Search results for: young children with learning disabilities

Authors: Kenneth Philip Jones

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

Keywords: video games, motivation, behavioral, learning transfer

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Authors: Muskan Jain, Baiju Gopal

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Objectives: Intimacy in interpersonal relationships is integral to psychological health and everyday wellbeing; the focus is on intimacy, which can be described as feelings of closeness, connection, and belonging within relationships, which is influenced by an individual's gender identity as well as life experiences. The study aims to explore the experiences of intimacy of the non-binary gender; this marginalized community has increased risks of developing the imposter phenomenon. The study explores the influence of IP on the development and sustenance of intimacy in relationships. Methods: The present study accumulates detailed narratives from 10 non-binary young adults ages 18 to 25 in metropolitan cities of India. Thematic analysis was used for the data analysis. Results: Seven major themes have emerged revolving around internalized criticism and self-depreciating behavior, which causes distance between partners. The four themes that result in the internalization of criticism are lack of social stability, invalidation by social units, adverse life experiences, and estrangement due to gender identity. Three themes that encapsulate major difficulties in relationships are limited self-disclosure, inhibition of physical needs, and fear of taking space. The findings have been critically compared and contrasted with the existing body of literature in the domain, which sets the agenda for further inquiry. Conclusion: It is important for future studies to capture the experiences of non-binary genders in India to provide better therapeutic support in order to assist them in forming meaningful and authentic relationships, thus increasing overall wellbeing.

Keywords: imposter phenomenon, intimacy, internalized criticism, marginalized community

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Authors: A. Degale Desta, Cheng Jian

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

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

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

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

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

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Authors: Karina Lõbus, Silver Muld, Kadri Kööp, Mare Tupits

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Aim: The aim of the research is to describe the experiences and needs of fathers’ of children with cancer in coping with the child's disease. Background: Today, about 80% of children diagnosed with malignancy in developed countries survive. Despite the positive statistics, recovery is not always certain, treatment is often very intensive and long-term. Cancer is affecting an increasing number of the population, which is increasing the demand for quality care, but the nature of expected care is currently unclear. This topic is important for the development of professional practice, as nurses complain that their knowledge to deal with the relatives of a patient with a difficult diagnosis is limited and would therefore like additional information to deal with the situation. Design: Qualitative, empirical, descriptive research. Method: The data were collected through semi-structured interviews and analysed by inductive content analysis method. Interviews were conducted during Autumn 2020. 4 subjects participated in the research. Results and Conclusions: The thesis revealed that fathers had different experiences and needs in dealing with the child's illness. Fathers' experiences of coping with child's diseases encompassed experiences with information, social relationships, healthcare, changes in personal health and experiences regarding the child. Regarding information, the respondents pointed out bad experiences with the availability of information and the ability to convey the necessary information. Experiences regarding social relationships included experiences with relatives and strangers. Regarding healthcare, fathers mentioned experiences related to the child's health and healthcare professionals. In regards to personal health, fathers pointed out negative changes in their mental and physical health. In relation to the child, the subjects revealed experiences regarding changed values, way of life and raising the child. According to the research, fathers’ needs in relation to dealing with child's cancer included material, social, and spiritual needs. In regard to material needs, fathers pointed out the need for state assistance and the needs related to the surrounding environment. The needs concerning social belonging involved needs for a driving force and involvement in the treatment process. Regarding spiritual needs, fathers expressed mixed feelings towards the need for outside and professional help.

Keywords: father, coping, cancer, child, experience, need

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Authors: Manik Sharma, Ganapathy Krishnamurthi

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The cost of some drugs and medical treatments has risen in recent years, that many patients are having to go without. A classification project could make researchers more efficient. One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatment takes, on average, more than 10 years and costs hundreds of millions of dollars. If successful, we could dramatically improve the industry's ability to model cellular images according to their relevant biology. In turn, greatly decreasing the cost of treatments and ensure these treatments get to patients faster. This work aims at solving a part of this problem by creating a cellular image classification model which can decipher the genetic perturbations in cell (occurring naturally or artificially). Another interesting question addressed is what makes the deep-learning model decide in a particular fashion, which can further help in demystifying the mechanism of action of certain perturbations and paves a way towards the explainability of the deep-learning model.

