Search results for: student teacher training

Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Dana Page, Erica McClure, Kate Snider, Jenni Pollard, Tim Landrum, Jeff Valentine

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Extensive research has been conducted on students with emotional and behavioral disorders (EBD) and their rates of challenging behavior. In the past, however, less attention has been given to their academic achievement and outcomes. Recent research examining outcomes for students with EBD has indicated that these students receive lower grades, are less likely to pass classes, and experience higher rates of school dropout than students without disabilities and students with other high incidence disabilities. Given that between 2% and 20% of the school-age population is likely to have EBD (though many may not be identified as such), this is no small problem. Despite the need for increased examination of this population’s academic achievement, research on the actual performance of students with EBD has been minimal. This study reports the results of a meta-analysis of the limited research examining academic achievement of students with EBD, including effect sizes of assessment scores and discussion of moderators potentially impacting academic outcomes. Researchers conducted a thorough literature search to identify potentially relevant documents before screening studies for inclusion in the systematic review. Screening identified 35 studies that reported results of academic assessment scores for students with EBD. These studies were then coded to extract descriptive data across multiple domains, including placement of students, participant demographics, and academic assessment scores. Results indicated possible collinearity between EBD disability status and lower academic assessment scores, despite a lack of association between EBD eligibility and lower cognitive ability. Quantitative analysis of assessment results yielded effect sizes for academic achievement of student participants, indicating lower performance levels and potential moderators (e.g., race, socioeconomic status, and gender) impacting student academic performance. In addition to discussing results of the meta-analysis, implications and areas for future research, policy, and practice are discussed.

Keywords: students with emotional behavioral disorders, academic achievement, systematic review, meta-analysis

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Authors: Jiabei Zhang

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Although adapted physical education (APE) teacher preparation programs are available in the nation, a consistent standards-based competency evaluation scale for preparing of qualified personnel for teaching children with disabilities in APE cannot be identified in the literature. The purpose of this study was to develop a standard-based competency evaluation scale for assessing qualifications for teaching children with disabilities in APE. Standard-based competencies were reviewed and identified based on research evidence documented as effective in teaching children with disabilities in APE. A standard-based competency scale was developed for assessing qualifications for teaching children with disabilities in APE. This scale included 20 standard-based competencies and a 4-point Likert-type scale for each standard-based competency. The first standard-based competency is knowledgeable of the causes of disabilities and their effects. The second competency is the ability to assess physical education skills of children with disabilities. The third competency is able to collaborate with other personnel. The fourth competency is knowledgeable of the measurement and evaluation. The fifth competency is to understand federal and state laws. The sixth competency is knowledgeable of the unique characteristics of all learners. The seventh competency is the ability to write in behavioral terms for objectives. The eighth competency is knowledgeable of developmental characteristics. The ninth competency is knowledgeable of normal and abnormal motor behaviors. The tenth competency is the ability to analyze and adapt the physical education curriculums. The eleventh competency is to understand the history and the philosophy of physical education. The twelfth competency is to understand curriculum theory and development. The thirteenth competency is the ability to utilize instructional designs and plans. The fourteenth competency is the ability to create and implement physical activities. The fifteenth competency is the ability to utilize technology applications. The sixteenth competency is to understand the value of program evaluation. The seventeenth competency is to understand professional standards. The eighteenth competency is knowledgeable of the focused instruction and individualized interventions. The nineteenth competency is able to complete a research project independently. The twentieth competency is to teach children with disabilities in APE independently. The 4-point Likert-type scale ranges from 1 for incompetent to 4 for highly competent. This scale is used for assessing if one completing all course works is eligible for receiving an endorsement for teaching children with disabilities in APE, which is completed based on the grades earned on three courses targeted for each standard-based competency. A mean grade received in three courses primarily addressing a standard-based competency will be marked on a competency level in the above scale. The level 4 is marked for a mean grade of A one receives over three courses, the level 3 for a mean grade of B over three courses, and so on. One should receive a mean score of 3 (competent level) or higher (highly competent) across 19 standard-based competencies after completing all courses specified for receiving an endorsement for teaching children with disabilities in APE. The validity, reliability, and objectivity of this standard-based competency evaluation scale are to be documented.

