• School Mathematics
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• v.5 no.2
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• pp.209-221
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• 2003

This study focuses on the necessity of developing the material for teachers who are involved in diagnosing and prescribing students' mathematical errors. And it also intends to stimulate the related research of this area. For this, it tries to suggest the fundamental components-(1)types and frequencies of errors, (2) diagnostic test kit, (3)causes of errors, (4)ideas for prevention, (5)ideas for correction, (6)practice for settlement, and (7) performance test kit and frame of the teaching guide book for the teachers according to the general procedure of diagnosis and prescription. Finally it provides the concrete research areas for the future study.

• School Mathematics
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• v.10 no.2
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• pp.259-277
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• 2008

Conventional assessments only provide a single summary score that indicates the overall performance level or achievement level of a student in a single learning area. For assessments to be more effective, test should provide useful diagnostic information in addition to single overall scores. Cognitive diagnosis modeling provides useful information by estimating individual knowledge states by assessing whether an examinee has mastered specific attributes measured by the test(Embretson, 1990; DiBello, Stout, & Rousses, 1995; Tatsuoka, 1995). Attributes are skills or cognitive processes that are required to perform correctly on a particular item. By the results of this study, students, parents, and teachers would be able to see where a student stands with respect to mastering the attributes. Such information could be used to guide the learner and teacher toward areas requiring more study. By being able to assess where they stand in regard to the attributes that compose an item, students can plan a more effective learning path to be desired proficiency levels. It would be very helpful to the examinee if score reports can provide the scale scores as well as the skill profiles. While the scale scores are believed to provide students' math ability by reporting only one score point, the skill profiles can offer a skill level of strong, weak or mixed for each student for each skill.

• Journal of Engineering Education Research
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• v.16 no.2
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• pp.58-68
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• 2013

Few colleges offer remedial basic math courses for college freshmen who have not passed math placement tests or whose scholastic aptitude test score in mathematics is low. This research is aiming for the curriculum development of basic college mathematics and its effective implementation. First, an in depth statistical analysis on the basic math courses for universities in Seoul area has been done. Second, diagnostic test and longitudinal study have been carried out for one institute. Based on these, basic concepts and areas critical for the success of Calculus course are extracted. Standards and contents for the remedial math courses are suggested.

• Journal of The Korean Association For Science Education
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• v.27 no.4
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• pp.346-353
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• 2007

In order to be efficient in teaching, a teacher should understand the current learner's level through diagnostic evaluation. This study has examined the major issues arising from the noble diagnostic assessment tool based on the theory of knowledge space. The knowledge state analysis method is actualizing the theory of knowledge space for practical use. The knowledge state analysis method is very advantageous when a certain group or individual student's knowledge structure is analyzed especially for strong hierarchical subjects such as mathematics, physics, chemistry, etc. Students' knowledge state helps design an efficient teaching plan by referring their hierarchical knowledge structure. The knowledge state analysis method can be enhanced by computer due to fast data processing. In addition, each student's knowledge can be improved effectively through individualistic feedback depending on individualized knowledge structure. In this study, we have developed a diagnostic assessment test for measuring student's learning outcome which is unattainable from the conventional examination. The diagnostic assessment test was administered to middle school students and analyzed by the knowledge state analysis method. The analyzed results show that students' knowledge structure after learning found to be more structured and well-defined than the knowledge structure before the learning.

• The Mathematical Education
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• v.61 no.2
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• pp.257-271
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• 2022

As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.

• Journal of The Korean Association of Information Education
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• v.6 no.1
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• pp.1-12
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• 2002

The purpose of this paper is to design and implement a LED (LEarner Diagnosis system). Mathematics which has learning hierarchies is difficult to learn, if learners don't understand previous learning contents. Before a learner starts learning, the LED offers diagnostic problems to diagnose the learner's understanding ability of the previous learning contents. If the learner don't understand it, then the LED present supplementary contents. After finishing the study, the learner has to pass the test to diagnose whether the learner do mastery learning. The LED makes learners do supplement previous contents, if learners don't understand it, and master all of the contents, learners can achieve learning object.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.101-112
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• 1997

