Search results for: learning benefits

Authors: Jamie L. Stangel

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Increased popularity and visibility of college athletics have contributed to an environment in which institutions -most of which lack self-sufficient athletics department budgets- reallocate monies from the university general fund and seek additional funding sources to keep up with increasing levels of spending on athletics. Given the prevalence of debates on student debt levels, coach salaries, and athlete pay, empirical evidence on whether this spending yields an expected return on investment is necessary. This study considered the relationship between the independent variable of the winning percentage of the men’s basketball team at a mid-major university, moderated by NCAA tournament appearance, number of applicants, number of enrollments, average SAT score of students, and donor giving to the university general and athletic funds. The results indicate that, other than a small correlation between athletic success and number of applicants, only when NCAA tournament appearance is used as a moderating variable, these purported benefits are not supported, suggesting the need for a reevaluation of athletic department spending and perceptions on tangible and intangible benefits for universities.

Keywords: athletic success, enrollment, NCAA, private giving

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Authors: Yosr Ghozzi

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The Cyber-Physical Systems terminology has been well received by the industrial community and specifically appropriated in educational settings. Indeed, our latest educational activities are based on the development of experimental platforms on an industrial scale. In fact, we built a collaborative learning model because of an international market study that led us to place ourselves at the heart of this technology. To align with these findings, a competency-based approach study was conducted, and program content was revised by reflecting the projectbased approach. Thus, this article deals with the development of educational devices according to a generated curriculum and specific educational activities while respecting the repository of skills adopted from what constitutes the educational cyber-physical production systems and the laboratories that are compliant and adapted to them. The implementation of these platforms was systematically carried out in the school's workshops spaces. The objective has been twofold, both research and teaching for the students in mechatronics and logistics of the electromechanical department. We act as trainers and industrial experts to involve students in the implementation of possible extension systems around multidisciplinary projects and reconnect with industrial projects for better professional integration.

Keywords: education 4.0, competency-based learning, teaching factory, project-based learning, cyber-physical systems, industry 4.0

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Authors: Maha Awad M. Almuttairi

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This research aimed to measure the impact of the use of a mobile learning (iTunes U) app for the development of metacognition skills delivered in the enrichment program offered to gifted students at the secondary level in Jeddah. The author targeted a group of students on an experimental scale to evaluate the achievement. The research sample consisted of a group of 38 gifted female students. The scale of evaluation of the metacognition skills used to measure the performance of students in the enrichment program was as follows: Satisfaction scale for the assessment of the technique used and the final product form after completion of the program. Appropriate statistical treatment used includes Paired Samples T-Test Cronbach’s alpha formula and eta squared formula. It was concluded in the results the difference of α≤ 0.05, which means the performance of students in the skills of metacognition in favor of using iTunes U. In light of the conclusion of the experiment, a number of recommendations and suggestions were present; the most important benefit of mobile learning applications is to provide enrichment programs for gifted students in the Kingdom of Saudi Arabia, as well as conducting further research on mobile learning and gifted student teaching.

Keywords: enrichment program, gifted students, metacognition skills, mobile learning

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

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Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

Procedia PDF Downloads 138

Authors: Nihal Esam Abuzinadah, Areej Abbas Malibari, Arwa Abdulaziz Allinjawi, Paul Krause

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Deaf and hearing-impaired students face many obstacles throughout their education, especially with learning applied sciences such as computer programming. In addition, there is no clear signs in the Arabic Sign Language that can be used to identify programming logic terminologies such as while, for, case, switch etc. However, hearing disabilities should not be a barrier for studying purpose nowadays, especially with the rapid growth in educational technology. In this paper, we develop an Avatar based system to teach computer programming to deaf and hearing-impaired students using Arabic Signed language with new signs vocabulary that is been developed for computer programming education. The system is tested on a number of high school students and results showed the importance of visualization in increasing the comprehension or understanding of concepts for deaf students through the avatar.