Keywords: evaluation scale, teacher preparation, adapted physical education teachers, and children with disabilities

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Authors: Abdulkader Helwan

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Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

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Authors: Lisa M. Coleman

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The focus on disability, LGBTQ+, and internationalization has certainly been the subject of much research and programmatic across higher education. Many universities have entered into global partnerships with varying success and challenges across the various areas, including laws and policies. Attentiveness to the specific nuances of global inclusion, diversity, equity, belonging, and access (GIDBEA) and the leadership to support these efforts is crucial to the development of longstanding success across the programs. There have been a number of shifts related to diversification across student and alumni bodies. These shifts include but are not limited to how people identify gender, race, and sexuality (and the intersections across such identities), as well as trends across emerging and diverse disability communities. NYU is the most international campus in the United States, with the most campuses and sites outside of its county of origin and the most international students and exchange programs than any other university. As a result, the ongoing work related to GIDEBA is at the center of much of the leadership, administrative, and research efforts. Climate assessment work across NYU’s diverse global campus landscape will serve as the foundation to exemplify best practices related to data collection and dissemination, community and stakeholder engagement, and effective implementation of innovative strategies to close gap areas as identified. The data (quantitative and qualitative) and related research findings represent data collected from close to 22,000 stakeholders across the NYU campuses. The case study centers on specific methodological considerations, data integrity, stakeholder engagement from across student-faculty, staff, and alumni constituencies, and tactics to advance specific GIDBEA initiatives related to navigating shifting landscapes. Design thinking, incubation, and co-creation strategies have been employed to expand, leverage, actualize, and implement GIDBEA strategies that are – concrete, measurable, differentiated, and specific to global sites and regions and emerging trends.

Keywords: disability, LGBTQ+, DEI, research, case studies

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Authors: Abubakar-Zagoon Adams, Emmanuel Intsiful, Haruna Zagoon-Sayeed

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Within the Ghanaian context, the promotion of Technical and Vocational Education and Training (TVET) has been faced with many obstacles which are of great concern to national development. One of the obstacles that have been identified as having some negative impact on TVET promotion is the poor public perception of TVET. Poor public perception, as identified in the sub-sectors report in a number of Ghana Education Service reports, has received little attention in both research and the government’s effort to address the poor performance of the TVET sub-sector. This study investigated TVET stakeholders in the Ayawaso-West Municipality in the Greater Accra Region of Ghana to ascertain knowledge of technical and vocational education in Ghana. This study explored parents’ and students’ views and knowledge about technical and vocational education. The study adopted an exploratory research design and a qualitative research approach. Thirty-six (36) participants were selected by employing a purposive sampling technique. Twelve (ten parents and two school personnel) out of the total sample were engaged in key informant interviews, whereas three focus group discussions were conducted with students, eight in each group. The study found that the public has fair knowledge (positive) about TVET, and the other side of the coin knows that TVET is only meant for school dropouts, underprivileged students, and weak academic students. The study recommended that the government should intensify public education on TVET, deliberate investment should be made in TVET infrastructure, as well as proper regulation of the sub-sector.

Keywords: public perception, TVET promotion, socioeconomic, self-employment

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Authors: Innocent Atuhe, Babra Nalwadda, Grace Mulyowa, Annabella Habinka Ejiri

