findings revealed that schools having an average class-size of 30 and below with use of instructional materials obtained better results than schools having more than 30 and above. The main score were higher in the school in schools having 30 and below than schools with 30 and above. It was therefore recommended that government, stakeholders and NGOs should provide more classrooms and supply of adequate instructional materials in all primary schools in the state to cater for small class-size.

Keywords: class-size, instructional materials, learning, academic achievement

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Authors: Onesimus Bulus Shiaki

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The study investigated the effect of cooperative learning strategy on mathematics achievement and retention among senior secondary school students of different abilities in Taraba State Nigeria. Cooperative learning strategy could hopefully contribute to students’ achievement which will spur the teachers to develop strategies for better learning. The quasi-experimental of pretest, posttest and control group design was adopted in this study. A sample of one hundred and sixty-four (164) Senior Secondary Two (SS2) students were selected from a population of twelve thousand, eight hundred and seventy-three (12,873) SS2 Students in Taraba State. Two schools with equivalent mean scores in the pre-test were randomly assigned to experimental and control groups. The experimental group students were stratified according to ability levels of low, medium and high. The experimental group was guided by the research assistants using the cooperative learning instructional package. After six weeks post-test was administered to the two groups while the retention test was administered two weeks after the post-test. The researcher developed a 50-item Mathematics Achievement Test (MAT) which was validated by experts obtaining the reliability coefficient of 0.87. Mean scores and standard deviations were used to answer the research questions while the Analysis of Co-variance (ANCOVA) was used to test the hypotheses. Major findings from the statistical analysis showed that cooperative learning strategy has a significant effect on the mean achievement of students as well as retention among students of high, medium and low ability in mathematics. However, cooperative learning strategy has no effect on the interaction of ability level and retention. Based on the results obtained, it was therefore recommended that the adoption of the use of cooperative learning strategy in the teaching and learning of mathematics in senior secondary schools be initiated, maintained and sustained for the benefit of senior secondary school students in Taraba State. Periodic Government sponsored in-service training in form of long vacation training programme, workshops, conferences and seminars on the nature, scope, and use of cooperative learning strategy should be organized for senior secondary school mathematics teachers in Taraba state.

Keywords: ability level, cooperative learning, mathematics achievement, retention

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Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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The use of brain stem auditory evoked potential (BAEP) is a common way to study the auditory function of people, a way to learn the functionality of a part of the brain neuronal groups that intervene in the learning process by studying the behaviour of wave VI. The latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of innocuous, low-cost, and easy-access techniques such as, among others, the BAEP that can help us to detect early possible neurodevelopmental difficulties for their subsequent assessment and cure. To date and to the authors' best knowledge, only the latency data obtained, observing the first to V waves and mainly in the left ear, were taken into account. This work shows that it is essential to take into account both ears; with these latest data, it has been possible had diagnosed more precise some cases than with the previous data had been diagnosed as 'normal' despite showing signs of some alteration that motivated the new consultation to the specialist.

Keywords: ear, neurodevelopment, auditory evoked potentials, intervals of normality, learning disabilities

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Authors: S. R. M. Krishna, R. Santosh Kumar, V. Kamakshi Prasad

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Machine learning techniques are applied for the analysis of the impact of mental illness on the burden of disease. It is calculated using the disability-adjusted life year (DALY). DALYs for a disease is the sum of years of life lost due to premature mortality (YLLs) + No of years of healthy life lost due to disability (YLDs). The critical analysis is done based on the Data sources, machine learning techniques and feature extraction method. The reviewing is done based on major databases. The extracted data is examined using statistical analysis and machine learning techniques were applied. The prediction of the impact of mental illness on the population using machine learning techniques is an alternative approach to the old traditional strategies, which are time-consuming and may not be reliable. The approach makes it necessary for a comprehensive adoption, innovative algorithms, and an understanding of the limitations and challenges. The obtained prediction is a way of understanding the underlying impact of mental illness on the health of the people and it enables us to get a healthy life expectancy. The growing impact of mental illness and the challenges associated with the detection and treatment of mental disorders make it necessary for us to understand the complete effect of it on the majority of the population.

