Search results for: average ensemble learning

Authors: Bina Kumari, Subir K. Sarkar, Pradipta Bandyopadhyay

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The autocorrelation function of the density fluctuation is studied in each of the two phases in a Gibbs Ensemble Monte Carlo (GEMC) simulation of the problem of phase separation for a square well potential with various values of its range. We find that the normalized autocorrelation function is described very well as a linear combination of an exponential function with a time scale τ₂ and a stretched exponential function with a time scale τ₁ and an exponent α. Dependence of (α, τ₁, τ₂) on the parameters of the GEMC algorithm and the range of the square well potential is investigated and interpreted. We also analyse the issue of how to choose the parameters of the GEMC simulation optimally.

Keywords: autocorrelation function, density fluctuation, GEMC, simulation

Procedia PDF Downloads 156

Authors: Guantao Bai

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Public space is an indispensable and important component of the urban built environment. How to more accurately evaluate the usage characteristics of public space can help improve its spatial quality. Compared to traditional survey methods, computer vision technology based on deep learning has advantages such as dynamic observation and low cost. This study takes the public space of Shaping Park as an example and, based on deep learning computer vision technology, processes and analyzes the image data of the public space to obtain the spatial usage characteristics and spatiotemporal characteristics of the public space. Research has found that the spontaneous activity time in public spaces is relatively random with a relatively short average activity time, while social activities have a relatively stable activity time with a longer average activity time. Computer vision technology based on deep learning can effectively describe the spatial usage characteristics of the research area, making up for the shortcomings of traditional research methods and providing relevant support for creating a good public space.

Keywords: computer vision, deep learning, public spaces, using features

Procedia PDF Downloads 36

Authors: Benjaporn Yaemjamuang

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The purposes of this study were to investigate the success of project-based learning and to evaluate the performance and level of satisfaction of tourism students who participated in the study. This paper drew upon a data collection from a senior tourism students survey conducted in Rajamangala University during summer 2013. The purposive sampling was utilized to obtain the sample which included 45 tourism students. The pretest and posttest method was utilized. The findings revealed that the majority of respondents had gained higher knowledge after the posttest significantly. The respondents’ knowledge increased about 53.33 percent from pretest to posttest. Also, the findings revealed the top three highest level of satisfaction as follows: 1) the role of teacher and students, 2) the research activities of the project-based learning, 3) the learning methods of the project-based learning. Moreover, the mean score of all categories was 3.98 with a standard deviation of 0.88 which indicated that the average level of satisfaction was high.

Keywords: performance, project-based learning, satisfaction, tourism

Procedia PDF Downloads 265

Authors: Prasan Briboonnanggoul

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The major objective of this research study was to explore some aspects of the performance culture of musical folk drama called Ligkey. This study was undertaken in an effect to focus on the specific functions of orchestra which accompanied Ligkey on Thai musical instruments in Chachoengsao Province. The process of study and exploration consisted of questionnaire, interview, a tape recording of an interview and photographs of performances which all of them were analyzed for the finding. The information obtained from the study indicated that Ligkey still received stable attention from people despite lesser performances affected by economics crisis. Almost all of the performances were organized and supported by both the public sector and the private sector. Based on the summary and finding of this study, a) there were ten Ligkey ensemble and ten orchestra which were Mon orchestra, not the precedent and the predecessor known as Thai orchestra; b) a variety of functions performed by musicians must harmonize discipline, punctuality, patience, no negligence, proficiency in performance; c) folklore melodies known as Plengnapad were performed as usual, but folklore melodies and songs known as Plangsongchan got lesser and got a tendency towards extinction because of the plot which corresponded with a market-driven entertainment. Therefore, a purpose-built schema of the preservation of Thai folklore songs was that they should have been recognized by both the performers and the audiences and patronized by the public sector via the government media to publicize the value of popular art form.

Keywords: Pipat Ensemble, Ligkey, Amphur Muaeng, Chachoengsao Province

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Authors: Lihong Wu, Raymond Young

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The ability to learn is one of the most critical skills in the information age. However, many students do not have a clear understanding of what learning is, what they are learning, and why they are learning. Many students study simply to pass rather than to learn something useful for their career and their life. They have a misconception about learning and a wrong attitude towards learning. This research explores student attitudes to study in management education and explores how to intercede to lead students from shallow to deeper modes of learning.

