Search results for: open and distant learning programme

Authors: Rebecca Oakes

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In the New Zealand public health system, work has been taking place to use electronic systems to convey data from the ‘floor to the board’ that makes patient needs, and therefore nursing work, visible. For nurses, these developments in health information technology puts us in a very new and exciting position of being able to articulate the work of nursing through a language understood at all levels of an organisation, the language of acuity. Nurses increasingly have a considerable stake-hold in patient acuity data. Patient acuity systems, when used well, can assist greatly in demonstrating how much work is required, the type of work, and when it will be required. The New Zealand Safe Staffing Unit is supporting New Zealand nurses to create a culture of shared governance, where nursing data is informing policies, staffing methodologies and forecasting within their organisations. Assisting organisations to understand their acuity data, strengthening user confidence in using electronic patient acuity systems, and ensuring nursing and midwifery workload is accurately reflected is critical to the success of the safe staffing programme. Nurses and midwives have the capacity via an acuity tool to become key informers of organisational planning. Quality patient care, best use of health resources and a quality work environment are essential components of a safe, resilient and well resourced organisation. Nurses are the key informers of this information. In New Zealand a national level approach is paving the way for significant changes to the understanding and use of patient acuity and nursing workload information.

Keywords: nursing workload, patient acuity, safe staffing, New Zealand

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Authors: Qi Wang

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Chinese has become a powerful worldwide language and millions of learners are studying it all over the words. Chinese is a tone language with unique meaningful characters, which makes foreign learners master it with more difficulties. On the other hand, as each foreign language, the learners of Chinese first will learn the basic Chinese Sound Structure (the initials and finals, tones, Neutral Tone and Tone Sandhi). It’s quite common that in the following studies, teachers made a lot of efforts on drilling and error correcting, in order to help students to pronounce correctly, but ignored the training of suprasegmental features (e.g. stress, intonation). This paper analysed the oral data based on our graduation students (two-year program) from 2006-2013, presents the intonation pattern of our graduates to speak Chinese as second language -high and plain with heavy accents, without lexical stress, appropriate stop endings and intonation, which led to the misunderstanding in different real contexts of communications and the international official Chinese test, e.g. HSK (Chinese Proficiency Test), HSKK (HSK Speaking Test). This paper also demonstrated how the Chinese to use the suprasegmental features strategically in different functions and moods (declarative, interrogative, imperative, exclamatory and rhetorical intonations) in order to train the learners to achieve better Communicative Performance.

Keywords: second language learning, suprasegmental, communication, HSK (Chinese Proficiency Test)

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Authors: Haikal Achmad Thaha, Pujo Laksono, Dhamma Nibbana Putra

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Customer service is one of the core interest in a service sector of a company, whether as the core business or as service part of the operation. Most of the time, the people and the previous research in service industry is focused on finding the best business model solution for the service sector, usually to decide between total in house customer service, outsourcing, or something in between. Conventionally, to take this decision is some important part of the management job, and this is a process that usually takes some time and staff effort, meanwhile market condition and overall company needs may change and cause loss of income and temporary disturbance in the companies operation . However, in this paper we have offer a new concept model to assist decision making process in service industry. This model will featured information platform as central tool to integrate service industry operation. The result is service information model which would ideally increase response time and effectivity of the decision making. it will also help service industry in switching the service solution system quickly through machine learning when the companies growth and the service solution needed are changing.

Keywords: service industry, customer service, machine learning, decision making, information platform

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Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

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The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

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Authors: Mehrnoush Tajnia, Simin Sadeghi-Saeb

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Students and teachers have different perceptions of effectiveness of instruction. Comparing students’ and teachers’ beliefs and finding the mismatches between them can increase L2 students’ satisfaction. Few studies have taken into account the beliefs of both students and teachers on different aspects of pedagogy and the effect of learners’ level of education and contexts on effective foreign language teacher practices. Therefore, the present study was conducted to compare students’ and teachers’ perceptions on effective foreign language teaching. A sample of 303 learners and 54 instructors from different private language institutes and universities participated in the study. A questionnaire was developed to elicit participants’ beliefs on effective foreign language teaching and learning. The analysis of the results revealed that: a) there is significant difference between the students’ beliefs about effective teacher practices and teachers’ belief, b) Class level influences students’ perception of effective foreign language teacher, d) There is a significant difference of opinion between those learners who study foreign languages at university and those who study foreign language in private institutes with respect to effective teacher practices. The present paper concludes that finding the gap between students’ and teachers’ beliefs would help both of the groups to enhance their learning and teaching.

