scholarly journals Learning System for Japanese Kanji Calligraphy with Computerized Supervision

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1071
Author(s):  
Shin ◽  
Rahim ◽  
Chang
Keyword(s):  
Error Rates ◽  
Learning System ◽  
Traditional Practice ◽  
Human Expert ◽  
Learning Efficiency ◽  
Traditional Manner ◽  
Human Teacher

The most popular way of learning oriental calligraphy has been by practicing the calligraphy under the supervision of a human teacher, but finding a good instructor can be difficult. There are a number of studies in the literature that have evaluated calligraphic characters in holistic ways, but such systems do not support detailed supervision of scripting errors. This study proposes a Kanji calligraphy learning system with computerized supervision and analyzes the learning efficiency of the system, where the supervision includes symmetries between strokes. The proposed system compares a written calligraphic character of a user to the model of a human expert, and indicates error spots with explanations. An experiment with 22 participants proved that this system was more efficient at reducing the number of scripting errors in comparison to the traditional manner of a human expert. The main contribution of this paper was to identify and reveal the efficacy of computerized supervision in comparison to a human supervisor. The proposed system decreased the writing-error-rates of learners from 32.7% to 3.4%, whereas the traditional practice reduced the error rates from 31.0% to 6.8%. This result shows that computerized supervision is more efficient than human supervision for learning calligraphy.

An English pronunciation learning system for Japanese students based on diagnosis of critical pronunciation errors

ReCALL ◽  
2004 ◽  
Vol 16 (1) ◽  
pp. 173-188 ◽  
Author(s):  
YASUSHI TSUBOTA ◽  
MASATAKE DANTSUJI ◽  
TATSUYA KAWAHARA
Keyword(s):  
Error Rate ◽  
Error Detection ◽  
Native Speakers ◽  
Error Rates ◽  
Learning System ◽  
Second Phase ◽  
Acoustic Model ◽  
Japanese Students ◽  
Speech Data ◽  
English Pronunciation

We have developed an English pronunciation learning system which estimates the intelligibility of Japanese learners' speech and ranks their errors from the viewpoint of improving their intelligibility to native speakers. Error diagnosis is particularly important in self-study since students tend to spend time on aspects of pronunciation that do not noticeably affect intelligibility. As a preliminary experiment, the speech of seven Japanese students was scored from 1 (hardly intelligible) to 5 (perfectly intelligible) by a linguistic expert. We also computed their error rates for each skill. We found that each intelligibility level is characterized by its distribution of error rates. Thus, we modeled each intelligibility level in accordance with its error rate. Error priority was calculated by comparing students' error rate distributions with that of the corresponding model for each intelligibility level. As non-native speech is acoustically broader than the speech of native speakers, we developed an acoustic model to perform automatic error detection using speech data obtained from Japanese students. As for supra-segmental error detection, we categorized errors frequently made by Japanese students and developed a separate acoustic model for that type of error detection. Pronunciation learning using this system involves two phases. In the first phase, students experience virtual conversation through video clips. They receive an error profile based on pronunciation errors detected during the conversation. Using the profile, students are able to grasp characteristic tendencies in their pronunciation errors which in effect lower their intelligibility. In the second phase, students practise correcting their individual errors using words and short phrases. They then receive information regarding the errors detected during this round of practice and instructions for correcting the errors. We have begun using this system in a CALL class at Kyoto University. We have evaluated system performance through the use of questionnaires and analysis of speech data logged in the server, and will present our findings in this paper.

Generating Personalized Navigation Learning Path Based on Knowledge Map

2014 ◽  
Vol 4 (2) ◽  
pp. 1-17
Author(s):  
Xiang Jun Huang ◽  
Chao Zhang ◽  
Qing Hua Zheng
Keyword(s):  
User Satisfaction ◽  
Information Overload ◽  
Rapid Development ◽  
Learning System ◽  
Knowledge Map ◽  
Learning Efficiency ◽  
Learning Path ◽  
Learning Instruction ◽  
E Learning ◽  
On Line