Keywords: hearing-impaired students, isolation, self-esteem, learning difficulties

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Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

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Authors: Barry Ardley, Abi Hunt, Nick Taylor

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As a theoretical and practical framework, this study uses the student-as-producer approach to learning in higher education, as adopted by the Lincoln International Business School, University of Lincoln, UK. Students as producer positions learners as skilled and capable agents, able to participate as partners with tutors in live research projects. To illuminate the nature of this approach to learning and to highlight its critical issues, the authors report on two guided student consultancy projects. These were set up with the assistance of two local organisations in the city of Lincoln, UK. Using the student as a producer model to deliver the projects enabled learners to acquire and develop a range of key skills and knowledge not easily accessible in more traditional educational settings. This paper presents a systematic case study analysis of the eight organising principles of the student-as-producer model, as adopted by university tutors. The experience of tutors implementing students as producers suggests that the model can be widely applied to benefit not only the learning and teaching experiences of higher education students and staff but additionally a university’s research programme and its community partners.

Keywords: consultancy, learning, student as producer, research

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

Procedia PDF Downloads 171

Authors: Abdelmenen Abobghala, Mahmud Ahmed, Mohamed Alwaheshi, Anwar Fanan, Meftah Mehdawi, Ahmed Abuhatira

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This research aims to predict academic performance and identify weak points in students to aid teachers in understanding their learning needs. Both quantitative and qualitative methods are used to identify difficult test items and the factors causing difficulties. The study uses interventions like focus group discussions, interviews, and action plans developed by the students themselves. The research questions explore the predictability of final grades based on mock exams and assignments, the student's response to action plans, and the impact on learning performance. Ethical considerations are followed, respecting student privacy and maintaining anonymity. The research aims to enhance student engagement, motivation, and responsibility for learning.

Keywords: prediction, academic performance, weak points, understanding, learning, quantitative methods, qualitative methods, formative assessments, feedback, emotional responses, intervention, focus group discussion, interview, action plan, student engagement, motivation, responsibility, ethical considerations

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Ratima Damkham, Natasha Dejdumrong, Priyakorn Pusawiro

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This paper explored the solution in game design to help game designers in the educational game designing using digital educational game model canvas (DEGMC) and digital educational game form (DEGF) based on Outcome-based Education program. DEGMC and DEGF can help designers develop an overview of the game while designing and planning their own game. The way to clearly assess players’ ability from learning outcomes and support their game learning design is by using the tools. Designers can balance educational content and entertainment in designing a game by using the strategies of the Business Model Canvas and design the gameplay and players’ ability assessment from learning outcomes they need by referring to the Constructive Alignment. Furthermore, they can use their design plan in this research to write their Game Design Document (GDD). The success of the research was evaluated by four experts’ perspectives in the education and computer field. From the experiments, the canvas and form helped the game designers model their game according to the learning outcomes and analysis of their own game elements. This method can be a path to research an educational game design in the future.

Keywords: constructive alignment, constructivist theory, educational game, outcome-based education

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Authors: Josef Steinberger, Vanni Zavarella, Hristo Tanev

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Natural disasters cause tens of thousands of victims and massive material damages. We refer to all those events caused by natural disasters, such as damage on people, infrastructure, vehicles, services and resource supply, as micro events. This paper addresses the problem of micro - event detection in online media sources. We present a natural language grammar learning algorithm and apply it to online news. The algorithm in question is based on distributional clustering and detection of word collocations. We also explore the extraction of micro-events from social media and describe a Twitter mining robot, who uses combinations of keywords to detect tweets which talk about effects of disasters.

Keywords: online news, natural language processing, machine learning, event extraction, crisis computing, disaster effects, Twitter

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Authors: Malini Ganapathy, Sarjit Kaur

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Maximising learning with higher-order thinking skills with Information and Communications Technology (ICT) has been deep-rooted and emphasised in various developed countries such as the United Kingdom, the United States of America and Singapore. The transformation of the education curriculum in the Malaysia Education Development Plan (PPPM) 2013-2025 focuses on the concept of Higher Order Thinking (HOT) skills which aim to produce knowledgeable students who are critical and creative in their thinking and can compete at the international level. HOT skills encourage students to apply, analyse, evaluate and think creatively in and outside the classroom. In this regard, the National Education Blueprint (2013-2025) is grounded based on high-performing systems which promote a transformation of the Malaysian education system in line with the vision of Malaysia’s National Philosophy in achieving educational outcomes which are of world class status. This study was designed to investigate ESL students’ learning practices on the emphasis of promoting HOTs while using ICT in their curricula. Data were collected using a stratified random sampling where 100 participants were selected to take part in the study. These respondents were a group of undergraduate students who undertook ESL courses in a public university in Malaysia. A three-part questionnaire consisting of demographic information, students’ learning experience and ICT utilization practices was administered in the data collection process. Findings from this study provide several important insights on students’ learning experiences and ICT utilization in developing HOT skills.

