11,025 research outputs found

    Toward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization

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    Most online e-learning systems often demand the pre-requisite requirements between course modules and/or some relationship measures between involved concepts to be explicitly inputed by the course instructors so that an optimizer can be ultimately used to find an optimal learning sequence of involved concepts or modules for each individual learner after considering his/her past performance, learner's profile, learning style, etc. However, relying solely on the course instructor's input on the relationship among the involved concepts can be imprecise possibly due to the individual biases by human experts. Furthermore, the decision will become more complicated when various instructors hold conflicting views on the relationship among the involved concepts that may hinder any reasonable deduction. Therefore, we propose in this paper a complete system framework that can perform an explicit semantic analysis on the course materials, possibly aided by the relevant Wiki articles for any missing information about the involved concepts, to formulate the individual concepts, and followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures. Lastly, an evolutionary optimizer will be used to return the optimal learning sequence after considering multiple experts' recommended learning sequences possibly containing conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework. Our empirical evaluation clearly revealed the possible advantages of our proposal with many possible directions for future investigation. © 2012 IEEE.published_or_final_versio

    Applying an evolutionary approach for learning path optimization in the next-generation e-learning systems

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    Learning analytics is targeted to better understand and optimize the process of learning and its environments through the measurement, collection and analysis of learners' data and contexts. To advise people's learning in a specific subject, most intelligent e-learning systems would require course instructors to explicitly input some prior knowledge about the subject such as all the pre-requisite requirements between course modules. Yet human experts may sometimes have conflicting views leading to less desirable learning outcomes. In a previous study, we proposed a complete system framework of learning analytics to perform an explicit semantic analysis on the course materials, followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures, and lastly employing a simple yet efficient evolutionary approach to return the optimal learning sequence. In this paper, we carefully consider to enhance the original evolutionary optimizer with the hill-climbing heuristic, and also critically evaluate the impacts of various experts' recommended learning sequences possibly with conflicting views to optimize the learning paths for the next-generation e-learning systems. More importantly, the integration of heuristics can make our proposed framework more self-adaptive to less structured knowledge domains with conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework for learning analytics. Our empirical evaluation clearly revealed many possible advantages of our proposal with interesting directions for future investigation. © 2013 IEEE.published_or_final_versio

    Managing stimulation of regional innovation subjects’ interaction in the digital economy

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    The reported study was funded by RFBR according to the research project No. 18-01000204_a, No. 16-07-00031_a, No. 18-07-00975_a.Purpose: The article is devoted to solving fundamental scientific problems in the scope of the development of forecasting modeling methods and evaluation of regional company’s innovative development parameters, synthesizing new methods of big data processing and intelligent analysis, as well as methods of knowledge eliciting and forecasting the dynamics of regional innovation developments through benchmarking. Design/Methodology/Approach: For regional economic development, it is required to identify the mechanisms that contribute to (or impede) the innovative economic development of the regions. The synergetic approach to management is based on the fact that there are multiple paths of IS development (scenarios with different probabilities), although it is necessary to reach the required attractor by meeting the management goals. Findings: The present research is focused on obtainment of new knowledge in creating a technique of multi-agent search, collection and processing of data on company’s innovative development indicators, models and methods of intelligent analysis of the collected data. Practical Implications: The author developed recommendations before starting the process of institutional changes in a specific regional innovation system. The article formulates recommendations on the implementation of institutional changes in the region taking into account the sociocultural characteristics of the region’s population. Originality/Value: It is the first time, when a complex of models and methods is based on the use of a convergent model of large data volumes processing is presented.peer-reviewe

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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