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Background: Atopic Dermatitis (AD) is one of the most prevalent and growing chronic inflammatory skin diseases in African prisons. AD care is limited in African due to a lack of information about the disease amongst primary care workers, limited access to dermatologists, lack of proper training of healthcare workers, and shortage of appropriate treatments. We designed and implemented the Prisons Telederma project based on the recommendations of the International Society of Atopic Dermatitis. We aimed at; i) increase awareness and understanding of teledermatology among prison health workers and ii) improve treatment outcomes of prisoners with atopic dermatitis through increased access to and utilization of consultant dermatologists through teledermatology in Uganda prisons. Approach: We used Store-and-forward Teledermatology (SAF-TD) to increase access to dermatologist-led care for prisoners and prison staff with AD. We conducted five days of training for prison health workers using an adapted WHO training guide on recognizing neglected tropical diseases through changes on the skin together with an adapted American Academy of Dermatology (AAD) Childhood AD Basic Dermatology Curriculum designed to help trainees develop a clinical approach to the evaluation and initial management of patients with AD. This training was followed by blended e-learning, webinars facilitated by consultant Dermatologists with local knowledge of medication and local practices, apps adjusted for pigmented skin, WhatsApp group discussions, and sharing pigmented skin AD pictures and treatment via zoom meetings. We hired a team of Ugandan Senior Consultant dermatologists to draft an iconographic atlas of the main dermatoses in pigmented African skin and shared this atlas with prison health staff for use as a job aid. We had planned to use MySkinSelfie mobile phone application to take and share skin pictures of prisoners with AD with Consultant Dermatologists, who would review the pictures and prescribe appropriate treatment. Unfortunately, the National Health Service withdrew the app from the market due to technical issues. We monitored and evaluated treatment outcomes using the Patient-Oriented Eczema Measure (POEM) tool. We held four advocacy meetings to persuade relevant stakeholders to increase supplies and availability of first-line AD treatments such as emollients in prison health facilities. Results: We have the very first iconographic atlas of the main dermatoses in pigmented African skin. We increased; i) the proportion of prison health staff with adequate knowledge of AD and teledermatology from 20% to 80%; ii) the proportion of prisoners with AD reporting improvement in disease severity (POEM scores) from 25% to 35% in one year; iii) increased proportion of prisoners with AD seen by consultant dermatologist through teledermatology from 0% to 20% in one year and iv)Increased the availability of AD recommended treatments in prisons health facilities from 5% to 10% in one year. Our study contributes to the use, evaluation, and verification of the use of teledermatology to increase access to specialist dermatology services to the most hard to reach areas and vulnerable populations such as that of prisoners.

Keywords: teledermatology, prisoners, reaching, un-reachable

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Authors: Radin Siti Aishah Radin A Rahman, Norasmah Othman, Zaidatol Akmaliah Lope Pihie, Hariyaty Ab. Wahid

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The recent instability in economy was found to be influencing the situation in Malaysia whether directly or indirectly. Taking that into consideration, the government needs to find the best approach to balance its citizen’s socio-economic strata level urgently. Through education platform is among the efforts planned and acted upon for the purpose of balancing the effects of the influence, through the exposure of social entrepreneurial activity towards youth especially those in higher institution level. Armed with knowledge and skills that they gained, with the support by entrepreneurial culture and environment while in campus; indirectly, the students will lean more on making social entrepreneurship as a career option when they graduate. Following the issues of marketability and workability of current graduates that are becoming dire, research involving how far the willingness of student to create social innovation that contribute to the society without focusing solely on personal gain is relevant enough to be conducted. With that, this research is conducted with the purpose of identifying the level of entrepreneurial intention and social entrepreneurship among higher institution students in Malaysia. Stratified random sampling involves 355 undergraduate students from five public universities had been made as research respondents and data were collected through surveys. The data was then analyzed descriptively using min score and standard deviation. The study found that the entrepreneurial intention of higher education students are on moderate level, however it is the contrary for social entrepreneurship activities, where it was shown on a high level. This means that while the students only have moderate level of willingness to be a social entrepreneur, they are very committed to created social innovation through the social entrepreneurship activities conducted. The implication from this study can be contributed towards the higher institution authorities in prediction the tendency of student in becoming social entrepreneurs. Thus, the opportunities and facilities for realizing the courses related to social entrepreneurship must be created expansively so that the vision of creating as many social entrepreneurs as possible can be achieved.

Keywords: entrepreneurial intention, higher education institutions (HEIs), social entrepreneurship, social entrepreneurial activity, gender

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Authors: Chen Chan Wei

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The purpose of this study was to investigate the impact of swim 1500m, 10000m run, VO2 max, and body fat on Olympic distance triathlon competition performance. The subjects were thirteen college triathletes with endurance training, with an average age, height and weight of 20.61±1.04 years (mean ± SD), 171.76±8.54 cm and 65.32±8.14 kg respectively. All subjects were required to take the tests of swim 1500m, run 10000m, VO2 max, body fat, and participate in the Olympic distance triathlon competition. First, the swim 1500m test was taken in the standardized 50m pool, with a depth of 2m, and the 10000m run test on the standardized 400m track. After three days, VO2 max was tested with the MetaMax 3B and body fat was measured with the DEXA machine. After two weeks, all 13 subjects joined the Olympic distance triathlon competition at the 2016 New Taipei City Asian Cup. The relationships between swim 1500m, 10000m run, VO2 max, body fat test, and Olympic distance triathlon competition performance were evaluated using Pearson's product-moment correlation. The results show that 10000m run and body fat had a significant positive correlation with Olympic distance triathlon performance (r=.830, .768), but VO2 max has a significant negative correlation with Olympic distance triathlon performance (r=-.735). In conclusion, for improved non-draft Olympic distance triathlon performance, triathletes should focus on running than swimming training and can be measure VO2 max to prediction triathlon performance. Also, managing body fat can improve Olympic distance triathlon performance. In addition, swimming performance was not significantly correlated to Olympic distance triathlon performance, possibly because the 2016 New Taipei City Asian Cup age group was not a drafting competition. The swimming race is the shortest component of Olympic distance triathlons. Therefore, in a non-draft competition, swimming ability is not significantly correlated with overall performance.

