Search results for: online teaching and learning

Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

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Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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Authors: Clement Yeboah, Eva Laryea

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A pretest-posttest within subjects experimental design was employed to examine the effects of a computerized basic statistics learning game on achievement and statistics-related anxiety of students enrolled in introductory graduate statistics course. Participants (N = 34) were graduate students in a variety of programs at state-funded research university in the Southeast United States. We analyzed pre-test posttest differences using paired samples t-tests for achievement and for statistics anxiety. The results of the t-test for knowledge in statistics were found to be statistically significant, indicating significant mean gains for statistical knowledge as a function of the game-based intervention. Likewise, the results of the t-test for statistics-related anxiety were also statistically significant, indicating a decrease in anxiety from pretest to posttest. The implications of the present study are significant for both teachers and students. For teachers, using computer games developed by the researchers can help to create a more dynamic and engaging classroom environment, as well as improve student learning outcomes. For students, playing these educational games can help to develop important skills such as problem solving, critical thinking, and collaboration. Students can develop an interest in the subject matter and spend quality time to learn the course as they play the game without knowing that they are even learning the presupposed hard course. The future directions of the present study are promising as technology continues to advance and become more widely available. Some potential future developments include the integration of virtual and augmented reality into educational games, the use of machine learning and artificial intelligence to create personalized learning experiences, and the development of new and innovative game-based assessment tools. It is also important to consider the ethical implications of computer game-based pedagogy, such as the potential for games to perpetuate harmful stereotypes and biases. As the field continues to evolve, it will be crucial to address these issues and work towards creating inclusive and equitable learning experiences for all students. This study has the potential to revolutionize the way basic statistics graduate students learn and offers exciting opportunities for future development and research. It is an important area of inquiry for educators, researchers, and policymakers and will continue to be a dynamic and rapidly evolving field for years to come.

Keywords: pretest-posttest within subjects, computer game-based learning, statistics achievement, statistics anxiety

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Authors: K. P. v. R. de Silva, R. W. Senevirathna, M. M. A. J. Kumara, J. P. M. Kumarasinghe, R. L. Gunawardana, S. M. Uluwitiya, G. C. P. Jayawickrama, W. K. T. I. Madushani

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Introduction: The limited literature supporting the utilization of mechanical bowel preparation (MBP) for patients undergoing laparoscopic anterior resection (LAR) remains a notable issue. This study was conducted to examine the clinical consequences of anastomosis in colorectal surgery with MBP compared to cases where MBP was not utilized (no-MBP) in the context of LAR. Methods: This was a retrospective comparative study conducted in the professorial surgical wards of the teaching hospital karapitiya (THK). Colorectal cancer patients(n=306) participated in the study, including 151 MBP patients and 155 no-MBP patients, where the postoperative complications and mortality rates were compared. Results: The anastomotic leakage rate was 2.6%(n=4) in the no-MBP group and 6.0%(n=9) in the MBP group (p=0.143). The postoperative paralytic ileus rate was 18.5%(n=28) and 5.8%(n=9) in the MBP group and no-MBP group, respectively, displaying a statistically significant difference (p=0.001). Wound infection, pneumonia, urinary tract infection, and cardiac complication rates also were higher in the MBP group. The overall mortality rate was 1.3%(n=3) in the no-MBP group and 2.0%(n=2) in the MBP group. Conclusions: The evidence concludes that MBP increases post-operative complications. Therefore, prophylactic MBP in LAR has not been proven to benefit patients. However, further research is necessary to understand the comparative effects of MBP versus no preparation comprehensively.

Keywords: MBP, anastomosis, LAR, paralytic ileus

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Authors: Lizel Bertie, Kim Johnston

