Search results for: mobile learning

Authors: Ellen A. Kalinga, R. B. Bagile Burchard, Lena Trojer

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Tanzania secondary schools in rural areas are geographically and socially isolated, hence face a number of problems in getting learning materials resulting in poor performance in National examinations. E-learning as defined to be the use of information and communication technology (ICT) for supporting the educational processes has motivated Tanzania to apply ICT in its education system. There has been effort to improve secondary school education using ICT through several projects. ICT for e-learning to Tanzania rural secondary school is one of the research projects conceived by the University of Dar-es-Salaam through its College of Engineering and Technology. The main objective of the project is to develop a tool to enable ICT support rural secondary school. The project is comprehensive with a number of components, one being development of e-learning management system (e-LMS) for Tanzania secondary schools. This paper presents strategies of developing e-LMS. It shows the importance of integrating action research methodology with the modeling methods as presented by model driven architecture (MDA) and the usefulness of Unified Modeling Language (UML) on the issue of modeling. The benefit of MDA will go along with the development based on software development life cycle (SDLC) process, from analysis and requirement phase through design and implementation stages as employed by object oriented system analysis and design approach. The paper also explains the employment of open source code reuse from open source learning platforms for the context sensitive development of the e-LMS for Tanzania secondary schools.

Keywords: Action Research Methodology, OOSA&D, MDA, UML, Open Source LMS.

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Authors: A. Otjen, S. Keller

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A Montana university has used applied consumer research in experiential learning with non-profit clients for over a decade. Through trial and error, a successful protocol has been established from problem statement through formative research to integrated marketing campaign execution. In this paper, we describe the protocol and its applications. Analysis was completed to determine the effectiveness of the campaigns and the results of how pre- and post-consumer research mark societal change because of media.

Keywords: Marketing, experiential learning, consumer behavior, community partner.

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: Image forensics, computer graphics, classification, deep learning, convolutional neural networks.

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Authors: Izaz Ahmad, Farhatullah, Shahbaz Ali, Farhad Ali, Faiza, Hazrat Junaid, Farhan Zaid

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Current trends in remote health monitoring to monetize on the Internet of Things applications have been raised in efficient and interference free communications in Wireless Body Area Network (WBAN) scenario. Co-existence interference in WBANs have aggravates the over-congested radio bands, thereby requiring efficient Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategies and improve interference management. Existing solutions utilize simplistic heuristics to approach interference problems. The scope of this research article is to investigate reinforcement learning for efficient interference management under co-existing scenarios with an emphasis on homogenous interferences. The aim of this paper is to suggest a smart CSMA/CA mechanism based on reinforcement learning called QIM-MAC that effectively uses sense slots with minimal interference. Simulation results are analyzed based on scenarios which show that the proposed approach maximized Average Network Throughput and Packet Delivery Ratio and minimized Packet Loss Ratio, Energy Consumption and Average Delay.

Keywords: WBAN, IEEE 802.15.4 Standard, CAP Super-frame, Q-Learning.

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Authors: Fiasal Assiri, Joanna Wincenciak, David Morrison-Love

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With the rapid spread of the COVID-19 pandemic, the Saudi government suspended students from going to school to combat the outbreak. As e-learning was not applied at all in schools, online teaching and learning have been revived in Saudi Arabia by providing a new platform called ‘Madrasati’. The Decomposed Theory of Planned Behaviour (DTPB) is used to examine individuals’ intention behaviour in many fields. Nevertheless, the factors that affect teachers’ continuance intention of the Madrasati platform have not yet been investigated. The purpose of this paper is to present a conceptual model in light with DTPB. To enhance the predictability of the model, the study incorporates other variables including learning content quality and interactivity as sub-factors under the perceived usefulness, students and government influences under the subjective norms, and technical support and prior e-learning experience under the perceived behavioural control. The model will be further validated using a mixed methods approach. Such findings would help administrators and stakeholders to understand teachers’ needs and develop new methods that might encourage teachers to continue using Madrasati effectively in their teaching.

Keywords: Madrasati, Decomposed Theory of Planned Behaviour, continuance intention, attitude, subjective norms, perceived behavioural control.

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Authors: Jung-Eun Park, Kyung-Whan Oh

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Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.

Keywords: Intelligent Clustering, Multi-Agent System, PCA, SOM, VC(Variance Criterion)

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: Data mining, knowledge discovery, machine learning, similarity measurement, supervised classification.

