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Journal articles on the topic 'Learning discrepancy'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Frederickson, N., and R. Reason. "Discrepancy Definitions of Specific Learning Difficulties." Educational Psychology in Practice 10, no. 4 (January 1995): 195–205. http://dx.doi.org/10.1080/0266736950100401.

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2

Hui, Bowen, and Robert Campbell. "Discrepancy between Learning and Practicing Digital Citizenship." Journal of Academic Ethics 16, no. 2 (January 20, 2018): 117–31. http://dx.doi.org/10.1007/s10805-018-9302-9.

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3

Reynolds, Cecil R. "Two Key Concepts in the Diagnosis of Learning Disabilities and the Habilitation of Learning." Learning Disability Quarterly 15, no. 1 (February 1992): 2–12. http://dx.doi.org/10.2307/1510559.

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Two key concepts in diagnosing learning disabilities (“severe discrepancy” and “process dysfunction”) are reviewed and their relationship to the habilitation of learning is discussed. Specific guidelines are delineated for correctly calculating a severe discrepancy between an individual's age and ability and level of academic attainment. Methods and reasons for evaluating processing skills are also discussed. Process models of deficit-centered remediation are dismissed in favor of strength models of remediation.

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4

Cahan, Sorel, Dafna Fono, and Ronit Nirel. "The Regression-Based Discrepancy Definition of Learning Disability." Journal of Learning Disabilities 45, no. 2 (April 7, 2010): 170–78. http://dx.doi.org/10.1177/0022219409355480.

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5

Li, Leida, Yu Zhou, Ke Gu, Yuzhe Yang, and Yuming Fang. "Blind Realistic Blur Assessment Based on Discrepancy Learning." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 11 (November 2020): 3859–69. http://dx.doi.org/10.1109/tcsvt.2019.2947450.

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Viering, Tom J., Jesse H. Krijthe, and Marco Loog. "Nuclear discrepancy for single-shot batch active learning." Machine Learning 108, no. 8-9 (June 26, 2019): 1561–99. http://dx.doi.org/10.1007/s10994-019-05817-y.

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7

Ashton, Chris. "In Defence of Discrepancy Definitions of Specific Learning Difficulties." Educational Psychology in Practice 12, no. 3 (October 1996): 131–40. http://dx.doi.org/10.1080/0266736960120301.

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8

Cervellera, Cristiano, and Danilo Maccio. "Learning With Kernel Smoothing Models and Low-Discrepancy Sampling." IEEE Transactions on Neural Networks and Learning Systems 24, no. 3 (March 2013): 504–9. http://dx.doi.org/10.1109/tnnls.2012.2236353.

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9

Brynjarsdóttir, Jenný, and Anthony OʼHagan. "Learning about physical parameters: the importance of model discrepancy." Inverse Problems 30, no. 11 (October 29, 2014): 114007. http://dx.doi.org/10.1088/0266-5611/30/11/114007.

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10

Simpson, Robert G., and Joseph A. Buckhalt. "A Non-Formula Discrepancy Model to Identify Learning Disabilities." School Psychology International 11, no. 4 (November 1990): 273–79. http://dx.doi.org/10.1177/0143034390114004.

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MacMillan, Donald L., Frank M. Gresham, and Kathleen M. Bocian. "Discrepancy Between Definitions of Learning Disabilities and School Practices." Journal of Learning Disabilities 31, no. 4 (July 1998): 314–26. http://dx.doi.org/10.1177/002221949803100401.

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12

Zhang, Lingling, Jun Liu, Minnan Luo, Xiaojun Chang, and Qinghua Zheng. "Deep Semisupervised Zero-Shot Learning with Maximum Mean Discrepancy." Neural Computation 30, no. 5 (May 2018): 1426–47. http://dx.doi.org/10.1162/neco_a_01071.

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Due to the difficulty of collecting labeled images for hundreds of thousands of visual categories, zero-shot learning, where unseen categories do not have any labeled images in training stage, has attracted more attention. In the past, many studies focused on transferring knowledge from seen to unseen categories by projecting all category labels into a semantic space. However, the label embeddings could not adequately express the semantics of categories. Furthermore, the common semantics of seen and unseen instances cannot be captured accurately because the distribution of these instances may be quite different. For these issues, we propose a novel deep semisupervised method by jointly considering the heterogeneity gap between different modalities and the correlation among unimodal instances. This method replaces the original labels with the corresponding textual descriptions to better capture the category semantics. This method also overcomes the problem of distribution difference by minimizing the maximum mean discrepancy between seen and unseen instance distributions. Extensive experimental results on two benchmark data sets, CU200-Birds and Oxford Flowers-102, indicate that our method achieves significant improvements over previous methods.