Keywords: effective teacher, effective teaching, students’ beliefs, teachers’ beliefs

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Authors: Talita Veldsman, Elzette Fritz

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Many children attending special education present with sensory integration difficulties that hamper their learning and behaviour. These learners can benefit from therapeutic interventions as part of their classroom curriculum that can address sensory development and allow for holistic development to take place. A research study was conducted by utilizing socio-drama as a therapeutic intervention in the classroom in order to develop sensory integration skills. The use of socio-drama as therapeutic intervention proved to be a successful multi-disciplinary approach where education and psychology could build a bridge of growth and integration. The paper describes how socio-drama was used in the classroom and how these sessions were designed. The research followed a qualitative approach and involved six Afrikaans-speaking children attending special secondary school in the age group 12-14 years. Data collection included observations during the session, reflective art journals, semi-structured interviews with the teacher and informal interviews with the adolescents. The analysis found improved self-confidence, better social relationships, sensory awareness and self-regulation in the participants after a period of a year.

Keywords: education, sensory integration, sociodrama, classroom intervention, psychology

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

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Authors: My Ha, Suwei Lin, Huiying Zou

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This study aimed at investigating the association between teacher support and academic resilience in a developing country. Using the data from PISA 2018 Student Questionnaire and Cognitive Tests, the study provided evidence of the significant impact teacher support had on reading literacy among 15-year-old students from low socio-economic status (SES) homes in Vietnam. From a total of 5773 Vietnamese participants from all backgrounds, a sample of 1765 disadvantaged students was drawn for analysis. As a result, 32 percent of the low SES sample was identified as resilient. Through their response to the PISA items regarding the frequency of support they received from teachers, the result of Latent Class Analysis (LCA) divides children into three subgroups: High Support (74.6%), Fair Support (21.6%), and Low Support (3.8%). The high support group reported the highest proportion of resilient students. Meanwhile, the low support group scored the lowest mean on reading test and had the lowest rate of resilience. Also, as the level of support increases, reading achievement becomes less dependent on socioeconomic status, reflected by the decrease in both the slope and magnitude of their correlation. Logistic regression revealed that 1 unit increase in standardized teacher support would lead to an increase of 29.1 percent in the odds of a student becoming resilient. The study emphasizes the role of supportive teachers in promoting resilience, as well as lowering educational inequity in general.

Keywords: academic resilience, disadvantaged students, teacher support, inequity, PISA

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Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

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Authors: Teja Koduru

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Untreated Lyme disease can lead to neurological, cardiac, and dermatological complications. Rapid diagnosis of the erythema migrans (EM) rash, a characteristic symptom of Lyme disease is therefore crucial to early diagnosis and treatment. In this study, we aim to utilize deep learning frameworks including Tensorflow and Keras to create deep convolutional neural networks (DCNN) to detect images of acute Lyme Disease from images of erythema migrans. This study uses a custom database of erythema migrans images of varying quality to train a DCNN capable of classifying images of EM rashes vs. non-EM rashes. Images from publicly available sources were mined to create an initial database. Machine-based removal of duplicate images was then performed, followed by a thorough examination of all images by a clinician. The resulting database was combined with images of confounding rashes and regular skin, resulting in a total of 683 images. This database was then used to create a DCNN with an accuracy of 93% when classifying images of rashes as EM vs. non EM. Finally, this model was converted into a web and mobile application to allow for rapid diagnosis of EM rashes by both patients and clinicians. This tool could be used for patient prescreening prior to treatment and lead to a lower mortality rate from Lyme disease.

