Search results for: learning behaviour

Authors: Mehrnoush Tajnia, Simin Sadeghi-Saeb

Abstract:

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

Procedia PDF Downloads 304

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

Abstract:

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

Procedia PDF Downloads 163

Authors: Kaori Narita, Juan de Dios Tena Horrillo, Claudio Detotto

Abstract:

Decisions on replacement of leaders are of significance and high prevalence in any organization, and concerns many of its stakeholders, whether it is a leader in a political party or a CEO of a firm, as indicated by high media coverage of such events. This merits an investigation into the consequences and implications of a leadership change on the performances and behavior of organizations and their workers. Sport economics provides a fruitful field to explore these issues due to the high frequencies of managerial changes in professional sports clubs and the transparency and regularity of observations of team performance and players’ abilities. Much of the existing research on managerial change focuses on how this affects the performance of an organization. However, there is scarcely attention paid to the consequences of such events on the behavior of individuals within the organization. Changes in behavior and attitudes of a group of workers due to a managerial change could be of great interest in management science, psychology, and operational research. On the other hand, these changes cannot be observed in the final outcome of the organization, as this is affected by many other unobserved shocks, for example, the stress level of workers with the need to deal with a difficult situation. To fill this gap, this study shows the first attempt to evaluate the impact of managerial change on players’ behaviors such as attack intensity, aggressiveness, and efforts. The data used in this study is from the top tier Italian football league (“Serie A”), where an average of 13 within season replacements of head coaches were observed over the period of seasons from 2000/2001 to 2017/18. The preliminary estimation employs Pooled Ordinary Least Square (POLS) and club-season Fixed Effect (FE) in order to assess the marginal effect of having a new manager on the number of shots, corners and red/yellow cards after controlling for a home-field advantage, ex ante abilities and current positions in the league of a team and their opponent. The results from this preliminary estimation suggest that the teams do not show a significant difference in their behaviors before and after the managerial change. To build on these preliminary results, other methods, including propensity score matching and non-linear model estimates, will be used. Moreover, the study will further investigate these issues by considering other measurements of attack intensity, aggressiveness, and efforts, such as possessions, a number of fouls and the athletic performance of players, respectively. Finally, the study is going to investigate whether these results vary over the characteristics of a new head coach, for example, their age and experience as a manager and a player. Thus far, this study suggests that certain behaviours of individuals in an organisation are not immediately affected by a change in leadership. To confirm this preliminary finding and lead to a more solid conclusion, further investigation will be conducted in the aforementioned manner, and the results will be elaborated in the conference.

Keywords: behaviour, effort, manager characteristics, managerial change, sport economics

Procedia PDF Downloads 119

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

Abstract:

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

Procedia PDF Downloads 63

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

Abstract:

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

Procedia PDF Downloads 163

Authors: Bei Shan Lin, Chun Mei Lu, Ya Ping Chen, Li Chen Lu

Abstract:

This study explores the ethical issues concerning the use of physical restraint in geriatric care. Physical restraint use in a medical care setting is seen as a controversial form of treatment that has occurred over decades. There is no doubt that people nowadays are living longer than previous generations. The ageing process is inevitable. Common disease such as impaired comprehension, memory loss, and trouble expressing one’s self contribute to the difficulty that these older patients have in adapting to medical institution. For these reasons, physical restraint is often used in reducing the risk of falling, managing wandering behaviour, preventing agitation, and promoting patient compliance in geriatric care. It can mean that physical restraints are considered as a common practice that is used in the care of older patients. It is most commonly used for three specific purposes, including procedural restraint, restraint to prevent falls, and behavioural restraints. Although there have been well documented instances of morbidity and mortality recognised as being potential risks associated with physical restraint use, it continues to be permitted and used in healthcare, often in the name of safety. However, there is insufficient evidence supporting the effectiveness of physical restraint use reducing injuries from falls and controlling challenging behaviour in geriatric care settings. There is barely any empirical evidence of either a scientific basis or clinical trials have evaluated the improvement in patient safety following physical restraint. In difficult clinical situations, guidelines and practical suggestions for Healthcare professionals to comply requirements can help those making appropriate decisions and to facilitate better judgement regarding physical restraint use. The following recommendations are given for physical restraint use in long-term care settings: an interdisciplinary team approach to assess, evaluate, and treat underlying diseases to determine if treatment can ease issues precipitating physical restraint use; a clearly stated purpose of treatment plan should be made after weighing up the risk of physical restraint use against the risk of without physical restraint use; a care plan for physical restraint has to include individualised treatment planning, informed consent, identification and remedial action to avoid negative consequences, regular assessment and modification, reduction and removal of risks; patients and their families must have the opportunity to consider and give voluntary informed consent prior to physical restraint utilisation; patients, family members, and Healthcare professionals should be educated on use and adverse consequences of physical restraints in order to make raise awareness of potential risks and to take appropriate steps to prevent unnecessary harm; after physical restraint removal, Healthcare professionals should discuss with patients and family members about their experience, feelings, and any anxieties regarding the treatment. Physical restraint should always be considered a last resort as deprive patient’s freedom, control, and individuality. Healthcare professionals should emphasise on providing individualized care, interdisciplinary decision-making process, and creative and collaborative alternatives to promote older patient’s rights, dignity and overall well-being as much as possible.

