• The Journal of Korean Association of Computer Education
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• v.10 no.6
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• pp.1-10
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• 2007

Programming is emphasized in information(computer science) education course domestically and in foreign countries, and novices are given ample opportunities to experience programming. Programming error is a critical factor which makes it difficult to learn programming for novices. However, if they are given appropriate feedback, it can have positive influence on programming learning. In this paper, we design programming learning supporting system for novice through error feedback and provide some implementations for EPL 'Dolittle'. This system has four features as highlighting, guiding messages, object tree, and step-execution.

• Korean Journal of Cognitive Science
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• v.28 no.1
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• pp.65-90
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• 2017

Rewards or penalties become informative only when contingent on an immediately preceding response. Our goal was to determine if the brain responds differently to motivational events depending on whether they provide feedback with the contingencies effective for learning. Event-related fMRI data were obtained from 22 volunteers performing a visuomotor categorical task. In learning-condition trials, participants learned by trial and error to make left or right responses to letter cues (16 consonants). Monetary rewards (+500) or penalties (-500) were given as feedback (learning feedback). In random-condition trials, cues (4 vowels) appeared right or left of the display center, and participants were instructed to respond with the appropriate hand. However, rewards or penalties (random feedback) were given randomly (50/50%) regardless of the correctness of response. Feedback-associated BOLD responses were analyzed with ANOVA [trial type (learning vs. random) x feedback type (reward vs. penalty)] using SPM8 (voxel-wise FWE p < .001). The right caudate nucleus and right cerebellum showed activation, whereas the left parahippocampus and other regions as the default mode network showed deactivation, both greater for learning trials than random trials. Activations associated with reward feedback did not differ between the two trial types for any brain region. For penalty, both learning-penalty and random-penalty enhanced activity in the left insular cortex, but not the right. The left insula, however, as well as the left dorsolateral prefrontal cortex and dorsomedial prefrontal cortex/dorsal anterior cingulate cortex, showed much greater responses for learning-penalty than for random-penalty. These findings suggest that learning-penalty plays a critical role in learning, unlike rewards or random-penalty, probably not only due to its evoking of aversive emotional responses, but also because of error-detection processing, either of which might lead to changes in planning or strategy.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.4
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• pp.125-137
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• 2021

PURPOSE: The growing number of people exposed to a static sitting posture has resulted in an increase in people with a poor posture out of the optimally aligned posture because of the low awareness of a correct sitting posture. Learning the correct sitting posture by applying sensory feedback is essential because a poor posture has negative consequences for the spine. Therefore, this study examined the effects of the sensory feedback types on learning correct sitting posture. METHODS: Thirty-six healthy adult males were assigned to a visual feedback group, a tactile feedback group, and a visuotactile feedback group to learn the correct sitting posture by applying sensory feedback. The spine angle, muscle activity, and muscle thickness were measured in the sitting position using retro-reflexive markers, electromyography, and ultrasound immediately after, five minutes, and 10 minutes after intervention. RESULTS: The intervention time was significantly shorter in the visuotactile feedback group than the visual feedback group (p < .05). Compared to the pre-intervention, the repositioning error angles of the thoracic and lumbar vertebrae of all groups were reduced significantly immediately after intervention and after five minutes. After 10 minutes, there was a significant difference in the thoracic and lumbar repositioning error angles of the tactile feedback group and the visuotactile feedback group (p < .05). No significant difference was noted at any time compared to the pre-intervention in all groups (p > .05). CONCLUSION: The use of tactile and visuotactile feedback in intervention to correct the sitting posture is proposed.

• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.897-903
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• 1991

In this paper we propose a hybrid position/force controller of a robot manipulator using feedback error learning rule and neural networks. The neural network is constructed from inverse dynamics. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained well, it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using PUMA 560 manipulator.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.602-608
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• 2011

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. The NN for the online feedback-error learning can composed that the input layer consists of six units for the inputs $x_i$, i=1~6, the hidden layer consists of two hidden units for hidden outputs $o_j$, j=1~2, and the output layer consists of two units for the outputs ${\tau}_k$, k=1~2. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels. The initial q value was set to [0, 5, ${\pi}$].

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.176.4-176
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• 2001

In this paper, a novel type of iterative learning controller is studied. The proposed learning algorithm utilizes not only the error signal of the previous iteration but also the delayed error signal of the current iteration. The delayed error signal is adopted to improve the convergence speed. The convergence condition is examined and the result shows that the proposed learning algorithm shows the fast convergence speed under the same convergence condition of the traditional iterative learning algorithm. The simulation examples are presented to confirm the validity of the proposed ILC algorithm.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.196-200
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used fur on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.136-139
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.17-24
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• 2004

We present a P-type iterative learning control(ILC) scheme for uncertain robotic systems that perform the same tasks repetitively. The proposed ILC scheme comprises a linear feedback controller consisting of position error, and a feedforward and feedback teaming controller updated by current velocity error. As the learning iteration proceeds, the joint position and velocity mrs converge uniformly to zero. By adopting the learning gain dependent on the iteration number, we present joint position and velocity error bounds which converge at the arbitrarily tuned rate, and the joint position and velocity errors converge to zero in the iteration domain within the adopted error bounds. In contrast to other existing P-type ILC schemes, the proposed ILC scheme enables analysis and tuning of the convergence rate in the iteration domain by designing properly the learning gain.