The purpose of this study was to examine usefulness of KEDI-Individual Basic Learning Skills Test as a diagnostic tool of learning disorders(LD). Learning disorder group consisted of two subgroups, verbal learning disorder group(VLD, n＝34) and nonverbal learning disorder group(NVLD, n＝14). Comparison group consisted of Dysthymia Disorder subgroup(n＝11) and Normal subgroup(n＝20). Performance of intelligence test and achievement test was examined in all 4 subgroups. In KEDI-WISC, VLD subgroup revealed primary problems in vocabulary, information and verbal-auditory attention test. NVLD group revealed primary problems in almost all performance tests such as visual acuity, psycho-motor coordination speed and visual-spatial organizations ability subtest. In KEDI-Individual Basic Learning Test, VLD group revealed primary problems in phonological coding process, word recognition and mathematics. For successful classification of LD children, the importance of achievement test and intelligence test was discussed by discriminant analysis and factor analysis. The results indicate that KEDI-Individual Basic Learning Skills is of considerable usefulness in diagnosing LD, but must be used in subtests, and additional tests must be conducted for thorough exploration of LD.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.1-35
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• 2024

The assumption of homoscedasticity is one of the most crucial assumptions for many parametric tests used in the biological sciences. The aim of this paper is to compare the empirical probability of type I error and the power of ten parametric and two non-parametric tests for homoscedasticity with simulations under different types of distributions, number of groups, number of samples per group, variance ratio and significance levels, as well as through empirical data from an agricultural experiment. According to the findings of the simulation study, when there is no violation of the assumption of normality and the groups have equal variances and equal number of samples, the Bhandary-Dai, Cochran's C, Hartley's Fmax, Levene (trimmed mean) and Bartlett tests are considered robust. The Levene (absolute and square deviations) tests show a high probability of type I error in a small number of samples, which increases as the number of groups rises. When data groups display a nonnormal distribution, researchers should utilize the Levene (trimmed mean), O'Brien and Brown-Forsythe tests. On the other hand, if the assumption of normality is not violated but diagnostic plots indicate unequal variances between groups, researchers are advised to use the Bartlett, Z-variance, Bhandary-Dai and Levene (trimmed mean) tests. Assessing the tests being considered, the test that stands out as the most well-rounded choice is the Levene's test (trimmed mean), which provides satisfactory type I error control and relatively high power. According to the findings of the study and for the scenarios considered, the two non-parametric tests are not recommended. In conclusion, it is suggested to initially check for normality and consider the number of samples per group before choosing the most appropriate test for homoscedasticity.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3996-4004
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• 2022

The data collected by the In-Situ Decommissioning (ISD) sensors are time-specific, age-specific, and developmental stage-specific. Research has been done on the stream data collected by ISD testbed in the recent few years to seek both frequent episodes and abnormal frequent episodes. Frequent episodes in the data stream have confirmed the daily cycle of the sensor responses and established sequences of different types of sensors, which was verified by the experimental setup of the ISD Sensor Network Test Bed. However, the discovery of abnormal frequent episodes remained a challenge because these abnormal frequent episodes are very small signals and may be buried in the background noise of voltage and current changes. In this work, we proposed Advanced Data Analytics (ADA) methods that are applied to the baseline data to identify frequent episodes and extended our approach by adding more features extracted from the baseline data to discover abnormal frequent episodes, which may lead to the early indicators of ISD system failures. In the study, we have evaluated our approach using the baseline data, and the performance evaluation results show that our approach is able to discover frequent episodes as well as abnormal frequent episodes conveniently.

• Communications of Mathematical Education
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• v.29 no.1
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• pp.111-130
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• 2015

The purpose of the study was to investigate the differences between two groups of students according to information recognition styles such as visual learners and linguistic learners. Two instructional methods, algeblocks and factorization formula, were utilized to introduce the factorization. Four students were participated for the study, and two of them were visual learners and the other two were linguistic learners based on learning style test. Interviews and the diagnostic tests were implemented before the instructions which were lasted for 6 sessions. After the instructions all the participants were interviewed and the researchers also interviewed them 5 days later. The results of the study were the followings: 1. All the participants regardless of their learning style revealed that algeblocks were helpful in understanding the factorization. 2. Visual learners were more likely using algeblocks, while the linguistic learners were more enthusiastic and proficient in using formula to solve the problems. 3. Five days later, two types of learning style students revealed different tendencies. Visual learners mainly used algeblocks, and linguistic learners were not enthusiastic about using algeblocks and one of them did not use them at all. 4. Five days later, two visual learners could not remember the formula, but linguistic learners could remember the formula in somewhat different level.