Search results for: learning challenges

Authors: Nhon Do, Tuyen Trong Tran, Phan Hoai Truong

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Nowadays e-Learning is more popular, in Vietnam especially. In e-learning, materials for studying are very important. It is necessary to design the knowledge base systems and expert systems which support for searching, querying, solving of problems. The ontology, which was called Computational Object Knowledge Base Ontology (COB-ONT), is a useful tool for designing knowledge base systems in practice. In this paper, a design method for knowledge base systems in education using COKB-ONT will be presented. We also present the design of a knowledge base system that supports studying knowledge and solving problems in higher mathematics.

Keywords: artificial intelligence, knowledge base systems, ontology, educational software.

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Authors: Ozlem Bozok, Yusuf Bozok

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The importance of using mother tongue and translation in foreign language classrooms cannot be ignored and translation can be utilized as a method in English Language Teaching courses. There exist researches advocating or objecting to the use of translation in foreign language learning but they all have a point in common: Translation should be used as an aid to teaching, not an end in itself. In this research, prospective English language teachers’ opinions about translation use and use of mother tongue in foreign language teaching are investigated and according to the findings, some explanations and recommendations are made.

Keywords: Exposure to foreign language, translation, foreign language learning, prospective teachers’ opinions, use of L1.

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Authors: Mirjana Jeleč Raguž, Barbara Pisker

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Development of knowledge based society carries multiple challenges to the higher education system. Some of the challenges laid before the higher education sector of countries which aspire to become knowledge based societies are: the entrepreneurial leadership of the higher education institutions, finding new sources of financing in order to minimize dependence on public resources, creating connections with the labor market, commercial utilization of R&D results, promotion of innovations as well as the overall promotion of science excellence relevant to the economic sector. Within a framework of this paper and its main subject of research, the challenge which is being put before the higher education institutions is an effort of establishing regional mission of higher education through the open collaboration with regional key factors, both private and public. Development of the mentioned collaboration and its contribution to the overall regional development in Croatia is the main subject of empirical research in this paper.

Keywords: Croatia, Higher Education Institutions, Regional Role, Science-Industry Interaction

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Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: Concept approximation, granular computing, reducts, rough set theory, rule induction.

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Authors: N. H. Mvungi

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Control of commutation of switched reluctance (SR) motor has been an area of interest for researchers for sometime now with mixed successes in addressing the inherent challenges. New technologies, processing schemes and methods have been adopted to make sensorless SR drive a reality. There are a number of conceptual, offline, analytical and online solutions in literature that have varying complexities and achieved equally varying degree of robustness and accuracies depending on the method used to address the challenges and the SR drive application. Magnetic coupling is one such challenge when using active probing techniques to determine rotor position of a SR motor from stator winding. This paper studies the effect of back-of-core saturation on the detected rotor position and presents results on measurement made on a 4- phase SR motor. The results shows that even for a four phase motor which is excited one phase at a time and using the electrically opposite phase for active position probing, the back-of-core saturation effects should not be ignored.

Keywords: Sensorless, SR motor, saturation effects, detection.

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Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: Cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma.

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Authors: Truong Quang Dang Khoa, Masahiro Nakagawa

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Chaos and fractals are novel fields of physics and mathematics showing up a new way of universe viewpoint and creating many ideas to solve several present problems. In this paper, a novel algorithm based on the chaotic sequence generator with the highest ability to adapt and reach the global optima is proposed. The adaptive ability of proposal algorithm is flexible in 2 steps. The first one is a breadth-first search and the second one is a depth-first search. The proposal algorithm is examined by 2 functions, the Camel function and the Schaffer function. Furthermore, the proposal algorithm is applied to optimize training Multilayer Neural Networks.

Keywords: learning and evolutionary computing, Chaos Optimization Algorithm, Artificial Neural Networks, nonlinear optimization, intelligent computational technologies.