Keywords: ethics healthcare, geriatric care, healthcare, physical restraint

Procedia PDF Downloads 127

Authors: Teja Koduru

Abstract:

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

Procedia PDF Downloads 228

Authors: Shanshan Zhou, Massimo Battaglia

Abstract:

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

Procedia PDF Downloads 713

Authors: Sarah Beaver, Renee Bryce

Abstract:

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

Procedia PDF Downloads 70

Authors: Baladitya Swaika, Rahul Khatry

Abstract:

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

Procedia PDF Downloads 166

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

Abstract:

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

Procedia PDF Downloads 427

Authors: Abdulrahman Sumayli

Abstract:

Natural Zinc has many significant biological functions, including developments and sustainable of bones and wound healing. Metallic zinc has recently been explored as potential biomaterials that have preferable biodegradable, biocompatible, and mechanical properties. Pure metal zinc has a preferable physical and mechanical properties for biodegradable and biocompatible applications such as density and modulus of elasticity. The aim of the research is to make different Zn-based metallic alloys and test them effectively to be used as biocompatible and biodegradable materials in the field biomedical application. Microstructure study of the as-cast alloys will be examined using SEM (scanning electron microscope) followed by X-ray diffraction investigated so as to evaluate phase constitution of the designed alloys. After that, immersion test and electrochemical test will be applied to the designed alloys so as to study bio corrosion behaviour of the proposed alloys. Finally, in vitro cytocompatibility well conducted to study biocompatibility of the made alloys.

Keywords: Zn-based alloys, biodegradable and biocompatible materials, cytotoxicity test, neutron synchrotron imaging

Procedia PDF Downloads 129

Authors: Bhrugesh Radadiya, Jaydeep Shah

Abstract:

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

Procedia PDF Downloads 720

Authors: Abiodun Musbau Lawal, Sunday Samson Babalola, Uzor Friday Ordu

Abstract:

Issue of employees counterproductive work behavior in government owned organization in emerging economies has continued to be a major concern. This study investigated the factors of pay satisfaction, intent to leave and age as predictors of counterproductive work behavior among non-academic employee in a Nigerian federal government owned university. A sample of 200 non-academic employees completed questionnaires. Hierarchical multiple regression was conducted to determine the contribution of each of the predictor variables on the criterion variable on counterproductive work behavior. Results indicate that age of participants (β = -.18; p < .05) significantly independently predicted CWB by accounting for 3% of the explained variance. Addition of pay satisfaction (β = -.14; p < .05) significantly accounted for 5% of the explained variance, while intent to leave (β = -.17; p < .05) further resulted in 8% of the explained variance in counterproductive work behavior. The importance of these findings with regards to reduction in counterproductive work behavior is highlighted.

