Search results for: task based learning.

Authors: Kandarpa Kumar Sarma

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The key to the continued success of ANN depends, considerably, on the use of hybrid structures implemented on cooperative frame-works. Hybrid architectures provide the ability to the ANN to validate heterogeneous learning paradigms. This work describes the implementation of a set of Distributed and Hybrid ANN models for Character Recognition applied to Anglo-Assamese scripts. The objective is to describe the effectiveness of Hybrid ANN setups as innovative means of neural learning for an application like multilingual handwritten character and numeral recognition.

Keywords: Assamese, Feature, Recurrent.

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Authors: Olugbade Damola, Adekomi Adebimbo, Sofowora Olaniyi Alaba

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One of the major problems facing education in Nigeria is the provision of quality Science and Technology education. Inadequate teaching facilities, non-usage of innovative teaching strategies, ineffective classroom management, lack of students’ motivation and poor integration of ICT has resulted in the increase in percentage of students who failed Basic Science and Technology in Junior Secondary Certification Examination for National Examination Council in Nigeria. To address these challenges, the Federal Government came up with a road map on education. This was with a view of enhancing quality education through integration of modern technology into teaching and learning, enhancing quality assurance through proper monitoring and introduction of innovative methods of teaching. This led the researcher to investigate how MOODLE LMS could be used to enhance students’ learning outcomes in BST. A sample of 120 students was purposively selected from four secondary schools in Ogbomoso. The experimental group was taught using MOODLE LMS, while the control group was taught using the conventional method. Data obtained were analyzed using mean, standard deviation and t-test. The result showed that MOODLE LMS was an effective learning platform in teaching BST in junior secondary schools (t=4.953, P<0.05). Students’ attitudes towards BST was also enhanced through MOODLE LMS (t=15.632, P<0.05). The use of MOODLE LMS significantly enhanced students’ retention (t=6.640, P<0.05). In conclusion, the Federal Government efforts at enhancing quality assurance through integration of modern technology and e-learning in Secondary schools proved to have yielded good result has students found MOODLE LMS to be motivating and interactive. Attendance was improved.

Keywords: MOODLE, learning management system, quality assurance, basic science and technology.

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Authors: Salah Algabli

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As online learning takes center stage, Arabic language instructors face the imperative to adapt their practices for the digital realm. This study investigates the experiences of online Arabic instructors to unveil the pedagogical opportunities and challenges this format presents. Utilizing a transcendental phenomenological approach with 15 diverse participants, the research shines a light on the unique realities of online language teaching at the university level, specifically in the United States. The study proposes theoretical and practical solutions to maximize the benefits of online language learning while mitigating its challenges. Recommendations cater to instructors, researchers, and program coordinators, paving the way for enhancing the quality of online Arabic language education. The findings highlight the need for pedagogical approaches tailored to the online environment, ultimately shaping a future where both instructors and learners thrive in this digital landscape.

Keywords: Online Arabic language learning, pedagogical opportunities and challenges, online Arabic teachers, online language instruction, digital pedagogy.

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Authors: J. Barbarán, M. Díaz, I. Esteve, D. Garrido, L. Llopis, B. Rubio

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Wireless Sensor and Actor Networks (WSANs) constitute an emerging and pervasive technology that is attracting increasing interest in the research community for a wide range of applications. WSANs have two important requirements: coordination interactions and real-time communication to perform correct and timely actions. This paper introduces a methodology to facilitate the task of the application programmer focusing on the coordination and real-time requirements of WSANs. The methodology proposed in this model uses a real-time component model, UM-RTCOM, which will help us to achieve the design and implementation of applications in WSAN by using the component oriented paradigm. This will help us to develop software components which offer some very interesting features, such as reusability and adaptability which are very suitable for WSANs as they are very dynamic environments with rapidly changing conditions. In addition, a high-level coordination model based on tuple channels (TC-WSAN) is integrated into the methodology by providing a component-based specification of this model in UM-RTCOM; this will allow us to satisfy both sensor-actor and actor-actor coordination requirements in WSANs. Finally, we present in this paper the design and implementation of an application which will help us to show how the methodology can be easily used in order to achieve the development of WSANs applications.

