Search results for: Learning Management System

Authors: Doseong Eom, Jeongmin Kim, Kwang Ryel Ryu

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Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.

Keywords: building energy management, machine learning, operation planning, simulation-based optimization

Procedia PDF Downloads 316

Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

Procedia PDF Downloads 257

Authors: Chevon Fernando, Banuka Athuraliya

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The purpose of this research is to provide a solution to the students’ time management, which usually becomes an issue because students must study and manage their personal commitments. "TimeTune," an AI-based study planner that provides an opportunity to maneuver study timeframes by incorporating modern machine learning algorithms with calendar applications, is unveiled as the ideal solution. The research is focused on the development of LSTM models that connect to the Google Calendar API in the process of developing learning paths that would be fit for a unique student's daily life experience and study history. A key finding of this research is the success in building the LSTM model to predict optimal study times, which, integrating with the real-time data of Google Calendar, will generate the timetables automatically in a personalized and customized manner. The methodology encompasses Agile development practices and Object-Oriented Analysis and Design (OOAD) principles, focusing on user-centric design and iterative development. By adopting this method, students can significantly reduce the tension associated with poor study habits and time management. In conclusion, "TimeTune" displays an advanced step in personalized education technology. The fact that its application of ML algorithms and calendar integration is quite innovative is slowly and steadily revolutionizing the lives of students. The excellence of maintaining a balanced academic and personal life is stress reduction, which the applications promise to provide for students when it comes to managing their studies.

Keywords: personalized learning, study planner, time management, calendar integration

Procedia PDF Downloads 36

Authors: Lucian Pîslaru-Dănescu, George-Claudiu Zărnescu, Laurențiu Constantin Lipan, Rareș-Andrei Chihaia

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In recent years, the utilization of supercapacitors for energy storage (ES) devices that are designed for energy harvesting (EH) applications has increased substantially. The use of supercapacitors as energy storage devices in hybrid energy harvesting systems allows the miniaturization of electronic structures for energy storage. This study is concerned with the concept of energy management capacitors – supercapacitors and the new electronic structures for energy storage used for energy harvesting devices. Supercapacitors are low-voltage devices, and electronic overvoltage protection is needed for powering the source. The power management device that uses these proposed new electronic structures for energy storage is better than conventional electronic structures used for this purpose, like rechargeable batteries, supercapacitors, and hybrid systems. A hybrid energy harvesting system with energy storage management is able to simultaneously use several energy sources with recovery from the environment. The power management device uses a summing electronic block to combine the electric power obtained from piezoelectric composite plates and from a photovoltaic conversion system. Also, an overvoltage protection circuit used as a voltage detector and an improved concept of charging supercapacitors is presented. The piezoelectric composite plates are realized only by pressing two printed circuit boards together without damaging or prestressing the piezoceramic elements. The photovoltaic conversion system has the advantage that the modules are covered with glass plates with nanostructured film of ZnO with the role of anti-reflective coating and to improve the overall efficiency of the solar panels.

Keywords: supercapacitors, energy storage, electronic overvoltage protection, energy harvesting

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Authors: Horng-Jyh Chen, Yi-Fang Chen, Chien-Liang Lin

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In this paper, the authors try to investigate the determinants of behavioral intention of the blended e-learning course for senior students at the Active Ageing University in Taiwan. Due to lower proficiency in the use of computers and less experience on learning styles of the blended e-learning course for senior students will be expected quite different from those for most young students. After more than five weeks course for two years the questionnaire survey is executed to collect data for statistical analysis in order to understand the determinants of the behavioral intention for senior students. The object of this study is at one of the Active Ageing University in Taiwan total of 84 senior students in the blended e-learning for the ceramics teaching course. The research results show that only the perceived usefulness of the blended e-learning course has significant positive relationship with the behavioral intention.

Keywords: Active Aging University, blended e-learning, ceramics teaching course, behavioral intention

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Authors: Ehi E. Aimiuwu

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As of 2017, many researchers in educational journals are still wondering if students are effectively and efficiently engaged in active learning in the online learning environment. The goal of this qualitative single case study and narrative research is to explore if students are actively engaged in their online learning. Seven online students in the United States from LinkedIn and residencies were interviewed for this study. Eleven online learning techniques from research were used as a framework.  Data collection tools were used for the study that included a digital audiotape, observation sheet, interview protocol, transcription, and NVivo 12 Plus qualitative software.  Data analysis process, member checking, and key themes were used to reach saturation. About 85.7% of students preferred individual grading. About 71.4% of students valued professor’s interacting 2-3 times weekly, participating through posts and responses, having good internet access, and using email.  Also, about 57.1% said students log in 2-3 times weekly to daily, professor’s social presence helps, regular punctuality in work submission, and prefer assessments style of research, essay, and case study.  About 42.9% appreciated syllabus usefulness and professor’s expertise.