Keywords: Lyme, untreated Lyme, erythema migrans rash, EM rash

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Authors: Shams Alyusof

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This work is to enrich the studies of the frames due to their prominent role in pure mathematics as well as in applied mathematics and many applications in computer science and engineering. Recently, there are remarkable studies of operators that preserve frames on some spaces, and this research could be considered as an extension of such studies. Indeed, this paper is to we characterize weighted composition operators that preserve frames in weighted Hardy spaces on the open unit disk. Moreover, it shows that this characterization does not apply to generalized weighted composition operators on such spaces. Nevertheless, this study could be extended to provide more specific characterizations.

Keywords: frames, generalized weighted composition operators, weighted Hardy spaces, analytic functions

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Authors: Shanshan Zhou, Massimo Battaglia

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Identity plays a critical role when an organization faces disasters. Individuals reflect on their working identities and identify themselves with the group and the organization, which facilitate collective sensemaking under crisis situations and enable coordinated actions to respond to and recover from disasters. In addition, an organization’s identity links it to its regional community, which fosters the mobilization of resources and contributes to rapid recovery. However, identity is also problematic for disaster prevention because of its persistence. An organization’s ego-defenses system prohibits the rethink of its identity and a rigid identity obstructs disaster prevention. This research aims to tackle the ‘problem’ of identity by study in-depth a case of an Italian multi–utility which experienced the 2012 Northern Italy earthquakes. Collecting data from 11 interviews with top managers and key players in the local community and archived materials, we find that the earthquakes triggered the rethink of the organization’s identity, which got reinforced afterward. This research highlighted the importance of identity in disaster response and recovery. More importantly, it explored the solution of overcoming the barrier of ego-defense that is to transform the organization into a learning organization which constantly rethinks its identity.

Keywords: community identity, disaster, identity, organizational learning

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Fossarello Maurizio, Mattana Giorgio, Tatti Filippo.

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The most widely performed surgical procedure in Open-Angle Glaucoma (OAG) is trabeculectomy. Although this filtering procedure is extremely effective, surgical failure and postoperative complications are reported. Due to the its invasive nature and possible complications, trabeculectomy is usually reserved, in practice, for patients who are refractory to medical and laser therapy. Recently, a number of micro-invasive surgical techniques (MIGS: Micro-Invasive Glaucoma Surgery), have been introduced in clinical practice. They meet the criteria of micro-incisional approach, minimal tissue damage, short surgical time, reliable IOP reduction, extremely high safety profile and rapid post-operative recovery. Xen45 Gel Implant (Allergan, Dublin, Ireland) is one of the MIGS alternatives, and consists in a porcine gelatin tube designed to create an aqueous flow from the anterior chamber to the subconjunctival space, bypassing the resistance of the trabecular meshwork. In this study we report the results of this technique as a favorable option in the treatment of OAG for its benefits in term of efficacy and safety, either alone or in combination with cataract surgery. This is a retrospective, single-center study conducted in consecutive OAG patients, who underwent Xen45 Gel Stent implantation alone or in combination with phacoemulsification, from October 2018 to June 2019. The primary endpoint of the study was to evaluate the reduction of both IOP and number of antiglaucoma medications at 12 months. The secondary endpoint was to correlate filtering bleb morphology evaluated by means of anterior segment OCT with efficacy in IOP lowering and eventual further procedures requirement. Data were recorded on Microsoft Excel and study analysis was performed using Microsoft Excel and SPSS (IBM). Mean values with standard deviations were calculated for IOPs and number of antiglaucoma medications at all points. Kolmogorov-Smirnov test showed that IOP followed a normal distribution at all time, therefore the paired Student’s T test was used to compare baseline and postoperative mean IOP. Correlation between postoperative Day 1 IOP and Month 12 IOP was evaluated using Pearson coefficient. Thirty-six eyes of 36 patients were evaluated. As compared to baseline, mean IOP and the mean number of antiglaucoma medications significantly decreased from 27,33 ± 7,67 mmHg to 16,3 ± 2,89 mmHg (38,8% reduction) and from 2,64 ± 1,39 to 0,42 ± 0,8 (84% reduction), respectively, at 12 months after surgery (both p < 0,001). According to bleb morphology, eyes were divided in uniform group (n=8, 22,2%), subconjunctival separation group (n=5, 13,9%), microcystic multiform group (n=9, 25%) and multiple internal layer group (n=14, 38,9%). Comparing to baseline, there was no significative difference in IOP between the 4 groups at month 12 follow-up visit. Adverse events included bleb function decrease (n=14, 38,9%), hypotony (n=8, 22,2%) and choroidal detachment (n=2, 5,6%). All eyes presenting bleb flattening underwent needling and MMC injection. The higher percentage of patients that required secondary needling was in the uniform group (75%), with a significant difference between the groups (p=0,03). Xen45 gel stent, either alone or in combination with phacoemulsification, provided a significant lowering in both IOP and medical antiglaucoma treatment and an elevated safety profile.