Keywords: Sensor networks, real time and embedded systems.

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Authors: Faisal Kalota

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Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: Analytics, Big Data in Education, Hadoop, Learning Analytics.

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Authors: Arun Prasad Jaganathan

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In today's rapidly evolving technological landscape, the need for efficient and proactive maintenance management solutions has become increasingly evident across various industries. Traditional approaches often suffer from drawbacks such as reactive strategies, leading to potential downtime, increased costs, and decreased operational efficiency. In response to these challenges, this paper proposes an AI-enabled approach to object-based maintenance management aimed at enhancing reliability and efficiency. The paper contributes to the growing body of research on AI-driven maintenance management systems, highlighting the transformative impact of intelligent technologies on enhancing object reliability and operational efficiency.

Keywords: AI, machine learning, predictive maintenance, object-based maintenance, expert team scheduling.

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Authors: Hikmat A. M. Abd-El-Jaber, Tengku M. T. Sembok

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The information on the Web increases tremendously. A number of search engines have been developed for searching Web information and retrieving relevant documents that satisfy the inquirers needs. Search engines provide inquirers irrelevant documents among search results, since the search is text-based rather than semantic-based. Information retrieval research area has presented a number of approaches and methodologies such as profiling, feedback, query modification, human-computer interaction, etc for improving search results. Moreover, information retrieval has employed artificial intelligence techniques and strategies such as machine learning heuristics, tuning mechanisms, user and system vocabularies, logical theory, etc for capturing user's preferences and using them for guiding the search based on the semantic analysis rather than syntactic analysis. Although a valuable improvement has been recorded on search results, the survey has shown that still search engines users are not really satisfied with their search results. Using ontologies for semantic-based searching is likely the key solution. Adopting profiling approach and using ontology base characteristics, this work proposes a strategy for finding the exact meaning of the query terms in order to retrieve relevant information according to user needs. The evaluation of conducted experiments has shown the effectiveness of the suggested methodology and conclusion is presented.

Keywords: information retrieval, user profiles, semantic Web, ontology, search engine.

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: Recognition, CNN, convolutional neural network, Yi character, divergence.

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Authors: Elizabeth B. Varghese, M. Wilscy

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A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.

Keywords: Face Recognition, Vector Quantization, Integrated Adaptive Fuzzy Clustering, Self Organization Map.

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Authors: Nagore Guerra Bilbao, Clemente Lobato Fraile

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Mentoring is provided by professionals with a higher level of experience and competence as part of the professional development of a university faculty. This paper explores the characteristics of the mentoring provided by those teachers participating in the development of an active methodology program run at the University of the Basque Country: to examine and to analyze mentors’ performance with the aim of providing empirical evidence regarding its value as a lifelong learning strategy for teaching staff. A total of 183 teachers were trained during the first three programs. The analysis method uses a coding technique and is based on flexible, systematic guidelines for gathering and analyzing qualitative data. The results have confirmed the conception of mentoring as a methodological innovation in higher education. In short, university teachers in general assessed the mentoring they received positively, considering it to be a valid, useful strategy in their professional development. They highlighted the methodological expertise of their mentor and underscored how they monitored the learning process of the active method and provided guidance and advice when necessary. Finally, they also drew attention to traits such as availability, personal commitment and flexibility in. However, a minority critique is pointed to some aspects of the performance of some mentors.

Keywords: Higher education, Mentoring, Professional development, University teachers.

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Authors: H. Nemmour, Y. Chibani

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Decision fusion is one of hot research topics in classification area, which aims to achieve the best possible performance for the task at hand. In this paper, we investigate the usefulness of this concept to improve change detection accuracy in remote sensing. Thereby, outputs of two fuzzy change detectors based respectively on simultaneous and comparative analysis of multitemporal data are fused by using fuzzy integral operators. This method fuses the objective evidences produced by the change detectors with respect to fuzzy measures that express the difference of performance between them. The proposed fusion framework is evaluated in comparison with some ordinary fuzzy aggregation operators. Experiments carried out on two SPOT images showed that the fuzzy integral was the best performing. It improves the change detection accuracy while attempting to equalize the accuracy rate in both change and no change classes.