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13

Reynolds, Cecil R. "Measuring the Aptitude-Achievement Discrepancy in Learning Disability Diagnosis." Remedial and Special Education 6, no. 5 (September 1985): 37–48. http://dx.doi.org/10.1177/074193258500600508.

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Etesami, Jalal, Negar Kiyavash, and Todd Coleman. "Learning Minimal Latent Directed Information Polytrees." Neural Computation 28, no. 9 (September 2016): 1723–68. http://dx.doi.org/10.1162/neco_a_00874.

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We propose an approach for learning latent directed polytrees as long as there exists an appropriately defined discrepancy measure between the observed nodes. Specifically, we use our approach for learning directed information polytrees where samples are available from only a subset of processes. Directed information trees are a new type of probabilistic graphical models that represent the causal dynamics among a set of random processes in a stochastic system. We prove that the approach is consistent for learning minimal latent directed trees. We analyze the sample complexity of the learning task when the empirical estimator of mutual information is used as the discrepancy measure.

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15

Jayanta, I. Nyoman Laba, Kadek Dewi Suryantari, and Made Sumantri. "An Analysis of Discrepancy between the Lesson Plan and the Implementation of Curriculum 2013 in Teaching and Learning Process in SD Negeri 4 Kaliuntu." Journal of Education Research and Evaluation 1, no. 2 (May 2, 2017): 73. http://dx.doi.org/10.23887/jere.v1i2.9839.

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This research aimed to determine how large the discrepancy between the ideal condition and the implementation of 2013 curriculum in teaching and learning process in SD Negeri 4 Kaliuntu reviewed from the lesson plan and teaching learning process. This research was evaluative research which apply discrepancy model. Measurement of the program effectiveness was done by comparing the ideal conditions, based on Permendikbud No. 103 of 2014, with the real conditions on the implementation of 2013 Curriculum at SD Negeri 4 Kaliuntu. Lesson plan data, was measured by the study documentation methode. Meanwhile, the teaching and learning process data was measured by observation methode. Subject of this research consisted of 6 teachers of SD Negeri 4 Kaliuntu. The data scores was form for all variables were analyzed by using the Wilcoxon’s signed rank test. The sign difference and score difference were calculated with a predetermined standard. The result of the analysis indicate that the large of discrepancy between ideal conditions (based on Permendikbud No. 103 of 2014) and the implementation of 2013 Curriculum in SD Negeri 4 Kaliuntu in terms of lesson plan is 12,17%. The discrepancy lies on indicators of learning activities, assessment of learning result, media and learning resources. Reviewed from the teaching learning process, the discrepancy occurs at 19.17%. The discrepancy occurs in the indicator of giving apperception, convey of evaluation techniques that will be used by teachers, students are directed reflection of the activities that have been implemented, and the provision of follow-up by giving remedial, enrichment, or giving assignments.

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John, David N., Michael Schick, and Vincent Heuveline. "Learning Model Discrepancy of an Electric Motor with Bayesian Inference." Proceedings 33, no. 1 (November 25, 2019): 11. http://dx.doi.org/10.3390/proceedings2019033011.

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Uncertainty Quantification (UQ) is highly requested in computational modeling and simulation, especially in an industrial context. With the continuous evolution of modern complex systems demands on quality and reliability of simulation models increase. A main challenge is related to the fact that the considered computational models are rarely able to represent the true physics perfectly and demonstrate a discrepancy compared to measurement data. Further, an accurate knowledge of considered model parameters is usually not available. e.g., fluctuations in manufacturing processes of hardware components or noise in sensors introduce uncertainties which must be quantified in an appropriate way. Mathematically, such UQ tasks are posed as inverse problems, requiring efficient methods to solve. This work investigates the influence of model discrepancies onto the calibration of physical model parameters and further considers a Bayesian inference framework including an attempt to correct for model discrepancy. A polynomial expansion is used to approximate and learn model discrepancy. This work extends by discussion and specification of a guideline on how to define the model discrepancy term complexity, based on the available data. Application to an electric motor model with synthetic measurements illustrates the importance and promising perspective of the method.

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17

Short, Elizabeth J., Lynne Feagans, James D. McKinney, and Mark I. Appelbaum. "Longitudinal Stability of LD Subtypes Based on Age- and IQ-Achievement Discrepancies." Learning Disability Quarterly 9, no. 3 (August 1986): 214–25. http://dx.doi.org/10.2307/1510467.