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Parental emotion regulation plays a central role in the socialisation of emotion, especially when teaching young children to cope with negative emotions. Despite evidence which shows non-supportive parental responses to children’s expression of negative emotions has implications for the social and emotional development of the child, few studies have investigated risk factors which impact parental emotion socialisation processes. The current study aimed to explore the extent to which parental stress contributes to both difficulties in parental emotion regulation and non-supportive parental responses to children’s expression of negative emotions. In addition, the study examined whether parental use of expressive suppression as an emotion regulation strategy facilitates the influence of parental stress on non-supportive responses by testing the relations in a mediation model. A sample of 140 Australian adults, who identified as parents with children aged 5 to 10 years, completed an online questionnaire. The measures explored recent symptoms of depression, anxiety, and stress, the use of expressive suppression as an emotion regulation strategy, and hypothetical parental responses to scenarios related to children’s expression of negative emotions. A mediated regression indicated that parents who reported higher levels of stress also reported higher levels of expressive suppression as an emotion regulation strategy and increased use of non-supportive responses in relation to young children’s expression of negative emotions. These findings suggest that parents who experience heightened symptoms of stress are more likely to both suppress their emotions in parent-child interaction and engage in non-supportive responses. Furthermore, higher use of expressive suppression strongly predicted the use of non-supportive responses, despite the presence of parental stress. Contrary to expectation, no indirect effect of stress on non-supportive responses was observed via expressive suppression. The findings from the study suggest that parental stress may become a more salient manifestation of psychological distress in a sub-clinical population of parents while contributing to impaired parental responses. As such, the study offers support for targeting overarching factors such as difficulties in parental emotion regulation and stress management, not only as an intervention for parental psychological distress, but also the detection and prevention of maladaptive parenting practices.

Keywords: emotion regulation, emotion socialisation, expressive suppression, non-supportive responses, parental stress

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Martina Dickson, Lilly Tennant

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The young Emirati female university students of today are the first generation of women in the UAE for whom higher education as become not only a possibility, but almost an expectation. Young women in the UAE today make up around 77% of students in higher education institutes in the country. However, the societal expectations placed upon these women in terms of early marriage, child-bearing and rearing are similar to those placed upon their mothers and grandmothers in a time where women were not expected to go to university. A large proportion of female university students in the UAE are mothers of young children, or become mothers whilst at the university. This creates a challenging situation for young student mothers, where two weeks’ maternity leave is typical across institutions. The context of this study is in one such institution in the UAE. We have employed a mixed method approach to gathering interview data from twenty mothers, and survey data from over one hundred mothers. The main findings indicate that mothers have strong desires for their institution to support them more, for example by the provision of nursery facilities and resting areas for new mothers, and giving them greater flexibility over course selections and schedules including the provision of online learning. However, the majority felt supported on a personal level by their tutors. The major challenges which they identified in returning to college after only two weeks’ leave included the inevitable health and lack of sleep issues when caring for a newborn, struggling to catch up with missed college work and handling their course load. We also explored the women's’ home support systems which were provided from a variety of extended family, spouses and paid domestic help.

Keywords: student mothers, challenges, supports, United Arab Emirates

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Authors: Christine Okello

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Physical Education (PE) curriculum provides school age students to explore issues that are likely to impact on health, safety, and well-being. The current curriculum includes the physical activity component, intended to improve physical fitness, social skills as well as building confidence. While this viewpoint is vital, there are challenges and stigma attached when specific issues are either ignored, inadequately addressed, or not seen to be important. The department stipulates that students attend a school that is closest to their home, to access available government transportation to and from school. Equivalently, parents of students with a disability decide where their children attend school. A choice between a regular classroom, mainstream Special Unit classroom, or a School for Specific Purposes (SSP). Parents who take their children to regular schools may be oblivious of the details of the curriculum. Physical Education outcomes does not stipulate the extent to which a student must perform or expected to perform. It is therefore due to the classroom teacher to adjust their teaching goals or outcomes to suit all students in their classroom. A student who can run a hundred meters race in 20 seconds may belong in the same classroom as a student in a wheelchair. While these students are challenged because of a lack of performance, teachers are challenged to effectively teach successful PE lessons, and on the other hand students without a disability may not be able to attain their optimum. This paper will identify areas of need, address the challenges, and explore a possible solution.

Keywords: special needs, disability, challenges, physical education

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Authors: Mina Malak Hanna Saad