Keywords: change detection, decision fusion, fuzzy logic, remote sensing.

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Authors: Ye. V. Lyubun

Abstract:

The problem of agricultural-soil pollution is closely linked to the production of ecologically pure foodstuffs and to human health. An important task, therefore, is to rehabilitate agricultural soils with the help of state-of-the-art biotechnologies, based on the use of metal-accumulating plants. In this work, on the basis of literature data and the results of prior research from this laboratory, plants were selected for which the growing technology is well developed and which are widespread locally: sugar sorghum (Sorghum saccharatum), sudangrass (Sorghum sudanense (Piper.) Stapf.), and sunflower (Helianthus annuus L.). I report on laboratory experiments designed to study the influence of synthetic indole-3- acetic acid and the extracellular indole-3-acetic acid released by the plant-growth-promoting rhizobacterium Azospirillum brasilense Sp245 on growth of and arsenic accumulation by these plants.

Keywords: Arsenic, bioaccumulation, plant-growth-promoting rhizobacteria, phytohormones.

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Authors: Ahmad Ghayoumi, Mehdi Ghayoumi

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Intelligent schools are those which use IT devices and technologies as media software, hardware and networks to improve learning process. On the other hand management improvement is best described as the process from which managers learn and improve their skills not only to benefit themselves but also their employing organizations Here, we present a model Management improvement System that has been applied on some schools and have made strict improvement.

Keywords: Intelligent school, Management development system, Learning station, Teaching station

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Authors: Alicia Y. C. Tang, Rukaini Hj. Abdullah, Sharifuddin M. Zain

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This paper discusses the development of a qualitative simulator (abbreviated QRiOM) for predicting the behaviour of organic chemical reactions. The simulation technique is based on the qualitative process theory (QPT) ontology. The modelling constructs of QPT embody notions of causality which can be used to explain the behaviour of a chemical system. The major theme of this work is that, in a qualitative simulation environment, students are able to articulate his/her knowledge through the inspection of explanations generated by software. The implementation languages are Java and Prolog. The software produces explanation in various forms that stresses on the causal theories in the chemical system which can be effectively used to support learning.

Keywords: Chemical reactions, explanation, qualitative processtheory, simulation

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Omer Cahana, Maya Herman, Ofer Levi

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: Accelerate MRI scans, image reconstruction, pyramid network, deep learning.

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Authors: Mario Kubek, Herwig Unger

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Due to the large amount of information in the World Wide Web (WWW, web) and the lengthy and usually linearly ordered result lists of web search engines that do not indicate semantic relationships between their entries, the search for topically similar and related documents can become a tedious task. Especially, the process of formulating queries with proper terms representing specific information needs requires much effort from the user. This problem gets even bigger when the user's knowledge on a subject and its technical terms is not sufficient enough to do so. This article presents the new and interactive search application DocAnalyser that addresses this problem by enabling users to find similar and related web documents based on automatic query formulation and state-ofthe- art search word extraction. Additionally, this tool can be used to track topics across semantically connected web documents.

Keywords: DocAnalyser, interactive web search, search word extraction, query formulation, source topic detection, topic tracking.

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Authors: Ahmad Ghayoumi, Mehdi Ghayoumi

Abstract:

Intelligent schools are those which use IT devices and technologies as media software, hardware and networks to improve learning process. On the other hand Strategic management is a field that deals with the major intended and emergent initiatives taken by general managers on behalf of owners, involving utilization of resources, to enhance the performance of firms in their external environments. Here, we present a model Strategic Management System that has been applied on some schools and have made strict improvement.