Keywords: cellular images, genetic perturbations, deep-learning, explainability

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Authors: Sung Uk Park, Young Su Kang, Sangmo Kang, Young Kweon Suh

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Mineral product, waste concrete (fine aggregates), waste in the optical field, industry, and construction employ separators to separate solids and classify them according to their size. Various sorting machines are used in the industrial field such as those operating under electrical properties, centrifugal force, wind power, vibration, and magnetic force. Study on separators has been carried out to contribute to the environmental industry. In this study, we perform CFD analysis for understanding the basic mechanism of the separation of waste concrete (fine aggregate) particles from air with a machine built with a rotor with blades. In CFD, we first performed two-dimensional particle tracking for various particle sizes for the model with 1 degree, 1.5 degree, and 2 degree angle between each blade to verify the boundary conditions and the method of rotating domain method to be used in 3D. Then we developed 3D numerical model with ANSYS CFX to calculate the air flow and track the particles. We judged the capability of particle separation for given size by counting the number of particles escaping from the domain toward the exit among 10 particles issued at the inlet. We confirm that particles experience stagnant behavior near the exit of the rotating blades where the centrifugal force acting on the particles is in balance with the air drag force. It was also found that the minimum particle size that can be separated by the machine with the rotor is determined by its capability to stay at the outlet of the rotor channels.

Keywords: environmental industry, separator, CFD, fine aggregate

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Authors: Revoti Prasad Bora, Nikita Katyal, Saurabh Yadav

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

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

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

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

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

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Authors: Willy Mbenza, Sho Kenjiro

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

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

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Authors: Houda Zouar, Hanane Sarnou

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

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

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Authors: Shaheeb Abdul Azeez, Siong-Choy Chong

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

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

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Authors: M. M. Akhmediev, J. R. Ashrapov, T. M. Akhmediev

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Hydrocephalus frequently occurs with spina bifida, and up to 80% of such patients need to be shunted. Objective: It’s sought to improve the results of the surgical treatment of hydrocephalus in children with spina bifida. Methods: We have analyzed the results of the surgical treatment of 80 patients aged between 1 month and 1,5-year-old with hydrocephalus and myelomeningocele. All patients underwent surgery in the period of 2013-2018. Results: In all patients, spina bifida was associated with hydrocephalus with a predominant extension of the posterior horns of the lateral ventricles in the form of colpocephaly, Chiari malformation type 2. Based on the method “Choose right shunt” the determination of the point of critical deformation of the ventricular system was established, 47 (58.8%) patients for the 1st stage underwent ventriculoperitoneal (VP) shunt surgery with a low-pressure valve, 28 (35.0%) patients with medium pressure and 5 (6.2%) with high-pressure valve. Under or over drainage complications were not observed in the postoperative period. The 2nd stage of surgery for myelomeningocele repair was planned in 1-2 months with the follow-up head ultrasonography and electromyography study. Conclusion: The implantable shunt systems parameters chosen before surgery in the surgical management of hydrocephalus in children with myelomeningocele are important in the causes of under or over drainage states, cerebrospinal fluid leakage from the myelomeningocele sac. Management of hydrocephalus should be performed by considering myelomeningocele affecting craniospinal compliance.

Keywords: hydrocephalus, spina bifida, myelomeningocele, ventriculoperitoneal (VP) shunt

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

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

Keywords: ESL, instruction, morphemic analysis, vocabulary

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

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

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

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Authors: Heichia Wang, Yalan Chao

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Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.

Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network

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Authors: Teresa Isabel Lozano Pérez, Sandra Soca Lozano

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In Cuba, the childcare is one of the programs prioritized by the Ministry of Public Health and the birth of a child becomes a desired and rewarding event for the family, which is prepared for the reception of a healthy child. When this does not happen and after the first months of the child's birth begin to appear developmental deviations that indicate the presence of a disorder, the event becomes a live event potentially negative and generates disruptions in the family health. A quantitative, descriptive, and cross-sectional research methodology was conducted to describe the impact on family health of diagnosis of autism in a sample of 25 families of children diagnosed with infantile autism at the University Pediatric Hospital Juan Manuel Marquez Havana, Cuba; in the period between January 2014 and May 2015. The sample was non probabilistic and intentional from the inclusion criteria selected. As instruments, we used a survey to identify the structure of the family, life events inventory and an instrument to assess the relative impact, adaptive resources of family and social support perceived (IRFA) to identify the diagnosis of autism as life event. The main results indicated that the majority of families studied were nuclear, small and medium and in the formation stage. All households surveyed identified the diagnosis of autism in a child as an event of great importance and negative significance for the family, taking in most of the families studied a high impact on the four areas of family health and impact enhancer of involvement in family health. All the studied families do not have sufficient adaptive resources to face this situation, sensing that they received social support frequently, mainly in information and emotional areas. We conclude that the diagnosis of autism one of the members of the families studied is valued as a life event highly significant with unfavorably way causing an enhancer impact of involvement in family health especially in the areas ‘health’ and ‘socio-psychological’. Among the social support networks health institutions, partners and friends are highlighted. We recommend developing intervention strategies in families of these children to support them in the process of adapting the diagnosis.

Keywords: family, family health, infantile autism, life event

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Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Daniela Velichová

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Plenty of ideas on how to adopt active learning methods in education are available nowadays. Mathematics is a subject where the active involvement of students is required in particular in order to achieve desirable results regarding sustainable knowledge and deep understanding. The present article is based on the outcomes of an Erasmus+ project DrIVE-MATH, that was aimed at developing a novel and integrated framework to teach maths classes in engineering courses at the university level. It is fundamental for students from the early years of their academic life to have agile minds. They must be prepared to adapt to their future working environments, where enterprises’ views are always evolving, where all collaborate in teams, and relations between peers are thought for the well-being of the whole - workers and company profit. This reality imposes new requirements on higher education in terms of adaptation of different pedagogical methods, such as project-based and active-learning methods used within the course curricula. Active learning methodologies are regarded as an effective way to prepare students to meet the challenges posed by enterprises and to help them in building critical thinking, analytic reasoning, and insight to the solved complex problems from different perspectives. Fostering learning-by-doing activities in the pedagogical process can help students to achieve learning independence, as they could acquire deeper conceptual understanding by experimenting with the abstract concept in a more interesting, useful, and meaningful way. Clear information about learning outcomes and goals might help students to take more responsibility for their learning results. Active learning methods implemented by the project team members in their teaching practice, eduScrum and Jigsaw in particular, proved to provide better scientific and soft skills support to students than classical teaching methods. EduScrum method enables teachers to generate a working environment that stimulates students' working habits and self-initiative as they become aware of their responsibilities within the team, their own acquired knowledge, and their abilities to solve problems independently, though in collaboration with other team members. This method enhances collaborative learning, as students are working in teams towards a common goal - knowledge acquisition, while they are interacting with each other and evaluated individually. Teams consisting of 4-5 students work together on a list of problems - sprint; each member is responsible for solving one of them, while the group leader – a master, is responsible for the whole team. A similar principle is behind the Jigsaw technique, where the classroom activity makes students dependent on each other to succeed. Students are divided into groups, and assignments are split into pieces, which need to be assembled by the whole group to complete the (Jigsaw) puzzle. In this paper, analysis of students’ perceptions concerning the achievement of deeper conceptual understanding in mathematics and the development of soft skills, such as self-motivation, critical thinking, flexibility, leadership, responsibility, teamwork, negotiation, and conflict management, is presented. Some new challenges are discussed as brought by introducing active learning methods in the basic mathematics courses. A few examples of sprints developed and used in teaching basic maths courses at technical universities are presented in addition.