Keywords: anterior segment OCT, bleb morphology, micro-invasive glaucoma surgery, open angle glaucoma, Xen45 gel implant

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Bhrugesh Radadiya, Jaydeep Shah

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In Indian foundry industry, there is a need of defect free casting with minimum production cost in short lead time. Casting defect is a very large issue in foundry shop which increases the rejection rate of casting and wastage of materials. The various parameters influences on casting process such as mold machine related parameters, green sand related parameters, cast metal related parameters, mold related parameters and shake out related parameters. The mold related parameters are most influences on casting defects in sand casting process. This paper review the casting produced by foundry with shrinkage and blow holes as a major defects was analyzed and identified that mold related parameters such as mold temperature, pouring temperature and runner size were not properly set in sand casting process. These parameters were optimized using different optimization techniques such as Taguchi method, Response surface methodology, Genetic algorithm and Teaching-learning based optimization algorithm. Finally, concluded that a Teaching-learning based optimization algorithm give better result than other optimization techniques.

Keywords: casting defects, genetic algorithm, parametric optimization, Taguchi method, TLBO algorithm

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Authors: Maria Antonietta Marongiu

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Propositional information in discourse is made coherent, intelligible, and persuasive through metadiscourse. The linguistic and rhetorical choices that writers/speakers make to organize and negotiate content matter are intended to help relate a text to its context. Besides, they help the audience to connect to and interpret a text according to the values of a specific discourse community. Based on these assumptions, this work aims to analyse the use of metadiscourse in the spoken performance of teachers in online EFL, ESP, and subject-teacher courses taught in English to non-native learners in higher education. In point of fact, the global spread of Covid 19 has forced universities to transition their in-class courses to online delivery. This has inevitably placed on the instructor a heavier interactional responsibility compared to in-class courses. Accordingly, online delivery needs greater structuring as regards establishing the reader/listener’s resources for text understanding and negotiating. Indeed, in online as well as in in-class courses, lessons are social acts which take place in contexts where interlocutors, as members of a community, affect the ways ideas are presented and understood. Following Hyland’s Interactional Model of Metadiscourse (2005), this study intends to investigate Teacher Talk in online academic courses during the Covid 19 lock-down in Italy. The selected corpus includes the transcripts of online EFL and ESP courses and subject-teachers online courses taught in English. The objective of the investigation is, firstly, to ascertain the presence of metadiscourse in the form of interactive devices (to guide the listener through the text) and interactional features (to involve the listener in the subject). Previous research on metadiscourse in academic discourse, in college students' presentations in EAP (English for Academic Purposes) lessons, as well as in online teaching methodology courses and MOOC (Massive Open Online Courses) has shown that instructors use a vast array of metadiscoursal features intended to express the speakers’ intentions and standing with respect to discourse. Besides, they tend to use directions to orient their listeners and logical connectors referring to the structure of the text. Accordingly, the purpose of the investigation is also to find out whether metadiscourse is used as a rhetorical strategy by instructors to control, evaluate and negotiate the impact of the ongoing talk, and eventually to signal their attitudes towards the content and the audience. Thus, the use of metadiscourse can contribute to the informative and persuasive impact of discourse, and to the effectiveness of online communication, especially in learning contexts.