Keywords: counterproductive, work behaviour, pay satisfaction, intent to leave

Procedia PDF Downloads 369

Authors: A. Khosrozadeh, M. A. Darabi, M. Xing, Q. Wang

Abstract:

Fabrication of a high-performance supercapacitor (SC) using a flexible cellulose-based composite film of polyaniline (PANI), reduced graphene oxide (RGO), and silver nanowires (AgNWs) is reported. The flexibility, high capacitive behaviour, and cyclic stability of the entire device make it a good candidate for wearable SCs. The results show that a capacitance as high as 73.4 F/g (1.6 F/cm2) at a discharge rate of 1.1 A/g is achieved by the device. In addition, the SC demonstrates a power density up to 468.8 W/kg and an energy density up to 5.1 wh/kg. The flexibility of the composite film is attributed to the binding effect of cellulose fibers as well as reinforcing effect of AgNWs. The excellent electrochemical performance of the device is found to be owing to the synergistic effect between PANI/RGO/AgNWs ternary in a cushiony cellulose matrix and porous structure of the composite.

Keywords: cellulose, polyaniline, reduced graphene oxide, silver, super capacitor

Procedia PDF Downloads 415

Authors: Shagil Akhtar, Syed Muneeb Iqbal, Mohammed R. Rahim

Abstract:

Common steel tubes with similar confines were used in simulation of tubes with distinctive type of corrugated sections. These corrugated cross-sections were arc-tangent, triangular, trapezoidal and square corrugated sections. The outcome of fluctuating structures of tube cross-section shape on the deformation feedback, collapse form and energy absorption characteristics of tubes under quasi-static axial compression have been prepared numerically. The finite element package of ANSYS Workbench was applied in the current analysis. The axial load-displacement products accompanied by the fold formation of disparate tubes were inspected and compared. Deviation of the initial peak load and the mean crushing force of the tubes with distinctive cross-sections were conscientiously examined.

Keywords: absorbed energy, axial loading, corrugated tubes, finite element, initial peak load, mean crushing force

Procedia PDF Downloads 375

Authors: Christopher J. Johnston

Abstract:

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

Procedia PDF Downloads 124

Authors: Goh Yung Hong, Mona Masood

Abstract:

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

Procedia PDF Downloads 513

Authors: Salha Mohamed Hussain

Abstract:

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

Procedia PDF Downloads 109

Authors: Happy Kumar Das, Md. Shahriar Shafiq

Abstract:

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

Procedia PDF Downloads 208

Authors: Shivangi Agrawal, Hsiu-I Ting

Abstract:

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 243

Authors: Pragnyashree Mishra, Shradhanjali Mohapatra

Abstract:

The present investigation is carried out to know the effect of foliar feeding of nitrogen and gibberellic acid on vegetative growth, flowering behaviour and yield of calendula variety ‘Golden Emporer’. The experiment was laid out in RBD in rabi season of 2013-14. There are 16 treatments are taken at different level such as nitrogen (at 0%,1%,2%,3%) and GA3 (at 50 ppm,100ppm,150 ppm). Among them maximum height at bud initiation stage was obtained at 3% nitrogen (27.00 cm) and at 150 ppm GA3 (26.5 cm), fist flowering was obtained at 3% nitrogen(60.00 days) and at 150 ppm GA3 (63.75 days), maximum flower stalk length was obtained at 3% nitrogen(3.50 cm) and at 150 ppm GA3 (5.42 cm),maximum duration of flowering was obtained at 3% nitrogen(46.00 days) and at 150 ppm GA3 (46.50days), maximum number of flower was obtained at 3% nitrogen (89.00per plant) and at 150 ppm GA3 (83.50 per plant), maximum flower weight was obtained at 3% nitrogen(1.25 gm per flower) and at 150 ppm GA3 (1.50 gm per flower), maximum yield was was obtained at 3% nitrogen (110.00 gm per plant) and at 150 ppm GA3 (105.00gm per plant) and minimum of all character was obtained when 0% nitrogen0 ppm GA3. All interaction between nitrogen and GA3 was found in significant except the yield .

Keywords: calendula, golden emporer, GA3, nitrogen and gibberellic acid

Procedia PDF Downloads 454

Authors: Vinay P., Srinivas K. S.