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New strategies for supplying banking services to the customer have been brought, which include online banking. Banks have begun to recall electronic banking (e-banking) as a manner to replace some conventional department features by means of the usage of the internet as a brand-new distribution channel. A few clients have at least one account at multiple banks and get admission to those debts through online banking. To test their present-day internet worth, customers need to log into each of their debts, get particular statistics, and paint closer to consolidation. Not only is it time-ingesting; however, but it is also a repeatable activity with a certain frequency. To solve this problem, the idea of account aggregation was delivered as a solution. Account consolidation in e-banking as a form of digital banking appears to build stronger dating with clients. An account linking service is usually known as a service that permits customers to manipulate their bank accounts held at exceptional institutions through a common online banking platform that places a high priority on safety and statistics protection. The object affords an outline of the account aggregation approach in e-banking as a distinct carrier in the area of e-banking. The advanced facts generation is becoming a vital thing in the improvement of financial services enterprise, specifically the banking enterprise. It has brought different ways of delivering banking to the purchaser, which includes net Banking. Banks began to study electronic banking (e-banking) as a means to update some of their traditional branch functions and the use of the net as a distribution channel. Some clients have at least multiple accounts throughout banks and get the right of entry to that money owed through the usage of e-banking offerings. To examine the contemporary internet's well-worth position, customers have to log in to each of their money owed, get the information and work on consolidation. This no longer takes sufficient time; however, it is a repetitive interest at a specified frequency. To address this point, an account aggregation idea is brought as an answer. E-banking account aggregation, as one of the e-banking kinds, appeared to construct a more potent dating with clients. Account Aggregation carrier usually refers to a service that allows clients to control their bank bills maintained in one-of-a-kind institutions via a common Internet banking working platform, with an excessive subject to protection and privateness. This paper offers an overview of an e-banking account aggregation technique as a new provider in the e-banking field.

Keywords: compatibility, complexity, mobile banking, observation, risk banking technology, Internet banks, modernization of banks, banks, account aggregation, security, enterprise developmente-banking, enterprise development

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Authors: Magali Canovi, Francesca Pucciarelli

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Over the past decade Langhe has developed as a wine tourism destination and has become increasingly popular on an international basis. Wine tourism has been recognized as an important business driver for wineries in Langhe and wine owners have taken advantage of this opportunity through developing a variety of tourism-related activities at their wineries, notably winery visits, wine tastings, cellar-door sales, B&Bs and/or restaurants. In order to promote these tourism-related activities and attract an increasing number of wine tourists, wineries have started to engage in social media. While tourism scholars are now well aware of the benefits social media provides to both travellers and service providers, the existing literature on social media from supplier’s perspective remains limited. Accordingly, this paper aims to fill this gap through providing new insights into how service providers, that is winery owners, exploit social media to promote tourism online. The paper explores the importance and the role of social media as part of wineries’ marketing strategies to promote wine tourism online. The focus lies on understanding, which motives drive winery owners to activate and implement social media activities in promoting wine tourism. A case study approach is adopted, using the North Italian wine region of Langhe in Piedmont. Empirical evidence is provided by a sample of 28 winery owners. An interpretivist approach to research is adopted in order to extend current understandings of social media within the context of wine tourism. In line with the interpretivist perspective, this paper uses discourse analysis (DA) as a methodological approach for analyzing and interpreting winery owners’ accounts. Three key findings emerge from this research. First, there is a general understanding among winery owners what social media represents an opportunity in promoting wine tourism – if not even a must have. Second, the majority of interviewed winery owners are currently applying to some extent social media to promote wine tourism online as well as to interact and engage with tourists directly. Lastly, a varying degree of usage of social media amongst wineries is identified, with some wineries not recognizing social media as a crucial tool in marketing communication strategies. On the other extent, some commonalities in strategies and platforms chosen can be detected by these wineries that actively participate in social media. In conclusion, the main contribution of this paper is that it extends current understandings of social media in the wine tourism context by offering valuable insights into how service providers perceive and engage in social media.

Keywords: langhe, promotion, social media, wine tourism

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Authors: Sadeq Al Yaari, Muhammad Alkhunayn, Ayman Al Yaari, Montaha Al Yaari, Adham Al Yaari, Sajedah Al Yaari, Fatehi Eissa

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Background & Aims: Audio-visual aids and computer-aided language instruction (CALI) have been documented to improve receptive skills, namely listening skills, in normal students. The increased listening has been attributed to the understanding of other interlocutors' speech, but recent experiments have suggested that audio-visual aids and CALI should be tested against the listening of students of special needs to see the effects of the former in the latter. This investigation described the effect of audio-visual aids and CALI on the performance of these students. Methods: Pre-and-posttests were administered to 40 students of special needs of both sexes at al-Malādh school for students of special needs aged between 8 and 18 years old. A comparison was held between this group of students and another similar group (control group). Whereas the former group underwent a listening course using audio-visual aids and CALI, the latter studied the same course with the same speech language therapist (SLT) with the classical method. The outcomes of the two tests for the two groups were qualitatively and quantitatively analyzed. Results: Significant improvement in the performance was found in the first group (treatment group) (posttest= 72.45% vs. pre-test= 25.55%) in comparison to the second (control) (posttest= 25.55% vs. pre-test= 23.72%). In comparison to the males’ scores, the scores of females are higher (1487 scores vs. 1411 scores). Suggested results support the necessity of the use of audio-visual aids and CALI in teaching listening at the schools of students of special needs.