Keywords: triathletes, olympic, non-drafting, correlation

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Authors: Jocelyn Solomons, Kraak

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Rhythmic movement, also referred to as “dance”, involves the execution of different motor skills as well as the integration and sequencing of actions between limbs, timing and spatial precision. The aim of this study was therefore to investigate and compare the effect of a 16-week rhythmic movement intervention on flexibility, dynamic balance, agility, power and local muscular endurance of academy rugby players in the Western Cape, according to positional groups. Players (N ¼ 54) (age 18.66 0.81 years; height 1.76 0.69 cm; weight 76.77 10.69 kg), were randomly divided into a treatment-control [TCA] (n ¼ 28) and a control-treatment [CTB] (n ¼ 26) group. In this crossover experimental design, the interaction effect of the treatment order and the treatment time between the TCA and CTB group, was determined. Results indicated a statistically significant improvement (p < 0.05) in agility2 (p ¼ 0.06), power2 (p ¼ 0.05), local muscular endurance1 (p ¼ 0.01) & 3 (p ¼ 0.01) and dynamic balance (p < 0.01). Likewise, forwards and backs also showed statistically significant improvements (p < 0.05) per positional groups. Therefore, a rhythmic movement intervention has the potential to improve rugby-specific bio-motor skills and furthermore, improve positional specific skills should it be designed with positional groups in mind. Future studies should investigate, not only the effect of rhythmic movement on improving specific rugby bio-motor skills, but the potential of its application as an alternative training method during off- season (or detraining phases) or as a recovery method.

Keywords: agility, dance, dynamic balance, flexibility, local muscular endurance, power, training

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Authors: Abigaile Rose Mary R. Capay, Janne Ly Castillon-Gilpo, Sheila A. Javier

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The transition to online learning has been a challenging feat on the mental health of tertiary students. This study investigated whether learning mindfulness strategies online would help in improving students’ imagination, conscientiousness, extraversion, agreeableness and emotional stability, as measured by the International Personality Item Pool (IPIP) Big Five Factor Markers, as well as their dispositional mindfulness as measured by the Mindfulness Attention Awareness Scale (MAAS). Fifty-two college students participated in the experiment. The 23 participants assigned to the treatment condition received 6-weekly experiential sessions of online mindfulness training and were advised to follow a daily mindfulness practice, while the 29 participants from the control group only received a 1-hour lecture. Scores were collected at pretest and posttest. Findings show that there was a significant difference in the pretest and posttest scores of students assigned in the treatment group, likewise medium effect sizes in the variables: dispositional mindfulness (t (22) = 2.64, p = 0.015, d = .550), extraversion (t (22) = 2.76, p = 0.011, d = 0.575), emotional stability (t (22) = 2.99, p = 0.007, d = .624), conscientiousness (t (22) = 2.74, p = 0.012, d = .572) and imagination (t (22) = 4.08, p < .001), but not for agreeableness (t (22) = 2.01, p = 0.057, d = .419). No significant differences were observed on the scores of the control group. Educational institutions are recommended to consider teaching basic mindfulness strategies to tertiary students, as a valuable resource in improving their mental health as they navigate through adjustments in online learning.

Keywords: mindfulness, school-based interventions, MAAS, IPIP Big Five Markers, experiment

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Authors: Somruay Apichatibutarapong

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The purpose of this study was to address and comparison of the attitudes towards the statistics course for undergraduate students. Data were collected from 120 students in Faculty of Sciences and Technology, Suan Sunandha Rajabhat University who enrolled in the statistics course. The quantitative approach was used to investigate the assessment and comparison of attitudes towards statistics course. It was revealed that the overall attitudes somewhat agree both in pre-test and post-test. In addition, the comparison of students’ attitudes towards the statistic course (Form A) has no difference in the overall attitudes. However, there is statistical significance in all dimensions and overall attitudes towards the statistics course (Form B).