Keywords: ML, DAL, YLD, YLL

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Authors: Eugenia Vomvoridi-Ivanovic

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It is broadly assumed that participation in field experiences will help pre-service teachers (PSTs) bridge theory to practice. However, this is often not the case since PSTs who are placed in classrooms with large numbers of students from diverse linguistic, cultural, racial, and ethnic backgrounds (culturally diverse students (CDS)) usually observe ineffective mathematics teaching practices that are in contrast to those discussed in their teacher preparation program. Over the past decades, the educational research community has paid increasing attention to investigating out-of-school learning contexts and how participation in such contexts can contribute to the achievement of underrepresented groups in Science, Technology, Engineering, and mathematics (STEM) education and their expanded participation in STEM fields. In addition, several research studies have shown that students display different kinds of mathematical behaviors and discourse practices in out-of-school contexts than they do in the typical mathematics classroom since they draw from a variety of linguistic and cultural resources to negotiate meanings and participate in joint problem solving. However, almost no attention has been given to exploring these contexts as field experiences for pre-service mathematics teachers. The purpose of this study was to explore how participation in a community based after-school field experience promotes understanding of the content pedagogy concepts introduced in elementary mathematics methods courses, particularly as they apply to teaching mathematics to CDS. This study draws upon a situated, socio-cultural theory of teacher learning that centers on the concept of learning as situated social practice, which includes discourse, social interaction, and participation structures. Consistent with exploratory case study methodology, qualitative methods were employed to investigate how a cohort of twelve participating pre-service teacher's approach to pedagogy and their conversations around teaching and learning mathematics to CDS evolved through their participation in the after-school field experience, and how they connected the content discussed in their mathematics methods course with their interactions with the CDS in the after-school. Data were collected over a period of one academic year from the following sources: (a) audio recordings of the PSTs' interactions with the students during the after-school sessions, (b) PSTs' after-school field-notes, (c) audio-recordings of weekly methods course meetings, and (d) other document data (e.g., PST and student generated artifacts, PSTs' written course assignments). The findings of this study reveal that the PSTs benefitted greatly through their participation in the after-school field experience. Specifically, after-school participation promoted a deeper understanding of the content pedagogy concepts introduced in the mathematics methods course and gained a greater appreciation for how students learn mathematics with understanding. Further, even though many of PSTs' assumptions about the mathematical abilities of CDS were challenged and PSTs began to view CDSs' cultural and linguistic backgrounds as resources (rather than obstacles) for learning, some PSTs still held negative stereotypes about CDS and teaching and learning mathematics to CDS in particular. Insights gained through this study contribute to a better understanding of how informal mathematics learning contexts may provide a valuable context for pre-service teacher's learning to teach mathematics to CDS.

Keywords: after-school mathematics program, pre-service mathematical education of teachers, qualitative methods, situated socio-cultural theory, teaching culturally diverse students

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Authors: Rashika Sharma

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The campuses at tertiary institutes can act as a social environment for peer to peer connections. However, socialization is not the only aspect that campuses provide. The campus can act as a learning environment that has often been termed as the campus curriculum. Many tertiary institutes have taken steps to make their campus a ‘green campus’ whereby initiatives have been taken to reduce their impact on the environment. However, as visible as these initiatives are, it is debatable whether these have any effect on students’ and their understanding of sustainable campus operations. Therefore, research was conducted to evaluate the effectiveness of sustainable campus operations in raising students’ awareness of sustainability. Students at two vocational institutes participated in this interpretive research with data collected through surveys and focus groups. The findings indicated that majority of vocational education students remained oblivious of sustainability initiatives on campuses.

Keywords: campus learning, education for sustainability, social learning, vocational education

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Authors: Hugo T. Jorge, Richard T. Simanke