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Authors: P. S. Seboletswe, Z. Mkhize, L. M. Katata-Seru

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Protorhus longifolia is known as a medicinal plant that has been used traditionally to treat various ailments such as hemiplegic paralysis, blood clotting related diseases, diarrhoea, heartburn, etc. The study reports a High-Performance Thin Layer Chromatography (HPTLC) fingerprint profile of Protorhus longifolia methanolic extract and its qualitative analysis of gallic acid, rutin, and quercetin. HPTLC analysis was achieved using CAMAG HPTLC system equipped with CAMAG automatic TLC sampler 4, CAMAG Automatic Developing Chamber 2 (ADC2), CAMAG visualizer 2, CAMAG Thin Layer Chromatography (TLC) scanner and visionCATS CAMAG HPTLC software. Mobile phase comprising toluene, ethyl acetate, formic acid (21:15:3) was used for qualitative analysis of gallic acid and revealed eight peaks while the mobile phase containing ethyl acetate, water, glacial acetic acid, formic acid (100:26:11:11) for qualitative analysis of rutin and quercetin revealed six peaks. HPTLC sillica gel 60 F254 glass plates (10 × 10) were used as the stationary phase. Gallic acid was detected at the R_f = 0.35; while rutin and quercetin were not evident in the extract. Further studies will be performed to quantify gallic acid in Protorhus longifolia leaves and also identify other biomarkers.

Keywords: Biomarkers, fingerprint profiling, gallic acid, HPTLC, Protorhus longifolia.

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Authors: Daniele Traversaro

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Problem: Senior citizens have been facing a challenging reality as a result of strict public health measures designed to protect people from the COVID-19 outbreak. These include the risk of social isolation due to the inability of the elderly to integrate with technology. Never before have Information and Communication Technology (ICT) skills become essential for their everyday life. Although third-age ICT education and lifelong learning are widely supported by universities and governments, there is a lack of literature on which teaching strategy/methodology to adopt in an entirely online ICT course aimed at third-age learners. This contribution aims to present an application of the Think-Pair-Share (TPS) learning method in an ICT third-age virtual classroom with an intergenerational approach to conducting online group labs and review activities. Research Question: Is collaborative learning suitable and effective, in terms of student engagement and learning outcomes, in an online ICT course for the elderly? Methods: In the TPS strategy a problem is posed by the teacher, students have time to think about it individually, and then they work in pairs (or small groups) to solve the problem and share their ideas with the entire class. We performed four experiments in the ICT course of the University of the Third Age of Genova (University of Genova, Italy) on the Microsoft Teams platform. The study cohort consisted of 26 students over the age of 45. Data were collected through online questionnaires. Two have been proposed, one at the end of the first activity and another at the end of the course. They consisted of five and three close-ended questions, respectively. The answers were on a Likert scale (from 1 to 4) except two questions (which asked the number of correct answers given individually and in groups) and the field for free comments/suggestions. Results: Groups achieve better results than individual students (with scores greater than one order of magnitude) and most students found TPS helpful to work in groups and interact with their peers. Insights: From these early results, it appears that TPS is suitable for an online third-age ICT classroom and useful for promoting discussion and active learning. Despite this, our work has several limitations. First of all, the results highlight the need for more data to be able to perform a statistical analysis in order to determine the effectiveness of this methodology in terms of student engagement and learning outcomes as future direction.

Keywords: Collaborative learning, information technology education, lifelong learning, older adult education, think-pair-share.

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Authors: Sobhy Fathy A. Hashesh

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This study aimed at investigating the effect of Andalus Knowledge Phases and Times (ANPT) model of learning and the effect of 'Intel Education Contribution in ANPT' on the development of students’ academic performance and emotional quotient. The society of the study composed of Andalus Private Schools, elementary school students (N=700), while the sample of the study composed of four randomly assigned groups (N=80) with one experimental group and one control group to study "ANPT" effect and the "Intel Contribution in ANPT" effect respectively. The study followed the quantitative and qualitative approaches in collecting and analyzing data to answer the study questions. Results of the study revealed that there were significant statistical differences between students’ academic performances and emotional quotients for the favor of the experimental groups. The study recommended applying this model on different educational variables and on other age groups to generate more data leading to more educational results for the favor of students’ learning outcomes.

Keywords: ANPT, Flipped Classroom, 5Es learning Model, Kagan structures.

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.