Keywords: English as a second language students, critical and creative thinking, learning, information and communication technology and higher order thinking skills

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Authors: Sara Corvino

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The aim of this study is to explore the potential of inclusive learning and assessment strategies to foster students' engagement with historical debates surrounding the field of graphic design. The goal is to respond to the diversity of L4 Graphic Design students, at Nottingham Trent University, in a way that instead of 'lowering standards' can benefit everyone. This research tests, measures, and evaluates the impact of a specific intervention, an assessment task, to develop students' critical visual analysis skills and stimulate a deeper engagement with the subject matter. Within the action research approach, this work has followed a case study research method to understand students' views and perceptions of a specific project. The primary methods of data collection have been: anonymous electronic questionnaire and a paper-based anonymous critical incident questionnaire. NTU College of Business Law and Social Sciences Research Ethics Committee granted the Ethical approval for this research in November 2019. Other methods used to evaluate the impact of this assessment task have been Evasys's report and students' performance. In line with the constructivist paradigm, this study embraces an interpretative and contextualized analysis of the collected data within the triangulation analytical framework. The evaluation of both qualitative and quantitative data demonstrates that active learning strategies and the disruption of thinking patterns can foster greater students' engagement and can lead to meaningful learning.

Keywords: active learning, assessment for learning, graphic design, higher education, student engagement

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Authors: Barzan Hadi Hama Karim

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The purpose of this research study is to determine how educators, students at the university level are using storytelling to support the educational process. This study provides a general framework about educational uses of storytelling as a pedagogical too to learn English language in the higher education and describes the different perceptions of people (teachers and students) at different levels. A survey is used to collect responses from a group of educators and students in educational settings to determine how they are using storytelling for educational purposes. The results show the current situation of educational uses of storytelling and explore some of the benefits and challenges educators face in implementing storytelling in their institutions. The purpose of our research is to investigate the impact of storytelling as a pedagogical tool to learn English language in higher education and its academic achievements on ESL students. It highlights findings that address the following questions: (1) How has storytelling been approached historically? (2) Is storytelling beneficial for students in early grades at university? (3) To what extent do teacher and student prefer storytelling as a pedagogical tool to teach and learn English language in higher education?

Keywords: storytelling, teacher's beliefs, student’s beliefs, student’s academic achievement, narrative, pedagogy, ESL

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Authors: P. Halder, A. Zaman

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It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Rafał Szmajser, Katarzyna Świetla, Mariusz Andrzejewski

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Robotic process automation (RPA) is a technology of repeatable business processes performed using computer programs, robots that simulate the work of a human being. This approach assumes replacing an existing employee with the use of dedicated software (software robots) to support activities, primarily repeated and uncomplicated, characterized by a low number of exceptions. RPA application is widespread in modern business services, particularly in the areas of Finance, Accounting and Human Resources Management. By utilizing this technology, the effectiveness of operations increases while reducing workload, minimizing possible errors in the process, and as a result, bringing measurable decrease in the cost of providing services. Regardless of how the use of modern information technology is assessed, there are also some doubts as to whether we should replace human activities in the implementation of the automation in business processes. After the initial awe for the new technological concept, a reflection arises: to what extent does the implementation of RPA increase the efficiency of operations or is there a Business Case for implementing it? If the business case is beneficial, in which business processes is the greatest potential for RPA? A closer look at these issues was provided by in this research during which the respondents’ view of the perceived advantages resulting from the use of robotization and automation in financial and accounting processes was verified. As a result of an online survey addressed to over 500 respondents from international companies, 162 complete answers were returned from the most important types of organizations in the modern business services industry, i.e. Business or IT Process Outsourcing (BPO/ITO), Shared Service Centers (SSC), Consulting/Advisory and their customers. Answers were provided by representatives of the positions in their organizations: Members of the Board, Directors, Managers and Experts/Specialists. The structure of the survey allowed the respondents to supplement the survey with additional comments and observations. The results formed the basis for the creation of a business case calculating tangible benefits associated with the implementation of automation in the selected financial processes. The results of the statistical analyses carried out with regard to revenue growth confirmed the correctness of the hypothesis that there is a correlation between job position and the perception of the impact of RPA implementation on individual benefits. Second hypothesis (H2) that: There is a relationship between the kind of company in the business services industry and the reception of the impact of RPA on individual benefits was thus not confirmed. Based results of survey authors performed simulation of business case for implementation of RPA in selected Finance and Accounting Processes. Calculated payback period was diametrically different ranging from 2 months for the Account Payables process with 75% savings and in the extreme case for the process Taxes implementation and maintenance costs exceed the savings resulting from the use of the robot.