Abstract:

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 188

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 329

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

Abstract:

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

Procedia PDF Downloads 12

Authors: Tarik Elaujali

Abstract:

This study examines a new media effective theory was developed by the author, it is called ‘The Effervescent Disk Theory’ (EDT). The theory main goal is to affect the unaffected audience who are either not exposing to a particular message or do not show interest in it. EDT suggest melting down messages that means to be affected within the media materials which are selected willingly by the audience themselves. A certain set of procedures to test EDT hypotheses were taken and illustrated in this study. A sample of 342 respondents (males & females) was collected from Tripoli University in Libya during the academic year 2013-2014. The designated sample is representing students who were failing to pass the English module for beginners’. This study aims to change the students’ negative notion about the importance of learning English, and to put their new idea into action. The theory seeks to affect audience cognition, emotions, and behaviors. EDT was applied in the present study alongside the media dependency theory. EDT hypotheses were confirmed, study results denoted that 73.6 percentage of the students responded positively and passed their English exam for beginners after being exposed selectively to their favorite TV program that contains a dissolved messages about the importance and vitality of learning English language.

Keywords: effervescent disk theory, selective exposure, media dependency, Libyan students

Procedia PDF Downloads 238

Authors: Mouli Banga, Chaya Ravindra

Abstract:

Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

Procedia PDF Downloads 12

Authors: Birgit Reime, Sonja Gscheidle, Toni Hübener, Lara Hübener

Abstract:

Suicide in individuals under 30 years is a global public health concern. The objective of this study was to identify strategies for the prevention of suicide and suicidal behavior preferred by adolescents and young adults who are vulnerable to suicidal behavior and by relevant experts. Using semi-structured interviews with n= 17 adolescents and young adults (18-25 years of age) and with n= 11 experts from relevant fields, we have applied an inductive approach and applied thematic content analysis. Six strategies for suicide prevention in young individuals were reported. These were digital solutions with appealing designs, anonymous support, trained peer support, spiritual support, improving existing structures, and raising suicide literacy. Accessibility of anonymous digital support may contribute to suicide prevention in young people.

Keywords: suicide prevention, adolescents, E-health, Germany

Procedia PDF Downloads 176

Authors: Mehran Memari, Bita Asadi

Abstract:

Second language (L2) classrooms in academic contexts usually consist of learners with diverse L2 proficiency levels. One solution for managing such heterogeneous classes and addressing individual needs of students is to improve learner autonomy by using technological innovations such as blogging. The focus of this study is on investigating the effects of blogging on improving the quality of Iranian university students' writings. For this aim, twenty-six Iranian university students participated in the study. Students in the experimental group (n=13) were required to blog daily while the students in the control group (n=13) were asked to write a daily schedule using paper and pencil. After a 3-month period of instruction, the five last writings of the students from both groups were rated by two experienced raters. Also, students' attitudes toward the traditional method and blogging were surveyed using a questionnaire and a semi-structured interview. The research results showed evidences in favor of the students who used blogging in their writing program. Also, although students in the experimental group found blogging more demanding than the traditional method, they showed an overall positive attitude toward the use of blogging as a way of improving their writing skills. The findings of the study have implications for the incorporation of computer-assisted learning in L2 academic contexts.

Keywords: blogging, computer-assisted learning, paper and pencil, writing

Procedia PDF Downloads 388

Authors: Sangeeta Rai

Abstract:

International education is Australia’s third largest export with significant revenue flowing to the economy in all state and territory jurisdictions. International students make significant economic, social and cultural contributions to all communities in which they are studying and often working. Among these international students are those from Nepal, who continue to seek Australian higher education in increasing numbers. This paper reports on findings from a literature review that highlights the gap in knowledge of the pedagogical issues that may need addressing in teaching Nepalese students in the higher education sector in Australia. Nepalese students bring to their studies a rich culture shaped by their country’s turbulent political and poor economic conditions. These factors may further contribute to their endeavors to seek education abroad to better themselves and their situation. This cohort of students faces various challenges undertaking their studies in Australia that may be due to factors including language, learning styles and engagement with peers. Hence, this paper highlights the importance of these students on Australian shores and forms the basis for further study on the issues and challenges that they face and those that need to be addressed by Australian educators.

Keywords: Nepalese students in Australia, challenges and coping mechanisms of Nepalese students, international students in Australia, socio-cultural background of Nepalese students

Procedia PDF Downloads 203