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Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of Machine Learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. Artificial Intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and two defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt ML techniques without rigorous testing, since they may be vulnerable to adversarial attacks, especially in security-critical areas such as the nuclear industry. We observed that while the adopted defence methods can effectively defend against different attacks, none of them could protect against all five adversarial attacks entirely.

Keywords: Resilient Machine Learning, attacks, defences, nuclear industry, crack detection.

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

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The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: EIoT, machine learning, anomaly detection, environment monitoring.

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Authors: Matthias Kirmse, Uwe Petersohn, Elief Paffrath

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As in today's semiconductor industries test costs can make up to 50 percent of the total production costs, an efficient test error detection becomes more and more important. In this paper, we present a new machine learning approach to test error detection that should provide a faster recognition of test system faults as well as an improved test error recall. The key idea is to learn a classifier ensemble, detecting typical test error patterns in wafer test results immediately after finishing these tests. Since test error detection has not yet been discussed in the machine learning community, we define central problem-relevant terms and provide an analysis of important domain properties. Finally, we present comparative studies reflecting the failure detection performance of three individual classifiers and three ensemble methods based upon them. As base classifiers we chose a decision tree learner, a support vector machine and a Bayesian network, while the compared ensemble methods were simple and weighted majority vote as well as stacking. For the evaluation, we used cross validation and a specially designed practical simulation. By implementing our approach in a semiconductor test department for the observation of two products, we proofed its practical applicability.

Keywords: Ensemble methods, fault detection, machine learning, semiconductor test.

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Authors: Hager Kammoun, Jean Charles Lamirel, Mohamed Ben Ahmed

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In this paper, a model for an information retrieval system is proposed which takes into account that knowledge about documents and information need of users are dynamic. Two methods are combined, one qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build “long term" knowledge about past queries and concepts in a collection of documents. The “long term" knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure.

Keywords: Information Retrieval Systems, machine learning, classification, Galois lattices, Self Organizing Map.

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Authors: Tsuyoshi Okita, Tatsuya Niwa

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For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem.

Keywords: Formal language, statistical inference problem, reduction.

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Authors: S. Kotsiantis, E. Koumanakos, D. Tzelepis, V. Tampakas

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This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. The decision of which particular method to choose is a complicated problem. A good alternative to choosing only one method is to create a hybrid forecasting system incorporating a number of possible solution methods as components (an ensemble of classifiers). For this purpose, we have implemented a hybrid decision support system that combines the representative algorithms using a stacking variant methodology and achieves better performance than any examined simple and ensemble method. To sum up, this study indicates that the investigation of financial information can be used in the identification of FFS and underline the importance of financial ratios.

Keywords: Machine learning, stacking, classifier.

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Authors: Chunming Xu

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Sparse representation which can represent high dimensional data effectively has been successfully used in computer vision and pattern recognition problems. However, it doesn-t consider the label information of data samples. To overcome this limitation, we develop a novel dimensionality reduction algorithm namely dscriminatively regularized sparse subspace learning(DR-SSL) in this paper. The proposed DR-SSL algorithm can not only make use of the sparse representation to model the data, but also can effective employ the label information to guide the procedure of dimensionality reduction. In addition,the presented algorithm can effectively deal with the out-of-sample problem.The experiments on gene-expression data sets show that the proposed algorithm is an effective tool for dimensionality reduction and gene-expression data classification.

Keywords: sparse representation, dimensionality reduction, labelinformation, sparse subspace learning, gene-expression data classification.

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Authors: Luz Janeth Ospina M.

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The fourth industrial revolution is changing the role of education substantially and, therefore, the role of instructors and learners at all levels. Education 4.0 is an imminent response to the needs of a globalized world where humans and technology are being aligned to enable endless possibilities, among them the need for students, as digital natives, to communicate effectively in at least one language besides their mother tongue, and also the requirement of developing theirs. This is an exploratory study in which a control group (N = 21), all of the students of Spanish as a foreign language at the university level, after taking a Spanish class, responded to an online questionnaire about the engagement, atmosphere, and environment in which their course was delivered. These aspects considered in the survey were relative to the instructor’s teaching style, including: (a) active, hands-on learning; (b) flexibility for in-class activities, easily switching between small group work, individual work, and whole-class discussion; and (c) integrating technology into the classroom. Strongly believing in these principles, the instructor deliberately taught the course in a SCALE-UP room, as it could facilitate such a positive and encouraging learning environment. These aspects are trends related to Education 4.0 and have become integral to the instructor’s pedagogical stance that calls for a constructive-affective role, instead of a transmissive one. As expected, with a learning environment that (a) fosters student engagement and (b) improves student outcomes, the subjects were highly engaged, which was partially due to the learning environment. An overwhelming majority (all but one) of students agreed or strongly agreed that the atmosphere and the environment were ideal. Outcomes of this study are relevant and indicate that it is about time for teachers to build up a meaningful correlation between humans and technology. We should see the trends of Education 4.0 not as a threat but as practices that should be in the hands of critical and creative instructors whose pedagogical stance responds to the needs of the learners in the 21st century.