Keywords: automation, outsourcing, business process automation, process automation, robotic process automation, RPA, RPA business case, RPA benefits

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Authors: Protik Basu, Indranil Ghosh, Pranab K. Dan

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Lean management is an integrated socio-technical system to bring about a competitive state in an organization. The purpose of this paper is to explore and integrate the role of customers and suppliers with the internal practices of the Indian manufacturing industries towards successful implementation of lean manufacturing (LM). An extensive literature survey is carried out. An attempt is made to build an exhaustive list of all the input manifests related to customers, suppliers and internal practices necessary for LM implementation, coupled with a similar exhaustive list of the benefits accrued from its successful implementation. A structural model is thus conceptualized, which is empirically validated based on the data from the Indian manufacturing sector. With the current impetus on developing the industrial sector, the Government of India recently introduced the Lean Manufacturing Competitiveness Scheme that aims to increase competitiveness with the help of lean concepts. There is a huge scope to enrich the Indian industries with the lean benefits, the implementation status being quite low. Hardly any survey-based empirical study in India has been found to integrate customers and suppliers with the internal processes towards successful LM implementation. This empirical research is thus carried out in the Indian manufacturing industries. The basic steps of the research methodology followed in this research are the identification of input and output manifest variables and latent constructs, model proposition and hypotheses development, development of survey instrument, sampling and data collection and model validation (exploratory factor analysis, confirmatory factor analysis, and structural equation modeling). The analysis reveals six key input constructs and three output constructs, indicating that these constructs should act in unison to maximize the benefits of implementing lean. The structural model presented in this paper may be treated as a guide to integrating customers and suppliers with internal practices to successfully implement lean. Integrating customers and suppliers with internal practices into a unified, coherent manufacturing system will lead to an optimum utilization of resources. This work is one of the very first researches to have a survey-based empirical analysis of the role of customers, suppliers and internal practices of the Indian manufacturing sector towards an effective lean implementation.

Keywords: customer management, internal manufacturing practices, lean benefits, lean implementation, lean manufacturing, structural model, supplier management

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

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Authors: Fu-Chi Chuang, Yu-Liang, Chang, Wen-Der Wang

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Our environment has been contaminated by many artificial chemicals, such as plastics, pesticides. Many of them have hormone-like activity and are classified as 'environmental hormone (also named endocrine disruptors)'. These chemicals interfere with or mimic hormones have adverse effects that persist into adulthood. Environmental education is an important way to teach students to become engaged in real-world issues that transcend classroom walls. Elementary education is the first stage to perform environmental education and it is an important component to help students develop adequate environmental knowledge, attitudes, and behavior. However, elementary teachers' knowledge plays a critical role in this mission. Therefore, we use a questionnaire to survey the knowledge of environmental hormone of elementary school teachers and their learning motivation of the environmental hormone-regarding knowledge. We collected 218 questionnaires from Taiwanese elementary teachers and the results indicate around 73% of elementary teachers do not have enough knowledge about environmental hormones. Our results also reveal the in-service elementary teachers’ learning motivation of environmental hormones knowledge is positively enhanced once they realized their insufficient cognitive ability of environmental hormones. We believe our study will provide the powerful reference for Ministry of Education to set up the policy of environmental education to enrich all citizens sufficient knowledge of the effects of the environmental hormone on organisms, and further to enhance our correct environmental behaviors.

Keywords: elementary teacher, environmental hormones, learning motivation, questionnaire

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Authors: Pataraporn Kheawwan

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Preceptorship represents an important aspect in new nurse orientation. However, there was no formal preceptor training program developed for nurse preceptor in Thailand. The purposes of this study were to develop and evaluate formal preceptor training program for nurse preceptors in King Chulalongkorn Memorial Hospital, Thailand. A research and development study design was utilized in this study. Participants were 37 nurse preceptors. The program contents were delivered by e-learning material, class lecture, group discussion followed by simulation training. Knowledge of the participants was assessed pre and post program. Skill and critical thinking were assessed using Preceptor Skill and Decision Making Evaluation form at the end of program. Statistical significant difference in knowledge regarding preceptor role and coaching strategies between pre and post program were found. All participants had satisfied skill and decision making score after completed the program. Most of participants perceived benefits of preceptor training course. In conclusion, The results of this study reveal that the newly developed preceptorship course is an effective formal training course for nurse preceptors.