With a rapid development of Internet, E-Learning is becoming a new learning mode. E-Learning is not limited by time and space. It also has a large number of on-line learning resource. However, it has many disadvantages for students, such as information overload, disorientation, low learning efficiency, low user satisfaction and so on. Our aim is to improve learning efficiency and user satisfaction by overcoming information overload and disorientation of E-Learning system. This paper proposes an algorithm by combining Spreading-Activation Theory and techniques of classifying and sorting knowledge. The algorithm can generate a near optimal navigation learning path(NLP) based on a student's target knowledge unit(TKU) and knowledge map(KM) which it belongs to. NLP provides students an appropriate learning instruction to effectively eliminate disorientation during the process when they are learning interested knowledge units. The essential tasks of the algorithm is to filter redundant information and sort candidate knowledge units. So its realization process can be divided into three phrases: first, generating candidate complement map to overcome information overload. Because the candidate complement map only contains essential candidate knowledge units and learning dependencies among them to master TKU. Second, constructing learning features to discrete the candidate complement map to implement techniques of sorting knowledge conveniently. Final, sorting candidate knowledge units to get an appropriate NLP by using a Secondary Sort Strategy(SSS). The experimental results have shown that our method is sound for improving learning efficiency and users' satisfaction.

Laboratory error reporting rates can change significantly with year-over-year examination

Diagnosis ◽  
2018 ◽  
Vol 5 (1) ◽  
pp. 15-19 ◽  
Author(s):  
Michael A. Noble ◽  
Veronica Restelli ◽  
Annemarie Taylor ◽  
Douglas Cochrane
Keyword(s):  
Patient Safety ◽  
Error Rates ◽  
Rate Of Change ◽  
Learning System ◽  
Medical Laboratory ◽  
Safety Issues ◽  
Laboratory Error ◽  
Incident Reporting Systems ◽  
Reporting Systems ◽  
Reporting Rates

AbstractBackground:Incident reporting systems are useful tools to raise awareness of patient safety issues associated with healthcare error, including errors associated with the medical laboratory.Methods:Previously, we presented the analysis of data compiled by the British Columbia Patient Safety & Learning System over a 3-year period. A second comparable set was collected and analyzed to determine if reported error rates would tend to remain stable or change.Results:Compared to the original set, the second set presented changes that were both materially and statistically significant. Overall, the total number of reports increased by 297% with substantial changes between the pre-examination, examination and post-examination phases (χ2: 993.925, DF=20; p<0.00001). While the rate of change for pre-examination (clerical and collection) errors were not significantly different than the total year results, the rate of change for reporting examination errors rose by 998%. While the exact reason for dramatic change is not clear, possible explanations are provided.Conclusions:Longitudinal error rate tracking is a useful approach to monitor for laboratory quality improvement.

scholarly journals PhyloPythiaS+: a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1603 ◽  
Author(s):  
Ivan Gregor ◽  
Johannes Dröge ◽  
Melanie Schirmer ◽  
Christopher Quince ◽  
Alice C. McHardy
Keyword(s):  
Error Rates ◽  
The Other ◽  
Marker Genes ◽  
Training Method ◽  
Human Expert ◽  
Training Sequences ◽  
Important Challenge ◽  
Taxonomic Characterization ◽  
Taxonomic Binning

Background.Metagenomics is an approach for characterizing environmental microbial communitiesin situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into ‘bins’ representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trainedPhyloPythiaSpackage, where a human expert decides on the taxa to incorporate in the model and identifies ‘training’ sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have.Results.We have developedPhyloPythiaS+, a successor to ourPhyloPythia(S)software. The new (+) component performs the work previously done by the human expert.PhyloPythiaS+also includes a newk-mer counting algorithm, which accelerated the simultaneous counting of 4–6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion.PhyloPythiaS+was compared toMEGAN,taxator-tk,Krakenand the genericPhyloPythiaSmodel. The results showed thatPhyloPythiaS+performs especially well for samples originating from novel environments in comparison to the other methods.Availability.PhyloPythiaS+in a virtual machine is available for installation under Windows, Unix systems or OS X on:https://github.com/algbioi/ppsp/wiki.

New Functions for Stimulating Learners' Motivation in a Web-Based e-Learning System

2010 ◽  
pp. 1709-1722
Author(s):  
Keita Matsuo ◽  
Leonard Barolli ◽  
Fatos Xhafa ◽  
Akio Koyama ◽  
Arjan Durresi
Keyword(s):  
System Performance ◽  
Ranking Function ◽  
Learning Systems ◽  
Learning System ◽  
The Internet ◽  
Learning Situation ◽  
Web Based ◽  
Learning Efficiency ◽  
E Learning

Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and during the last few years enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learningcompletion rate is low. One of the reasons is the low study desire and motivation. In our previous work, we implemented an e-learning system that is able to increase the learning efficiency by stimulating learners’ motivation. In this work, we designed and implemented new functionssuch as: interface changing function, new ranking function and learner’s learning situation checking function to improve the system performance.

scholarly journals Design Flow of English Learning System Based on Item Response Theory

