Search results for: meaningful learning characteristics

Authors: Chin-Cheh Yi, Pei-Wen Liao, Chin-Feng Huang, I-Hui Hwang

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With the proliferation of mobile computing technology, mobile learning (m-learning) will play a vital role in the rapidly growing electronic learning market. However, the acceptance of m-learning by individuals is critical to the successful implementation of m-learning systems. Thus, there is a need to research the factors that affect users- intention to use m-learning. Based on an updated information system (IS) success model, data collected from 350 respondents in Taiwan were tested against the research model using the structural equation modeling approach. The data collected by questionnaire were analyzed to check the validity of constructs. Then hypotheses describing the relationships between the identified constructs and users- satisfaction were formulated and tested.

Keywords: m-learning, information system success, users' satisfaction, perceived value.

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Authors: Melvin Ballera, Mosbah Mohamed Elssaedi, Ahmed Khalil Zohdy

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Collaborative problem solving in e-learning can take in the form of discussion among learner, creating a highly social learning environment and characterized by participation and interactivity. This paper, designed a collaborative learning environment where agent act as co-learner, can play different roles during interaction. Since different roles have been assigned to the agent, learner will assume that multiple co-learner exists to help and guide him all throughout the collaborative problem solving process, but in fact, alone during the learning process. Specifically, it answers the questions what roles of the agent should be incorporated to contribute better learning outcomes, how agent will facilitate the communication process to provide social learning and interactivity and what are the specific instructional strategies that facilitate learner participation, increased skill acquisition and develop critical thinking.

Keywords: Collaborative e-learning, collaborative problem solving, , imaginary co-learner, social learning.

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Authors: Mohamed Bendahmane, Brahim Elfalaki, Mohammed Benattou

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One of the major sources of learners' professional difficulties is their heterogeneity. Whether on cognitive, social, cultural or emotional level, learners being part of the same group have many differences. These differences do not allow to apply the same learning process at all learners. Thus, an optimal learning path for one, is not necessarily the same for the other. We present in this paper a model-oriented service to offer to each learner a personalized learning path to acquire the targeted skills.

Keywords: Service-oriented architecture, learning path, web service, personalization, trace analysis.

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Authors: Mohammed T. Taher, Usman Ghani, Ahmed S. Khan

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This paper compares the findings of two studies conducted to determine the effectiveness of simulation-based, hands-on and feedback mechanism on students learning by answering the following questions: 1). Does the use of simulation improve students’ learning outcomes? 2). How do students perceive the instructional design features embedded in the simulation program such as exploration and scaffolding support in learning new concepts? 3.) What is the effect of feedback mechanisms on students’ learning in the use of simulation-based labs? The paper also discusses the other aspects of findings which reveal that simulation by itself is not very effective in promoting student learning. Simulation becomes effective when it is followed by hands-on activity and feedback mechanisms. Furthermore, the paper presents recommendations for improving student learning through the use of simulation-based, hands-on, and feedback-based teaching methodologies.

Keywords: Simulation-based teaching, hands-on learning, feedback-based learning, scaffolding.

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Authors: A. Mirzaaghazadeh, H. Motameni, M. Karshenas, H. Nematzadeh

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Learning the gradient of neuron's activity function like the weight of links causes a new specification which is flexibility. In flexible neural networks because of supervising and controlling the operation of neurons, all the burden of the learning is not dedicated to the weight of links, therefore in each period of learning of each neuron, in fact the gradient of their activity function, cooperate in order to achieve the goal of learning thus the number of learning will be decreased considerably. Furthermore, learning neurons parameters immunes them against changing in their inputs and factors which cause such changing. Likewise initial selecting of weights, type of activity function, selecting the initial gradient of activity function and selecting a fixed amount which is multiplied by gradient of error to calculate the weight changes and gradient of activity function, has a direct affect in convergence of network for learning.

Keywords: Back propagation, Flexible, Gradient, Learning, Neural network, Pattern recognition.