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A twofold classification system based on discrepancy formulas was employed to subtype normal (NLD) and learning disabled (LD) children into educationally meaningful groups based upon simultaneous consideration of IQ-achievement and age-achievement discrepancies. 52 LD and 58 NLD children were classified into five groups: overachievers, target achievers, underachievers, slow learners, and disabled achievers. This classification system appeared useful by its ability to differentiate disabled learners, who were discrepant in reading achievement as predicted by both age and IQ, from slow learners, who were only discrepant in reading achievement as predicted by age. The longitudinal stability of the classification scheme was examined over a 3-year period. As expected, NLD students' achievement was more predictable over time than their LD peers' LD students became more disabled with age in spite of remedial services. The educational implications and theoretical relevance of discrepancy-based subtypes are discussed.

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Gardner, P., T. J. Rogers, C. Lord, and R. J. Barthorpe. "Learning model discrepancy: A Gaussian process and sampling-based approach." Mechanical Systems and Signal Processing 152 (May 2021): 107381. http://dx.doi.org/10.1016/j.ymssp.2020.107381.

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19

Humphries, Tom, and Janet Bone. "Validity of IQ-Achievement Discrepancy Criteria for Identifying Learning Disabilities." Canadian Journal of School Psychology 9, no. 2 (June 1994): 181–91. http://dx.doi.org/10.1177/082957359400900206.

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20

Boso, F., and D. M. Tartakovsky. "Learning on dynamic statistical manifolds." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2239 (July 2020): 20200213. http://dx.doi.org/10.1098/rspa.2020.0213.

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Hyperbolic balance laws with uncertain (random) parameters and inputs are ubiquitous in science and engineering. Quantification of uncertainty in predictions derived from such laws, and reduction of predictive uncertainty via data assimilation, remain an open challenge. That is due to nonlinearity of governing equations, whose solutions are highly non-Gaussian and often discontinuous. To ameliorate these issues in a computationally efficient way, we use the method of distributions, which here takes the form of a deterministic equation for spatio-temporal evolution of the cumulative distribution function (CDF) of the random system state, as a means of forward uncertainty propagation. Uncertainty reduction is achieved by recasting the standard loss function, i.e. discrepancy between observations and model predictions, in distributional terms. This step exploits the equivalence between minimization of the square error discrepancy and the Kullback–Leibler divergence. The loss function is regularized by adding a Lagrangian constraint enforcing fulfilment of the CDF equation. Minimization is performed sequentially, progressively updating the parameters of the CDF equation as more measurements are assimilated.

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21

Widyani, Ni Luh Sadewi, Ign I. Wyn Suwatra, and I. Wayan Widiana. "The Discrepancy of Curriculum 2013 in Teaching and Learning Process Based on Permendikbud No 65 of 2013 in SD Laboratorium Undiksha." Journal of Education Research and Evaluation 1, no. 2 (May 2, 2017): 57. http://dx.doi.org/10.23887/jere.v1i2.9840.

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This research aimed to determine how large the discrepancy between the ideal condition and the implementation of 2013 curriculum in teaching and learning process at SD Laboratorium Undiksha reviewed from the lesson plan, teaching learning process, assessment and learning outcomes, and the monitoring of learning process. This study was evaluative research which apply discrepancy model. Measurement of the program effectiveness was done by comparing the ideal conditions, based on Permendikbud No. 65 of 2013, with the real conditions on the implementation of 2013 Curriculum at SD Laboratorium Undiksha. Lesson plan, assessment process, and learning outcomes variable, were measured by the study documentation sheets. Meanwhile, the teaching and learning process variable was measured by observation sheets, and the monitoring of learning process variable was measured by questionnaire. Sample of the study consisted of 4 teachers of SD Laboratorium Undiksha. The scores form for all variables were analyzed using the Wilcoxon’s signed rank test. The sign difference and score difference were calculated with a predetermined standard. The result of the analysis showed that the large of discrepancy between ideal conditions (based on Permendikbud No. 65 of 2013) and the implementation of 2013 Curriculum at SD Laboratorium Undiksha in terms of lesson plan is 17,90%, teaching and learning process is 12,11%, assessment and learning outcomes is 6,67%, and the monitoring of learning process is 10,95%. The discrepancy is generally due to the difficulty in changing the mindset of most education staff, tardiness of the books’ distribution, and uneven training programs for educators.