Keywords: Intelligent school, Strategic management system, Learning station, Teaching station

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Authors: Luis A. Morales Rosales, Saul E. Pomares Hernandez, Gustavo Rodriguez Gomez

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Synchronous cooperative systems (SCS) bring together users that are geographically distributed and connected through a network to carry out a task. Examples of SCS include Tele- Immersion and Tele-Conferences. In SCS, the coordination is the core of the system, and it has been defined as the act of managing interdependencies between activities performed to achieve a goal. Some of the main problems that SCS present deal with the management of constraints between simultaneous activities and the execution ordering of these activities. In order to resolve these problems, orderings based on Lamport-s happened-before relation have been used, namely, causal, Δ-causal, and causal-total orderings. They mainly differ in the degree of asynchronous execution allowed. One of the most important orderings is the causal order, which establishes that the events must be seen in the cause-effect order as they occur in the system. In this paper we show that for certain SCS (e.g. videoconferences, tele-immersion) where some degradation of the system is allowed, ensuring the causal order is still rigid, which can render negative affects to the system. In this paper, we illustrate how a more relaxed ordering, which we call Fuzzy Causal Order (FCO), is useful for such kind of systems by allowing a more asynchronous execution than the causal order. The benefit of the FCO is illustrated by applying it to a particular scenario of intermedia synchronization of an audio-conference system.

Keywords: Event ordering, fuzzy causal ordering, happenedbefore relation and cooperative systems.

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Authors: C. W. Kan

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Cotton fibre is a commonly-used natural fibre because of its good fibre strength, high moisture absorption behaviour and minimal static problems. However, one of the main drawbacks of cotton fibre is wrinkling after washing, which is recently overcome by wrinkle-resistant treatment. 1,2,3,4-butanetetracarboxylic acid (BTCA) could improve the wrinkle-resistant properties of cotton fibre. Although the BTCA process is an effective method for wrinkle resistant application of cotton fabrics, reduced fabric strength was observed after treatment. Therefore, this paper would explore the use of atmospheric pressure plasma treatment under different discharge powers as a pretreatment process to enhance the application of BTCA process on cotton fabric without generating adverse effect. The aim of this study is to provide learning information to the users to know how the atmospheric pressure plasma treatment can be incorporated in textile finishing process with positive impact.

Keywords: Learning materials, atmospheric pressure plasma treatment, cotton, wrinkle-resistant, BTCA.

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Authors: Nebi Gedik

Abstract:

One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: Wave atom transform, statistical features, multi-resolution representation, mammogram.

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Authors: Young-Seok Choi

Abstract:

In healthy humans, the cortical brain rhythm shows specific mu (~6-14 Hz) and beta (~18-24 Hz) band patterns in the cases of both real and imaginary motor movements. As cerebellar ataxia is associated with impairment of precise motor movement control as well as motor imagery, ataxia is an ideal model system in which to study the role of the cerebellocortical circuit in rhythm control. We hypothesize that the EEG characteristics of ataxic patients differ from those of controls during the performance of a Brain-Computer Interface (BCI) task. Ataxia and control subjects showed a similar distribution of mu power during cued relaxation. During cued motor imagery, however, the ataxia group showed significant spatial distribution of the response, while the control group showed the expected decrease in mu-band power (localized to the motor cortex).

Keywords: Brain-computer interface, EEG, modulation, ataxia.

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper presents sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for CHD prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, coronary heart disease, ECG, electrocardiogram, ResNet, sliding window.

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Authors: Abeer Aljohani

Abstract:

The COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred as corona virus which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as Omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. Numerous COVID-19 cases have produced a huge burden on hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease based on the symptoms and medical history of the patient. As machine learning is a widely accepted area and gives promising results for healthcare, this research presents an architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard University of California Irvine (UCI) dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques on the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and Principal Component Analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, Receiver Operating Characteristic (ROC) and Area under Curve (AUC). The results depict that Decision tree, Random Forest and neural networks outperform all other state-of-the-art ML techniques. This result can be used to effectively identify COVID-19 infection cases.

Keywords: Supervised machine learning, COVID-19 prediction, healthcare analytics, Random Forest, Neural Network.

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur

Abstract:

In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.