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Authors: Satya Pilla

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This study explores how the mechanics of learning paves the way to engineering innovation. Theories related to learning in the new product/service innovation are reviewed from an organizational perspective, behavioral perspective, and engineering perspective. From this, an engineering team-s external interactions for knowledge brokering and internal composition for skill balance are examined from a learning and innovation viewpoints. As a result, an integrated learning model is developed by reconciling the theoretical perspectives as well as developing propositions that emphasize the centrality of learning, and its drivers, in the engineering product/service development. The paper also provides a review and partial validation of the propositions using the results of a previously published field study in the aerospace industry.

Keywords: Engineering Services, Integrated Learning, NewProduct Development, Service Innovation

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Authors: R.L. Sanders, L. McKeown

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An international cooperation between educators in Australia and the US has led to a reconceptualization of the teaching of a library science course at Appalachian State University. The pedagogy of Action Learning coupled with a 3D virtual learning environment immerses students in a social constructivist learning space that incorporates and supports interaction and reflection. The intent of this study was to build a bridge between theory and practice by providing students with a tool set that promoted personal and social reflection, and created and scaffolded a community of practice. Besides, action learning is an educational process whereby the fifty graduate students experienced their own actions and experience to improve performance.

Keywords: action learning, action research, reflection, metacognition, virtual worlds

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Authors: Jingyu Hou

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This paper proposes a new teaching and learning approach-project and module based teaching and learning (PMBTL). The PMBTL approach incorporates the merits of project/problem based and module based learning methods, and overcomes the limitations of these methods. The correlation between teaching, learning, practice and assessment is emphasized in this approach, and new methods have been proposed accordingly. The distinct features of these new methods differentiate the PMBTL approach from conventional teaching approaches. Evaluation of this approach on practical teaching and learning activities demonstrates the effectiveness and stability of the approach in improving the performance and quality of teaching and learning. The approach proposed in this paper is also intuitive to the design of other teaching units. 

Keywords: Computer science education, project and module based, software engineering.

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Authors: Khaled Halimi, Hassina Seridi-Bouchelaghem

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The purpose of this paper is to describe how learning analytics approaches based on social semantic web techniques can be applied to enhance the lifelong learning experiences in a connectivist perspective. For this reason, a prototype of a system called SoLearn (Social Learning Environment) that supports this approach. We observed and studied literature related to lifelong learning systems, social semantic web and ontologies, connectivism theory, learning analytics approaches and reviewed implemented systems based on these fields to extract and draw conclusions about necessary features for enhancing the lifelong learning process. The semantic analytics of learning can be used for viewing, studying and analysing the massive data generated by learners, which helps them to understand through recommendations, charts and figures their learning and behaviour, and to detect where they have weaknesses or limitations. This paper emphasises that implementing a learning analytics approach based on social semantic web representations can enhance the learning process. From one hand, the analysis process leverages the meaning expressed by semantics presented in the ontology (relationships between concepts). From the other hand, the analysis process exploits the discovery of new knowledge by means of inferring mechanism of the semantic web.

Keywords: Connectivism, data visualization, informal learning, learning analytics, semantic web, social web.

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Authors: Keisuke Nakamura, Kiyoshi Akama, Hiroshi Mabuchi

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E-Learning systems are used by many learners and teachers. The developer is developing the e-Learning system. However, the developer cannot do system construction to satisfy all of users- demands. We discuss a method of constructing e-Learning systems where learners and teachers can design, try to use, and share extending system functions that they want to use; which may be nally added to the system by system managers.

Keywords: Clause-Rule-Model, database-access, e-Learning, Web-Application.

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Authors: Maryam Honarmand

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Regarding to the fast growth of computer, internet, and virtual learning in our country (Iran) and need computer-based learning systems and multimedia tools as an essential part of such education, designing and implementing such systems would help teach different field such as science. This paper describes the basic principle of multimedia. At the end, with a description of learning science to the infant students, the method of this system will be explained.

Keywords: Multimedia tools, computer based learning, science, student.

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Authors: Hyun-Woo Cho

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Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: Process Monitoring, kernel methods, multivariate filtering, data-driven techniques, quality improvement.