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Lacan’s work addresses overarching issues concerning the scientific intelligibility of the subject in its philosophical sense. Even though his reflection is not, strictly speaking, philosophy of science, it contains many traits that are typical of this branch of philosophy. However, the relation between Lacan’s early thought and the philosophy of science of the time is often disregarded or only incompletely accounted for in Lacanian scholarship. French philosopher of science Emile Meyerson was often implicitly or explicitly referred to in Lacan’s works, yet few publications can be found on their relationship. The objective of this paper is to contribute to the analysis of this relationship, indicating some of its possible implications. For this, the convergence between Meyerson’s doctrine of science and Lacan’s works between 1936 and 1953 is discussed, as well as the conditions under which Lacan’s reception of Meyerson’s ideas take place. In conclusion, it is argued that this convergence allows for the clarification of important issues in Lacan’s early work, such as the concept of imago, his views on the nature of truth, and his thesis of the anthropomorphism of natural sciences. Meyerson’s argument for the permanence of common sense within science makes Lacan’s claims on the anthropomorphism of natural sciences more understandable. Similarly, Meyerson’s views on the epistemological shortfall of the Principle of Identity sheds some light on Lacan’s 1936 critique of associationistic concepts of engram and truth and may be at the origins of his antirealist and anti-idealist stances. Meyerson’s Principle of Identity is also related to some aspects of Lacan’s concept of imago. The imago understood as the unconscious condition for the identity in time of family figures in childhood, would be an excellent expression of the Principle of Identity. In this sense, the Principle of Identity may be linked to the concept of imaginary as developed by Lacan in the 1950s. However, Lacan considerably distorts Meyerson’s views in his 1936 critique of Freud’s concept of libido. Finally, a possible relationship between Lacan’s late concept of the real and Meyerson’s concept of the irrational is suggested.

Keywords: imaginary, Lacan, Meyerson, philosophy of science, real

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Authors: Hala Shaari, Nuredin Ahmed

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Teaching computer programming is recognized to be difficult and a real challenge. The biggest problem faced by novice programmers is their lack of understanding of basic programming concepts. A visualized learning tool was developed and used by volunteered first-year students for two semesters. The purposes of this paper are firstly, to emphasize factors which directly affect the performance of our students negatively. Secondly, to examine whether the proposed tool would improve their performance and learning progression. The results of adopting this tool were conducted using a pre-survey and post-survey questionnaire. As a result, students who used the learning tool showed better performance in their programming subject.

Keywords: factors, novice, programming, visualization

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Authors: Cahit Perkgöz

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Artificial intelligence has brought new approaches to solving problems in almost every research field in recent years. One of these topics is shape design and optimization, which has the possibility of applications in many fields, such as nanotechnology and electronics. A properly constructed cost function can eliminate the need for labeled data required in deep learning and create desired shapes. In this work, the network parameters are optimized differentially, which differs from traditional approaches. The methods are tested for physics-related structures and successful results are obtained. This work is supported by Eskişehir Technical University scientific research project (Project No: 20ADP090)

Keywords: deep learning, shape design, optimization, artificial intelligence

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi

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An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.

Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power

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Authors: Arpapron Phokajang

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The objectives of this research were to study the internet usage, including; date, time, description of using service, network service, telephone charge, and to study the internet usage behavior on mobile phones of the Faculty of Management Science students at Suan Sunandha Rajabhat University. The samples consisted of 395 students from the Faculty of Management Science. Questionnaires were used for collecting the data. Descriptive statistics used in this research including percentage, mean, and standard deviation. The findings of this research found that most respondents were female, aged between 21 and 25 years old, used the monthly AIS network service calls on Monday to Friday around 6.01-12.00 p.m., the internet usage behavior on mobile phones for entertainment was found in the highest level in all aspects, and education, business and commerce, and communication were found in the moderate level and using the internet to watch YouTube in the highest level also.

Keywords: faculty of management science, internet usage behavior, mobile phones, Suan Sunandha Rajabhat University

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

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Authors: Khaled Alotaibi

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This paper deals with developing reasoning thinking and the intrinsic-extrinsic motivation for learning, and enhancing the academic achievement of a sample of students at Teachers' College in King Saud University. The study sample included 58 students who were divided randomly into two groups; one was an experimental group with 20 students and the other was a control group with 22 students. The following tools were used: e-courses by using I-map, Reasoning Thinking Tes, questionnaire to measure the intrinsic-extrinsic motivation for learning and an academic achievement test. Experimental group was taught using e-courses by using I-map, while the control group was taught by using traditional education. The results showed that: - There were no statistically significant differences between the experimental group and the control group in Reasoning thinking skills. - There were statistically significant differences between the experimental group and the control group in the intrinsic-extrinsic motivation for learning in favor of the experimental group. - There were statistically significant differences between the experimental group and the control group in academic achievement in favor of the experimental group.