Keywords: statistics attitude, student’s attitude, statistics, attitude test

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Betty Kibirige

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Students entering the University of Zululand (UNIZULU) Science Faculty mostly come with skills that allow them to prepare for exams and pass them in order to satisfy requirements for entry into a tertiary Institution. Some students hail from deep rural schools with limited facilities, while others come from well-resourced schools. Personal experience has shown that it may take a student the whole time at a tertiary institution following the same skills as those acquired in high school as a sure means of entering the next level in their development, namely a postgraduate program. While it is apparent that at this point in human history, it is totally impossible to teach all the possible content in any one subject, many academics approach teaching and learning from the traditional point of view. It therefore became apparent to explore ways of developing a graduate that will be able to approach life with skills that allows them to navigate knowledge by applying conceptual and critical thinking skills. Recently, the Science Faculty at the University of Zululand introduced two Engineering programs. In an endeavour to approach the development of the Engineering graduate in this institution to be able to tackle problem-solving in the present-day excessive information availability, it became necessary to study and review approaches used by various academics in order to settle for a possible best approach to the challenge at hand. This paper focuses on the development of a deep rural child in a graduate with conceptual and critical thinking skills as major attributes possessed upon graduation. For this purpose, various approaches were studied. A combination of these approaches was repackaged to form an approach that may appear novel to UNIZULU and the rural child, especially for the Engineering discipline. The approach was checked by offering quiz questions to students participating in an engineering module, observing test scores in the targeted module and make comparative studies. Test results are discussed in the article. It was concluded that students’ graduate attributes could be tailored subconsciously to indeed include conceptual and critical thinking skills, but through more than one approach depending mainly on the student's high school background.

Keywords: graduate attributes, conceptual skills, critical thinking skills, traditional approach

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Authors: Ramune Kasperaviciene, Jurgita Motiejuniene, Dalia Venckiene

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Familiarising students with strategies for transferring cultural meanings (intertextual units, culture-specific idioms, culture-specific items, etc.) should be part of a comprehensive translator training programme. The present paper focuses on strategies for transferring such meanings into other languages and explores possibilities for introducing these methods and practice to translation students. The authors (university translation teachers) analyse the means of transferring cultural meanings from English into Lithuanian in a specific travel book, attribute these means to theoretically grounded strategies, and make calculations related to the frequency of adoption of specific strategies; translation students are familiarised with concepts and methods related to transferring cultural meanings and asked to put their theoretical knowledge into practice, i.e. interpret and translate certain culture-specific items from the same source text, and ground their decisions on theory; the comparison of the strategies employed by the professional translator of the source text (as identified by the authors of this study) and by the students is made. As a result, both students and teachers gain valuable experience, and new practices of conducting translation classes for a specific purpose evolve. Conclusions highlight the differences and similarities of non-professional and professional choices, summarise the possibilities for introducing methods of transferring cultural meanings to students, and round up with specific considerations of the impact of theoretical knowledge and the degree of experience on decisions made in the translation process.

Keywords: cultural meanings, culture-specific items, strategies for transferring cultural meanings, translator training

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Authors: Leena Alfarani

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Although mobile devices are ubiquitous on university campuses, teacher-readiness for mobile learning has yet to be fully explored in the non-western nations. This study shows that two main factors affect the adoption and use of m-learning among female teachers within a university in Saudi Arabia—resistance to change and perceived social culture. These determinants of the current use and intention to use of m-learning were revealed through the analysis of an online questionnaire completed by 165 female faculty members. This study reveals several important issues for m-learning research and practice. The results further extend the body of knowledge in the field of m-learning, with the findings revealing that resistance to change and perceived social culture are significant determinants of the current use of and the intention to use m-learning.