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Authors: Chia-Jung Chen, Rong-Shine Lin, Rong-Guey Chang

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As networking has become popular, Web-learning tends to be a trend while designing a tool. Moreover, five-axis machining has been widely used in industry recently; however, it has potential axial table colliding problems. Thus this paper aims at proposing an efficient web-learning collision detection tool on five-axis machining. However, collision detection consumes heavy resource that few devices can support, thus this research uses a systematic approach based on web knowledge to detect collision. The methodologies include the kinematics analyses for five-axis motions, separating axis method for collision detection, and computer simulation for verification. The machine structure is modeled as STL format in CAD software. The input to the detection system is the g-code part program, which describes the tool motions to produce the part surface. This research produced a simulation program with C programming language and demonstrated a five-axis machining example with collision detection on web site. The system simulates the five-axis CNC motion for tool trajectory and detects for any collisions according to the input g-codes and also supports high-performance web service benefiting from C. The result shows that our method improves 4.5 time of computational efficiency, comparing to the conventional detection method.

Keywords: Collision detection, Five-axis machining, Separating axis.

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Authors: C. Cruz, A. Breda

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The world is changing and, consequently, the young people need to acquire more sophisticated tools and skills to lead with the new societies’ challenges. In the curriculum of the Portuguese education system, in the profile of students leaving compulsory education, the critical thinking and creative thinking are pointed out as skills to be developed, as well as the capacity of interconnect different knowledge and applicate them in different contexts and learning areas. Unlike primary school teachers, teachers specialized in a specific area sometimes reveal more difficulties in developing interdisciplinary approaches in the classrooms and, despite the effort, the interdisciplinarity is not a common practice in schools. Statements like "Mathematics is everywhere" are unquestionable, however, some math teachers continue to develop an abstract teaching of mathematics devoid of any connection with reality. Good mathematical problems in real contexts are promising in the development of interdisciplinary pedagogical practices. However, these problems are often addressed by teachers in multidisciplinary rather than interdisciplinary contexts or are not addressed at all due several reasons, which range from insecurity in working on disciplinary domains with which they are not comfortable to a lack of pedagogical resources. In this study this issue is approached through a case study involving Mathematics teachers, which, in their professional development scope, attended a training aimed at stimulating interdisciplinary practices in real contexts, namely related to the COVID-19 pandemic.

Keywords: Interdisciplinarity, Mathematics, professional development, teacher training.

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125 Hz to 8000 Hz. The data contain patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R2 values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R2 values for the best models for KNN ranges from .89 to .95. The best imputation models received R2 between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our imputation models versus constant imputations by a two percent increase.

Keywords: Machine Learning, audiograms, data imputations, single imputations.

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning (ML) archetypal that could forecast the COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID-19 cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organization (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data are split into 8:2 ratio for training and testing purposes to forecast future new COVID-19 cases. Support Vector Machine (SVM), Random Forest (RF), and linear regression (LR) algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID-19 cases is evaluated. RF outperformed the other two ML algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n = 30. The mean square error obtained for RF is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis, RF algorithm can perform more effectively and efficiently in predicting the new COVID-19 cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest.

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: Cyber-security, Intrusion Detection Systems, Temporal Graph Network, Anomaly Detection.

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Authors: N. Z. Azlan, H. Yamaura

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The experimental study of position control of a light weight and small size robotic finger during non-contact motion is presented in this paper. The finger possesses fingertip pinching and self adaptive grasping capabilities, and is made of a seven bar linkage mechanism with a slider in the middle phalanx. The control system is tested under the Proportional Integral Derivative (PID) control algorithm and Recursive Least Square (RLS) based Feedback Error Learning (FEL) control scheme to overcome the uncertainties present in the plant. The experiments conducted in Matlab Simulink and xPC Target environments show that the overall control strategy is efficient in controlling the finger movement.

Keywords: Anthropomorphic finger, position control, feedback error learning, experimental study

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Authors: Salem Alajmi, Bader Altaweel

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Kuwait, like many parts of the world, has started facing the dangerous growth of electrical and electronic waste. This growth has been noted in last two decades, emerging with the development of mobile phones, computers, TVs, as well as other electronic devices and electrical equipment. Kuwait is already among the highest global producers of electronic waste (e-waste) in kg per capita. Furthermore, Kuwait is among the global countries that set high-level future targets in renewable energy projects. Accumulation of this electronic waste, as well as accelerated renewable energy projects, will lead to the increase of future threats to the country. In this research, factors that lead to the increase the e-waste in Kuwait are presented. Also, the current situations of dealing with e-waste in the country as well as the associated challenges are examined. The impact of renewable energy projects on future e-waste accumulation is considered. Moreover, this research proposes the best strategies and practices toward successfully dealing with the waste of electronic devices and renewable energy technologies.