Keywords: listening, receptive skills, audio-visual aids, CALI, special needs

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: Evgeny E. Tereshatov, C. M. Folden

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Ionic liquids and deep eutectic mixtures represent so-called non-conventional solvents. The former, composed of discrete ions, is a salt with a melting temperature below 100°С. The latter, consisting of hydrogen bond donors and acceptors, is a mixture of at least two compounds, resulting in a melting temperature depression in comparison with that of the individual moiety. These systems also can be water-immiscible, which makes them applicable for metal extraction. This work will cover interactions of In, Tl, Ir, and Rh in hydrochloric acid media with eutectic mixtures and Er, Ir, and At in a gas phase with chemically modified α-detectors. The purpose is to study chemical systems based on non-conventional solvents in terms of their interaction with metals. Once promising systems are found, the next step is to modify the surface of α-detectors used in the online element production at cyclotrons to get the detector chemical selectivity. Initially, the metal interactions are studied by means of the liquid-liquid extraction technique. Then appropriate molecules are chemisorbed on the surrogate surface first to understand the coating quality. Finally, a detector is covered with the same molecule, and the metal sorption on such detectors is studied in the online regime. It was found that chemical treatment of the surface can result in 99% coverage with a monolayer formation. This surface is chemically active and can adsorb metals from hydrochloric acid solutions. Similarly, a detector surface was modified and tested during cyclotron-based experiments. Thus, a procedure of detectors functionalization has been developed, and this opens an interesting opportunity of studying chemisorption of elements which do not have stable isotopes.

Keywords: mechanism, radioisotopes, solvent extraction, gas phase sorption

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Authors: Michael Lousis

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The systematic identification of the most conspicuous and significant errors made by learners during three-years of testing of their progress in learning Arithmetic are presented in this article. How these errors have changed over three-years of school instruction of Arithmetic also is shown. The sample is comprised of two hundred (200) English students and one hundred and fifty (150) Greek students. These students were purposefully selected according to their participation in each testing session in the development of the three-year Kassel Project in England and Greece, in both domains simultaneously in Arithmetic and Algebra. The data sample includes six test-scripts corresponding to three testing sessions in both Arithmetic and Algebra respectively.

Keywords: arithmetic, errors, Kassel Project, progress of learning

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Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

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Authors: Ihsan Ullah Khan, Asim Kareem, Naveed Saif

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This research study examined the application of innovative strategies for improving writing skills of Secondary level students. It also examined the steps taken by Secondary level teachers for the improvement of writing skills of their students. Effective written communication is the problem faced by all the ESL students at secondary level. The objective of the study was to help the secondary level students to overcome this problem. More specifically, this research study aimed to guide the teachers, teaching at secondary level, to bring innovation in their teaching by showing the results of innovative strategies. In order to know about the practices of the teachers, inside the classroom, data was calculated through rating scale questionnaire. After that experimental study was carried out. For the experimental study a 10th grade class was selected. Results were drawn by analyzing the pre and post-tests of the students with the help of independent sample t-test. The results showed that a significant change occurred in the writing skills of the students, belonging to Treatment group. No improvement was observed in the writing skills of the students, belonging to Control group. Thus this research study proved to be a great contribution by guiding the teachers to bring a significant change in the writing skills of the students.

Keywords: writing skills, innovative strategies, teachers, students, treatment group, control group

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Authors: Laurence C. Hanes, Robert J. Stone

