Search results for: young children with learning disabilities

Authors: Nitza Davidovitch, Yael Yossel-Eisenbach

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The purpose of the study was to inquire whether there was a change in the shaping of undergraduate students’ digitally-oriented study pattern in the pre-Covid (2016-2017) versus post-Covid period (2022-2023), as affected by three factors: social background characteristics, high school, and academic background characteristics. These two-time points were cauterized by dramatic changes in teaching and learning at institutions of higher education. The data were collected via cross-sectional surveys at two-time points, in the 2016-2017 academic school year (N=443) and in the 2022-2023 school year (N=326). The questionnaire was distributed on social media and it includes questions on demographic background characteristics, previous studies in high school and present academic studies, and questions on learning and reading habits. Method of analysis: A. Statistical descriptive analysis, B. Mean comparison tests were conducted to analyze the variations in the mean score for the digitally-oriented learning pattern variable at two-time points (pre- and post-Covid) in relation to each of the independent variables. C. Analysis of variance was performed to test the main effects and the interactions. D. Applying linear regression, the research aimed to examine the combined effect of the independent variables on shaping students' digitally-oriented learning habits. The analysis includes four models. In all four models, the dependent variable is students’ perception of digitally oriented learning. The first model included social background variables; the second model included scholastic background as well. In the third model, the academic background variables were added, and the fourth model includes all the independent variables together with the variable of period (pre- and post-COVID). E. Factor analysis confirms using the principal component method with varimax rotation; the variables were constructed by a weighted mean of all the relevant statements merged to form a single variable denoting a shared content world. The research findings indicate a significant rise in students’ perceptions of digitally-oriented learning in the post-COVID period. From a gender perspective, the impact of COVID on shaping a digital learning pattern was much more significant for female students. The socioeconomic status perspective is eliminated when controlling for the period, and the student’s job is affected - more than all other variables. It may be assumed that the student’s work pattern mediates effects related to the convenience offered by digital learning regarding distance and time. The significant effect of scholastic background on shaping students’ digital learning patterns remained stable, even when controlling for all explanatory variables. The advantage that universities had over colleges in shaping a digital learning pattern in the pre-COVID period dissipated. Therefore, it can be said that after COVID, there was a change in how colleges shape students’ digital learning patterns in such a way that no institutional differences are evident with regard to shaping the digital learning pattern. The study shows that period has a significant independent effect on shaping students’ digital learning patterns when controlling for the explanatory variables.

Keywords: learning pattern, COVID, socioeconomic status, digital learning

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Authors: Sherrene Bogle, Verlia Bogle, Tyrone Anderson

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In recent years, organizations have become increasingly interested in the possibility of analyzing social media as a means of gaining meaningful feedback about their products and services. The aspect based sentiment analysis approach is used to predict the sentiment for Twitter datasets for Digicel and Lime, the main mobile companies in Jamaica, using supervised learning classification techniques. The results indicate an average of 82.2 percent accuracy in classifying tweets when comparing three separate classification algorithms against the purported baseline of 70 percent and an average root mean squared error of 0.31. These results indicate that the analysis of sentiment on social media in order to gain customer feedback can be a viable solution for mobile companies looking to improve business performance.

Keywords: machine learning, sentiment analysis, social media, supervised learning

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Authors: Prashant Verma

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Commercial banks are facing immense pressure, including financial disintermediation, interest rate volatility and digital ways of finance. Retaining an existing customer is 5 to 25 less expensive than acquiring a new one. This paper explores customer churn prediction, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models. The results show that out of the various machine learning models, Random Forest which predicts the churn with 78% accuracy, has been found to be the most powerful model for the scenario. Customer vintage, customer’s age, average balance, occupation code, population code, average withdrawal amount, and an average number of transactions were found to be the variables with high predictive power for the churn prediction model. The model can be deployed by the commercial banks in order to avoid the customer churn so that they may retain the funds, which are kept by savings bank (SB) customers. The article suggests a customized campaign to be initiated by commercial banks to avoid SB customer churn. Hence, by giving better customer satisfaction and experience, the commercial banks can limit the customer churn and maintain their deposits.