Keywords: class facilitation, course management, online teaching, online education, student engagement

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Authors: Thi Chang Duyen Can

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Althoughthe COVID-19 epidemic is under control, online education technology in Vietnam will still thrive in the learner-centered trend. Most of the Vietnamese students are now ready to familiarize themselves with and access to online learning. Even in some cases, online learning, if combined with new tools, is far more effective and exciting for students than some traditional instruction. However, little research has been conducted to explore Vietnamese teachers’ difficulties in moderating learner-centered E-learning. Therefore, the study employed the mixed method (n=9) to (i) uncover the challenges faced by Vietnamese teachers in English language online classes using learner-centred approach and (ii) propose the recommendations to improve the quality of online training in universities.

Keywords: learner-centered e-learning, english language classes, teachers' challenges, online learning

Procedia PDF Downloads 75

Authors: Hamed Hossein Afshari, Shahrzad Jalali, Amir Hossein Ghods, Bijan Raahemi

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This paper introduces an automated clustering solution that applies to WiFi/Bluetooth sensing data and is later used for traffic management applications. The paper initially summarizes a number of clustering approaches and thereafter shows their performance for noise removal. In this context, clustering is used to recognize WiFi and Bluetooth MAC addresses that belong to passengers traveling by a public urban transit bus. The main objective is to build an intelligent system that automatically filters out MAC addresses that belong to persons located outside the bus for different routes in the city of Ottawa. The proposed intelligent system alleviates the need for defining restrictive thresholds that however reduces the accuracy as well as the range of applicability of the solution for different routes. This paper moreover discusses the performance benefits of the presented clustering approaches in terms of the accuracy, time and space complexity, and the ease of use. Note that results of clustering can further be used for the purpose of the origin-destination estimation of individual passengers, predicting the traffic load, and intelligent management of urban bus schedules.

Keywords: WiFi-Bluetooth sensing, cluster analysis, artificial intelligence, traffic management

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

Procedia PDF Downloads 229

Authors: Raminder Kaur

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This paper considers the emerging area of integrating Virtual Reality (VR) technologies into the teaching of Visual Anthropology, Research Methods, and the Anthropology of Contemporary India in the University of Sussex. If deployed in a critical and self-reflexive manner, there are several advantages to VR-based immersive learning: (i) Based on data available for British schools, it has been noted that ‘Learning through experience can boost knowledge retention by up to 75%’. (ii) It can tutor students to learn with and from virtual worlds, devising new collaborative methods where suited. (iii) It can foster inclusive learning by aiding students with SEN and disabilities who may not be able to explore such areas in the physical world. (iv) It can inspire and instill confidence in students with anxieties about approaching new subjects, realms, or regions. (v) It augments our provision of ‘smart classrooms’ synchronised to the kinds of emerging immersive learning environments that students come from in schools.

Keywords: virtual reality, anthropology, immersive learning, university

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Authors: Glenn A. Palmer, Lorenzo Bowman, Juanita Johnson-Bailey

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The ubiquitous economic upheaval that has gripped the global environment in the past few years displaced many workers through unemployment or underemployment. Globally, this disruption has caused many adult workers to seek additional education or skills to remain competitive, and acquire the ability and options to find gainful employment. While many learners have availed themselves of some opportunities to be retrained and retooled at locations within their communities, others have explored those options through the online learning environment. This paper examines the empirical research that explores the various strategies that are used in the adult online learning community that could also foster transformative learning.

Keywords: online learning, transformational learning, adult education, economic crisis, unemployment

Procedia PDF Downloads 455

Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting, and tracking of physical items in combination with Internet of Things developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm-based management and decision-making does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyse on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue, and environmental impact. Evolutionary computing can be very effective in finding the optimal combination of sets of some objects and, finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and evolutionary computing in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management, and its uptake has become a continuing trend.