Keywords: discourse analysis, metadiscourse, online EFL and ESP teaching, rhetoric

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Authors: Christopher J. Johnston

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This paper presents the finding of a research study in which novice (first and second year) elementary teachers (grades Kindergarten – six) evaluated various mathematics Virtual Manipulatives, websites, and Applets (tools) for use in mathematics instruction. Participants identified the criteria they used for evaluating these types of resources and provided recommendations for or against five pre-selected tools. During the study, participants participated in three data collection activities: (1) A brief Likert-scale survey which gathered information about their attitudes toward technology use; (2) An identification of criteria for evaluating technology tools; and (3) A review of five pre-selected technology tools in light of their self-identified criteria. Data were analyzed qualitatively using four theoretical categories (codes): Software Features (41%), Mathematics (26%), Learning (22%), and Motivation (11%). These four theoretical categories were then grouped into two broad categories: Content and Instruction (Mathematics and Learning), and Surface Features (Software Features and Motivation). These combined, broad categories suggest novice teachers place roughly the same weight on pedagogical features as they do technological features. Implications for mathematics teacher educators are discussed, and suggestions for future research are provided.

Keywords: mathematics education, novice teachers, technology, virtual manipulatives

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Authors: Goh Yung Hong, Mona Masood

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This paper explores the effects of gamification on lower secondary school students’ motivation and engagement in the classroom. Two-group posttest-only experimental design were employed to study the influence of gamification teaching method (GTM) when compared with conventional teaching method (CTM) on 60 lower secondary school students. The Student Engagement Instrument (SEI) and Intrinsic Motivation Inventory (IMI) were used to assess students’ intrinsic motivation and engagement level towards the respective teaching method. Finding indicates that students who completed the GTM lesson were significantly higher in intrinsic motivation to learn than those from the CTM. Although the result were insignificant and only marginal difference in the engagement mean, GTM still show better potential in raising student’s engagement in class when compared with CTM. This finding proves that the GTM is likely to solve the current issue of low motivation to learn and low engagement in class among lower secondary school students in Malaysia. On the other hand, despite being not significant, higher mean indicates that CTM positively contribute to higher peer support for learning and better teacher and student relationship when compared with GTM. As a conclusion, gamification approach is flexible and can be adapted into many learning content to enhance the intrinsic motivation to learn and to some extent, encourage better student engagement in class.

Keywords: conventional teaching method, gamification teaching method, motivation, engagement

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Authors: Salha Mohamed Hussain

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As part of the review on the Malay Language curriculum and pedagogy in Singapore conducted in 2010, some recommendations were made to nurture active learners who are able to use the Malay Language efficiently in their daily lives. In response to the review, a new Malay Language teaching and learning package for primary school, called CEKAP (Cungkil – Elicit; Eksplorasi – Exploration; Komunikasi – Communication; Aplikasi – Application; Penilaian – Assessment), was developed from 2012 and implemented for Primary 1 in all primary schools from 2015. Resources developed in this package include the text book, activity book, teacher’s guide, big books, small readers, picture cards, flash cards, a game kit and Information and Communication Technology (ICT) resources. The development of the CEKAP package is continuous until 2020. This paper will look at a model incorporated in the development of the teaching materials in the new Malay Language Curriculum for Primary Schools and the rationale for each phase of development to ensure that the resources meet the needs of every pupil in the teaching and learning of Malay Language in the primary schools. This paper will also focus on the preliminary findings of the effectiveness of the model based on the feedback given by members of the working and steering committees. These members are academicians and educators who were appointed by the Ministry of Education to provide professional input on the soundness of pedagogical approach proposed in the revised syllabus and to make recommendations on the content of the new instructional materials. Quantitative data is derived from the interviews held with these members to gather their input on the model. Preliminary findings showed that the members provided positive feedback on the model and that the comprehensive process has helped to develop good and effective instructional materials for the schools. Some recommendations were also gathered from the interview sessions. This research hopes to provide useful information to those involved in the planning of materials development for teaching and learning.