Keywords: savings bank, customer churn, customer retention, random forests, machine learning, under-sampling

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Authors: Robin Ferguson

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The context which this paper explores is the provision of Technical Vocational Education and Training (TVET) of southern African workers at sea on local and foreign vessels using a blended learning approach. The pedagogical challenge of providing quality education in this context is that multiple African and foreign languages and cultural norms are found amongst the all-male crew; and there are widely differing levels of education, low levels of digital literacy and limited connectivity. The methodology used is a nested case study. The study describes the mechanisms used to provide ongoing, real-time workplace TVET on two foreign vessels. Some training was done in person when the vessels came into port, however, the majority of the TVET was achieved from shore to ship using a combination of commonly available Google and Microsoft Apps and WhatsApp. Voice, video and text in multiple languages were used to accommodate different learning styles. The learning was supported by the development of learning networks using social media. This paper also reflects on the shore-based organisational change processes required to support sea learning. The conceptual framework used is the Theory of Practice Architectures (TPA) as is provides a site-ontological perspective of the sayings/thinkings, doings and relatings of this workplace training which is multiplanar as it plays out at sea and ashore, in-person and on-line. Using TPA, the overarching practice architectures and supporting structures which confound or enable these learning practices are revealed. The contribution which this paper makes is an insight into an innovative vocational pedagogy which promotes ICT-mediated learning amongst workers who suffer from low levels of literacies and limited ICT-access and who work and live in remote places. It is a pedagogy of possibility which crosses the digital divide.

Keywords: theory of practice architecture, microsoft, google, whatsapp, vocational pedagogy, mariners, distributed workplaces

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Authors: Ulriikka Savela-Huovinen, Hanni Muukkonen, Auli Toom

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The development of workplace as a learning environment has been emphasized in research field of workplace learning. The prior literature on sensory performance emphasized the individual’s competences as assessor, while the competences in the collaborative interactional and knowledge creation practices as workplace learning method are not often mentioned. In the present study aims to find out what kinds of competences and contexts are central when assessor conducts food sensory evaluation in authentic professional context. The aim was to answer the following questions: first, what kinds of competences does sensory evaluation require according to assessors? And second, what kinds of contexts for sensory evaluation do assessors report? Altogether thirteen assessors from three Finnish food companies were interviewed by using semi-structural thematic interviews to map practices and development intentions as well as to explicate already established practices. The qualitative data were analyzed by following the principles of abductive and inductive content analysis. Analysis phases were combined and their results were considered together as a cross-analysis. When evaluated independently required competences were perception, knowledge of specific domains and methods and cognitive skills e.g. memory. Altogether, 42% of analysis units described individual evaluation contexts, 53% of analysis units described collaborative interactional contexts, and 5% of analysis units described collaborative knowledge creation contexts. Related to collaboration, analysis reviewed learning, sharing and reviewing both external and in-house consumer feedback, developing methods to moderate small-panel evaluation and developing product vocabulary collectively between the assessors. Knowledge creation contexts individualized from daily practices especially in cases product defects were sought and discussed. The study findings contribute to the explanation that sensory assessors learn extensively from one another in the collaborative interactional and knowledge creation context. Assessors learning and abilities to work collaboratively in the interactional and knowledge creation contexts need to be ensured in the development of the expertise.

Keywords: assessor, collaboration, competences, contexts, learning and practices, sensory evaluation

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Authors: C. Treerattanaphan, P. Boonpramuk, P. Singla

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Frequent, continuous speech training has proven to be a necessary part of a successful speech therapy process, but constraints of traveling time and employment dispensation become key obstacles especially for individuals living in remote areas or for dependent children who have working parents. In order to ameliorate speech difficulties with ample guidance from speech therapists, a website has been developed that supports speech therapy and training for people with articulation disorders in the standard Thai language. This web-based program has the ability to record speech training exercises for each speech trainee. The records will be stored in a database for the speech therapist to investigate, evaluate, compare and keep track of all trainees’ progress in detail. Speech trainees can request live discussions via video conference call when needed. Communication through this web-based program facilitates and reduces training time in comparison to walk-in training or appointments. This type of training also allows people with articulation disorders to practice speech lessons whenever or wherever is convenient for them, which can lead to a more regular training processes.