Keywords: active learning methods, collaborative learning, conceptual understanding, eduScrum, Jigsaw, soft skills

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Authors: Lorena Blasco-Arcas, Francesca Pucciarelli

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Business contexts have become globalised and digitalised, which requires that managers develop a strong sense of cross-cultural intelligence while working in geographically distant teams by means of digital technologies. How to better equip future managers on these kinds of skills has been put forward as a critical issue in Business Schools. In pursuing these goals, higher education is shifting from a passive lecture approach, to more active and experiential learning approaches that are more suitable to learn skills. For example, through the use of case studies, proposing plausible business problem to be solved by students (or teams of students), these institutions have focused for long in fostering learning by doing. Though, case studies are no longer enough as a tool to promote active teamwork and experiential learning. Moreover, digital advancements applied to educational settings have enabled augmented classrooms, expanding the learning experience beyond the class, which increase students’ engagement and experiential learning. Different authors have highlighted the benefits of digital engagement in order to achieve a deeper and longer-lasting learning and comprehension of core marketing concepts. Clickers, computer-based simulations and business games have become fairly popular between instructors, but still are limited by the fact that are fictional experiences. Further exploration of real digital platforms to implement real, live projects in the classroom seem relevant for marketing and business education. Building on this, this paper describes the development of an experiential learning activity in class, in which students developed a communication campaign in teams using the BuzzFeed platform, and subsequently implementing the campaign by using other social media platforms (e.g. Facebook, Instagram, Twitter…). The article details the procedure of using the project for a marketing module in a Bachelor program with students located in France, Italy and Spain campuses working on multi-campus groups. Further, this paper describes the project outcomes in terms of students’ engagement and analytics (i.e. visits achieved). the project included a survey in order to analyze and identify main aspects related to how the learning experience is influenced by the cultural competence developed through working in geographically distant and culturally diverse teamwork. Finally, some recommendations to use project-based social media tools while working with virtual teamwork in the classroom are provided.

Keywords: cultural competences, experiential learning, social media, teamwork, virtual group work

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Authors: Yingqi Cui, Changran Huang, Raymond Lee

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In this paper, we proposed an AI Tutor using ontology and natural language process techniques to generate a computer science domain knowledge graph and answer users’ questions based on the knowledge graph. We define eight types of relation to extract relationships between entities according to the computer science domain text. The AI tutor is separated into two agents: learning agent and Question-Answer (QA) agent and developed on JADE (a multi-agent system) platform. The learning agent is responsible for reading text to extract information and generate a corresponding knowledge graph by defined patterns. The QA agent can understand the users’ questions and answer humans’ questions based on the knowledge graph generated by the learning agent.

Keywords: artificial intelligence, natural Language processing, knowledge graph, intelligent agents, QA system

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Authors: Naseem Akhter, Rozina Khattak, Arshad Munir

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The Islamic social and family system is very clear concerning will, choice, consent and negation of compulsion and force in human life. Marriage is not only a civil contract but also a religious and spiritual contract between spouse (man and woman), which allows them for each other to live gladly, joyfully and legally in the society. It is an immortal and perpetual association between man and woman, which is filled with sympathetic affection, kindness, compassion and security. Islam gives specific rights to parents and guardians to set up the marriage ceremony and get done it as a respectful family occasion, confer their blessing and advice for a life partner of their children. The rights of parents and guardians are summed up in the term of "Willayah”. Islam does not permit parents, guardians and other relatives to compel their children regarding the marriage of their choice, because the groom and the bride are the real parties of the contract. Therefore, their willingness is of prime importance in order to spend whole life with each other. The Holy Prophet (peace and blessings of Allah be upon him) prohibits forcing a virgin to marriage without her permission, whether this is her father or someone else. The right of free consent to choose a life partner is the basic right for the human which is God (Allah) gifted. Unfortunately, forced marriage is a common practice in Pakistani society that has no link with Islam. This article is being written in the same context.

Keywords: choice, consent, forced marriage, Islam, parents, spouse

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Authors: Frantisek Mosna

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E-learning has become an efficient and widespread means in process of education at all branches of human activities. Statistics is not an exception. Unfortunately the main focus in the statistics teaching is usually paid to the substitution to formulas. Suitable web-sites can simplify and automate calculation and provide more attention and time to the basic principles of statistics, mathematization of real-life situations and following interpretation of results. We introduce our own web-sites for hypothesis testing. Their didactic aspects, technical possibilities of individual tools for their creating, experience and advantages or disadvantages of them are discussed in this paper. These web-sites do not substitute common statistical software but significantly improve the teaching of the statistics at universities.