Keywords: Malay language, materials development, model, primary school

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Authors: Happy Kumar Das, Md. Shahriar Shafiq

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This paper portrays an assumed change in the primary teacher education program in Bangladesh. An initiative has been taken with a vision to ensure an integrated approach to developing trainee teachers’ knowledge and understanding about learning at a deeper level, and with that aim, the Diploma in Primary Education (DPEd) program replaces the Certificate-in-Education (C-in-Ed) program in Bangladeshi context for primary teachers. The stated professional values of the existing program such as ‘learner-centered’, ‘reflective’ approach to pedagogy tend to contradict the practice exemplified through the delivery mechanism. To address the challenges, through the main two components (i) Training Institute-based learning and (ii) School-based learning, the new program tends to cover knowledge and value that underpin the actual practice of teaching. These two components are given approximately equal weighting within the program in terms of both time, content and assessment as the integration seeks to combine theoretical knowledge with practical knowledge and vice versa. The curriculum emphasizes a balance between the taught modules and the components of the practicum. For example, the theories of formative and summative assessment techniques are elaborated through focused reflection on case studies as well as observation and teaching practice in the classroom. The key ideology that is reflected through this newly developed program is teacher’s belief in ‘holistic education’ that can lead to creating opportunities for skills development in all three (Cognitive, Social and Affective) domains simultaneously. The proposed teacher education program aims to address these areas of generic skill development alongside subject-specific learning outcomes. An exploratory study has been designed in this regard where 7 Primary Teachers’ Training Institutes (PTIs) in 7 divisions of Bangladesh was used for experimenting DPEd program. The analysis was done based on document analysis, periodical monitoring report and empirical data gathered from the experimental PTIs. The findings of the study revealed that the intervention brought positive change in teachers’ professional beliefs, attitude and skills along with improvement of school environment. Teachers in training schools work together for collective professional development where they support each other through lesson study, action research, reflective journals, group sharing and so on. Although the DPEd program addresses the above mentioned factors, one of the challenges of the proposed program is the issue of existing capacity and capabilities of the PTIs towards its effective implementation.

Keywords: Bangladesh, effective implementation, primary teacher education, reflective approach

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Authors: Shivangi Agrawal, Hsiu-I Ting

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Entrepreneurship is an essential source of economic growth and a prominent factor influencing socio-economic development. Entrepreneurship education educates and enhances entrepreneurial activity. This study aims to understand current trends in entrepreneurship education and evaluate the effectiveness of diverse entrepreneurship education programs. An increasing number of universities offer entrepreneurship education courses to create and successfully continue entrepreneurial ventures. Despite the prevalence of entrepreneurship education, research studies lack inconsistency about the effectiveness of entrepreneurship education to promote and develop entrepreneurship. Strategies to develop entrepreneurial attitudes and intentions among individuals are hindered by a lack of understanding of entrepreneurs' educational purposes, components, methodology, and resources required. Lack of adequate entrepreneurship education has been linked with low self-efficacy and lack of entrepreneurial intent. Moreover, in the age of digitisation and during the COVID-19 pandemic, digital learning platforms (e.g., online entrepreneurship education courses and programs) and other digital tools (e.g., digital game-based entrepreneurship education) have become more relevant to entrepreneurship education. This paper contributes to the continuation of academic literature in entrepreneurship education by evaluating and assessing current trends in entrepreneurship education programs, leading to better understanding to reduce gaps between entrepreneurial development requirements and higher education institutions.