Keywords: Active learning, education 4.0, higher education, pedagogical stance.

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Authors: Dongwan Kang, Joohyung Oh, Chaetae Im

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Advancement of communication technologies and smart devices in the recent times is leading to changes into the integrated wired and wireless communication environments. Since early days, businesses had started introducing environments for mobile device application to their operations in order to improve productivity (efficiency) and the closed corporate environment gradually shifted to an open structure. Recently, individual user's interest in working environment using mobile devices has increased and a new corporate working environment under the concept of BYOD is drawing attention. BYOD (bring your own device) is a concept where individuals bring in and use their own devices in business activities. Through BYOD, businesses can anticipate improved productivity (efficiency) and also a reduction in the cost of purchasing devices. However, as a result of security threats caused by frequent loss and theft of personal devices and corporate data leaks due to low security, companies are reluctant about adopting BYOD system. In addition, without considerations to diverse devices and connection environments, there are limitations in detecting abnormal behaviors, such as information leaks, using the existing network-based security equipment. This study suggests a method to detect abnormal behaviors according to individual behavioral patterns, rather than the existing signature-based malicious behavior detection, and discusses applications of this method in BYOD environment.

Keywords: BYOD, Security, Anomaly Behavior Detection.

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Authors: Badr Hssina, Belaid Bouikhalene, Abdelkrim Merbouha

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This article presents our prototype MASET (Multi Agents System for E-Tutoring Learners engaged in online collaborative work). MASET that we propose is a system which basically aims to help tutors in monitoring the collaborative work of students and their various interactions. The evaluation of such interactions by the tutor is based on the results provided by the automatic analysis of the interaction indicators. This system is predicated upon the middleware JADE (Java Agent Development Framework) and e-learning Moodle platform. The MASET environment is modeled by AUML which allows structuring the different interactions between agents for the fulfillment and performance of online collaborative work. This multi-agent system has been the subject of a practical experimentation based on the interactions data between Master Computer Engineering and System students.

Keywords: AUML, Collaborative work, E-learning, E-tutoring, JADE, Moodle, SMA, Web Agent.

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Authors: S. Pancucci

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Professional development is the focus of this study. It reports on questionnaire data that examined the perceived effectiveness of the Train the Trainer model of technology professional development for elementary teachers. Eighty-three selected teachers called Information Technology Coaches received four half-day and one after-school in-service sessions. Subsequently, coaches shared the information and skills acquired during training with colleagues. Results indicated that participants felt comfortable as Information Technology Coaches and felt well prepared because of their technological professional development. Overall, participants perceived the Train the Trainer model to be effective. The outcomes of this study suggest that the use of the Train the Trainer model, a known professional development model, can be an integral and interdependent component of the newer more comprehensive learning community professional development model.

Keywords: change, education, learning community, professional development, school improvement, technology coach, Train the Trainer.

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based transformer models, which had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the website of University of California, Irvine (UCI), which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean   Absolute Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: Air quality prediction, deep learning algorithms, time series forecasting, look-back window.

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: Basketball, deep learning, feature extraction, single-camera, tracking.

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Authors: A. Harmse, R. Goede

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Students with high level skills are in demand, especially in scare skill environments. If universities wish to be successful and competitive, its students need to be adequately equipped with the necessary tools. Work Integrated Learning (WIL) is an essential component of the education of a student. The relevance of higher education should be assessed in terms of how it meets the needs of society and the world of work in a global economy. This paper demonstrates how to use Habermas's theory of communicative action to reflect on students- perceptions on their integration in the work environment to achieve social integration and financial justification. Interpretive questionnaires are used to determine the students- view of how they are integrated into society, and contributing to the economy. This paper explores the use of Habermas-s theory of communicative action to give theoretical and methodological guidance for the practice of social findings obtained in this inquiry.

Keywords: Discourse, Habermas, Information Systems Education, Theory of Communicative Action, Work Integrated Learning.