Keywords: E-waste, landfill, environmental management, valuable metals, hazardous materials.

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Authors: Eric P. Jiang

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As the Internet continues to grow at a rapid pace as the primary medium for communications and commerce and as telecommunication networks and systems continue to expand their global reach, digital information has become the most popular and important information resource and our dependence upon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and criminal enterprises. In this paper, we propose a new machine learning based network intrusion detection framework for cyber security. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is applied to a collected set of network audit data to generate a profile of normal network behavior and in the intrusion detection stage, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anomalies should these events are sufficiently far from the expected normal behavior. The proposed framework is particularly applicable to the situations where there is only a small amount of labeled network training data available, which is very typical in real world network environments.

Keywords: classification, data analysis and mining, network intrusion detection, semi-supervised learning.

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Authors: Saleh Komies, Mona Alharbi, Razan Alhayyani, Mozah Almulhim, Roseanne Khawaja, Ahmed Alradhi

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One of the proposed solutions that face some challenges is encouraging sustainable energy consumption across Saudi Arabia through crowdfunding platforms. To address these challenges, we need to determine the level of awareness of crowdfunding and green projects, as well as the preferences and willingness of Saudis to utilize crowdfunding as an alternative funding source for green projects in Saudi Arabia. In this study, we aim to determine the influence of environmental awareness and concern on the propensity to crowdfund green projects. The survey is being conducted as part of environmental initiatives to assess public perceptions and opinions on crowdfunding green projects in Saudi Arabia. A total of 450 responses to an online questionnaire distributed via convenience and snowball sampling were utilized for data analysis. The survey reveals that Saudis have a low understanding of crowdfunding concepts and a relatively high understanding of implementing green projects. The public is interested in crowdfunding green projects if there is a return on investment.

Keywords: Crowdfunding, green projects, renewable energy, Saudi Arabia, solar farms, wind resources.

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Authors: Konstantinos E. Evangelidis, Evangelos Kehris, Theodore H. Kaskalis

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We propose that Virtual Learning Environments (VLEs) should be designed by taking into account the characteristics, the special needs and the specific operating rules of the academic institutions in which they are employed. In this context, we describe a VLE module that extends the support of the organization and delivery of course material by including administration activities related to the various stages of teaching. These include the co-ordination, collaboration and monitoring of the course material development process and institution-specific course material delivery modes. Our specialized module, which enhances VLE capabilities by Helping Educators and Learners through a Laboratory Assistance System, is willing to assist the Greek tertiary technological sector, which includes Technological Educational Institutes (T.E.I.).

Keywords: Virtual learning environments, Teachingcoordination, Laboratorial education, Technological institutes.

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Authors: Ang Ling Weay, Mona Masood

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This paper discusses the potential benefits of an interactive multimedia information representation in enhancing students’ critical thinking aligned with history reasoning in learning history amongst Secondary School students in Malaysia. Two modes of multimedia information representation were implemented; chronologic and thematic information representations. A qualitative study of an unstructured interview was conducted among two history teachers, one history education lecturer, two i-think experts, and five students from Form Four secondary school. The interview was to elicit their opinions on the implementation of thinking maps and interactive multimedia information representation in history learning. The key elements of the interactive multimedia (e.g. multiple media, user control, interactivity and use of timelines and concept maps) were then considered to improve the learning process. Findings of the preliminary investigation reveal that the interactive multimedia information representations have the potential benefits to be implemented as an instructional resource in enhancing students’ higher order thinking skills (HOTs). This paper concludes by giving suggestions for future work.

Keywords: Multimedia Information Representation, Critical Thinking, History Reasoning, Chronological and Thematic Information Representation.

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Authors: Amira, A. Goma, U. E. Mahrous

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Measuring the effect of perinatal lead exposure on learning ability of offspring is considered as a sensitive and selective index for providing an early marker for central nervous system damage produced by this toxic metal. A total of 35 Sprague-Dawley adult rats were used to investigate the effect of lead acetate toxicity on behavioral patterns of adult female rats and learning ability of offspring. Rats were allotted into 4 groups, group one received 1g/l lead acetate (n=10), group two received 1.5g/l lead acetate (n=10), group three received 2g/l lead acetate in drinking water (n=10) and control group did not receive lead acetate (n=5) from 8^th day of pregnancy till weaning of pups.