Keywords: entrepreneurship education, digital technologies, academic entrepreneurship, COVID-19

Procedia PDF Downloads 239

Authors: Norbert Szulc, Cezary Wieczorkowski, Igor Baranowski

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In this paper, a control system that bridges the gap in support for Unmanned Surface Vessels in the PX4 Opensource Autopilot was developed. The system is designed for an omnidirectional water craft with four motors. A modular autopilot architecture design centred around publish-subscribe interprocess communication was used. The paper presents the implementation and integration process of a generic surface vehicle controller capable of driving any configuration of motors through the recently introduced in control allocator in PX4 autopilot. The proposed approach was successfully tested in a case study through implementation on the ASV Perkoz.

Keywords: control system, PX4, drones, rovers, surface vessels, omnidirectional

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Authors: Vinay P., Srinivas K. S.

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Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown.

Keywords: low light image enhancement, real-time video, computer vision, machine learning

Procedia PDF Downloads 180

Authors: Yooncheol Lee, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

This paper deals with the establishment of a short-term operational plan for an air conditioner for efficient energy management of exhibition hall. The short-term operational plan is composed of a time series of operational schedules, which we have searched using genetic algorithms. Establishing operational schedule should be considered the future trends of the variables affecting the exhibition hall environment. To reflect continuously changing factors such as external temperature and occupant, short-term operational plans should be updated in real time. But it takes too much time to evaluate a short-term operational plan using EnergyPlus, a building emulation tool. For that reason, it is difficult to update the operational plan in real time. To evaluate the short-term operational plan, we designed prediction models based on machine learning with fast evaluation speed. This model, which was created by learning the past operational data, is accurate and fast. The collection of operational data and the verification of operational plans were made using EnergyPlus. Experimental results show that the proposed method can save energy compared to the reactive control method.

Keywords: exhibition hall, energy management, predictive model, simulation-based optimization

Procedia PDF Downloads 323

Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Shobha U. Jadhav

Abstract:

Onion is most important vegetable crop grown throughout the world. Onion suffers from pest and fungal diseases but these fungicides cause pollution and disturb microbial balance of soil. Under integrated fungal disease management programme cost effective and eco- friendly component like plant extract are used to control plant pathogens. Alternaria porri, Fusarium oxysporium, Stemphylium vesicarium are soil-borne pathogens of onion. Effect of three different plant extracts (Ocimum sanctum L., Xanthium strumarium B. and H. Withania somnifera Dunal)at five different concentration Viz, 10, 25, 50, 75, and 100 percentage on these pathogens was studied by food poisoning technique. Ocimum sanctum gave 84.21% growth of Alternaria porri at 10% extract concentration and 10.52% growth in 100% extract concentration. As compared to Fusarium oxysporium and Stemphylium vesicarium, Alternaria porri give good inhibitory response. In Xanthium strumarium B. and H. at 10% extract concentration 46.42% growth and at 100% extract concentration 28.57% growth of Fusarium oxysporum was observed. Fusarium oxysporum give good inhibitory response as compared to Alternaria porri and Stemphylium vesicarium. In Withania somnifera Dunal in 10% extract concentration 84.21% growth and in 100% extract concentration 21.05% growth of Stemphylium vesicarium was recorded. Stemphylium vesicarium give good inhibitory response as compared to Alternaria porri and Fusarium oxysporum.

Keywords: pathogen, onion, plant, extract

Procedia PDF Downloads 368

Authors: L. Maiza, A. Cantagrel, M. Forestier, G. Laucoin, T. Regali

Abstract:

Evaluation of High Frequency Trading (HFT) impact on financial markets is very important for traders who use market analysis to detect winning transaction opportunity. Analysis of HFT data on tobacco commodity market is discussed here and interesting linear relationship has been shown between trading frequency and difference between averaged trading prices above and below considered trading frequency. This may open new perspectives on markets data understanding and could provide possible interpretation of Adam Smith invisible hand.

Keywords: financial market, high frequency trading, analysis, impacts, Adam Smith invisible hand

Procedia PDF Downloads 344