Keywords: reasoning, thinking, intrinsic motivation, active learning

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Authors: Mridul Sharma, Praveen Saroha

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In today's world, everyone has become so busy in their to-do tasks and daily routine that they tend to ignore some of the basal components of our life, including exercise and diet. This comparative study analyzes the pathways of the relationship between exercise and brain plasticity and also includes another variable diet to study the effects of diet on learning by answering questions including which diet is known to be the best learning supporter and what are the recommended quantities of the same. Further, this study looks into inter-relation between diet and exercise, and also some other approach of the relation between diet and exercise on learning apart from through Brain Derived Neurotrophic Factor (BDNF).

Keywords: brain derived neurotrophic factor, brain plasticity, diet, exercise

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Authors: Daodu Joshua Rotimi

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This study investigated the Efficacy of Self-Assessment Metacognitive Strategy on Academic performance in Energy Concepts among Upper Basic Science Students in Ankpa, Kogi State, Nigeria. The research design adopted for the study was a Quasi-experimental control group design which employed a pretest, posttest of the experimental and control groups. The population of the study consisted of one hundred and twenty-four (124) JSSII Students; sixty-five (65) for the experimental group and (59) for the control group. The instrument used for the study was the Energy Concept Performance Test (ECPT), with a reliability coefficient of 0.80. Two research questions were answered using descriptive statistics of mean and standard deviation, while two hypotheses were tested using a t-test at P≤0.05 level of significance. The findings of the study revealed that the use of the Self-Assessment Metacognitive Strategy has a positive effect on students’ performance in energy concepts among upper Basic Science Students leading to high academic performance; also, there is no significant difference in the mean Academic Performance scores between Male and Female students taught Energy Concept using Self-Assessment Metacognitive Strategy. Based on the research findings, recommendations were made, which include that Secondary school teachers should be encouraged the use Self-Assessment Metacognitive strategy so as to make the learning process attractive, interactive and enriching to the learners.

Keywords: metacognition, self-assessment, performance, efficacy

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Mariana Paz Sajon, Paula Cecilia Primogerio, Mariana Albarracin

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This study recovers an experience of teacher training carried out in an Undergraduate Business School from a private university in Buenos Aires, Argentina. The purpose of the project was to provide teachers with an opportunity to reflect on their teaching practices at the university. The aim of the study is to systematize lessons and challenges that emerge from this teacher training experience. A group of teachers who showed a willingness to learn teaching abilities was selected to work. They completed a formative journey working in learning communities starting from the immersion in different aspects of teaching and learning, class observations, and an individual and collaborative reflection exercise in a systematic way among colleagues. In this study, the productions of the eight teachers who are members of the learning communities are analyzed, framed in an e-portfolio that they prepared during the training journey. The analysis shows that after the process of shared reflection, traits related to powerful teaching and meaningful learning have appeared in the classes. For their part, teachers reflect having reached an awareness of their own practices, identifying strengths and opportunities for improvement, and the experience of sharing their own way and knowing the successes and failures of others was valued. It is an educational journey of pedagogical transformation of the teachers, which is infrequent in business education, which could lead to a change in teaching practices for the entire Business School. The present study involves theoretical and pedagogic aspects of education in a business school in Argentina and its flow-on implications for the workplace that may be transferred to other educational contexts.

Keywords: Argentina, learning community, meaningful learning, powerful teaching, reflective practice

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Authors: Alfia Sari

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The implementation of the Content Language Integrated Learning (CLIL) approach in Indonesia has shown positive impacts in several educational institutions. Several studies have proven the benefits of implementing the CLIL approach, including the development of students’ language and content subject knowledge. Interestingly, one primary school in Surabaya, Indonesia, has been successfully implementing the CLIL approach. The students achieved high content and language subject scores, and the school was accredited A. A study on how the CLIL approach was practiced is important to investigate how teachers implemented it and how students benefited from it. Therefore, this present study attempted to investigate the implementation of the CLIL approach in this school to characterize good practices that can be implemented in other schools. A case study was conducted to observe its implementation in the third-grade classes (English, Science, and Math) by using the Protocol for Language Arts Teaching Observation (PLATO). The findings indicated that the CLIL teaching in this school accommodated the content and language well (scores 3-4). The content and language were clearly integrated, and the teachers successfully carried out the subjects in English. Teachers offered students opportunities to listen, speak, read, and write using the target language. This study described some characteristics of CLIL teaching in primary school that can be used as examples for future CLIL teachers to integrate the content and language in their teaching practices.