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Authors: Masaki Omata, Shumma Hosokawa

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An experiment to verify the relationships between physiological indexes of an e-learner and the presence or absence of an operation during e-learning is described. Electroencephalogram (EEG), hemoencephalography (HEG), skin conductance (SC), and blood volume pulse (BVP) values were measured while participants performed experimental learning tasks. The results show that there are significant differences between the SC values when reading with clicking on learning materials and the SC values when reading without clicking, and between the HEG ratio when reading (with and without clicking) and the HEG ratio when resting for four of five participants. We conclude that the SC signals can be used to estimate whether or not a learner is performing an active task and that the HEG ratios can be used to estimate whether a learner is learning.

Keywords: E-learning, physiological index, physiological signal, state of learning.

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Authors: Nabil El Kadhi, Minerva M. Bunagan

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The dynamism in education, particularly in the area of teaching, learning and assessment has caused Higher Education Institutions (HEIs) worldwide to seek for ways to continuously improve their educational processes. HEIs use outcomes of institutional audits, assessments and accreditations, for improvement. In this study, the published institutional audit reports of HEIs in the Sultanate of Oman were analyzed to produce features of good practice; identify challenges along Teaching, Learning Assessment (TLA); and propose a framework that puts major emphasis in having a quality-assured TLA, including a set of principles that can be used as basis in succeeding an institutional visit. The TLA framework, which shows the TLA components, characteristics of the components, related expectation, including implementation tool/ strategy and pitfalls can be used by HEIs to have an adequate understanding of the scope of audit and be able to satisfy institutional audit requirements. The scope of this study can be widened by exploring the other requirements of the Institutional Audits in the Sultanate of Oman, particularly the area on Governance and Management and Student Support Services.

Keywords: Accreditation, audit, quality assurance, teaching, learning and assessment.

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Authors: David Hallberg

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It is believed that major account on language diversity must be taken in learning, and especially in learning using ICT. This paper-s objective is to exhibit language and communication barriers in learning, to approach the topic from socioculture and cognitivist perspectives, and to give exploratory solutions of handling such barriers. The review is mainly conducted by approaching the journal Computers & Education, but also an initially broad search was conducted. The results show that not much attention is paid on language and communication barriers in an immediate relation to learning using ICT. The results shows, inter alia, that language and communication barriers are caused because of not enough account is taken on both the individual-s background and the technology.

Keywords: communication barriers, cognitive, ICT, language barriers, learning, socioculture

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Authors: Sejong Oh

Abstract:

Classification is an important topic in machine learning and bioinformatics. Many datasets have been introduced for classification tasks. A dataset contains multiple features, and the quality of features influences the classification accuracy of the dataset. The power of classification for each feature differs. In this study, we suggest the Classification Influence Index (CII) as an indicator of classification power for each feature. CII enables evaluation of the features in a dataset and improved classification accuracy by transformation of the dataset. By conducting experiments using CII and the k-nearest neighbor classifier to analyze real datasets, we confirmed that the proposed index provided meaningful improvement of the classification accuracy.

Keywords: accuracy, classification, dataset, data preprocessing

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Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

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In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: Evolving learning, knowledge extraction, knowledge graph, text mining.

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Authors: Sepideh Fazeli, Fariba Bahrami

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Investigating language acquisition is one of the most challenging problems in the area of studying language. Syllable learning as a level of language acquisition has a considerable significance since it plays an important role in language acquisition. Because of impossibility of studying language acquisition directly with children, especially in its developmental phases, computer models will be useful in examining language acquisition. In this paper a computer model of early language learning for syllable learning is proposed. It is guided by a conceptual model of syllable learning which is named Directions Into Velocities of Articulators model (DIVA). The computer model uses simple associational and reinforcement learning rules within neural network architecture which are inspired by neuroscience. Our simulation results verify the ability of the proposed computer model in producing phonemes during babbling and early speech. Also, it provides a framework for examining the neural basis of language learning and communication disorders.

Keywords: Brain modeling, computer models, language acquisition, reinforcement learning.

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Authors: G. Cossu, A. Esposito, G. Picco, C. Scrizzi, A. Tartaglia, E. Tresso

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A collaboration among the Hospital S. Giovanni Battista of Turin, the Politecnico of Turin, and the MUST company is described. The content of the collaboration has been and is the use of ICT-s, e-learning, and blended learning for the internal professional education, training, and keeping up to date of the personnel of the hospital. A platform for the delivery of the teaching materials has been built, including an evaluation and self-evaluation tool. The first on line courses have been developed and delivered and many more are in preparation. The first results of the monitoring of the efficacy of the online education have been positive.