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Authors: Aditya K, Dinesh P, Ramesh Bhakthavatchalu

Abstract:

This paper explains a project based learning method where autonomous mini-robots are developed for research, education and entertainment purposes. In case of remote systems wireless sensors are developed in critical areas, which would collect data at specific time intervals, send the data to the central wireless node based on certain preferred information would make decisions to turn on or off a switch or control unit. Such information transfers hardly sums up to a few bytes and hence low data rates would suffice for such implementations. As a robot is a multidisciplinary platform, the interfacing issues involved are discussed in this paper. The paper is mainly focused on power supply, grounding and decoupling issues.

Keywords: CPLD, power supply, decoupling, grounding.

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Authors: S. B. Kotsiantis, P. E. Pintelas

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Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.

Keywords: data mining, machine learning, pattern recognition.

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Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: Compliance Course, Corporate Training, Learner Behaviours, xAPI.

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Authors: Iman Badr

Abstract:

The concept of flexible manufacturing is highly appealing in gaining a competitive edge in the market by quickly adapting to the changing customer needs. Scheduling jobs on flexible manufacturing systems (FMSs) is a challenging task of managing the available flexibility on the shop floor to react to the dynamics of the environment in real-time. In this paper, an agent-oriented scheduling framework that can be integrated with a real or a simulated FMS is proposed. This framework works in stochastic environments with a dynamic model of job arrival. It supports a hierarchical cooperative scheduling that builds on the available flexibility of the shop floor. Testing the framework on a model of a real FMS showed the capability of the proposed approach to overcome the drawbacks of the conventional approaches and maintain a near optimal solution despite the dynamics of the operational environment.

Keywords: Autonomous agents, Flexible manufacturing systems(FMS), Manufacturing scheduling, Real-time systems.

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Authors: Chris Fersterer, Col Fay, Tobias Danielmeier, Kat Achterberg, Scott Willis

Abstract:

New Zealand, an island nation, has an extensive coastline peppered with small communities of iconic buildings known as Bachs. Post WWII, these modest buildings were constructed by their owners as retreats and generally were small, low cost, often using recycled material and often they fell below current acceptable building standards. In the latter part of the 20^th century, real estate prices in many of these communities remained low and these areas became permanent residences for people attracted to this affordable lifestyle choice. The Blueskin Resilient Communities Trust (BRCT) is an organisation that recognises the vulnerability of communities in low lying settlements as now being prone to increased flood threat brought about by climate change and sea level rise. Some of the inhabitants of Blueskin Bay, Otago, NZ have already found their properties to be un-insurable because of increased frequency of flood events and property values have slumped accordingly. Territorial authorities also acknowledge this increased risk and have created additional compliance measures for new buildings that are less than 2 m above tidal peaks. Community resilience becomes an additional concern where inhabitants are attracted to a lifestyle associated with a specific location and its people when this lifestyle is unable to be met in a suburban or city context. Traditional models of social housing fail to provide the sense of community connectedness and identity enjoyed by the current residents of Blueskin Bay. BRCT have partnered with the Otago Polytechnic Design School to design a new form of community housing that can react to this environmental change. It is a longitudinal project incorporating participatory approaches as a means of getting people ‘on board’, to understand complex systems and co-develop solutions. In the first period, they are seeking industry support and funding to develop a transportable and fully self-contained housing model that exploits current technologies. BRCT also hope that the building will become an educational tool to highlight climate change issues facing us today. This paper uses the Climate Safe House (CSH) as a case study for education in architectural sustainability through experiential learning offered as part of the Otago Polytechnics Bachelor of Design. Students engage with the project with research methodologies, including site surveys, resident interviews, data sourced from government agencies and physical modelling. The process involves collaboration across design disciplines including product and interior design but also includes connections with industry, both within the education institution and stakeholder industries introduced through BRCT. This project offers a rich learning environment where students become engaged through project based learning within a community of practice, including architecture, construction, energy and other related fields. The design outcomes are expressed in a series of public exhibitions and forums where community input is sought in a truly participatory process.

Keywords: Community resilience, problem based learning, project based learning, case study.

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