2017 ◽  
Vol 12 (12) ◽  
pp. 91
Author(s):  
Yuemei Liu ◽  
Xuetao Zhao
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Design Flow ◽  
Learning System ◽  
Item Selection ◽  
Good Effect ◽  
English Learning ◽  
Response Theory ◽  
Learning Efficiency ◽  
Self Adaptive

The popularity of computer technology in English teaching has led to the establishment of many English learning platforms, but the enhancement of students’ English proficiency is limited due to the lack of relevance, self-adaptive test questions and analytical ability. The project management theory is introduced into English learning, which can provide students with teaching content and test questions that are more suitable for their own actual situation through a more intelligent, personalized way. At the same time, the static and dynamic database model based on students’ own learning behavior is constructed to facilitate storage of students’ learning record. Combined with the advantages of hierarchical selection, SH method and improved polynomial model, this paper puts forward a new type of item section model. This paper introduces the basic theory and related technology, and then makes an in-depth study on the demand analysis of English learning system. Finally, this paper realizes the design of English learning system based on item response theory and validates the good effect of English item selection from the perspective of application. The system provides teachers and students with convenient learning strategies, item selection strategies, test strategies and academic performance strategies. The introduction of item response theory enables the system to become truly student-centered and provides a more comprehensive and self-adaptive learning model, which is of great significance for improving the learning efficiency of English learning and the learning efficiency of college students in China.

Impact of Augmented Reality on Programming Language Learning: Efficiency and Perception

2017 ◽  
Vol 56 (2) ◽  
pp. 254-271 ◽  
Author(s):  
Chin-Hung Teng ◽  
Jr-Yi Chen ◽  
Zhi-Hong Chen
Keyword(s):  
Augmented Reality ◽  
Language Learning ◽  
Programming Language ◽  
Three Dimensional ◽  
Spatial Relationship ◽  
Learning System ◽  
Learning Efficiency ◽  
Science And Technology Education ◽  
3D Applications ◽  
Enhanced Learning

Although the learning of programming language is critical in science and technology education, it might be difficult for some students, especially novices. One possible reason might be the fact that programming language, especially for three-dimensional (3D) applications, is too complex and abstract for these students to understand. Programming for 3D applications requires understanding the spatial relationship of 3D objects and hence needs a visualization technique more. In view of this, this article presents an augmented reality (AR)-enhanced learning system that offers visual representation and interactivity to help students learn programming for 3D applications. To examine the influences of such an AR-enhanced system on student learning, a within-group experiment with 34 college students was conducted. All students used both of an AR-enhanced version and an ordinary version. The findings revealed that the AR-enhanced version made students have better learning efficiency than the ordinary system. In addition, the AR-enhanced system also made students have enhanced perceptions in terms of system usability, flow experience, and usage perception. Based on the results, further development of AR-enhanced learning systems is also suggested and discussed.

Perceptual Characteristics of Consonant Production in Apraxia of Speech and Aphasia

2019 ◽  
Vol 28 (4) ◽  
pp. 1411-1431 ◽  
Author(s):  
Lauren Bislick ◽  
William D. Hula
Keyword(s):  
Error Rate ◽  
Error Rates ◽  
Group Differences ◽  
Diagnostic Process ◽  
Apraxia Of Speech ◽  
Contextual Variables ◽  
Error Type ◽  
Type Distribution ◽  
Phonetic Features ◽  
Syllable Position

Purpose This retrospective analysis examined group differences in error rate across 4 contextual variables (clusters vs. singletons, syllable position, number of syllables, and articulatory phonetic features) in adults with apraxia of speech (AOS) and adults with aphasia only. Group differences in the distribution of error type across contextual variables were also examined. Method Ten individuals with acquired AOS and aphasia and 11 individuals with aphasia participated in this study. In the context of a 2-group experimental design, the influence of 4 contextual variables on error rate and error type distribution was examined via repetition of 29 multisyllabic words. Error rates were analyzed using Bayesian methods, whereas distribution of error type was examined via descriptive statistics. Results There were 4 findings of robust differences between the 2 groups. These differences were found for syllable position, number of syllables, manner of articulation, and voicing. Group differences were less robust for clusters versus singletons and place of articulation. Results of error type distribution show a high proportion of distortion and substitution errors in speakers with AOS and a high proportion of substitution and omission errors in speakers with aphasia. Conclusion Findings add to the continued effort to improve the understanding and assessment of AOS and aphasia. Several contextual variables more consistently influenced breakdown in participants with AOS compared to participants with aphasia and should be considered during the diagnostic process. Supplemental Material https://doi.org/10.23641/asha.9701690

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