Keywords: blended learning, mobile learning, technology adoption, devices

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Authors: Justyna Mroczkowska

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Social worker's ability to provide culturally sensitive assistance in child protection is taken for granted; given limited training opportunities and lack of clear guidance, practitioners report working with migrant families more demanding in comparison to working with native families. In this study, the author developed and factor analyzed the Norwegian Cultural Self-Efficacy Scale to describe the level of cultural capability among Norwegian child protection professionals. The study aimed to determine the main influencing factors to cultural efficacy and examine the relationship between self-efficacy and perceived difficulty in working with migrant families. The scale was administered to child protection workers in Norway (N=251), and the reliability of the scale measured by Cronbach's alpha coefficient was .904. The confirmatory factor analysis of social work cultural self-efficacy found support for four separate but correlated subscales: Assessment, Communication, Support Request, and Teamwork. Regression analyses found the experience in working with migrant families, training and support from external agencies, and colleague support to be significant predictors of cultural self-efficacy. Self-efficacy in assessment skills and self-efficacy in communication skills were moderately related to the perceived difficulty to work with migrant families. The findings conclude with previous research and highlight the need for both professional development programs and institutional resources to be provided to support the practitioner's preparation for multicultural practice in child protection.

Keywords: child protection, cultural self-efficacy, cultural competency, migration, resources

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Authors: Cheng-Yu Tsai, Wen-Te Liu, Shin-Mei Hsu, Yin-Tzu Lin, Chi Wu

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Background: Obstructive Sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. In addition, Body parameters were identified high predictive importance for OSAS severity. However, the effects of body parameters on OSAS severity remain unclear. Objective: In this study, the objective is to establish a prediction model for OSAS by using body parameters and investigate the effects of body parameters in OSAS. Methodologies: Severity was quantified as the polysomnography and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). Four levels of OSAS severity were classified by the apnea and hypopnea index (AHI) with American Academy of Sleep Medicine (AASM) guideline. Body parameters, including neck circumference, waist size, and body mass index (BMI) were obtained from questionnaire. Next, dividing the collecting subjects into two groups: training and testing groups. The training group was used to establish the random forest (RF) to predicting, and test group was used to evaluated the accuracy of classification. Results: There were 3330 subjects recruited in this study, whom had been done polysomnography for evaluating severity for OSAS. A RF of 1000 trees achieved correctly classified 79.94 % of test cases. When further evaluated on the test cohort, RF showed the waist and BMI as the high import factors in OSAS. Conclusion It is possible to provide patient with prescreening by body parameters which can pre-evaluate the health risks.

Keywords: apnea and hypopnea index, Body parameters, obstructive sleep apnea syndrome, Random Forest

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Authors: Dativa Maria Aliddeki, Fred Monje, Godfrey Nsereko, Benon Kwesiga, Daniel Kadobera, Alex Riolexus Ario

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Background: Cholera is a severe gastrointestinal disease caused by Vibrio cholera. It has caused several pandemics. On 26 February 2018, a suspected cholera outbreak, with one death, occurred in School X in Hoima Municipality, western Uganda. We investigated to identify the scope and mode of transmission of the outbreak, and recommend evidence-based control measures. Methods: We defined a suspected case as onset of diarrhea, vomiting, or abdominal pain in a student or staff of School X or their family members during 14 February–10 March. A confirmed case was a suspected case with V. cholerae cultured from stool. We reviewed medical records at Hoima Hospital and searched for cases at School X. We conducted descriptive epidemiologic analysis and hypothesis-generating interviews of 15 case-patients. In a retrospective cohort study, we compared attack rates between exposed and unexposed persons. Results: We identified 15 cases among 75 students and staff of School X and their family members (attack rate=20%), with onset from 25-28 February. One patient died (case-fatality rate=6.6%). The epidemic curve indicated a point-source exposure. On 24 February, a student brought fried fish from her home in a fishing village, where a cholera outbreak was ongoing. Of the 21 persons who ate the fish, 57% developed cholera, compared with 5.6% of 54 persons who did not eat (RR=10; 95% CI=3.2-33). None of 4 persons who recooked the fish before eating, compared with 71% of 17 who did not recook it, developed cholera (RR=0.0, 95%CIFisher exact=0.0-0.95). Of 12 stool specimens cultured, 6 yielded V. cholerae. Conclusion: This cholera outbreak was caused by eating fried fish, which might have been contaminated with V. cholerae in a village with an ongoing outbreak. Lack of thorough cooking of the fish might have facilitated the outbreak. We recommended thoroughly cooking fish before consumption.