Keywords: knowledge, surface learning, deep learning, education

Procedia PDF Downloads 467

Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Prachya Kamonphet

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The purposes of this research were to study the learning motivation factors for Pre-cadets in Armed Forces Academies Preparatory School, Ministry of Defense. The subjects were 320 Pre-cadets (from all 3-year classes of Pre-cadets, the academic year 2015). The research instruments were questionnaires. The collected data were analyzed by means of Descriptive Statistic and One-Way Analysis of Variance. The results of this study were as follows: The relation between the Pre-cadets’ average grade and the motivation in studying was significance.In the aspect of the environment related to Pre-cadets’ families and the motivation in studying.In the aspect of the environment related to Pre-cadets’ studying, it was found that teaching method, learning place, educational media, relationship between teachers and Pre-cadets, relationship between Pre-cadets and their friends, and relationship between Pre-cadets and the commanders were significant.

Keywords: learning motivation factors, learning motivation, armed forces academies preparatory school, learning

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Authors: Kamalpreet Kaur, Renu Dhir

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Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%.

Keywords: climate, satellite images, prediction, classification

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Wanwadee Neamsakul

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VARK learning style is an effective method of learning that could enhance all skills of the students like visual (V), auditory (A), read/write (R), and kinesthetic (K). This learning style benefits the students in terms of professional competencies, critical thinking and lifelong learning which are the desirable characteristics of the nursing students. This study aimed to develop a model of teaching and learning clinical practicum at postpartum ward for nursing students by using VARK learning styles, and evaluate the nursing students’ opinions about the developmental model. A methodology used for this study was research and development (R&D). The model was developed by focus group discussion with five obstetric nursing instructors who have experiences teaching Maternal Newborn and Midwifery I subject. The activities related to practices in the postpartum (PP) ward including all skills of VARK were assigned into the matrix table. The researcher asked the experts to supervise the model and adjusted the model following the supervision. Subsequently, it was brought to be tried out with the nursing students who practiced on the PP ward. Thirty third year nursing students from one of the northern Nursing Colleges, Academic year 2015 were purposive sampling. The opinions about the satisfaction of the model were collected using a questionnaire which was tested for its validity and reliability. Data were analyzed using descriptive statistics. The developed model composed of 27 activities. Seven activities were developed as enhancement of visual skills for the nursing students (25.93%), five activities as auditory skills (18.52%), six activities as read and write skills (22.22%), and nine activities as kinesthetic skills (33.33%). Overall opinions about the model were reported at the highest level of average satisfaction (mean=4.63, S.D=0.45). In the aspects of visual skill (mean=4.80, S.D=0.45) was reported at the highest level of average satisfaction followed by auditory skill (mean=4.62, S.D=0.43), read and write skill (mean=4.57, S.D=0.46), and kinesthetic skill (mean=4.53, S.D=0.45) which were reported at the highest level of average satisfaction, respectively. The nursing students reported that the model could help them employ all of their skills during practicing and taking care of the postpartum women and newborn babies. They could establish self-confidence while providing care and felt proud of themselves by the benefits of the model. It can be said that using VARK learning style to develop the model could enhance both nursing students’ competencies and positive attitude towards the nursing profession. Consequently, they could provide quality care for postpartum women and newborn babies effectively in the long run.

Keywords: model, nursing students, postpartum ward, teaching and learning clinical practicum

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Authors: Loai Abdallah, Malik Yousef

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A domain space for the data should reflect the actual similarity between objects. Since objects belonging to the same cluster usually share some common traits even though their geometric distance might be relatively large. In general, the Euclidean distance of data points that represented by large number of features is not capturing the actual relation between those points. In this study, we propose a new method to construct a different space that is based on clustering to form a new distance metric. The new distance space is based on ensemble clustering (EC). The EC distance space is defined by tracking the membership of the points over multiple runs of clustering algorithm metric. Over this distance, we train the decision trees classifier (DT-EC). The results obtained by applying DT-EC on 10 datasets confirm our hypotheses that embedding the EC space as a distance metric would improve the performance.