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Video games have reached a point of huge commercial success as well as wide familiarity with audiences both young and old. Much attention and research have also been directed towards serious games and their potential learning affordances. It is little surprise that the field of virtual heritage has taken a keen interest in using serious games to present cultural heritage information to users, with applications ranging from museums and cultural heritage institutions, to academia and research, to schools and education. Many researchers have already documented their efforts to develop and distribute virtual heritage serious games. Although attempts have been made to create classifications of the different types of virtual heritage games (somewhat akin to the idea of game genres), no formal taxonomy has yet been produced to define the different types of cultural heritage and historical information that can be presented through these games at a content level, and how the information can be manifested within the game. This study proposes such a taxonomy. First the informational content is categorized as heritage or historical, then further divided into tangible, intangible, natural, and analytical. Next, the characteristics of the manifestation within the game are covered. The means of manifestation, level of demonstration, tone, and focus are all defined and explained. Finally, the potential learning outcomes of the content are discussed. A demonstration of the taxonomy is then given by describing the informational content and corresponding manifestations within several examples of virtual heritage serious games as well as commercial games. It is anticipated that this taxonomy will help designers of virtual heritage serious games to think about and clearly define the information they are presenting through their games, and how they are presenting it. Another result of the taxonomy is that it will enable us to frame cultural heritage and historical information presented in commercial games with a critical lens, especially where there may not be explicit learning objectives. Finally, the results will also enable us to identify shared informational content and learning objectives between any virtual heritage serious and/or commercial games.

Keywords: informational content, serious games, taxonomy, virtual heritage

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Authors: Michalis Linardakis, Vasilis Grammatikopoulos, Athanasios Gregoriadis, Kalliopi Trouli

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Discrete choice models belong to the family of Conjoint analysis that are applied on the preferences of the respondents towards a set of scenarios that describe alternative choices. The scenarios have been pre-designed to cover all the attributes of the alternatives that may affect the choices. In this study, we examine how preschool educators integrate physical activities into their everyday teaching practices through the use of discrete choice models. One of the advantages of discrete choice models compared to other more traditional data collection methods (e.g. questionnaires and interviews that use ratings) is that the respondent is called to select among competitive and realistic alternatives, rather than objectively rate each attribute that the alternatives may have. We present the effort to construct and choose representative attributes that would cover all possible choices of the respondents, and the scenarios that have arisen. For the purposes of the study, we used a sample of 50 preschool educators in Greece that responded to 4 scenarios (from the total of 16 scenarios that the orthogonal design resulted), with each scenario having three alternative teaching practices. Seven attributes of the alternatives were used in the scenarios. For the analysis of the data, we used multinomial logit model with random effects, multinomial probit model and generalized mixed logit model. The conclusions drawn from the estimated parameters of the models are discussed.

Keywords: conjoint analysis, discrete choice models, educational data, multivariate statistical analysis

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Authors: Gabriel Ocheleka Aniedi A. Udo, Patum Wasinda

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The study was carried out to appraise the impact of ICT-based teaching methodologies (video-taped instructions and Power Point presentations) on academic performance and retention of secondary school students in Physics, with particular interest in Newton Laws of Motion. The study was conducted in Cross River State, Nigeria, with a quasi-experimental research design using non-randomised pre-test and post-test control group. The sample for the study consisted of 176 SS2 students drawn from four intact classes of four secondary schools within the study area. Physics Achievement Test (PAT), with a reliability coefficient of 0.85, was used for data collection. Mean and Analysis of Covariance (ANCOVA) was used in the treatment of the obtained data. The results of the study showed that there was a significant difference in the academic performance and retention of students taught using video-taped instructions and those taught using power point presentations. Findings of the study showed that students taught using video-taped instructions had a higher academic performance and retention than those taught using power point presentations. The study concludes that the use of blended ICT-based teaching methods can improve learner’s academic performance and retention.

Keywords: video taped instruction (VTI), power point presentation (PPT), academic performance, retention, physics

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Authors: Joseph Wolff, Jeffrey Eilbott

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Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

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Authors: David F. Nettleton, Christian Wasiak, Jonas Dorissen, David Gillen, Alexandr Tretyak, Elodie Bugnicourt, Alejandro Rosales

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In this work, preliminary results are given for the modeling and calibration of two inline processes, pultrusion, and laser ablation, using machine learning techniques. The end product of the processes is the core of a medical guidewire, manufactured to comply with a user specification of diameter and flexibility. An ensemble approach is followed which requires training several models. Two state of the art machine learning algorithms are benchmarked: Kernel Recursive Least Squares (KRLS) and Support Vector Regression (SVR). The final objective is to build a precise digital model of the pultrusion and laser ablation process in order to calibrate the resulting diameter and flexibility of a medical guidewire, which is the end product while taking into account the friction on the forming die. The result is an ensemble of models, whose output is within a strict required tolerance and which covers the required range of diameter and flexibility of the guidewire end product. The modeling and automatic calibration of complex in-line industrial processes is a key aspect of the Industry 4.0 movement for cyber-physical systems.