Keywords: cholera, disease outbreak, foodborne, global health security, Uganda

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Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

Procedia PDF Downloads 267

Authors: Shankaran Sitarama

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Teamwork is an extremely effective pedagogical tool in engineering education. New Product Development (NPD) has been an effective strategy of companies to streamline and bring innovative products and solutions to customers. Thus, Engineering curriculum in many schools, some collaboratively with business schools, have brought NPD into the curriculum at the graduate level. Teamwork is invariably used during instruction, where students work in teams to come up with new products and solutions. There is a significant emphasis of grade on the semester long teamwork for it to be taken seriously by students. As the students work in teams and go through this process to develop the new product prototypes, their effectiveness and learning to a great extent depends on how they function as a team and go through the creative process, come together, and work towards the common goal. A core attribute of a successful NPD team is their creativity and innovation. The team needs to be creative as a group, generating a breadth of ideas and innovative solutions that solve or address the problem they are targeting and meet the user’s needs. They also need to be very efficient in their teamwork as they work through the various stages of the development of these ideas resulting in a POC (proof-of-concept) implementation or a prototype of the product. The simultaneous requirement of teams to be creative and at the same time also converge and work together imposes different types of tensions in their team interactions. These ideational tensions / conflicts and sometimes relational tensions / conflicts are inevitable. Effective teams will have to deal with the Team dynamics and manage it to be resilient enough and yet be creative. This research paper provides a computational analysis of the teams’ communication that is reflective of the team dynamics, and through a superimposition of latent semantic analysis with social network analysis, provides a computational methodology of arriving at patterns of visual interaction. These team interaction patterns have clear correlations to the team dynamics and provide insights into the functioning and thus the effectiveness of the teams. 23 student NPD teams over 2 years of a course on Managing NPD that has a blend of engineering and business school students is considered, and the results are presented. It is also correlated with the teams’ detailed and tailored individual and group feedback and self-reflection and evaluation questionnaire.

Keywords: team dynamics, social network analysis, team interaction patterns, new product development teamwork, NPD teams

Procedia PDF Downloads 116

Authors: Marina Karaseva

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The beginning of our century became a new digital epoch in the educational situation. Last decade the newest stage of this process had been initialized by the touch-screen mobile devices with program applications for them. The touch possibilities for learning fundamentals of music are of especially importance for music majors. The phenomenon of touching, firstly, makes it realistic to play on the screen as on music instrument, secondly, helps students to learn music theory while listening in its sound elements by music ear. Nowadays we can detect several levels of such mobile applications: from the basic ones devoting to the elementary music training such as intervals and chords recognition, to the more advanced applications which deal with music perception of non-major and minor modes, ethnic timbres, and complicated rhythms. The main purpose of the proposed paper is to disclose the main tendencies in this process and to demonstrate the most innovative features of music theory applications on the base of iOS and Android systems as the most common used. Methodological recommendations how to use these digital material musicologically will be done for the professional music education of different levels. These recommendations are based on more than ten year ‘iTheory’ teaching experience of the author. In this paper, we try to logically classify all types of ‘iTheory’mobile applications into several groups, according to their methodological goals. General concepts given below will be demonstrated in concrete examples. The most numerous group of programs is formed with simulators for studying notes with audio-visual links. There are link-pair types as follows: sound — musical notation which may be used as flashcards for studying words and letters, sound — key, sound — string (basically, guitar’s). The second large group of programs is programs-tests containing a game component. As a rule, their basis is made with exercises on ear identification and reconstruction by voice: sounds and intervals on their sounding — harmonical and melodical, music modes, rhythmic patterns, chords, selected instrumental timbres. Some programs are aimed at an establishment of acoustical communications between concepts of the musical theory and their musical embodiments. There are also programs focused on progress of operative musical memory (with repeating of sounding phrases and their transposing in a new pitch), as well as on perfect pitch training In addition a number of programs improvisation skills have been developed. An absolute pitch-system of solmisation is a common base for mobile programs. However, it is possible to find also the programs focused on the relative pitch system of solfegе. In App Store and Google Play Market online store there are also many free programs-simulators of musical instruments — piano, guitars, celesta, violin, organ. These programs may be effective for individual and group exercises in ear training or composition classes. Great variety and good sound quality of these programs give now a unique opportunity to musicians to master their music abilities in a shorter time. That is why such teaching material may be a way to effective study of music theory.