Keywords: big data, evolutionary computing, cloud, precision technologies

Procedia PDF Downloads 184

Authors: Jacky Liu

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This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

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Authors: Farhaz Amyn Rajabali, Collins Odour

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Mathematics has been there for over 4000 years and has been one of the very first educational topics explored by human civilization. Throughout the years, it has become a complex study and has derived so many other subjects. With advancements in ICT, most of the computation in mathematics is done using powerful computers. In many different countries, the children in primary and secondary schools face difficulties in learning mathematics, and this has many reasons behind it, one being the students don’t engage much with the mathematical concepts hence failing to understand them deeply. The objective of this system is to help the students understand this mathematical concept interactively, which in return will encourage the love for learning and increase thorough understanding of many concepts. Research was conducted among a group of samples and about 50% of respondents replied that they had never used an augmented reality application before. This means that the chances for this system to be accepted in the market are high due to its innovative idea. Around 60% of people did recommend the use of this system to learn mathematics. The study also showed several challenges in an educational system, including but not limited to lack of resources which was chosen by 30% of respondents, the challenge to read from textbooks (34.6%) and how hard it is to visualize concepts (46.2%). The survey question asked what benefits the users see using augmented reality to learn mathematics. The responses that were picked the most were increased student engagement and using real-world examples to understand concepts, both being 65.4% and followed by easy access to learning material at 61.5%, and increased knowledge retention at 50%. This shows that there are plenty of issues with an education system that can be addressed by software applications; now that the newer generation is so enthusiastic about electronic devices, it can actually be used to deliver good knowledge and skills to the upcoming students and mitigate most of the challenges faced currently. The study concludes that the implementation of the system is a best practice for the educational system especially leveraging a new technology that has the ability to attract the attention of many young students and use it to deliver information. It will also give rise to awareness of new technology and on multiple ways it can be implemented. Addressing the educational sector in developing countries using information technology is an imperative task since these kids studying now is the future of the country and will use what they learn and understand during their childhood will help them to make decisions about their lives in the future which will not only affect them personally but also affect the whole society in general.

Keywords: AR, mathematics, system development, augmented reality

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Authors: Chaima Babi, Said Gadri

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The rapid increase in fake news continues to grow at a very fast rate; this requires implementing efficient techniques that allow testing the re-liability of online content. For that, the current research strives to illuminate the fake news problem using deep learning DL and machine learning ML ap-proaches. We have developed the traditional LSTM (Long short-term memory), and the bidirectional BiLSTM model. A such process is to perform a training task on almost of samples of the dataset, validate the model on a subset called the test set to provide an unbiased evaluation of the final model fit on the training dataset, then compute the accuracy of detecting classifica-tion and comparing the results. For the programming stage, we used Tensor-Flow and Keras libraries on Python to support Graphical Processing Units (GPUs) that are being used for developing deep learning applications.

Keywords: machine learning, deep learning, natural language, fake news, Bi-LSTM, LSTM, multiclass classification

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Authors: Patrick Oehler, Nicholas Folger

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Following a new public management approach, Germany has widely reformed its higher education system around the turn of the millennium. Aimed at preparing the country’s publicly funded universities and applied science colleges for a century of glory, the reforms led to the introduction of rigid performance measurement and management practices, which disrupted the inert system on all levels. Yet, many of the new policies met significant resistance, and some of them had to be reversed over time. Ever since Germany has struggled to find a balance between its pre- and its post-millennial approach to performance measurement and management. This contribution combines insights of a joint research project, which was created and funded by the German Federal Ministry of Education and Research with the aim to better understand the effects of its performance measurement and management policies, including those the ministry had implemented over the previous decades. The research project combines researchers from 17 German research institutions who employed a wide range of theories from various disciplines and very diverse research methods to explain performance measurement and management and their consequences on the behavior of various stakeholders in higher education systems. In these projects, performance measurement and management have been researched from three angles—education, research, and third mission. The collaborative project differentiated functional and dysfunctional elements of common performance measurement and management practices, and identified key problems with these practices, such as (1) oversimplification of performance indicators, (2) ‘overmeasurement’ of performance in general, (3) excessive use of quantitative indicators, and (4), a myopic focus on research-focused indicators and a negligence of measures targeting education and third mission. To address these issues, the collaborative project developed alternative approaches to performance measurement and management, including suggestions for qualitative performance measures, improved supervision, review, and evaluations methods, and recommendations how to better balance education, research, and third mission. The authors would like to share the rich findings of the joint research project with an international audience and discuss their implications for alternative higher education systems.

Keywords: performance measurement, performance management, new public management, performance evaluation

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Nadjat Zerf Boudjettou, Fahima Nader, Rachid Chalal

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Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system.