Keywords: web-based remote training program, Thai speech therapy, articulation disorders, speech booster

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Authors: Ashwini Patil

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Caudal block is one of the most commonly used regional anesthetic techniques in children. Currently, fentanyl is used as an adjuvant to bupivacaine to prolong analgesia but fentanyl is a narcotic. Dexamethasone, a glucocorticoid with strong anti-inflammatory effects provides improvement in post-operative analgesia and post-operative side effects. However, its analgesic efficacy and safety in comparison with fentanyl has not been extensively studied. So the objective of this randomized controlled study is to compare dexamethasone with fentanyl as an adjuvant to bupivacaine for caudal block in children in relation to the duration of caudal analgesia, post-operative analgesic requirement and incidence of post-operative nausea and vomiting. This study included 100 children, aged 1–6 years, undergoing lower abdominal surgeries. Patients were randomized into two groups, 50 each to receive a combination of dexamethasone 0.2 mg/kg along with 1 ml/kg bupivacaine 0.25% (group A) or combination of fentanyl (1 ug/kg) along with 1ml/kg bupivacaine 0.25% (group B). In the post-operative period, pain was assessed using a Modified Objective Pain Scale (MOPS) until 12 hr after surgery and rescue analgesia is administered when MOPS score 4 or more is recorded. Residual motor block, number of analgesic doses required within 24 hr after surgery, sedation scores, intra-operative and post-operative hemodynamic variables, post-operative nausea and vomiting (PONV), and other adverse effects were recorded. Data is analysed using unpaired t test and Significance level of P< 0.05 is considered statistically significant. Group A showed a significantly longer time to first analgesic requirement than group B (p<0.05). The number of rescue analgesic doses required in the first 24 h was significantly less in group A (p<0.05). Group A showed significantly lower MOPS scores than group B(p<0.05). Intra-operative and post-operative hemodynamic variables, Modified Bromage Scale scores, and sedation scores were comparable in both the groups. Group A showed significantly fewer incidences of PONV compared with group B(p<0.05). This study reveals that adding dexamethasone to bupivacaine prolongs the duration of postoperative analgesia and decreases the incidence of PONV as compared to combination of fentanyl to bupivacaine after a caudal block in pediatric patients.

Keywords: bupivacaine, caudal analgesia, dexamethasone, pediatric

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Authors: Meng-Tzu Cheng, Louisa Rosenheck, Chen-Yen Lin, Eric Klopfer

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The purpose of the research is to explore some of the ways in which gameplay data can be analyzed to yield results that feedback into the learning ecosystem. Back-end data for all users as they played an MMOG, The Radix Endeavor, was collected, and this study reports the analyses on a specific genetics quest by using the data mining techniques, including the decision tree method. In the study, different reasons for quest failure between participants who eventually succeeded and who never succeeded were revealed. Regarding the in-game tools use, trait examiner was a key tool in the quest completion process. Subsequently, the results of decision tree showed that a lack of trait examiner usage can be made up with additional Punnett square uses, displaying multiple pathways to success in this quest. The methods of analysis used in this study and the resulting usage patterns indicate some useful ways that gameplay data can provide insights in two main areas. The first is for game designers to know how players are interacting with and learning from their game. The second is for players themselves as well as their teachers to get information on how they are progressing through the game, and to provide help they may need based on strategies and misconceptions identified in the data.

Keywords: MMOG, decision tree, genetics, gaming-learning interaction

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Authors: Sarolta Lipóczi, Ildikó Szabó

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The use of ICT in European Schools has increased over the last decade but there is still room for improvement. Also interactive technology is often used below its technical and pedagogical potentials. The pedagogical potential of interactive technology in classrooms has not yet reached classrooms in different countries and in a substantial way. To develop these materials cooperation between educational researchers and teachers from different backgrounds is necessary. INTACT project brings together experts from science education, mathematics education, social science education and foreign language education – with a focus on bilingual education – and teachers in secondary and primary schools to develop a variety of pedagogically qualitative interactive teaching and learning resources. Because of the backgrounds of the consortium members INTACT project focuses on the areas of science, mathematics and social sciences. To combine these two features (science/math and foreign language) the project focuses on bilingual education. A big issue supported by ‘interactiveness’ is social and collaborative learning. The easy way to communicate and collaborate offered by web 2.0 tools, mobile devices connected to the learning material allows students to work and learn together. There will be a wide range of possibilities for school co-operations at regional, national and also international level that allows students to communicate and cooperate with other students beyond the classroom boarders while using these interactive teaching materials. Opening up the learning scenario enhance the social, civic and cultural competences of the students by advocating their social skills and improving their cultural appreciation for other nations in Europe. To enable teachers to use the materials in indented ways descriptions of successful learning scenarios (i.e. using design patterns) will be provided as well. These materials and description will be made available to teachers by teacher trainings, teacher journals, booklets and online materials. The resources can also be used in different settings including the use of a projector and a touchpad or other technical interactive devices for the input i.e. mobile phones. Kecskemét College as a partner of INTACT project has developed two teaching and learning resources in the area of foreign language teaching. This article introduces these resources as well.

