• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

• Research in Mathematical Education
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• v.10 no.2 s.26
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• pp.115-124
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• 2006

In order to optimize a learning effect in mathematics, the results of the educational assessment must be effectively used by both teachers and students. The teacher using technology to provide students with performance feedback is becoming more prevalent in educational contexts worldwide but concern arises over the form of that feedback and the effects it has upon students' achievements. Also, feedback takes considerable time for teachers but their instructional time is limited. For these reasons, it is a significant matter how to select items effectively in order to give feedback to students after an assessment. In this study, we introduce the systematic selection method of feedback items using the regression analysis in order to provide effective feedback to students by teachers.

• Asia pacific journal of information systems
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• v.33 no.4
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• pp.1118-1134
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• 2023

Previous research has shown that employees tend to react more positively to corrective feedback from supervisors to the extent they perceive that they were treated with empathy, respect, and concern towards fair interpersonal treatment in receiving the feedback information. Then, to facilitate effective supervisory feedback and coaching, it would be useful for organizations to monitor the contents of feedback exchanges between supervisors and employees to make sure that supervisors are providing performance feedback using languages that are more likely to be perceived as interpersonally fair. Computer-aided text analysis holds potential as a useful tool that organizations can use to efficiently monitor the quality of the feedback messages that supervisors provide to their employees. In the current study, we applied computer-aided text analysis (using closed-vocabulary text analysis) and machine learning to examine the validity of language-based algorithms trained on supervisor language in performance feedback situations for predicting human ratings of feedback interpersonal fairness. Results showed that language-based algorithms predicted feedback interpersonal fairness with reasonable level of accuracy. Our findings provide supportive evidence for the promise of using employee language data for managing (and improving) performance management in organizations.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.345-353
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• 2006

Over the past few years, training professionals have become more pragmatic in their approach to technology-based media by using it to augment traditional forms of training delivery, such as classroom instruction and text-based materials. This trend has led to the rise of the term blended learning. Blended learning, an environment of e-learning, is a powerful learning solution created through a mixture of face-to-face and online learning delivered through a mix of media and superior learning experiences. In this article we design and implement an e-learning environment for blended learning. The environment focused on following factors: learning activity and participation of learners, and real time feedback of instructor.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.815-821
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• 2014

In this paper, we propose a novel CALL (Computer-Assisted Language Learning) system, which is called POMY (POSTECH Immersive English Study). In our system, students can study English while talking to characters in a computer-generated virtual environment. POMY also supports haptic feedback, so students can study English in a more interesting manner. Haptic feedback is provided by two platforms, a haptic chair and a force-feedback device. The haptic chair, which is equipped with an array of vibrotactile actuators, delivers directional information to the student. The force-feedback device enables the student to feel the physical properties of an object. These haptic systems help the student better understand English conversations and focus on studying. We conducted a user experiment and its results showed that our haptic-enabled English study contributes to better learning of English.

• The Journal of Korean Association of Computer Education
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• v.7 no.1
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• pp.141-149
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• 2004

Today there are many Web courseware systems for formative evaluation and feedback. Formative evaluation and feedback provided according to users' response in most Web courseware systems, however, are simple texts showing only whether correct or wrong, correct answers, relevant information, etc., far deviated from actual context. Thus such a system may weaken the corrective function of feedback and, as a result, reduce learners' understanding of contents and the possibility of learning transfer. In addition, according to the learning theory of constructivism, learning is influenced by the situation, in which it happens, and knowledge is learned and transferred differently depending on the context in which it is learned. In the background, this study designed and implemented a contextual error-correction feedback system that can provide feedback in a context closely related and similar to the relevant situation according to the response of learners when formative evaluation is carried out in Web courseware. In addition, it applied 'correction/correct-answer-providing feedback', 'relevant information providing feedback' and 'contextual error-correction feedback' to Web courseware for learning actual productivity S/W and verified if 'contextual error-correction feedback' is more effective than other two types of feedback for learners' academic achievement.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1277-1283
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• 1990

The iterative learning control synthesized in the frequency domain has been utilized for temperature control of a batch reactor. For this purpose, a feedback-assisted generalized learning control scheme was constructed first, and the convergence and robustness analyses were conducted in the frequency domain. The feedback-assisted learning operation was then implemented in a bench scale batch reactor where reaction heat is simulated using an electric heater. As a result, progressive reduction of temperature control error could be obviously observed as batch operation is repeated.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.330-333
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• 2005

In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.346-346
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• 2000

We proposed a method of second-order iterative learning control with feedback, which shows an enhancement of convergence speed and robustness to the disturbances in our previous study. In this paper, we show that the proposed second-order iterative learning control algorithm with feedback is more effective and has better convergence performance than the algorithm without feedback in the case of the existence of initial condition errors. And the convergence woof of the proposed algorithm in the case of the existence of initial condition error is given in detail, and the effectiveness of the Proposed algorithm is shown by simulation results.

• ETRI Journal
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• v.33 no.2
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• pp.240-250
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• 2011

To enable a relevance feedback paradigm to evolve itself by users' feedback, a reinforcement learning method is proposed. The feature space of the medical images is partitioned into positive and negative hypercubes by the system. Each hypercube constitutes an individual in a genetic algorithm infrastructure. The rules take recombination and mutation operators to make new rules for better exploring the feature space. The effectiveness of the rules is checked by a scoring method by which the ineffective rules will be omitted gradually and the effective ones survive. Our experiments on a set of 10,004 images from the IRMA database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to other existing approaches in the literature.