Keywords: ear training, innovation in music education, music theory, mobile devices

Procedia PDF Downloads 205

Authors: Juvanie C. Lapesigue

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Worldwide school closures result from a significant public health crisis that has affected the nation and the entire world. It has affected students, educators, educational organizations globally, and many other aspects of society. Academic institutions worldwide teach students using diverse approaches of various learning delivery modes. This paper investigates the competence and performance of physical education teachers using various learning delivery modes, including Distance learning, Blended Learning, and Homeschooling during online distance education. To identify the Gap between their age generation using various learning delivery that affects teachers' preparation for distance learning and evaluates how these modalities impact teachers’ competence and performance in the case of a pandemic. The respondents were the Senior High School teachers of the Department of Education who taught in Davao City before and during the pandemic. Purposive sampling was utilized on 61 Senior High School Teachers in Davao City Philippines. The result indicated that teaching performance based on pedagogy and assessment has significantly affected teaching performance in teaching physical education, particularly those Non-PE teachers teaching physical education subjects. It should be supplied with enhancement training workshops to help them be more successful in preparation in terms of teaching pedagogy and assessment in the following norm. Hence, a proposed unique training design for non-P.E. Teachers has been created to improve the teachers’ performance in terms of pedagogy and assessment in teaching P.E subjects in various learning delivery modes in the next normal.

Keywords: distance learning, learning delivery modes, P.E teachers, senior high school, teaching competence, teaching performance

Procedia PDF Downloads 94

Authors: Aziz Soubai

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The primary issue is that English as foreign language learners don’t use English outside the classroom. The only little exposure is inside the classroom, and that’s not enough to make them good language learners! Edmodo, like the other Learning Management Systems, can be used to encourage students to collaborate with each other and with global classrooms on projects where English is used- Some examples of collaboration with different schools will be mentioned and how the Substitution Augmentation Modification Redefinition (SAMR) model and its stages can be applied in the activities, especially for teachers who are hesitant to introduce technology or don’t have a lot of technical knowledge. There will also be some focus on Edmodo groups and on how flipped and blended learning can be used as an extension for classroom time and to help the teacher address language problems and improve students’ language skills, especially writing, reading and communication. It is also equally important to use Edmodo badges and certificates for motivating and engaging learners and gamifying the lesson.

Keywords: EFL learners, language classroom-learning management system, edmodo, SAMR, language skills

Procedia PDF Downloads 63

Authors: Mika Kaufman

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Behavioral health in school-age children is a great concern. Negative behaviors can affect mental and physical health and, if ignored, can lead to further problems later in life. Rural communities often lack resources for counselors, social workers, and mental health care in the hopes of intervening with children who exhibit negative behaviors. Because of this, schools in rural communities are more likely to have children with behavioral issues. Prevention programs to recognize and address these behavioral concerns can educate teachers about mental health, different negative behaviors that students might exhibit, and how to manage those behaviors and engage with students in a positive way.

Keywords: prevention programs, behavioral health, resources for teachers, rural schools

Procedia PDF Downloads 68

Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

Procedia PDF Downloads 324

Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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

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

Procedia PDF Downloads 72

Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

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Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: ABET, accreditation, benchmark collection, machine learning, program educational objectives, student outcomes, supervised multi-class classification, text mining

Procedia PDF Downloads 173

Authors: Derek O Reilly, Piotr Milczarski, Shane Dowdall, Artur Hłobaż, Krzysztof Podlaski, Hiram Bollaert

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Learning through creation of contextual games is a very promising way/tool for interdisciplinary and international group projects. During 2013 and 2014 we took part and organized two intensive students projects in different conditions. The projects enrolled 68 students and 12 mentors from 5 countries. In the paper we want to share our experience how to strengthen the chances to succeed in short (12-15 days long) student projects. In our case almost all teams prepared working prototype and the results were highly appreciated by external experts.

Keywords: contextual games, mobile games, GGULIVRR, walkabout, Erasmus intensive programme

Procedia PDF Downloads 502

Authors: Michiko Iwasaki, Jane M. Endres, Julia Y. Richards, Andrew Futterman

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The present study aimed to examine college students’ happiness during COVID19-pandemic. Using the online survey data from 96 college students in the U.S., a regression analysis was conducted to predict college students’ happiness. The results indicated that a four-predictor model (optimism, college students’ subjective wellbeing, coronavirus stress, and spirituality) explained 57.9% of the variance in student’s subjective happiness, F(4,77)=26.428, p<.001, R2=.579, 95% CI [.41,.66]. The study suggests the importance of learned optimism among college students.

Keywords: COVID-19, optimism, spirituality, well-being

Procedia PDF Downloads 226