Keywords: ensemble clustering, decision trees, classification, K nearest neighbors

Procedia PDF Downloads 160

Authors: Rukminingsih Rukmi

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This paper addresses the issue of L2 teaching speaking to ESL students by fostering their active learning through project-based learning. Project-based learning was employed in classrooms where teachers support students by giving sufficient guidance and feedback. The students drive the inquiry, engage in research and discovery, and collaborate effectively with teammates to deliver the final work product. The teacher provides the initial direction and acts as a facilitator along the way. This learning approach is considered helpful for fostering students’ active learning. that the steps in implementing of project-based learning that fosters students’ critical thinking in TEFL class are in the following: (1) Discussing the materials about Speaking Class, (2) Working with the group to construct scenario of ways on speaking practice, (3) Practicing the scenario, (4) Recording the speaking practice into video, and (5) Evaluating the video product. This research is aimed to develop a strategy of teaching speaking by implementing project-based learning to improve speaking skill in the second Semester of English Department of STKIP PGRI Jombang. To achieve the purpose, the researcher conducted action research. The data of the study were gathered through the following instruments: test, observation checklists, and questionnaires. The result was indicated by the increase of students’ average speaking scores from 65 in the preliminary study, 73 in the first cycle, and 82 in the second cycle. Besides, the results of the study showed that project-based learning considered to be appropriate strategy to give students the same amount of chance in practicing their speaking skill and to pay attention in creating a learning situation.

Keywords: active learning, project-based learning, speaking ability, L2 teaching speaking

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Authors: Lee Bih Ni

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This paper discusses that good learning involves all academic groups in the school. Blended learning is learning outside the classroom. Google Classroom is a free service learning app for schools, non-profit organizations and anyone with a personal Google account. Facilities accessed through computers and mobile phones are very useful for school teachers and students. Blended learning classrooms using both traditional and technology-based methods for teaching have become the norm for many educators. Using Google Classroom gives students access to online learning. Even if the teacher is not in the classroom, the teacher can provide learning. This is the supervision of the form of the teacher when the student is outside the school.

Keywords: blended learning, learning app, google classroom, schools

Procedia PDF Downloads 117

Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Pimploi Tirastittam, Suppara Charoenpoom

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Nowadays the asynchronous learning has granted the permission to the anywhere and anything learning via the technology and E-media which give the learner more convenient. This research is about the design of the blended and online learning for the asynchronous learning of the process management subject in order to create the prototype of this subject asynchronous learning which will create the easiness and increase capability in the learning. The pattern of learning is the integration between the in-class learning and online learning via the internet. This research is mainly focused on the online learning and the online learning can be divided into 5 parts which are virtual classroom, online content, collaboration, assessment and reference material. After the system design was finished, it was evaluated and tested by 5 experts in blended learning design and 10 students which the user’s satisfaction level is good. The result is as good as the assumption so the system can be used in the process management subject for a real usage.

Keywords: blended learning, asynchronous learning, design, process management

Procedia PDF Downloads 377

Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Heesuk Seo, Yongtae Kim

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Online learning started in the 1990s, the spread of the Internet has been through the era of e-learning paradigm of online education in the era of smart learning change. Reflecting the different nature of the mobile to anywhere anytime, anywhere was also allows the form of learning, it was also available through the learning content and interaction. We are developing a cloud system, 'TLINKS CLOUD' that allows you to configure the environment of the smart learning without the need for additional infrastructure. Using the big-data analysis for e-learning contents, we provide an integrated solution for e-learning tailored to individual study.

Keywords: authority tool, big data analysis, e-learning, HTML5

Procedia PDF Downloads 375

Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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World-wide spread of coronavirus grows the concern about planning for the excess demand of hospital services in response to COVID-19 pandemic. The surge in the hospital services demand beyond the current capacity leads to shortage of ICU beds and ventilators in some parts of US. In this study, we forecast the required number of hospital beds and possible shortage of beds in US during COVID-19 pandemic to be used in the planning and hospitalization of new cases. In this paper, we used a data on COVID-19 deaths and patients’ hospitalization besides the data on hospital capacities and utilization in US from publicly available sources and national government websites. we used a novel ensemble modelling of deep learning networks, based on stacking different linear and non-linear layers to predict the shortage in hospital beds. The results showed that our proposed approach can predict the excess hospital beds demand very well and this can be helpful in developing strategies and plans to mitigate this gap.