Keywords: bilingual educational settings, international cooperation, interactive teaching and learning resources, work across culture

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Authors: Hsin-Yu Lee, Ming-Zhong Li, Wen-Hsi Chiu, Su-Fen Cheng, Shwu-Wen Lin

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Background: The use of medical terminology is essential to professional nurses on clinical practice. However, most nursing students consider traditional lecture-based teaching of medical terminology as boring and overly conceptual and lack motivation to learn. It is thus an issue to be discussed on how to enhance nursing students’ self-directed learning and improve learning outcomes of medical terminology. Digital game-based learning is a learner-centered way of learning. Past literature showed that the most common game-based learning for language education has been immersive games and teaching games. Thus, this study selected role-playing games (RPG) and digital puzzle games for observation and comparison. It is interesting to explore whether digital game-based learning has positive impact on nursing students’ learning of medical terminology and whether students can adapt well on this type of learning. Results can be used to provide references for institutes and teachers on teaching medical terminology. These instructions give you guidelines for preparing papers for the conference. Use this document as a template if you are using Microsoft Word. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further at WASET. Define all symbols used in the abstract. Do not cite references in the abstract. Do not delete the blank line immediately above the abstract; it sets the footnote at the bottom of this column. Page margins are 1,78 cm top and down; 1,65 cm left and right. Each column width is 8,89 cm and the separation between the columns is 0,51 cm. Objective: The purpose of this research is to explore respectively the impact of RPG and puzzle game on nursing students’ self-directed learning and technology acceptance. The study further discusses whether different game types bring about different influences on students’ self-directed learning and technology acceptance. Methods: A quasi-experimental design was adopted in this study so that repeated measures between two groups could be conveniently conducted. 103 nursing students from a nursing college in Northern Taiwan participated in the study. For three weeks of experiment, the experiment group (n=52) received “traditional teaching + RPG” while the control group (n=51) received “traditional teaching + puzzle games”. Results: 1. On self-directed learning: For each game type, there were significant differences for the delayed tests of both groups as compared to the pre and post-tests of each group. However, there were no significant differences between the two game types. 2. On technology acceptance: For the experiment group, after the intervention of RPG, there were no significant differences concerning technology acceptance. For the control group, after the intervention of puzzle games, there were significant differences regarding technology acceptance. Pearson-correlation coefficient and path analysis conducted on the results of the two groups revealed that the dimension were highly correlated and reached statistical significance. Yet, the comparison of technology acceptance between the two game types did not reach statistical significance. Conclusion and Recommend: This study found that through using different digital games on learning, nursing students have effectively improved their self-directed learning. Students’ technology acceptances were also high for the two different digital game types and each dimension was significantly correlated. The results of the experimental group showed that through the scenarios of RPG, students had a deeper understanding of medical terminology, which reached the ‘Understand’ dimension of Bloom’s taxonomy. The results of the control group indicated that digital puzzle games could help students memorize and review medical terminology, which reached the ‘Remember’ dimension of Bloom’s taxonomy. The findings suggest that teachers of medical terminology could use digital games to assist their teaching according to their goals on cognitive learning. Adequate use of those games could help improve students’ self-directed learning and further enhance their learning outcome on medical terminology.