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Authors: Dinesh Kumar, C.S. Rai, Shakti Kumar

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Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.

Keywords: Face Recognition, Principal Component Analysis, Self Organizing Maps, Independent Component Analysis

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Authors: Chaiwat Waree

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The purposes of the research were to develop online lessons to strengthen the learning process of Master's degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University; to achieve the efficiency criteria of 80/80; and to study the satisfaction of students who use online lessons to strengthen the learning process of Master’s degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University. The sample consisted of 40 university students studying in semester 1, academic year 2012. The sample was determined by Purposive Sampling. Selected students were from the class which the researcher was the homeroom tutor. The tutor was responsible for the teaching of learning process. Tools used in the study were online lessons, 60-point performance test, and evaluation test of satisfaction of students on online lessons. Data analysis yielded the following results; 83.66/88.29 efficiency of online lessons measured against the criteria; the comparison of performance before and after taking online lessons using t-test yielded 29.67. The statistical significance was at 0.05; the average satisfaction level of forty students on online lessons was 4.46 with standard deviation of 0.68.

Keywords: Online Lessons, Curriculum and Instruction.

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Authors: Christina Van Wingerden

Abstract:

The aim of this paper is to propose pedagogical design for learning management systems (LMS) that offers greater inclusion for students based on a number of theoretical perspectives and delineated through an example. Considering the impact of COVID-19, including on student mental health, the research suggesting the importance of student sense of belonging on retention, success, and student well-being, the author describes intentional LMS design incorporating theoretically based practices informed by critical theory, feminist theory, indigenous theory and practices, and new materiality. This article considers important aspects of these theories and practices which attend to inclusion, identities, and socially just learning environments. Additionally, increasing student sense of belonging and mental health through LMS design influenced by adult learning theory and the community of inquiry model are described.  The process of thinking through LMS pedagogical design with inclusion intentionally in mind affords the opportunity to allow LMS to go beyond course use as a repository of documents, to an intentional community of practice that facilitates belonging and connection, something much needed in our times. In virtual learning environments it has been harder to discern how students are doing, especially in feeling connected to their courses, their faculty, and their student peers. Increasingly at the forefront of public universities is addressing the needs of students with multiple and intersecting identities and the multiplicity of needs and accommodations. Education in 2020, and moving forward, calls for embedding critical theories and inclusive ideals and pedagogies to the ways instructors design and teach in online platforms. Through utilization of critical theoretical frameworks and instructional practices, students may experience the LMS as a welcoming place with intentional plans for welcoming diversity in identities.

Keywords: Belonging, critical pedagogy, instructional design, Learning Management System, LMS.

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Authors: M. R. Aghaebrahimi, S. H. Zahiri, M. Amiri

Abstract:

The necessity of solving multi dimensional complicated scientific problems beside the necessity of several objective functions optimization are the most motive reason of born of artificial intelligence and heuristic methods. In this paper, we introduce a new method for multiobjective optimization based on learning automata. In the proposed method, search space divides into separate hyper-cubes and each cube is considered as an action. After gathering of all objective functions with separate weights, the cumulative function is considered as the fitness function. By the application of all the cubes to the cumulative function, we calculate the amount of amplification of each action and the algorithm continues its way to find the best solutions. In this Method, a lateral memory is used to gather the significant points of each iteration of the algorithm. Finally, by considering the domination factor, pareto front is estimated. Results of several experiments show the effectiveness of this method in comparison with genetic algorithm based method.

Keywords: Function optimization, Multiobjective optimization, Learning automata.

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Authors: Syed Sultan Ahmed

Abstract:

The current education system in India is adept in equipping and assessing the scholastic development of children. However, there is an immediate need to strengthen co-scholastic areas like life-skills, values and attitudes to equip students to face real life challenges. Audio-visual technology and their respective media can make a significant contribution to a value based learning curriculum. Thus, co-scholastic skills need to be effectively nurtured by a medium that is entertaining and impactful. Films in general have a tremendous impact in our society. Films with a positive message make a formidable learning experience that can influence and inspire generations of learners. Leveraging on this powerful medium, EduMedia India Pvt. Ltd. has introduced School Cinema a well researched film-based learning module supported by a fun and exciting workbook, designed to introduce and reaffirm life-skills and values to children, thereby having a positive influence on their attitudes.

Keywords: Co-Scholastics, Entertaining, Educative, Holistic- Development

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