Keywords: COVID-19, deep learning, ensembled models, hospital capacity planning

Procedia PDF Downloads 129

Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Saad Al-Baddai, Karema Al-Subari, Elmar Lang, Bernd Ludwig

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Empirical mode decomposition (EMD), a new data-driven of time-series decomposition, has the advantage of supposing that a time series is non-linear or non-stationary, as is implicitly achieved in Fourier decomposition. However, the EMD suffers of mode mixing problem in some cases. The aim of this paper is to present a solution for a common type of signals causing of EMD mode mixing problem, in case a signal suffers of an intermittency. By an artificial example, the solution shows superior performance in terms of cope EMD mode mixing problem comparing with the conventional EMD and Ensemble Empirical Mode decomposition (EEMD). Furthermore, the over-sifting problem is also completely avoided; and computation load is reduced roughly six times compared with EEMD, an ensemble number of 50.

Keywords: empirical mode decomposition (EMD), mode mixing, sifting process, over-sifting

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Authors: Tracy Ho, Huann-Shyang Lin, Mina Lin

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This study investigated the different learning path of students with different learning attitude and learning styles in Arts Creation. Based on direct instruction, guided-discovery learning, and discovery learning theories, a tablet app including the following three learning areas were developed for students: (1) replication and remix practice area, (2) guided creation area, and (3) free creation area. Thirty. students with different learning attitude and learning styles were invited to use this app. Students’ learning behaviors were categorized and defined. The results will provide both educators and researchers with insights that can form a useful foundation for designing different content and strategy with the application of new technologies in school teaching. It also sheds light on how an educational App can be designed to enhance Arts Creation.

Keywords: App, arts creation, learning attitude, learning style, tablet

Procedia PDF Downloads 245

Authors: S. Jagadeesh Kumar

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This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.

Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Adelia Desi Agnesita

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The advent of a new disease called covid-19 made many major changes in the world, one of which is the process of learning and teaching. Learning formerly offline but now is done online, which makes students need adaptation to the learning process. The covid-19 pandemic that occurs almost worldwide causes activities that involve many people to be avoided, one of which is learning to teach. In Indonesia, since March 2020, the process of college learning is turning into online/ long-distance learning. It's to prevent the spread of the covid-19. Student online learning presents some of the obstacles to poor signals, many of the tasks, lack of focus, difficulty sleeping, and resulting stress.

Keywords: learning, online, covid-19, pandemic

Procedia PDF Downloads 180

Authors: Chen-Ya Juan

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Problem-Based Learning uses vague questions to guide student thinking and enhance their self-learning and collaboration. Most teachers implement PBL in a physical classroom, where teachers can monitor and evaluate students’ learning progress and guide them to search resources for answers. However, the prevalence of the Covid-19 in the world had changed from physical teaching to distance teaching. This instruction used many cases and applied Problem-Based Learning combined on the distance teaching via the internet for college students. This study involved an experimental group with PBL and a control group without PBL. The teacher divided all students in PBL class into eight groups, and 7~8 students in each group. The teacher assigned different cases for each group of the PBL class. Three stages of instruction were developed, including background knowledge of Learning, case analysis, and solving problems for each case. This study used a quantitative research method, a two-sample t-test, to find a significant difference in groups with PBL and without PBL. Findings indicated that PBL incased the average score of special education knowledge. The average score was improved by 20.46% in the PBL group and 15.4% without PBL. Results didn’t show significant differences (0.589>0.05) in special education professional knowledge. However, the feedback of the PBL students implied learning more about the application, problem-solving skills, and critical thinking. PBL students were more likely to apply professional knowledge on the actual case, find questions, resources, and answers. Most of them understood the importance of collaboration, working as a team, and communicating with other team members. The suggestions of this study included that (a) different web-based teaching instruments influenced student’s Learning; (b) it is difficult to monitor online PBL progress; (c) online PBL should be implemented flexible and multi-oriented; (d) although PBL did not show a significant difference on the group with PBL and without PBL, it did increase student’s problem-solving skills and critical thinking.