Keywords: digital game-based learning, medical terminology, nursing education, self-directed learning, technology acceptance model

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Fatima Majeed Almonajim

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Panucum miliaceum L is a plant from the genus Gramineae, In the world, millets are regarded as a significant grain, however, they are very little exploited. Millet grain is abundant in nutrients and health-beneficial phenolic compounds, making it suitable as food and feed. The plant has received considerable attention for its high content of phenolic compounds, low glycemic index, the presence of unsaturated fats and lack of gluten which are beneficial to human health, and thus, have made the plant being effective in treating celiac disease, diabetes, lowering blood lipids (cholesterol) and preventing tumors. Moreover, the plant requires little water to grow, a property that is worth considering. This study provides an overview of the nutritional and health benefits provided by millet types grown in 2 areas Iraq and Iran, aiming to compare the effect of climate on the components of millet. In this research, millet samples collected from the both Babylon (Iraqi) and Isfahan (Iranian) types were extracted and after HPTLC, the resulted pattern of the two samples were compared. As a result, the Iranian millet showed more terpenoid compounds than Iraqi millet, and therefore, Iranian millet has a higher priority than Iraqi millet in increasing the human body's immunity. On the other hand, in view of the number of essential amino acids, the Iraqi millet contains more nutritional value compared to the Iranian millet. Also, due to the higher amount of histidine in the Iranian millet, compiled to the lack of gluten found from previous studies, we came to the conclusion that the addition of millet in the diet of children, more specifically those children with irritable bowel syndrome, can be considered beneficial. Therefore, as a component of dairy products, millet can be used in preparing food for children such as dry milk.

Keywords: HPTLC, phytochemicals, specialty foods, Panucum miliaceum L, nutrition

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Authors: Abigaba Sophia

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Artificial intelligence (AI) has transformed the education sector by revolutionizing educational frameworks by providing new opportunities and innovative advanced platforms for language translation during the teaching and learning process. In today's education sector, the primary key to scholarly communication is language; therefore, translation between different languages becomes vital in the process of communication. KIU-T being an International University, admits students from different nations speaking different languages, and English is the official language; some students find it hard to grasp a word during teaching and learning. This paper explores the practical aspect of using artificial intelligence technologies in an advanced language translation manner during teaching and learning. The impact of this technology is reflected in the education strategies to equip students with the necessary knowledge and skills for professional activity in the best way they understand. The researcher evaluated the demand for this practice since students have to apply the knowledge they acquire in their native language to their countries in the best way they understand. The main objective is to improve student's language competence and lay a solid foundation for their future professional development. A descriptive-analytic approach was deemed best for the study to investigate the phenomena of language translation intelligence alongside Microsoft Office during the teaching and learning process. The study analysed the responses of 345 students from different academic programs. Based on the findings, the researcher recommends using the artificial intelligence language translation technique during teaching, and this requires the wisdom of human content designers and educational experts. Lecturers and students will be trained in the basic knowledge of this technique to improve the effectiveness of teaching and learning to meet the student’s needs.

Keywords: artificial intelligence, language translation technique, teaching and learning process, Microsoft Office

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Authors: Sarah Maiter

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Heightened globalization, migration, displacement of citizens, and refugee needs is putting increasing demand for approaches to social services for diverse populations that responds to families to ensure the safety and protection of vulnerable members while providing supports and services. Along with this social works re-focus on socially just approaches to practice increasingly asks social workers to consider the challenging circumstances of families when providing services rather than a focus on individual shortcomings alone. Child protection workers then struggle to ensure safety of children while assessing the needs of families. This assessment can prove to be difficult when providing services to immigrant, refugee, and racially diverse families as understanding of and familiarity with these families is often limited. Furthermore, child protection intervention in western countries is state mandated having legal authority when intervening in the lives of families where child protection concerns have been identified. Within this context, racialized immigrant and refugee families are at risk of misunderstandings that can result in interventions that are overly intrusive, unhelpful, and harsh. Research shows disproportionality and overrepresentation of racial and ethnic minorities, and immigrant families in the child protection system. Reasons noted include: a) possibilities of racial bias in reporting and substantiating abuse, b) struggles on the part of workers when working with families from diverse ethno-racial backgrounds and who are immigrants and may have limited proficiency in the national language of the country, c) interventions during crisis and differential ongoing services for these families, d) diverse contexts of these families that poses additional challenges for families and children, and e) possible differential definitions of child maltreatment. While cultural and ethnic diversity in child rearing approaches have been cited as contributors to child protection concerns, this approach should be viewed cautiously as it can result in stereotyping and generalizing that then results in inappropriate assessment and intervention. However, poverty and the lack of social supports, both well-known contributors to child protection concerns, also impact these families disproportionately. Child protection systems, therefore, need to continue to examine policy and practice approaches with these families that ensures safety of children while balancing the needs of families. This presentation provides data from several research studies that examined definitions of child maltreatment among a sample of racialized immigrant families, experiences of a sample of immigrant families with the child protection system, concerns of a sample of child protection workers in the provision of services to these families, and struggles of families in the transitions to their new country. These studies, along with others provide insights into areas of consideration for practice that can contribute to safety for children while ensuring just and equitable responses that have greater potential for keeping families together rather than premature apprehension and removal of children to state care.