The obtained results revealed a dose dependent increase in the feeding time, drinking frequency, licking frequency, scratching frequency, licking litters, nest building and retrieving frequencies, while standing time increased significantly in rats treated with 1.5g/l lead acetate than other treated groups and control, on contrary lying time decreased gradually in a dose dependent manner. Moreover, movement activities were higher in rats treated with 1g/l lead acetate than other treated groups and control. Furthermore, time spent in closed arms was significantly lower in rats given 2g/l lead acetate than other treated groups, while, they spent significantly much time spent in open arms than other treated groups which could be attributed to occurrence of adaptation. Furthermore, number of entries in open arms was dose dependent. However, the ratio between open/closed arms revealed a significant decrease in rats treated with 2g/l lead acetate than control group.

Keywords: Lead toxicity, rats, learning ability, behavior.

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Authors: Alfonso Bastías, Álvaro González

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The actual grow of the infrastructure in develop country require sophisticate ways manage the operation and control the quality served. This research wants to concentrate in the operation of this infrastructure beyond the construction. The infrastructure-s operation involves an uncertain environment, where unexpected variables are present every day and everywhere. Decision makers need to make right decisions with right information/data analyzed most in real time. To adequately support their decisions and decrease any negative impact and collateral effect, they need to use computational tools called decision support systems (DSS), but now the main source of information came from common users thought an extensive crowdsourcing

Keywords: Crowdsourcing, Learning Systems, Decision Support Systems, Infrastructure, Construction.

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Authors: Kerstin Grunden, Ann Svensson, Berit Forsman, Christina Karlsson, Ayman Obeid

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The research question of the article is to explore whether the dialogue meetings method could be relevant for reflective learning among researchers and practitioners when welfare technology should be implemented in municipalities, or not. A testbed was planned to be implemented in a retirement home in a Swedish municipality, and the practitioners worked with a pre-study of that testbed. In the article, the dialogue between the researchers and the practitioners in the dialogue meetings is described and analyzed. The potential of dialogue meetings as an arena for learning and reflection among researchers and practitioners is discussed. The research methodology approach is participatory action research with mixed methods (dialogue meetings, focus groups, participant observations). The main findings from the dialogue meetings were that the researchers learned more about the use of traditional research methods, and the practitioners learned more about how they could improve their use of the methods to facilitate change processes in their organization. These findings have the potential both for the researchers and the practitioners to result in more relevant use of research methods in change processes in organizations. It is concluded that dialogue meetings could be relevant for reflective learning among researchers and practitioners when welfare technology should be implemented in a health care organization.

Keywords: Dialogue meetings, implementation, reflection, test bed, welfare technology, participatory action research.

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Authors: Ehigiamusoe, Uyi Kizito

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The paper examines the impact of money market on economic growth in Nigeria using data for the period 1980-2012. Econometrics techniques such as Ordinary Least Squares Method, Johanson’s Co-integration Test and Vector Error Correction Model were used to examine both the long-run and short-run relationship. Evidence from the study suggest that though a long-run relationship exists between money market and economic growth, but the present state of the Nigerian money market is significantly and negatively related to economic growth. The link between the money market and the real sector of the economy remains very weak. This implies that the market is not yet developed enough to produce the needed growth that will propel the Nigerian economy because of several challenges. It was therefore recommended that government should create the appropriate macroeconomic policies, legal framework and sustain the present reforms with a view to developing the market so as to promote productive activities, investments, and ultimately economic growth.

Keywords: Economic Growth, Investments, Money Market, Money Market Challenges, Money Market Instruments.