Keywords: problem-based learning, college students, distance learning, case analysis, problem-solving

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Authors: Md Rafiqul Islam, Ashir Ahmed, Anwaar Ulhaq, Abu Raihan M. Kamal, Yuan Miao, Hua Wang

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Mental health of people is equally important as of their physical health. Mental health and well-being are influenced not only by individual attributes but also by the social circumstances in which people find themselves and the environment in which they live. Like physical health, there is a number of internal and external factors such as biological, social and occupational factors that could influence the mental health of people. People living in poverty, suffering from chronic health conditions, minority groups, and those who exposed to/or displaced by war or conflict are generally more likely to develop mental health conditions. However, to authors’ best knowledge, there is dearth of knowledge on the impact of workplace (especially the highly stressed IT/Tech workplace) on the mental health of its workers. This study attempts to examine the factors influencing the mental health of tech workers. A publicly available dataset containing more than 65,000 cells and 100 attributes is examined for this purpose. Number of machine learning techniques such as ‘Decision Tree’, ‘K nearest neighbor’ ‘Support Vector Machine’ and ‘Ensemble’, are then applied to the selected dataset to draw the findings. It is anticipated that the analysis reported in this study would contribute in presenting useful insights on the attributes contributing in the mental health of tech workers using relevant machine learning techniques.

Keywords: mental disorder, diagnosis, occupational stress, IT workplace

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Authors: Nur Azlina Mohamed Mokmin, Mona Masood

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Guided by the theory of learning style, this study is based on the development of a multimedia learning application for students with mastery learning style. The learning material was developed by applying a graduated difficulty learning strategy. Algebraic fraction was chosen as the learning topic for this application. The effectiveness of this application in helping students learn is measured by giving a pre- and post-test. The result shows that students who learn using the learning material that matches their preferred learning style performs better than the students with a non-personalized learning material.

Keywords: algebraic fractions, graduated difficulty, mastery learning style, multimedia

Procedia PDF Downloads 477

Authors: Ashwini Mahesh Jagatap

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This paper presents the language learning strategy with respect to speaking skill with collaborative learning approach. Collaborative learning has been proven to be efficient learning methodology for all kinds of students. Students are working in groups of two or more, reciprocally searching for understanding, Solutions, or meanings, or creating a product. The presentation highlights the different stages which can be implemented during actual implementation of the methodology in the class room teaching learning process.

Keywords: collaborative classroom, collaborative learning approach, language skills, traditional teaching

Procedia PDF Downloads 544

Authors: Itsaree Padphai, Jiranan Pakpeian, Suksun Niponchai

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This study is a descriptive research which aims to: 1) Compare the difference of knowledge before and after using the e-Learning program entitled “Basic Cardiopulmonary Resuscitation for Students in an Emergency Medical Technician Diploma Program”, and 2) Assess the students’ satisfaction after using the said program. This research is a kind of teaching and learning management supplemented with the e-Learning system; therefore, the purposively selected samples are 44 first-year and class-16 students of an emergency medical technician diploma program who attend the class in a second semester of academic year 2012 in Sirindhorn College of Public Health, Khon Kaen province. The research tools include 1) the questionnaire for general information of the respondents, 2) the knowledge tests before and after using the e-Learning program, and 3) an assessment of satisfaction in using the e-Learning program. The statistics used in data analysis percentage, include mean, standard deviation, and inferential statistics: paired t-test. 1. The general information of the respondents was mostly 37 females representing 84.09 percent. The average age was 19.5 years (standard deviation was 0.81), the maximum age was 21 years, and the minimum age was 19 years respectively. Students (35 subjects) admitted that they preferred the methods of teaching and learning by using the e-Learning systems. This was totally 79.95 percent. 2. A comparison on the difference of knowledge before and after using the e-Learning program showed that the mean before an application was 6.64 (standard deviation was 1.94) and after was 18.84 (standard deviation 1.03), which was higher than the knowledge of students before using the e-Learning program with the statistical significance (P value < 0.001). 3. For the satisfaction after using the e-Learning program, it was found that students’ satisfaction was at a very good level with the mean of 4.93 (standard deviation was 0.11).

Keywords: e-Learning, cardiopulmonary resuscitation, diploma program, Khon Kaen Province

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Authors: Darshana Lal, Sonu Rana

Abstract:

Learning Resource Centers (LRC) are acquiring different kinds of documents like books, journals, thesis, dissertations, standard, databases etc. in print and e-form. This article deals with the different types of sources available in LRC. It also discusses the concept of the web, as a tool, as a multimedia system and the different interfaces available on the web. The reasons for establishing LRC are highlighted along with the assignments of LRC. Different features of LRC‘S like self-learning and group learning are described. It also implements a group of activities like reading, learning, educational etc. The use of LRC by students and faculties are given and concluded with the benefits.

Keywords: internet, search engine, resource centre, opac, self-learning, group learning

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