Keywords: child protection, child welfare services, immigrant families, racial and ethnic diversity

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Authors: Terence Soule, Tami Al Ghamdi

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To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population.

Keywords: transfer learning, genetic algorithm, evolutionary computation, source and target

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Authors: Husain A. Murad, Ali A. Dashti, Ali Al-Kandari

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The outbreak of Coronavirus during the Spring semester of 2020 brought new challenges for the teaching of vocational mass communication courses at universities in Kuwait. Using the Media Richness Theory (MRT), this study examines the response of 252 university students on mass communication programs. A questionnaire regarding their perceptions and preferences concerning modes of instruction on vocational courses online, focusing on the four factors of MRT: immediacy of feedback, capacity to include personal focus, conveyance of multiple cues, and variety of language. The outcomes show that immediacy of feedback predicted all criterion variables: suitability of distance learning (DL) for teaching vocational courses, sentiments of students toward DL, perceptions of easiness of evaluation of DL coursework, and the possibility of retaking DL courses. Capacity to include personal focus was another positive predictor of the criterion variables. It predicted students’ sentiments toward DL and the possibility of retaking DL courses. The outcomes are discussed in relation to implications for using DL, as well as constructing an agenda for DL research.

Keywords: distance learning, media richness theory, traditional learning, vocational media courses

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Authors: Lubna M. Zakaryia Mahmoud, Mohammed A. Dawood, Doaa A. Heiba

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A prospective double-blind placebo controlled trial carried out on 60 children known to be asthmatic who presented to the emergency department at Alexandria University of Children’s Hospital at El-Shatby with acute asthma exacerbations to assess the efficacy of adding inhaled magnesium sulfate to β-agonist, compared with β-agonist in saline, in the management of acute asthma exacerbations in children. The participants in the study were divided in two groups; Group A (study group) received inhaled salbutamol solution (0.15 ml/kg) plus isotonic magnesium sulfate 2 ml in a nebulizer chamber. Group B (control group): received nebulized salbutamol solution (0.15 ml/kg) diluted with placebo (2 ml normal saline). Both groups received inhaled solution every 20 minutes that was repeated for three doses. They were evaluated using the Pediatric Asthma Severity Score (PASS), oxygen saturation using portable pulse oximetry and peak expiratory flow rate using a portable peak expiratory flow meter at initially recorded as zero-minute assessment and every 20 minutes from the end of each nebulization (nebulization lasts 5-10 minutes) recorded as 20, 40 and 60-minute assessments. Regarding PASS, comparison showed non-significant difference with p-value 0.463, 0.472, 0.0766 at 20, 40 and 60 minutes. Regarding oxygen saturation, improvement was more significant towards group A starting from 40 min with significant p-value=0.000. At 60 min p-value=0.000. Although mean PEFR significantly improved from zero-min in both groups; however, improvement was more significant in group A with significant p-value = 0.015, 0.001, 0.001 at 20 min, 40 min and 60 min, respectively. The conclusion this study suggests is that inhaled magnesium sulfate is an efficient add on drug to standard β- agonist inhalation used in the treatment of moderate to severe asthma exacerbations.