Keywords: preceptor, preceptorship, new nurse, clinical education

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Authors: Ilona Buchem, Carolin Gellner

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Over the last few years, the call for the support of senior learners in the development of their digital literacy has become prevalent, mainly due to the progression towards ageing societies paired with advances in digitalisation in all spheres of life, including e-health and electronic patient record (EPA). While major research efforts in supporting senior learners in developing digital literacy have been invested so far in e-learning focusing on knowledge acquisition and cognitive tasks, little research exists in learning models which target virtual mentoring and coaching with the help of pedagogical agents and address the social dimensions of learning. Research from studies with students in the context of formal education has already provided methods for designing intelligent virtual agents in support of personalised learning. However, this research has mostly focused on cognitive skills and has not yet been applied to the context of mentoring/coaching of senior learners, who have different characteristics and learn in different contexts. In this paper, we describe how insights from previous research can be used to develop an intelligent virtual learning coach (agent) for senior learners with a focus on building the social relationship between the agent and the learner and the key task of the agent to socialize learners to the larger context of digital literacy with a focus on electronic health records. Following current approaches to mentoring and coaching, the agent is designed not to enhance and monitor the cognitive performance of the learner but to serve as a trusted friend and advisor, whose role is to provide one-to-one guidance and support sharing of experiences among learners (peers). Based on literature review and synopsis of research on virtual agents and current coaching/mentoring models under consideration of the specific characteristics and requirements of senior learners, we describe the design framework which was applied to design an intelligent virtual learning coach as part of the e-learning system for digital literacy of senior learners in the ePA-Coach project founded by the German Ministry of Education and Research. This paper also presents the results from the evaluation study, which compared the use of the first prototype of the virtual learning coach designed according to the design framework with a voice narration in a multimedia learning environment with senior learners. The focus of the study was to validate the agent design in the context of the persona effect (Lester et al., 1997). Since the persona effect is related to the hypothesis that animated agents are perceived as more socially engaging, the study evaluated possible impacts of agent coaching in comparison with voice coaching on motivation, engagement, experience, and digital literacy.

Keywords: virtual learning coach, virtual mentor, pedagogical agent, senior learners, digital literacy, electronic health records

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Authors: Sachin Nagargoje

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Complete labeled data is often difficult to obtain in a practical scenario. Even if one manages to obtain the data, the quality of the data is always in question. In shopping vertical, offers are the input data, which is given by advertiser with or without a good quality of information. In this paper, an author investigated the possibility of using a very simple Semi-supervised learning approach to increase the recall of unhealthy offers (has badly written Offer Title or partial product details) in shopping vertical domain. The author found that the semisupervised learning method had improved the recall in the Smart Phone category by 30% on A=B testing on 10% traffic and increased the YoY (Year over Year) number of impressions per month by 33% at production. This also made a significant increase in Revenue, but that cannot be publicly disclosed.

Keywords: semi-supervised learning, clustering, recall, coverage

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Authors: Eunice H. Li

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This e-poster explores some of the issues that are linked to Open Educational Resources (OER). It describes how OER is explained by experts in the field and relates its value in attaining and using knowledge. ‘Open', 'open pedagogy', self-direction, freedom, and autonomy are the main issues identified for the discussion. All of these issues make essential contributions to OER in one way or another. Nevertheless, there are seemingly areas of contentions with regard to applying these concepts in teaching and learning practices. For this e-Poster, it is the teaching-learning aspects of OER that it is primarily concerned with. The basis for the discussion comes from a 2013 critique of OER presented by Jeremy Knox of the University of Edinburgh, tutor of the MSc in Digital Education Programme. This discussion is also supported by the analysis of other research work and papers in this area. The general view on OER is that it is a useful tool for the advancement of learner-centred models of education, but in whatever context, pedagogy cannot be diminished and overlooked. It should take into consideration how to deal with the issues identified above in order to allow learners to gain full benefit from OER.