Keywords: calibration, data modeling, industrial processes, machine learning

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Authors: Shaleeza Sohail

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Self, and peer evaluations are some of the main components in almost all group assignments and projects in higher education institutes. These evaluations provide students an opportunity to better understand the learning outcomes of the assignment and/or project. A number of online systems have been developed for this purpose that provides automated assessment and feedback of students’ contribution in a group environment based on self and peer evaluations. All these systems lack a progressive aspect of these assessments and feedbacks which is the most crucial factor for ongoing improvement and life-long learning. In addition, a number of assignments and projects are designed in a manner that smaller or initial assessment components lead to a final assignment or project. In such cases, the evaluation and feedback may provide students an insight into their performance as a group member for a particular component after the submission. Ideally, it should also create an opportunity to improve for next assessment component as well. Self and Peer Progressive Assessment and Feedback System encourages students to perform better in the next assessment by providing a comparative analysis of the individual’s contribution score on an ongoing basis. Hence, the student sees the change in their own contribution scores during the complete project based on smaller assessment components. Self-Assessment Factor is calculated as an indicator of how close the self-perception of the student’s own contribution is to the perceived contribution of that student by other members of the group. Peer-Assessment Factor is calculated to compare the perception of one student’s contribution as compared to the average value of the group. Our system also provides a Group Coherence Factor which shows collectively how group members contribute to the final submission. This feedback is provided for students and teachers to visualize the consistency of members’ contribution perceived by its group members. Teachers can use these factors to judge the individual contributions of the group members in the combined tasks and allocate marks/grades accordingly. This factor is shown to students for all groups undertaking same assessment, so the group members can comparatively analyze the efficiency of their group as compared to other groups. Our System provides flexibility to the instructors for generating their own customized criteria for self and peer evaluations based on the requirements of the assignment. Students evaluate their own and other group members’ contributions on the scale from significantly higher to significantly lower. The preliminary testing of the prototype system is done with a set of predefined cases to explicitly show the relation of system feedback factors to the case studies. The results show that such progressive feedback to students can be used to motivate self-improvement and enhanced team skills. The comparative group coherence can promote a better understanding of the group dynamics in order to improve team unity and fair division of team tasks.

Keywords: effective group work, improvement of team skills, progressive feedback, self and peer assessment system

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Authors: Ofelia A. Damag

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The study was focused on the development and assessment of the illustrated language activities of the 1996 Edition of the Portage Guide to Early Education. It determined the extent of appropriateness, applicability, time efficiency and aesthetics of the illustrated language activities to be used as instructional material not only by teachers, but parents and caregivers as well. The eclectic research design was applied in this study using qualitative and quantitative methods. To determine the applicability and time efficiency of the study, a try out was done. Since the eclectic research design was used, it made use of a researcher-made survey questionnaire and focus group discussion. Analysis of the data was done through weighted mean and ANOVA. The respondents of the study were representatives of Special Education (SPED) teachers, caregivers and parents of a special-needs child, particularly with difficulties in learning basic language skills. The results of the study show that a large number of respondents are SPED teachers and caregivers and are mostly college graduates. Many of them have earned units towards Master’s studies. Moreover, a majority of the respondents have not attended seminars or in-service training in early intervention for them to be more competent in the area of specialization. It is concluded that the illustrated language activities under review in this study are appropriate, applicable, time efficient and aesthetic for use as a tool in teaching. The recommendations are focused on the advocacy for SPED teachers, caregivers and parents of special-needs children to be more consistent in the implementation of the new instructional materials as an aid in an intervention program.

Keywords: illustrated language activities, inclusion, portage guide to early education, special educational needs

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Authors: Karen V. Lee

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The significance of this study involves autoethnographic research about a music teacher learning about the socialization of gender issues in teaching. Mentorship involving intervention helps with the consequences influencing a transgendered music teacher. Basic storytelling methodology involves the qualitative method of research as a theoretical framework where the author provides a storied reflection about political issues surrounding teachers and the sociology of gender. Sub-themes involve counseling, adult education to ensure students and teachers receive social, emotional, physical, spiritual, and educational resources that evoke visceral, emotional responses from the audience. Major findings share how stories are helpful resources for others who struggle with the socialization of gender. It is hoped the research dramatizes an episodic yet incomplete story that highlights the circumstances surrounding the protagonist having his sex reassignment surgery during his undergraduate education degree. In conclusion, the research is a reflexive storied framework that embraces a positive outlook about a transgendered teacher during his masectomy. The sensory experience seeks verisimilitude by evoking lifelike and believable feelings from others. Thus, the scholarly importance of the sociology of gender and society provides transformative aspects that contributes to social change. Overall, the surgery surrounding the story about transgendered issues are not uncommon in society. Thus, continued education supports the moral mission to help teachers overcome and understand issues of gender that can socially impacts their professional lives as teachers.