Keywords: CLIL, ELT, young learners, case study

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Authors: Ramzi Rihane, Yassine Benayed

Abstract:

Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Miriam I. Jimenez-Perez, Mariana C. Orellana-Haro, Carolina Guzman-Brambila

Abstract:

As part of microbiology and dietetics courses, students of medicine and nutrition analyze the main pathogenic microorganisms and perform dietary analyzes. The course of microbiology describes in a general way the main pathogens including bacteria, viruses, fungi, and parasites, as well as their interaction with the human species. We hypothesize that lack of practical application of the course causes the students not to find the value and the clinical application of it when in reality it is a matter of great importance for healthcare in our country. The courses of the medical microbiology and dietetics are mostly theoretical and only a few hours of laboratory practices. Therefore, it is necessary the incorporation of new innovative techniques that involve more practices and community fieldwork, real cases analysis and real-life situations. The purpose of this intervention was to incorporate real-world learning experiences in the instruction of medical microbiology and dietetics courses, in order to improve the learning process, understanding and the application in the field. During a period of 6 months, medicine and nutrition students worked in a community of urban poverty. We worked with 90 children between 4 and 6 years of age from low-income families with no access to medical services, to give an infectious diagnosis related to nutritional status in these children. We expect that this intervention would give a different kind of context to medical microbiology and dietetics students improving their learning process, applying their knowledge and laboratory practices to help a needed community. First, students learned basic skills in microbiology diagnosis test during laboratory sessions. Once, students acquired abilities to make biochemical probes and handle biological samples, they went to the community and took stool samples from children (with the corresponding informed consent). Students processed the samples in the laboratory, searching for enteropathogenic microorganism with RapID™ ONE system (Thermo Scientific™) and parasites using Willis and Malloy modified technique. Finally, they compared the results with the nutritional status of the children, previously measured by anthropometric indicators. The anthropometric results were interpreted by the OMS Anthro software (WHO, 2011). The microbiological result was interpreted by ERIC® Electronic RapID™ Code Compendium software and validated by a physician. The results were analyses of infectious outcomes and nutritional status. Related to fieldwork community learning experiences, our students improved their knowledge in microbiology and were capable of applying this knowledge in a real-life situation. They found this kind of learning useful when they translate theory to a real-life situation. For most of our students, this is their first contact as health caregivers with real population, and this contact is very important to help them understand the reality of many people in Mexico. In conclusion, real-world or fieldwork learning experiences empower our students to have a real and better understanding of how they can apply their knowledge in microbiology and dietetics and help a much- needed population, this is the kind of reality that many people live in our country.

Keywords: real-world learning experiences, medical microbiology, dietetics, nutritional status, infectious status.

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Authors: Raimi, Kehinde Olawuyi, Omoare Ayodeji Motunrayo

Abstract:

In the last two decades, there has been rising youth unemployment, restiveness, and social vices in Nigeria. The relevance of Vocational Education for skills acquisition, global learning, and national development to address these problems cannot be underestimated. Thus, the need to economically empower Nigerian youths to be able to develop the nation and meet up in the ever-changing global learning and economy led to the assessment of Vocational Education as Synergy for the Skills Acquisition and Global Learning in Ogun State, Nigeria. One hundred and twenty out of 1,500 students were randomly selected for this study. Data were obtained through a questionnaire and were analyzed with descriptive statistics and Chi-square. The results of the study showed that 59.2% of the respondents were between 20 – 24 years of age, 60.8% were male, and 65.8% had a keen interest in Vocational Education. Also, 90% of the respondents acquired skills in extension/advisory, 78.3% acquired skills in poultry production, and 69.1% acquired skills in fisheries/aquaculture. The major constraints to Vocational Education are inadequate resource personnel (χ² = 10.25, p = 0.02), inadequate training facilities (x̅ = 2.46) and unstable power supply (x̅ = 2.38). Results of Chi-square showed significance association between constraints and Skills Acquisition (χ² = 12.54, p = 0.00) at p < 0.05 level of significance. It was established that Vocational Education significantly contributed to students’ skills acquisition and global learning. This study, therefore, recommends that inadequate personnel should be looked into by the school authority in order not to over-stretch the available staff of the institution while the provision of alternative stable power supply (solar power) is also essential for effective teaching and learning process.

Keywords: vocational education, skills acquisition, national development, global learning

Procedia PDF Downloads 128