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Authors: Babatunde Kasim Oladele

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One of the major objectives of the Nigeria national policy on education is the provision of equal educational opportunities to all citizens at different levels of education. With regards to higher education, an aspect of the policy encourages distance learning to be organized and delivered by tertiary institutions in Nigeria. This study therefore, determines how much of the Government resources are committed, how the resources are utilized and what alternative sources of funding are available for this system of education. This study investigated the trends in recurrent costs between 2004/2005 and 2013/2014 at University of Ibadan Distance Learning Centre (DLC). A descriptive survey research design was employed for the study. Questionnaire was the research instrument used for the collection of data. The population of the study was 280 current distance learning education students, 70 academic staff and 50 administrative staff. Only 354 questionnaires were correctly filled and returned. Data collected were analyzed and coded using the frequencies, ratio, average and percentages were used to answer all the research questions. The study revealed that staff salaries and allowances of academic and non-academic staff represent the most important variable that influences the cost of education. About 55% of resources were allocated to this sector alone. The study also indicates that costs rise every year with increase in enrolment representing a situation of diseconomies of scale. This study recommends that Universities who operates distance learning program should strive to explore other internally generated revenue option to boost their revenue. University of Ibadan, being the premier university in Nigeria, should be given foreign aid and home support, both financially and materially, to enable the institute to run a formidable distance education program that would measure up in planning and implementation with those of developed nation.

Keywords: Open education, distance education, University of Ibadan, cost of education, Nigeria.

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Authors: Ana Mafalda Gonçalves, Neuza Pedro

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The evolution of ICT has changed all sections of society and these changes have been creating an irreversible impact on higher education institutions, which are expected to adopt innovative technologies in their teaching practices. As theorical framework this study select Rogers theory of innovation diffusion which is widely used to illustrate how technologies move from a localized invented to a widespread evolution on organizational practices. Based on descriptive statistical data collected in a European higher education institution three years longitudinal study was conducted for analyzing and discussion the different stages of a LMS adoption process. Results show that ICT integration in higher education is not progressively successful and a linear process and multiple aspects must be taken into account.

Keywords: e-learning, higher education, LMS, innovation, technologies

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Authors: Carlos V. Regueiro, Jose E. Domenech, Roberto Iglesias, Jose L. Correa

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In this work a visual and reactive contour following behaviour is learned by reinforcement. With artificial vision the environment is perceived in 3D, and it is possible to avoid obstacles that are invisible to other sensors that are more common in mobile robotics. Reinforcement learning reduces the need for intervention in behaviour design, and simplifies its adjustment to the environment, the robot and the task. In order to facilitate its generalisation to other behaviours and to reduce the role of the designer, we propose a regular image-based codification of states. Even though this is much more difficult, our implementation converges and is robust. Results are presented with a Pioneer 2 AT on a Gazebo 3D simulator.

Keywords: Image-based State Codification, Mobile Robotics, ReinforcementLearning, Visual Behaviour.

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: Deep learning, data mining, gender predication, MOOCs.

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Authors: Abioye A. Oyenuga, Rao Bhamidimarri

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Hurling a successful Construction and Demolition Waste (C&DW) recycling operation around the globe is a challenge today, predominantly because secondary materials markets are yet to be integrated. Reducing, Reusing and recycling of (C&DW) have been employed over the years, and various techniques have been investigated. However, the economic and environmental viability of its application seems limited. This paper discusses the costs and benefits in using secondary materials and focus on investigating reuse and recycling process for five major types of construction materials: concrete, metal, wood, cardboard/paper and plasterboard. Data obtained from demolition specialists and contractors are considered and evaluated. The research paper found that construction material recovery process fully incorporate a 3R’s principle contributing to saving energy and natural resources. This scrutiny leads to the empathy of grand challenges in construction material recovery process. Recommendations to deepen material recovery process are also discussed.

Keywords: Construction & Demolition Waste (C&DW), 3R concept, Recycling, Reuse, Life-Cycle Assessment (LCA), Waste Management.

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Authors: Jacqueline M. Dias, Amina A Khowaja