Keywords: sociology of gender, transgender, music teachers, story, autoethnography as research, ideology

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Authors: Ting Gao, Mingyue He

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Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments.

Keywords: channel estimation, reconfigurable intelligent surface, wireless communication, deep learning

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Authors: Elaine Marta Pereira Aaltonen

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Effective school leadership materialised in educational settings through the high standard professional performance of senior and middle leaders, has increasingly become an education policy priority around the world due to a wide recognition that schools need knowledgeable, skilled, and committed leaders, along with great teachers, in order to ensure outstanding education at all levels of schooling. The scope of this paper is the work of middle leaders, whose direct influence on teachers and classroom teaching, thus, on student learning outcomes, is a key component for successful school systems. It particularly aims at sharing some of the findings obtained through an academic study recently carried out by the same researcher, which was focused on enhancing understanding about aspects related to the professional performance of educational middle leaders, applied to the context of the lower elementary school division of a private mainstream school located in Brazil. The master´s dissertation findings included identifying the roles performed by a team of educational middle leaders throughout the year of 2021, as well as gaining insights on their perceptions about the roles performed, both through an electronic questionnaire and individual face-to-face interviews. Not only the roles of the middle leaders who participated in the research have been identified through the qualitative case study undertaken, but additional research finding lying within the sphere of the categorisation of such roles, based upon coherent domains of practice, has possibly been made. Hence, the main purpose of this paper is to outline the findings concerning the current roles played by educational middle leaders.

Keywords: roles, middle leaders, educational leadership, school leadership, and management

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Authors: Adam Al-Harbi, Chanel Powell, Katelyn Ward

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In the age of social media and online body forums, the perception of body image among teenagers is constantly evolving. Growing up in an era of instant comparison facilitated by the internet, adolescents often struggle with self-identity, comparing their lives and bodies to countless others online. This environment has made it easier for them to access information on restrictive diets and fasting, contributing to the unfortunate glamorization of eating disorders. Although beauty standards have existed for centuries, social media amplifies these pressures. Platforms such as Instagram and TikTok have created spaces where teens are constantly exposed to idealized body types and beauty standards. This exposure often leads to harmful comparisons, fostering feelings of inadequacy and negative self-worth. Research indicates that adolescents, especially young girls, are at heightened risk for developing eating disorders like anorexia nervosa as they internalize these unrealistic standards. Anorexia nervosa, which has the highest mortality rate of any psychiatric disorder, is particularly alarming, with one in five anorexia deaths resulting from suicide. Young individuals with anorexia are twelve times more likely to die than their peers without the disorder. This paper aims to summarize the current state of literature comparing cognitive-behavioral therapy (CBT) and family-based therapy (FBT) in reducing morbidity, and mortality, and preventing symptom recurrence among adolescents with anorexia. CBT is a widely used treatment for eating disorders, particularly anorexia nervosa, while FBT is notable for its involvement of family members, which is crucial for developing teens and adolescents.

Keywords: psychology, CBT, FBT, eating disorders, anorexia, therapeutic modalities

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Authors: Sriraman Kothuri, Komal Teja Mattupalli

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In this paper we proposed a novel algorithm for recognizing hulls in a hand written digits. This is an extension to the work on “Digit Recognition Using Freeman Chain code”. In order to find out the hulls in a user given digit it is necessary to follow three steps. Those are pre-processing, Boundary Extraction and at last apply the Hull Detection system in a way to attain the better results. The detection of Hull Regions is mainly intended to increase the machine learning capability in detection of characters or digits. This can also extend this in order to get the hull regions and their intensities in Black Holes in Space Exploration.