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22

Callinan, Sarah, Everarda Cunningham, and Stephen Theiler. "Revisiting Discrepancy Theory in Learning Disabilities: What Went Wrong and Why We Should Go Back." Australian Journal of Guidance and Counselling 23, no. 1 (November 16, 2012): 1–17. http://dx.doi.org/10.1017/jgc.2012.22.

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The rise in popularity of Response to Intervention (RTI) as a method of identifying Learning Disabilities (LD) is partially due to the psychometric and theoretical issues inherent to the use of IQ tests in the once popular discrepancy method of identification. However, both RTI and discrepancy theories have their shortcomings, and criticisms directed at either method are usually applicable to both. This conceptual article puts forward a justification for using tests of the cognitive processes that are implicated in LD as a better method of LD identification. Although the unsuitability of the discrepancy method to accurately identify LD students is well established, it does represent the construct of LD well. Therefore, the discrepancy method can be used as an effective baseline measure against which improved identification procedures based on cognitive processes can be measured. Once these cognitive processes are more clearly defined, tests of these processes offer promise for LD identification.

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23

Finlan, Thomas G. "Do State Methods of Quantifying a Severe Discrepancy Result in Fewer Students with Learning Disabilities?" Learning Disability Quarterly 15, no. 2 (May 1992): 129–34. http://dx.doi.org/10.2307/1511014.

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Minimizing misclassification of students with LD is a major concern for policymakers, particularly if financial incentives are available for placing children in such programs. In the current study, individual states' methods of defining a severe discrepancy for determining LD eligibility are examined, as well as the way use of such methods influences misclassification. The percentage of the total population identified as LD by individual states (as reported to the federal government) was compared. Results showed variations from 2.19% to 8.66% in the percentage of students aged 7 to 16 identified as LD across states. Seven of the states in the lowest percentage decile used a method for determining a severe discrepancy; in comparison, only two of the states in the decile identifying the most students used a method of determining a severe discrepancy. It was concluded that use of any method to determine a severe discrepancy may help reduce the number of inappropriate placements resulting from labeling students as LD.

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Chen, Kaixuan, Lina Yao, Dalin Zhang, Xiaojun Chang, Guodong Long, and Sen Wang. "Distributionally Robust Semi-Supervised Learning for People-Centric Sensing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3321–28. http://dx.doi.org/10.1609/aaai.v33i01.33013321.

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Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, humangenerated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.

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25

Ravi, R. "Learning Discrepancy in relation to Personality Factors among Primary School Students." i-manager’s Journal on Educational Psychology 1, no. 3 (January 15, 2008): 55–60. http://dx.doi.org/10.26634/jpsy.1.3.450.

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Li, Lingling, Jie Zeng, Licheng Jiao, Pujiang Liang, Fang Liu, and Shuyuan Yang. "Online Active Extreme Learning Machine With Discrepancy Sampling for PolSAR Classification." IEEE Transactions on Geoscience and Remote Sensing 58, no. 3 (March 2020): 2027–41. http://dx.doi.org/10.1109/tgrs.2019.2952236.

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Dombrowski, Stefan C., Randy W. Kamphaus, and Cecil R. Reynolds. "After the Demise of the Discrepancy: Proposed Learning Disabilities Diagnostic Criteria." Professional Psychology: Research and Practice 35, no. 4 (2004): 364–72. http://dx.doi.org/10.1037/0735-7028.35.4.364.

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Cervellera, C., and M. Muselli. "Deterministic Design for Neural Network Learning: An Approach Based on Discrepancy." IEEE Transactions on Neural Networks 15, no. 3 (May 2004): 533–44. http://dx.doi.org/10.1109/tnn.2004.824413.

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Moták, Ladislav, Nathalie Huet, Catherine Gabaude, and Jean-Claude Bougeant. "Older drivers' self-regulation: Discrepancy reduction or region of proximal learning?" Psychology and Aging 27, no. 4 (2012): 1164–71. http://dx.doi.org/10.1037/a0028650.

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Saputro, Ekaresta Prihardjati, and Suharsimi Arikunto. "Keefektifan manajemen program pembelajaran BIPA (Bahasa Indonesia bagi Penutur Asing) di Kota Yogyakarta." Jurnal Akuntabilitas Manajemen Pendidikan 6, no. 1 (April 30, 2018): 122. http://dx.doi.org/10.21831/amp.v6i1.8066.