Keywords: data warehouse, data mining, knowledge discovery in database, KDD, medical knowledge management, Bayesian networks

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Tom O'Mahony

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This article considers the design of a student-centered learning environment in engineering education. The learning environment integrates a number of components, including project-based learning, collaborative learning, two-stage assignments, active learning lectures, and a flipped-classroom. Together these elements place the individual learner and their learning at the center of the environment by focusing on understanding, enhancing relevance, applying learning, obtaining rich feedback, making choices, and taking responsibility. The evolution of this environment from 2014 to the present day is outlined. The impact of this environment on learners and their learning is evaluated via student questionnaires that consist of both open and closed-ended questions. The closed questions indicate that students found the learning environment to be really interesting and enjoyable (rated as 4.7 on a 5 point scale) and encouraged students to adopt a deep approach towards studying the course materials (rated as 4.0 on a 5 point scale). A content analysis of the open-ended questions provides evidence that the project, active learning lectures, and flipped classroom all contribute to the success of this environment. Furthermore, this analysis indicates that the two-stage assessment process, in which feedback is provided between a draft and final assignment, is the key component and the dominant theme. A limitation of the study is the small class size (less than 20 learners per year), but, to some degree, this is compensated for by the longitudinal nature of the study.

Keywords: deep approaches, formative assessment, project-based learning, student-centered learning

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Authors: Benhenni Abdellaziz

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Irrigation is a limiting factor for agricultural production and socioeconomic development of many countries in the arid and semi-arid world. However, the sustainability of irrigation systems requires rational management of the water resource, which is becoming increasingly rare in these regions. The objective of this work is to apply a geographic information system (GIS) coupled with a model for calculating crop water requirements (CROPWATER) for the management of irrigation water in irrigated areas and offer managers an effective tool to better manage water resources in these areas. The application area of GIS is the irrigated perimeter of Western Middle Cheliff, which is located in a semi-arid region (Middle Cheliff). The scope in question is considerable agrarian dynamics and an increased need for irrigation of most crops.

Keywords: GIS, CROPWAT, irrigation, water management, middle cheliff

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Authors: Ivy Rose Mathew

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The purpose of this study is to outline the efficacy of technology and the opportunities it can bring to implement a successful delivery model in Distance Learning. Distance Learning has proliferated over the past few years across the world. Some of the current challenges faced by current students of distance education include lack of motivation, a sense of isolation and a need for greater and improved communication. Hence the author proposes a creative technology supported model for distance learning exactly mirrored on the traditional face to face learning that can be adopted by distance learning providers. This model suggests the usage of a range of technologies and social networking facilities, with the aim of creating a more engaging and sustaining learning environment to help overcome the isolation often noted by distance learners. While discussing the possibilities, the author also highlights the complexity and practical challenges of implementing such a model. Design/methodology/approach: Theoretical issues from previous research related to successful models for distance learning providers will be considered. And also the analysis of a case study from one of the largest private tertiary institution in Botswana, Botho University will be included. This case study illustrates important aspects of the distance learning delivery model and provides insights on how curriculum development is planned, quality assurance is done, and learner support is assured for successful distance learning experience. Research limitations/implications: While some of the aspects of this study may not be applicable to other contexts, a number of new providers of distance learning can adapt the key principles of this delivery model.

Keywords: distance learning, efficacy, learning experience, technology supported model

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Authors: Ajda Osifo, Fatma Elshafie

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Mobile-assisted language learning favors social-constructivist and connectivist theories to learning and adaptive approaches to teaching. It offers many opportunities to differentiated instruction in meaningful ways as it enables learners to become more collaborative, engaged and independent through additional dimensions such as web-based media, virtual learning environments, online publishing to an imagined audience and digitally mediated communication. MALL applications can be a tool for the teacher to personalize and adjust instruction according to the learners’ needs and give continuous feedback to improve learning and performance in the process, which support differentiated instruction practices. This paper explores the utilization of Mobile Assisted Language Learning applications as a supporting tool for effective differentiation in the language classroom. It reports overall experience in terms of implementing MALL to shape and apply differentiated instruction and expand learning options. This session is structured in three main parts: first, a review of literature and effective practice of academically responsive instruction will be discussed. Second, samples of differentiated tasks, activities, projects and learner work will be demonstrated with relevant learning outcomes and learners’ survey results. Finally, project findings and conclusions will be given.

Keywords: academically responsive instruction, differentiation, mobile learning, mobile-assisted language learning

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Authors: K. C. Wong

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This paper demonstrates a number of examples in the hope to shed some light on the possibility of designing future operating systems in a more adaptation-based manner. A modern operating system, we conceive, should possess the capability of 'learning' in such a way that it can dynamically adjust its services and behavior according to the current status of the environment in which it operates. In other words, a modern operating system should play a more proactive role during the session of providing system services to users. As such, a modern operating system is expected to create a computing environment, in which its users are provided with system services more matching their dynamically changing needs. The examples demonstrated in this paper show that user's resource access patterns 'learned' and determined during a session can be utilized to improve system performance and hence to provide users with a better and more effective computing environment. The paper also discusses how to use the frequency, the continuity, and the duration of resource accesses in a session to quantitatively measure and determine user's resource access patterns for the examples shown in the paper.