Keywords: chain code, machine learning, hull regions, hull recognition system, SASK algorithm

Procedia PDF Downloads 398

Authors: Maha Abdulmajeed, Nagwa Fahmy

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Improving journalism practice in the UAE requires a clear understanding of the mid-career training environment; what Arab journalists’ think about the professional training available to them, what training needs they have and still not achieved, and what factors they think it could help to improve the mid-career training outcomes. This research paper examines the validity and effectiveness of mid-career professional journalistic training in the UAE. The research focuses on Arab journalists’ perceptions and attitudes towards professional training, and the state of journalistic training courses available to them, in comparison to modern trends of professional training. The two main objectives of this paper are to examine how different factors affect the effectiveness of the mid-career training offered to Arab Journalists in UAE, whether they are institutional factories, socio-economic factors, personal factors, etc. Then, to suggest a practical roadmap to improve the mid-career journalism training in the UAE. The research methodology combines qualitative and quantitative approaches. As researchers conduct in-depth interviews with a sample of Arab journalists in the UAE, Media outlets in UAE encompass private and governmental entities, with media products in Arabic and/or English, online and/or offline as well. Besides, content analysis will be applied to the available online and offline journalistic training courses offered to Arab journalists’ in UAE along the past three years. Research outcomes are expected to be helpful and practical to improve professional training in the UAE and to determine comprehensive and concrete criteria to provide up-to-date professional training, and to evaluate its validity. Results and research outcomes can help to better understand the current status of mid-career journalistic training in the UAE, to evaluate it based on studying both; the targeted trainees and the up-to-date journalistic training trends.

Keywords: Arab journalists, Arab journalism culture, journalism practice, journalism and technology

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Anne E. Goodenough

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One of the most important roles of higher education professionals is to ensure that graduates have excellent employment prospects. This means providing students with the skills necessary to be immediately effective in the workplace. Increasingly, universities are seeking to achieve this by moving from lecture-based and campus-delivered curricula to more varied delivery, which takes students out of their academic comfort zone and allows them to engage with, and be challenged by, real world issues. One popular approach is integration of problem-based learning (PBL) projects into curricula. However, although the potential benefits of PBL are considerable, it can be difficult to devise projects that are meaningful, such that they can be regarded as mere ‘hoop jumping’ exercises. This study examines three-way partnerships between academics, students, and external link organizations. It studied the experiences of all partners involved in different collaborative projects to identify how benefits can be maximized and challenges overcome. Focal collaborations included: (1) development of real-world modules with novel assessment whereby the organization became the ‘client’ for student consultancy work; (2) frameworks where students collected/analyzed data for link organizations in research methods modules; (3) placement-based internships and dissertations; (4) immersive fieldwork projects in novel locations; and (5) students working as partners on staff-led research with link organizations. Focus groups, questionnaires and semi-structured interviews were used to identify opportunities and barriers, while quantitative analysis of students’ grades was used to determine academic effectiveness. Common challenges identified by academics were finding suitable link organizations and devising projects that simultaneously provided education opportunities and tangible benefits. There was no ‘one size fits all’ formula for success, but careful planning and ensuring clarity of roles/responsibilities were vital. Students were very positive about collaboration projects. They identified benefits to confidence, time-keeping and communication, as well as conveying their enthusiasm when their work was of benefit to the wider community. They frequently highlighted employability opportunities that collaborative projects opened up and analysis of grades demonstrated the potential for such projects to increase attainment. Organizations generally recognized the value of project outputs, but often required considerable assistance to put the right scaffolding in place to ensure projects worked. Benefits were maximized by ensuring projects were well-designed, innovative, and challenging. Co-publication of projects in peer-reviewed journals sometimes gave additional benefits for all involved, being especially beneficial for student curriculum vitae. PBL and student projects are by no means new pedagogic approaches: the novelty here came from creating meaningful three-way partnerships between academics, students, and link organizations at all undergraduate levels. Such collaborations can allow students to make a genuine contribution to knowledge, answer real questions, solve actual problems, all while providing tangible benefits to organizations. Because projects are actually needed, students tend to engage with learning at a deep level. This enhances student experience, increases attainment, encourages development of subject-specific and transferable skills, and promotes networking opportunities. Such projects frequently rely upon students and staff working collaboratively, thereby also acting to break down the traditional teacher/learner division that is typically unhelpful in developing students as advanced learners.

Keywords: higher education, employability, link organizations, innovative teaching and learning methods, interactions between enterprise and education, student experience

Procedia PDF Downloads 179