Keywords: e-learning, hypothesis testing, PHP, web-sites

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Authors: Abdirahim Mohamed, Sarita Rana Chhetri, Michael Sleath, Nadia Saleem

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Rationale: Internet usage has been very much integrated into our daily lives. Internet usage within a neurodevelopmental disorder population is also on the increase. Nevertheless, there is very little empirical research on how this population virtually protect themselves; along with how their parents can keep them safe online. This topic was an ever-growing concern to the parents within our services and in many cases would add to the stresses and mental health of parents. This ignited an idea within our team to conduct research to explore the perceived online risks within this population and how they keep themselves safe. In conjunction, we also explored how parents and caregivers monitor and safeguard their young people to the potential threats online. Our hypothesis was that the perceived risks will heavily outnumber the safeguarding measures implemented by this population. Method: Within the Coventry and Warwickshire NHS Partnership Trust Child and Adolescent Mental Health Service (CAMHS), we distributed qualitative questionnaires to all the clinical bases (N=80). Questions explored topics such as daily internet usage, safeguarding measures, and perceived threats. The researchers requested for all CAMHS clinicians to identify participants. Participants in this study were accessing CAMHS for neurodevelopmental specific interventions. Results: The data were analysed using both Excel and SPSS. Within SPSS, a MANOVA was conducted and found a significant difference between safeguarding measures and perceived online risks within responses (p ≤ 0.5). This supports our hypothesis that participants in this population are well versed in the safeguarding issues of the internet; however, struggle to implement appropriate preventative measures. Data were also screened using Excel and found that all parents and carers stated they 'monitored their child’s internet use'. Conclusion: Data suggest that parents/carers may require more specific intervention to equip them with preventative measures due to the clear discrepancy between perceived risks and safeguarding measures. More research may also need to be conducted around this area to determine appropriate methodology to explore this topic further.

Keywords: Internet, health , how safe are we , internet health check

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Authors: Sophonie Sarielle Jean Jacques Bertrand

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Background: Acute flaccid paralysis (PFA) is the sudden weakness or paralysis of muscles seen in children under 15 years of age. According to the WHO, PFA remains a real public health problem. For Haiti, the PFA represents a national priority. This study aims to describe the epidemiological profile of cases of Acute Flaccid Paralysis (PFA) in Haiti from 2018-2020. Methods: A descriptive cross-sectional study covering the period of 2018-2021 was carried out. epidemiological surveillance data PFA exported to Integrated Monitoring Evaluation Surveillance (MESI) were used. Sociodemographic variables were studied. Prevalence and clinical mortality rate were calculated. Epi Info 7.2 and Excel 2016 were used for data analysis. Results: 76 AFP cases were recorded for the period, or 13 (17%) in 2018, 23 (30%) in 2019, 8 (11%) in 2020 32 (42%) in 2021. Children aged 5-14 years accounted for 36% of cases (n= 26). The M/F sex ratio was 0.52, with a predominance of the female sex. The clinical mortality rate was 2.6%. The prevalence was 1.77/100,000 people. Conclusion: From 2018-2021, 76 cases of PFA cases were recorded in the 10 departments of the country, of which the West department was the most affected. Maintaining high vaccination coverage and a standard acute flaccid paralysis surveillance system are essential for the eradication of this condition. Strengthen epidemiological surveillance of PFA.