Keywords: nebulized, magnesium sulfate, acute asthma , pediatric

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Authors: Gopal Chandra Saha, Sumanta Daw

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Warm-up is an essential component for optimizing performance in various sports before a physical fitness training session. This study investigated the immediate comparative effect of Self-Myofascial Release through vibration rolling (VR), non-vibration rolling (NVR), and static stretching as a part of a warm-up treatment on the functional fitness of young adults. Functional fitness is a classification of training that prepares the body for real-life movements and activities. For the present study 20male physical education students were selected as subjects. The age of the subjects was ranged from 20-25 years. The functional fitness variables undertaken in the present study were flexibility, muscle strength, agility, static and dynamic balance of the lower extremity. Each of the three warm-up protocol was administered on consecutive days, i.e. 24 hr time gap and all tests were administered in the morning. The mean and SD were used as descriptive statistics. The significance of statistical differences among the groups was measured by applying ‘F’-test, and to find out the exact location of difference, Post Hoc Test (Least Significant Difference) was applied. It was found from the study that only flexibility showed significant difference among three types of warm-up exercise. The observed result depicted that VR has more impact on myofascial release in flexibility in comparison with NVR and stretching as a part of warm-up exercise as ‘p’ value was less than 0.05. In the present study, within the three means of warm-up exercises, vibration roller showed better mean difference in terms of NVR, and static stretching exercise on functional fitness of young physical education practitioners, although the results were found insignificant in case of muscle strength, agility, static and dynamic balance of the lower extremity. These findings suggest that sports professionals and coaches may take VR into account for designing more efficient and effective pre-performance routine for long term to improve exercise performances. VR has high potential to interpret into an on-field practical application means.

Keywords: self-myofascial release, functional fitness, foam roller, physical education

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Authors: Sudheer Dhume, T. Prasad

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There are distinct signs of Business Education losing its sheen. It is more so in developing countries. One of the reasons is the value addition at the end of 2 year MBA program is not matching with the requirements of present times and expectations of the students. In this backdrop, Pedagogy Innovation has become prerequisite for making our MBA programs relevant and useful. This paper is the description and analysis of innovative Action Learning pedagogical approach adopted by a group of faculty members at NITIE Mumbai. It not only promotes multidisciplinary research but also enhances integration of the functional areas skillsets in the students. The paper discusses the theoretical bases of this pedagogy and evaluates the effectiveness of it vis-à-vis conventional pedagogical tools. The evaluation research using Bloom’s taxonomy framework showed that this blended method of Business Education is much superior as compared to conventional pedagogy.

Keywords: action learning, blooms taxonomy, business education, innovation, pedagogy

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Authors: Priyashri Kamlesh Sridhar, Suranga Nanayakkara

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Current frameworks in assessment follow lesson delivery and rely heavily on test performance or teacher’s observations. This, however, neglects the underlying cognitive load during the learning process. Identifying the pivotal points when the load occurs helps design effective pedagogies and tools that respond to learners’ cognitive state. There has been limited research on quantifying cognitive load in preschoolers, real-time. In this study, we recorded electrodermal activity and heart rate variability (HRV) from 10 kindergarteners performing executive function tasks and Johnson Woodcock test of cognitive abilities. Preliminary findings suggest that there are indeed sensitive task-dependent markers in skin conductance (number of SCRs and average amplitude of SCRs) and HRV (mean heart rate and low frequency component) captured during the learning process.

Keywords: early childhood, learning, methodologies, pedagogies

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Authors: A. Pedro, T. Goldschmidt

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Socio-emotional development is considered to be an essential prerequisite for school readiness. To our best knowledge, research on socio-emotional development specifically from the views of teachers in the South African context is limited. This study explored preschool teachers’ understanding of the role that socio-emotional development plays in preparing the child for school. Using the social learning theory, a qualitative approach with an exploratory design was used for the study. A total of 12 preschool teachers from both community-based and school-based preschools were purposively recruited. Upon receiving ethics clearance from the University of the Western Cape and the Western Cape Education Department, semi-structured interviews were conducted and analysed by utilizing Braun and Clarke’s (2006) six phases of thematic analysis. Participants’ rights, anonymity, and confidentiality were upheld throughout the research process. Findings reveal that preschool teachers emphasise the importance of holistic development for school readiness. Teachers deemed socio-emotional development as absolutely crucial for preparing children for school as it eases the transition to formal schooling and adaptation to the classroom environment.