Keywords: E-learning, blended learning, on line education, ICT.

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Authors: Rishi Ruttun

Abstract:

One of the major features of hypermedia learning is its non-linear structure, allowing learners, the opportunity of flexible navigation to accommodate their own needs. Nevertheless, such flexibility can also cause problems such as insufficient navigation and disorientation for some learners, especially those with Field Dependent cognitive styles. As a result students learning performance can be deteriorated and in turn, they can have negative attitudes with hypermedia learning systems. It was suggested that visual elements can be used to compensate dilemmas. However, it is unclear whether these visual elements improve their learning or whether problems still exist. The aim of this study is to investigate the effect of students cognitive styles and visual elements on students learning performance and attitudes in hypermedia learning environment. Cognitive Style Analysis (CSA), Learning outcome in terms of pre and post-test, practical task, and Attitude Questionnaire (AQ) were administered to a sample of 60 university students. The findings revealed that FD students preformed equally to those of FI. Also, FD students experienced more disorientation in the hypermedia learning system where they depend a lot on the visual elements for navigation and orientation purposes. Furthermore, they had more positive attitudes towards the visual elements which escape them from experiencing navigation and disorientation dilemmas. In contrast, FI students were more comfortable, did not get disturbed or did not need some of the visual elements in the hypermedia learning system.

Keywords: Hypermedia learning, cognitive styles, visual elements, support, learning performance, attitudes and perceptions

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Authors: Yi-Chun Chang, Jian-Wei Li

Abstract:

Web-based cooperative learning focuses on (1) the interaction and the collaboration of community members, and (2) the sharing and the distribution of knowledge and expertise by network technology to enhance learning performance. Numerous research literatures related to web-based cooperative learning have demonstrated that cooperative scripts have a positive impact to specify, sequence, and assign cooperative learning activities. Besides, literatures have indicated that role-play in web-based cooperative learning environments enhances two or more students to work together toward the completion of a common goal. Since students generally do not know each other and they lack the face-to-face contact that is necessary for the negotiation of assigning group roles in web-based cooperative learning environments, this paper intends to further extend the application of genetic algorithm (GA) and propose a GA-based algorithm to tackle the problem of role assignment in web-based cooperative learning environments, which not only saves communication costs but also reduces conflict between group members in negotiating role assignments.

Keywords: genetic algorithm (GA), role assignment, role-play; web-based cooperative learning.

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Authors: S. Sarumathi, P. Ranjetha, C. Saraswathy, M. Vaishnavi, S. Geetha

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Clustering is an unsupervised learning technique for aggregating data objects into meaningful classes so that intra cluster similarity is maximized and inter cluster similarity is minimized in data mining. However, no single clustering algorithm proves to be the most effective in producing the best result. As a result, a new challenging technique known as the cluster ensemble approach has blossomed in order to determine the solution to this problem. For the cluster analysis issue, this new technique is a successful approach. The cluster ensemble's main goal is to combine similar clustering solutions in a way that achieves the precision while also improving the quality of individual data clustering. Because of the massive and rapid creation of new approaches in the field of data mining, the ongoing interest in inventing novel algorithms necessitates a thorough examination of current techniques and future innovation. This paper presents a comparative analysis of various cluster ensemble approaches, including their methodologies, formal working process, and standard accuracy and error rates. As a result, the society of clustering practitioners will benefit from this exploratory and clear research, which will aid in determining the most appropriate solution to the problem at hand.

Keywords: Clustering, cluster ensemble methods, consensus function, data mining, unsupervised learning.

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Authors: Ken Stevens

Abstract:

Networked schools have become a feature of education systems in countries that seek to provide learning opportunities in schools located beyond major centres of population. The internet and e-learning have facilitated the development of virtual educational structures that complement traditional schools, encouraging collaborative teaching and learning to proceed. In rural New Zealand and in the Atlantic Canadian province of Newfoundland and Labrador, e-learning is able to provide new ways of organizing teaching, learning and the management of educational opportunities. However, the future of e-teaching and e-learning in networked schools depends on the development of professional education programs that prepare teachers for collaborative teaching and learning environments in which both virtual and traditional face to face instruction co-exist.