Keywords: epidemiology, PFA, Haïti, MESI

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Authors: F. Uriel, M. M. Fernandez Liporace

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In the last decade, academic achievement in higher education has become a topic of agenda in Argentina, regarding the high figures of adjustment problems, academic failure and dropout, and the low graduation rates in the context of massive classes and traditional teaching methods. Psychological variables, such as perceived social support, academic motivation and learning styles and strategies have much to offer since their measurement by tests allows a proper diagnose of their influence on academic achievement. Framed in a major research, several studies analysed multiple samples, totalizing 5135 students attending Argentinean public universities. The first goal was aimed at the identification of statistically significant differences in psychological variables -perceived social support, learning styles, learning strategies, and academic motivation- by age, gender, and degree of academic advance (freshmen versus sophomores). Thus, an inferential group differences study for each psychological dependent variable was developed by means of student’s T tests, given the features of data distribution. The second goal, aimed at examining associations between the four psychological variables on the one hand, and academic achievement on the other, was responded by correlational studies, calculating Pearson’s coefficients, employing grades as the quantitative indicator of academic achievement. The positive and significant results that were obtained led to the formulation of different predictive models of academic achievement which had to be tested in terms of adjustment and predictive power. These models took the four psychological variables above mentioned as predictors, using regression equations, examining predictors individually, in groups of two, and together, analysing indirect effects as well, and adding the degree of academic advance and gender, which had shown their importance within the first goal’s findings. The most relevant results were: first, gender showed no influence on any dependent variable. Second, only good achievers perceived high social support from teachers, and male students were prone to perceive less social support. Third, freshmen exhibited a pragmatic learning style, preferring unstructured environments, the use of examples and simultaneous-visual processing in learning, whereas sophomores manifest an assimilative learning style, choosing sequential and analytic processing modes. Despite these features, freshmen have to deal with abstract contents and sophomores, with practical learning situations due to study programs in force. Fifth, no differences in academic motivation were found between freshmen and sophomores. However, the latter employ a higher number of more efficient learning strategies. Sixth, freshmen low achievers lack intrinsic motivation. Seventh, models testing showed that social support, learning styles and academic motivation influence learning strategies, which affect academic achievement in freshmen, particularly males; only learning styles influence achievement in sophomores of both genders with direct effects. These findings led to conclude that educational psychologists, education specialists, teachers, and universities must plan urgent and major changes. These must be applied in renewed and better study programs, syllabi and classes, as well as tutoring and training systems. Such developments should be targeted to the support and empowerment of students in their academic pathways, and therefore to the upgrade of learning quality, especially in the case of freshmen, male freshmen, and low achievers.

Keywords: academic achievement, academic motivation, coping, learning strategies, learning styles, perceived social support

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Authors: Akshat Kumar, Vidushi

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This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

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Authors: John White

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This study investigated the effects of practice schedules on learning and performance in golf putting, specifically focusing on the impact of increasing contextual interference (CI). University students (n=7) were randomly assigned to blocked, random, or increasing practice schedules. During acquisition, participants performed 135 putting trials using different weighted golf balls. The blocked group followed a specific sequence of ball weights, while the random group practiced with the balls in a random order. The increasing group started with a blocked schedule, transitioned to a serial schedule, and concluded with a random schedule. Retention and transfer tests were conducted 24 hours later. The results indicated that high levels of CI (random practice) were more beneficial for learning than low levels of CI (blocked practice). The increasing practice schedule, incorporating blocked, serial, and random practice, demonstrated advantages over traditional blocked and random schedules. Additionally, EEG was used to explore the neurophysiological effects of the increasing practice schedule.

Keywords: skill acquisition, motor control, learning, contextual interference

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Authors: Angsumalin Puntho

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Disruptive technology has had a great influence on our everyday lives and the existence of an organization. Geologists in the public sector need to keep up with digital technology and be able to work and collaborate in a more effective manner. The result from SWOT and 7S McKinsey analyses suggest that there are inadequate IT personnel, no individual digital literacy development plan, and a misunderstanding of management policies. The Office of Civil Service Commission develops digital literacy skills that civil servants and government officers should possess in order to work effectively; it consists of nine dimensions, including computer skills, internet skills, cyber security awareness, word processing, spreadsheets, presentation programs, online collaboration, graphics editors and cyber security practices; and six steps of digital literacy development including self-assessment, individual development plan, self-learning, certified test, learning reflection, and practices. Geologists can use digital literacy as a learning tool to develop themselves for better career opportunities.

Keywords: disruptive technology, digital technology, digital literacy, computer skills

Procedia PDF Downloads 104