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Penelitian ini bertujuan untuk mengevaluasi manajemen program pembelajaran BIPA di lembaga BIPA Kota Yogyakarta. Penelitian ini merupakan penelitian evaluasi dengan pendekatan kombinasi kualitatif dan kuantitatif. Model evaluasi yang digunakan adalah discrepancy evaluation model. Hasil penelitian menunjukkan bahwa implementasi manajemen program pembelajaran BIPA (1) aspek perencanaan program mencapai 93%, kesenjangan 7% dengan kriteria “sangat efektif”; (2) aspek pengorganisasian program mencapai 99%, kesenjangan 1% dengan kriteria “sangat efektif”; (3) aspek pelaksanaan program mencapai 93%, kesenjangan 7% dengan kriteria “sangat efektif”; (4) aspek pengawasan program mencapai 86%, kesenjangan 14% dengan kriteria “sangat efektif”. Secara keseluruhan keefektifan manajemen program pembelajaran BIPA memiliki kriteria “sangat efektif”, dengan capaian 93% kesenjangan 7%.Kata kunci: evaluasi; keefektifan; manajemen program pembelajaran BIPA THE EFFECTIVENESS OF BIPA (INDONESIAN LANGUAGE FOR FOREIGNER) PROGRAM MANAGEMENT IN YOGYAKARTAAbstractThis study is aimed to evaluate the management of BIPA learning program of BIPA course in Yogyakarta. This research is an evaluation research with the combination of qualitative and quantitative approach. Evaluation model used in this research is discrepancy evaluation model. The result of the study shows that the implementation of the management of BIPA learning program (1) the planning program is 93% performance, 7% discrepancy with criteria “very effective”; (2) the organizing aspect is 99% performance and 1% discrepancy with criteria “very effective”; (3) the implementing aspect is 93% performance and 7% discrepancy with criteria “very effective”; (4) the monitoring aspect is 86% performance 14% discrepancy with criteria “very effective”. After all the effectiveness of the management of BIPA learning program have “very effective” criteria, with 93% performance and 7% discrepancy.Keywords: evaluation, the effectiveness, BIPA program management

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Albuquerque, Cristina Petrucci. "Representations of Learning Disabilities in Portugal." Psychologica 62, no. 2 (December 18, 2019): 75–93. http://dx.doi.org/10.14195/1647-8606_62-2_5.

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It is essential to study learning disabilities (LD) representations by education professionals since they are the ones who are more directly involved in their identification and intervention. This research analyzes the representations of the concept of LD of 310 Portuguese education professionals (regular education teachers, special education teachers, and psychologists). Through a questionnaire that consists of three parts, we examine the perspectives of the professionals regarding: 1) the identification criteria (e.g., exclusion; discrepancy; RTI) and the phenomenology of LD (manifestations; specific nature of LD); 2) the use, broad or narrow, of the LD term; and 3) the Portuguese legislation regarding LD. The results obtained indicate the agreement with the manifestations of LD (e.g., low achievement), with the criterion of discrepancy and with a dimensional view of LD. The professionals considered that LD may be due to contextual factors (e.g., socioeconomic disadvantage, family interactions, and inadequate teaching conditions). Participants also expressed agreement with a very comprehensive use of the term (e.g., in the absence of a diagnosis, in school failure situations). Education professionals also expressed a negative perspective regarding the educational support given to LD in Portugal. The comparisons between professional groups have documented the existence of certain significant differences.

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Hein, Helle, and Ulo Lepik. "LEARNING TRAJECTORIES OF DYNAMICAL SYSTEMS." Mathematical Modelling and Analysis 17, no. 4 (September 1, 2012): 519–31. http://dx.doi.org/10.3846/13926292.2012.706654.

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The aim of the present paper is to describe the method that is capable of adjusting the parameters of a dynamical system so that the trajectories gain certain specified properties. Three problems are considered: (i) learning fixed points, (ii) learning to periodic trajectories, (iii) restrictions on the trajectories. An error function, which measures the discrepancy between the actual and desired trajectories is introduced. Numerical results of several examples, which illustrate the efficiency of the method, are presented.

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Obradovic, Svetlana, and Nadezda Krstic. "Teachers’ intuition and knowledge in detecting specific learning disabilities." Zbornik Instituta za pedagoska istrazivanja 44, no. 2 (2012): 316–31. http://dx.doi.org/10.2298/zipi1202316o.