Keywords: Advanced Placement, Cybercells, Extranet, Intranet.

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Authors: Mesfer Al Duhayyim, Paul Newbury

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This paper proposes an effective adaptive e-learning system that uses a coloured concept map to show the learner's knowledge level for each concept in the chosen subject area. A Fuzzy logic system is used to evaluate the learner's knowledge level for each concept in the domain, and produce a ranked concept list of learning materials to address weaknesses in the learner’s understanding. This system obtains information on the learner's understanding of concepts by an initial pre-test before the system is used for learning and a post-test after using the learning system. A Fuzzy logic system is used to produce a weighted concept map during the learning process. The aim of this research is to prove that such a proposed novel adapted e-learning system will enhance learner's performance and understanding. In addition, this research aims to increase participants' overall understanding of their learning level by providing a coloured concept map of understanding followed by a ranked concepts list of learning materials.

Keywords: Adaptive e-learning system, coloured concept map, fuzzy logic, ranked concept list.

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Authors: Bekim Fetaji, Majlinda Fetaji

Abstract:

The development and use of mobile devices as well as its integration within education systems to deliver electronic contents and to support real-time communications was the focus of this research. In order to investigate the software engineering issues in using mobile devices a research on electronic content was initiated. The Developed MP3 mobile software solution was developed as a prototype for testing and developing a strategy for designing a usable m-learning environment. The mobile software solution was evaluated using mobile device using the link: http://projects.seeu.edu.mk/mlearn. The investigation also tested the correlation between the two mobile learning indicators: electronic content and attention, based on the Task Based learning instructional method. The mobile software solution ''M-Learn“ was developed as a prototype for testing the approach and developing a strategy for designing usable m-learning environment. The proposed methodology is about what learning modeling approach is more appropriate to use when developing mobile learning software.

Keywords: M-learning, mobile software development, mobiledevices, learning instructions, task based learning.

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Authors: David D. Hampton

Abstract:

Lesson study is used as an instructional technique to promote both student and faculty learning. However, little is known about the usefulness of learning communities in supporting results of lesson study on the self-efficacy and development for tenure-track faculty. This study investigated the impact of participation in a lesson study learning community on 34 new faculty members at a mid-size Midwestern University, specifically regarding implementing lesson study evaluations by new faculty on their reported self-efficacy. Results indicate that participation in a lesson study learning community significantly increased faculty members’ lesson study self-efficacy as well as grant and manuscript production over one academic year. Suggestions for future lesson study around faculty learning communities are discussed.

Keywords: Lesson study, learning community, lesson study self-efficacy, new faculty.

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Authors: Sumitra Nuanmeesri

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The research focus on study, analyze and design the model of the infrastructure and computer networks for distance education, online learning via new media, e-learning and blended learning. The collected information from study and analyze process that information was evaluated by the index of item objective congruence (IOC) by 9 specialists to design model. The results of evaluate the model with the mean and standard deviation by the sample of 9 specialists value is 3.85. The results showed that the infrastructure and computer networks are designed to be appropriate to a great extent appropriate to a great extent.

Keywords: Blended Learning, New Media, Infrastructure and Computer Network, Tele-Education, Online Learning.

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Authors: Seongsoo Lee, Jong-Bae Lee, Wookkang Lee, Duyen Hai Pham

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In the image sensors, the acquired image often differs from the real image in luminance or chrominance due to fabrication defects or nonlinear characteristics, which often lead to pixel defects or sensor failure. Therefore, the image acquisition characteristics of image sensors should be measured and tested before they are mounted on the target product. In this paper, the standardized test and measurement methods of image sensors are introduced. It applies standard light source to the image sensor under test, and the characteristics of the acquired image is compared with ideal values.

Keywords: Image Sensor, Image Acquisition Characteristics, Defect, Failure, Standard, Test, Measurement.

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Authors: Yasser F. Hassan

Abstract:

Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: Cellular automata, neural cellular automata, deep learning, classification.

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Authors: [email protected]

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data need a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM), ensemble learning with hyper parameters optimization, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: Machine learning, Deep learning, cancer prediction, breast cancer, LSTM, Score-Level Fusion.

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