Keywords: open, pedagogy, e-learning technologies, autonomy, knowledge

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Authors: Eric Agrie Ambele

Abstract:

The norms and models for learning pronunciation in relation to the teaching and learning of English pronunciation are key issues nowadays in English Language Teaching in ESL contexts. This paper discusses these issues based on a study on the attitudes of some Francophone university students in Cameroon towards three English accents spoken in Cameroon: Cameroon Francophone English (CamFE), Cameroon English (CamE), and Hyperlectal Cameroon English (near standard British English). With the desire to know more about the treatment that these English accents receive among these students, an aspect that had hitherto received little attention in the literature, a language attitude questionnaire, and the matched-guise technique was used to investigate this phenomenon. Two methods of data analysis were employed: (1) the percentage count procedure, and (2) the semantic differential scale. The findings reveal that the participants’ attitudes towards the selected accents vary in degree. Though Hyperlectal CamE emerged first, CamE second and CamFE third, no accent, on average, received a negative evaluation. It can be deduced from this findings that, first, CamE is gaining more and more recognition and can stand as an autonomous accent; second, that the participants all rated Hyperlectal CamE higher than CamE implies that they would be less motivated in a context where CamE is the learning model. By implication, in the teaching of English pronunciation to francophone learners learning English in Cameroon, Hyperlectal Cameroon English should be the model.

Keywords: teaching pronunciation, English accents, Francophone learners, attitudes

Procedia PDF Downloads 193

Authors: Daniel Asiamah Ameyaw

Abstract:

This research attempts to investigate how lecturer evaluation makes a difference in enhancing higher education teaching and learning processes. The research questions to guide this research work states first as, “What are the perspectives on the difference made by evaluating academic teachers in order to enhance higher education teaching and learning processes?” and second, “What are the implications of the findings for Policy and Practice?” Data for this research was collected mainly through interviewing and partly documents review. Data analysis was conducted under the framework of grounded theory. The findings showed that for individual lecturer level, lecturer evaluation provides a continuous improvement of teaching strategies, and serves as source of data for research on teaching. At the individual student level, it enhances students learning process; serving as source of information for course selection by students; and by making students feel recognised in the educational process. At the institutional level, it noted that lecturer evaluation is useful in personnel and management decision making; it assures stakeholders of quality teaching and learning by setting up standards for lecturers; and it enables institutions to identify skill requirement and needs as a basis for organising workshops. Lecturer evaluation is useful at national level in terms of guaranteeing the competencies of graduates who then provide the needed manpower requirement of the nation. Besides, it mentioned that resource allocation to higher educational institution is based largely on quality of the programmes being run by the institution. The researcher concluded, that the findings have implications for policy and practice, therefore, higher education managers are expected to ensure that policy is implemented as planned by policy-makers so that the objectives can successfully be achieved.

Keywords: academic quality, higher education, lecturer evaluation, teaching and learning processes

Procedia PDF Downloads 143

Authors: Azusa Katsumata

Abstract:

Where there is collaboration between universities and companies in the educational context, companies seek ‘knowledge’ from universities and provide a ‘place of practice’ to them. Several universities have introduced activities aimed at the mutual enlightenment of a diversity of people in career education. However, several programs emphasize on delivering results, and on practicing the prepared materials as planned. Few programs focus on unexpected failures and setbacks. This way of learning is important in career education so that classmates can help each other, overcome difficulties, draw out each other’s strengths, and learn from them. Seijo University in Tokyo offered Tokyo Tourism, a Project-Based Learning course, as a first-year career education course until 2016. In cooperation with a travel agency, students participate in planning actual tourism products for foreigners visiting Japan, undertake tours serving as guides. This paper aims to study the 'learning platform' created by a series of processes such as the fieldwork, planning tours, the presentation, selling the tourism products, and guiding the tourists. We conducted a questionnaire to measure the development of work-related skills in class. From the results of the questionnaire, we can see, in the example of this class, that students demonstrated an increased desire to be pro-active and an improved motivation to learn. Students have not, however, acquired policy or business skills. This is appropriate for first-year careers education, but we need to consider how this can be incorporated into future courses. In the questionnaire filled out by the students after the class, the following results were found. Planning and implementing travel products while learning from each other, and helping the teams has led to improvements in the student workforce. This course is a collaborative project between Japanese universities and the 2020 Tokyo Olympics and Paralympic Games committee.

Keywords: university career education, platform of learning, project-based learning, collaboration between university and company

Procedia PDF Downloads 160