Keywords: adaptation-based systems, operating systems, resource access patterns, system performance

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Authors: Eulalia Torras, Andreu Bellot

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To know whether students’ achievements are the result of online interaction and not just a consequence of individual differences themselves, it seems essential to link the teaching presence and social presence to the types of knowledge built. The research aim is to analyze the social presence in relation to two types of knowledge, declarative and procedural. Qualitative methodology has been used. The analysis of the contents was based on an observation protocol that included community of enquiry indicators and procedural and declarative knowledge indicators. The research has been conducted in three phases that focused on an observational protocol and indicators, results and conclusions. Results show that the teaching-learning processes have been characterized by the patterns of presence and types of knowledge. Results also show the importance of social presence support provided by the teacher and the students, not only in regard to the nature of the instructional support but also concerning how it is presented to the student and the importance that is attributed to it in the teaching-learning process, that is, what it is that assistance is offered on. In this study, we find that the presence based on procedural guidelines and declarative reflection, the management of shared meaning on the basis of the skills and the evidence of these skills entail patterns of learning. Nevertheless, the importance that the teacher attributes to each support aspect has a bearing on the extent to which the students reflect more on the given task.

Keywords: education, online, teaching and learning processes, knowledge

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Authors: Badri Gechbaia

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Adaptive management as a fundamental element of the concept of the assurance of economy`s sustainability to the economic security of the system-synergetic type has been considered. It has been proved that the adaptive sustainable development is a transitional phase from the extensive one and later on from the rapid growth to the sustainable development. It has been determined that the adaptive system of the strategic assurance of the sustainability of the economy to the economic security threats is formed on the principles of the domination in its complex of the subsystems with weightier adaptive characteristics that negate the destructive influence of external and internal environmental factors on the sustainability of the national economy.

Keywords: adaptive management, adaptive properties, economic security, strategic assurance

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Authors: Raffaele Danilo Esposito, Domingo Planes Meseguer, Maria Del Pilar Dorado Rodriguez

Abstract:

As far as we know, it is still unavailable a commercial solution which would allow to manage, openly and configurable up to user needs, the huge amount of data generated in a modern Radiation Oncology Department. Currently, available information management systems are mainly focused on Record & Verify and clinical data, and only to a small extent on physical data. Thus, results in a partial and limited use of the actually available information. In the present work we describe the implementation at our department of a centralized information management system based on a web server. Our system manages both information generated during patient planning and treatment, and information of general interest for the whole department (i.e. treatment protocols, quality assurance protocols etc.). Our objective it to be able to analyze in a simple and efficient way all the available data and thus to obtain quantitative evaluations of our treatments. This would allow us to improve our work flow and protocols. To this end we have implemented a relational data base which would allow us to use in a practical and efficient way all the available information. As always we only use license free software.

Keywords: information management system, radiation oncology, medical physics, free software

Procedia PDF Downloads 229

Authors: Joseph Osodo, Motsa Thobekani Phila

Abstract:

The study investigated the impact of e-learning on the performance of history learners in Eswatini general certificate of secondary education in the Manzini region of Eswatini. The study was guided by the theory of connectivism. The study had three objectives which were to find out the significance of e-learning during the COVID-19 era in learning History subject; challenges faced by history teachers’ and learners’ in e-learning; and how the challenges were mitigated. The study used a qualitative research approach and descriptive research design. Purposive sampling was used to select eight History teachers and eight History learners from four secondary schools in the Manzini region. Data were collected using face to face interviews. The collected data were analyzed and presented in thematically. The findings showed that history teachers had good knowledge on what e-learning was, while students had little understanding of e-learning. Some of the forms of e-learning that were used during the pandemic in teaching history in secondary schools included TV, radio, computer, projectors, and social media especially WhatsApp. E-learning enabled the continuity of teaching and learning of history subject. The use of e-learning through the social media was more convenient to the teacher and the learners. It was concluded that in some secondary school in the Manzini region, history teacher and learners encountered challenges such as lack of finances to purchase e-learning gadgets and data bundles, lack of skills as well as access to the Internet. It was recommended that History teachers should create more time to offer additional learning support to students whose performance was affected by the COVID-19 pandemic effects.

Keywords: e-learning, performance, COVID-19, history, connectivism

Procedia PDF Downloads 68