Keywords: early childhood, preschool teachers, school readiness, socio-emotional development

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Authors: Yosr Ghozzi

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The Cyber-Physical Systems terminology has been well received by the industrial community and specifically appropriated in educational settings. Indeed, our latest educational activities are based on the development of experimental platforms on an industrial scale. In fact, we built a collaborative learning model because of an international market study that led us to place ourselves at the heart of this technology. To align with these findings, a competency-based approach study was conducted, and program content was revised by reflecting the projectbased approach. Thus, this article deals with the development of educational devices according to a generated curriculum and specific educational activities while respecting the repository of skills adopted from what constitutes the educational cyber-physical production systems and the laboratories that are compliant and adapted to them. The implementation of these platforms was systematically carried out in the school's workshops spaces. The objective has been twofold, both research and teaching for the students in mechatronics and logistics of the electromechanical department. We act as trainers and industrial experts to involve students in the implementation of possible extension systems around multidisciplinary projects and reconnect with industrial projects for better professional integration.

Keywords: education 4.0, competency-based learning, teaching factory, project-based learning, cyber-physical systems, industry 4.0

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Authors: Maha Awad M. Almuttairi

Abstract:

This research aimed to measure the impact of the use of a mobile learning (iTunes U) app for the development of metacognition skills delivered in the enrichment program offered to gifted students at the secondary level in Jeddah. The author targeted a group of students on an experimental scale to evaluate the achievement. The research sample consisted of a group of 38 gifted female students. The scale of evaluation of the metacognition skills used to measure the performance of students in the enrichment program was as follows: Satisfaction scale for the assessment of the technique used and the final product form after completion of the program. Appropriate statistical treatment used includes Paired Samples T-Test Cronbach’s alpha formula and eta squared formula. It was concluded in the results the difference of α≤ 0.05, which means the performance of students in the skills of metacognition in favor of using iTunes U. In light of the conclusion of the experiment, a number of recommendations and suggestions were present; the most important benefit of mobile learning applications is to provide enrichment programs for gifted students in the Kingdom of Saudi Arabia, as well as conducting further research on mobile learning and gifted student teaching.

Keywords: enrichment program, gifted students, metacognition skills, mobile learning

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

Abstract:

Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Sara ElElimy, Samir Moustafa

Abstract:

Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

Procedia PDF Downloads 128

Authors: Praveen S. Muthukumarana, Achala C. Aponso

Abstract:

A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

Procedia PDF Downloads 133

Authors: Barry Ardley, Abi Hunt, Nick Taylor

Abstract:

As a theoretical and practical framework, this study uses the student-as-producer approach to learning in higher education, as adopted by the Lincoln International Business School, University of Lincoln, UK. Students as producer positions learners as skilled and capable agents, able to participate as partners with tutors in live research projects. To illuminate the nature of this approach to learning and to highlight its critical issues, the authors report on two guided student consultancy projects. These were set up with the assistance of two local organisations in the city of Lincoln, UK. Using the student as a producer model to deliver the projects enabled learners to acquire and develop a range of key skills and knowledge not easily accessible in more traditional educational settings. This paper presents a systematic case study analysis of the eight organising principles of the student-as-producer model, as adopted by university tutors. The experience of tutors implementing students as producers suggests that the model can be widely applied to benefit not only the learning and teaching experiences of higher education students and staff but additionally a university’s research programme and its community partners.

Keywords: consultancy, learning, student as producer, research

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Authors: Haveesha Buddhdev

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The most important right for a human being in a society is the freedom of expression as stated by Article 18 and 19 of the Universal Declaration of Human rights pledged by member states of United Nations. Will it be fair to expect him/her to be of sound mental health if this right is taken away? Religion as a primary social institution controls many rights, freedoms and duties of people in a society. It does so by imposing certain values and beliefs on people which would either enhance quality of life or curb their freedom adversely thus affecting individual mental health. This paper aims to study the positive and negative role that religion plays in influencing one’s freedom of expression. This paper will focus on reviewing existing studies on the positive and negative impacts of religion on mental health. It will also contain data collected by the researcher about the impacts of religion on freedom of expression which will be obtained by surveying a sample of 30 adolescents and young adults. The researcher will use a Likert scale for these purpose, with response options ranging from strongly disagree to strongly agree and quantify it accordingly. Descriptive statistics would be used to analyse the data. Such research would help to identify possible problems faced by adolescents and young adults when it comes to religio-cultural ethos and also facilitate further researches to study the role that religion plays in mental health.

Keywords: cultural Ethos, freedom of expression, adolescent mental health, social science

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

Abstract:

This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

Abstract:

Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

Procedia PDF Downloads 158