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Clinical education is one of the most important components of a nursing curriculum as it develops the students’ cognitive, psychomotor and affective skills. Clinical teaching ensures the integration of knowledge into practice. As the numbers of students increase in the field of nursing coupled with the faculty shortage, clinical preceptors are the best choice to ensure student learning in the clinical settings. The clinical preceptor role has been introduced in the undergraduate nursing programme. In Pakistan, this role emerged due to a faculty shortage. Initially, two clinical preceptors were hired. This study will explore clinical preceptors views and experiences of precepting Bachelor of Science in Nursing (BScN) students in an undergraduate program. A case study design was used. As case studies explore a single unit of study such as a person or very small number of subjects; the two clinical preceptors were fundamental to the study and served as a single case. Qualitative data were obtained through an iterative process using in depth interviews and written accounts from reflective journals that were kept by the clinical preceptors. The findings revealed that the clinical preceptors were dedicated to their roles and responsibilities. Another, key finding was that clinical preceptors’ prior knowledge and clinical experience were valuable assets to perform their role effectively. The clinical preceptors found their new role innovative and challenging; it was stressful at the same time. Findings also revealed that in the clinical agencies there were unclear expectations and role ambiguity. Furthermore, clinical preceptors had difficulty integrating theory into practice in the clinical area and they had difficulty in giving feedback to the students. Although this study is localized to one university, generalizations can be drawn from the results. The key findings indicate that the role of a clinical preceptor is demanding and stressful. Clinical preceptors need preparation prior to precepting students on clinicals. Also, institutional support is fundamental for their acceptance. This paper focuses on the views and experiences of clinical preceptors undertaking a newly established role and resonates with the literature. The following recommendations are drawn to strengthen the role of the clinical preceptors: A structured program for clinical preceptors is needed along with mentorship. Clinical preceptors should be provided with formal training in teaching and learning with emphasis on clinical teaching and giving feedback to students. Additionally, for improving integration of theory into practice, clinical modules should be provided ahead of the clinical. In spite of all the challenges, ten more clinical preceptors have been hired as the faculty shortage continues to persist.

Keywords: Baccalaureate nursing education, clinical education, clinical preceptors, nursing curriculum.

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Authors: M. Kobylanski, D. Buskulic, P.-H. Duron, D. Revuz, F. Ruggieri, E. Sandier, C. Tijus

Abstract:

In order to design a cooperative e-learning platform, we observed teams of Teacher [T], Computer Scientist [CS] and exerciser's programmer-designer [ED] cooperating for the conception of a self-correcting exercise, but without the use of such a device in order to catch the kind of interactions a useful platform might provide. To do so, we first run a task analysis on how T, CS and ED should be cooperating in order to achieve, at best, the task of creating and implementing self-directed, self-paced, repeatable self-correcting exercises (RSE) in the context of open educational resources. The formalization of the whole process was based on the “objectives, activities and evaluations” theory of educational task analysis. Second, using the resulting frame as a “how-to-do it” guide, we run a series of three contrasted Hackathon of RSE-production to collect data about the cooperative process that could be later used to design the collaborative e-learning platform. Third, we used two complementary methods to collect, to code and to analyze the adequate survey data: the directional flow of interaction among T-CS-ED experts holding a functional role, and the Means-End Problem Solving analysis. Fourth, we listed the set of derived recommendations useful for the design of the exerciser as a cooperative e-learning platform. Final recommendations underline the necessity of building (i) an ecosystem that allows to sustain teams of T-CS-ED experts, (ii) a data safety platform although offering accessibility and open discussion about the production of exercises with their resources and (iii) a good architecture allowing the inheritance of parts of the coding of any exercise already in the data base as well as fast implementation of new kinds of exercises along with their associated learning activities.

Keywords: Distance open educational resources, pedagogical alignment, self-correcting exercises, teacher’s involvement, team roles.

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Authors: Nhon Do, Thuong Huynh, An Pham

Abstract:

Nowadays, organizing a repository of documents and resources for learning on a special field as Information Technology (IT), together with search techniques based on domain knowledge or document-s content is an urgent need in practice of teaching, learning and researching. There have been several works related to methods of organization and search by content. However, the results are still limited and insufficient to meet user-s demand for semantic document retrieval. This paper presents a solution for the organization of a repository that supports semantic representation and processing in search. The proposed solution is a model which integrates components such as an ontology describing domain knowledge, a database of document repository, semantic representation for documents and a file system; with problems, semantic processing techniques and advanced search techniques based on measuring semantic similarity. The solution is applied to build a IT learning materials management system of a university with semantic search function serving students, teachers, and manager as well. The application has been implemented, tested at the University of Information Technology, Ho Chi Minh City, Vietnam and has achieved good results.

Keywords: document retrieval system, knowledgerepresentation, document representation, semantic search, ontology.

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