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The aim of the study was to investigate primary school teachers? proficiency in detecting the ability-achievement discrepancy as a landmark of possible specific developmental learning disabilities (SLD). Twenty-two teachers in five schools attempted to select, in accordance with their perception and out of a larger preliminary sample, those students whose school results revealed: (a) discrepancy between school achievement and general abilities (the group of purportedly disharmonic children, GPD) or (b) concordance between general abilities and achievement (the group of purportedly harmonic children, GPH). The children were tested by REVISK, while teachers re-assessed students? reading, writing and arithmetic performance against a simple structured questionnaire based on demands of the approved elementary school program delineated by the Ministry of Education of the Republic of Serbia. Research results indicate that more than 60% of children originally qualified to GPH have actually shown significant discrepancy between targeted scholastic skills and (normal) general intelligence. The data suggested some association between students? disparity in attainment and teachers? attribution accuracy, while the only homogenous quantitative marker of misplaced children were decreased values on some of the REVISK Verbal subscale tests. This study has shown that teachers can use their professional knowledge to enhance their capability to detect children with specific learning disabilities. In absence of criterion-referenced tests of reading, writing and mathematics, a structured approach to the projected course of skill progress might support teachers? confidence regarding likely SLD.

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Mustafa, Pinton Setya. "Model Discrepancy sebagai Evaluasi Program Pendidikan." PALAPA 9, no. 1 (May 25, 2021): 182–98. http://dx.doi.org/10.36088/palapa.v9i1.1067.

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Programs designed to advance education continue to progress and develop dynamically. Improving the program requires an evaluation process. There are various evaluation models used in improving educational programs depending on the aspects needed for improvement or refinement. The purpose of this research is to discuss the basic concepts of the discrepancy model evaluation made by Provus. This research is a library study with data obtained through the study of relevant books and articles and then analyzed using a qualitative approach consisting of: (1) data reduction, (2) data display, and (3) drawing conclusions. Evaluation The discrepancy model is one type of appropriate approach in evaluating an educational and learning program. The procedures used in the discrepancy evaluation are (1) design, (2) installation, (3) process, (4) product, and (5) comparison or the fifth in the form of costs and benefits if needed. The result of the discrepancy model evaluation is knowing the gap between the expected conditions and the reality on the ground, so that it can be a guide for the next step in making decisions.

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Oktapiani, Marliza. "Evaluasi Proses Program Pendidikan Keaksaraan Dasar Perspektif Discrepancy Evaluation Model." Journal Of Administration and Educational Management (ALIGNMENT) 3, no. 1 (June 29, 2020): 55–65. http://dx.doi.org/10.31539/alignment.v3i1.1307.

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The purpose of this study is to find out how to evaluate the process of literacy education programs from the perspective of the discrepancy evaluation model. The method used in this research is qualitative method. The results of this study indicate that there are gaps in the aspects of the program process which have three gaps including the preparation of learning tools (syllabus and lesson plans), improvement of reading culture, assessment of learning processes and assessment of final results. The achievements and impacts of the basic literacy education program have not been maximal in meeting basic literacy education program standards. In conclusion, the program analyzed its gaps and provided problem solving in every aspect based on the stage of the discrepancy evaluation model. Keywords: Process Evaluation, Basic Literacy, Discrepancy Evaluation Model

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36

Mayes, Susan Dickerson, and Susan L. Calhoun. "Test of the Definition of Learning Disability Based on the Difference between IQ and Achievement." Psychological Reports 97, no. 1 (August 2005): 109–16. http://dx.doi.org/10.2466/pr0.97.1.109-116.

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A learning disability is commonly defined as a discrepancy between IQ and achievement. This has been criticized for identifying too many children as having a learning disability who have high IQs and average academic achievement. Such overidentification as actually occurred was assessed in 473 referred children (8–16 years, M= 10, SD = 2) with normal intelligence. Learning disability was defined as a significant discrepancy ( p<.05) between predicted and obtained achievement in reading, mathematics, or written expression on the Wechsler Individual Achievement Test. Predicted achievement was based on the child's WISC-III Full Scale IQ. Overidentification was considered to occur when a child scored at or above age level in reading, mathematics, and writing but still had a significant discrepancy between predicted and obtained achievement by virtue of a high IQ. Learning disability was diagnosed in 312 (66%) of the children. There was no overidentification because all children had one or more WIAT scores below the normative level for their age, i.e., < 100. Further, only 7% of the children were identified with a learning disability based on a WIAT score in the 90s. These children had a mean IQ of 123 and were rated by their teachers and parents as having learning problems.

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Maki, Kathrin E., and Sarah R. Adams. "Specific Learning Disabilities Identification: Do the Identification Methods and Data Matter?" Learning Disability Quarterly 43, no. 2 (February 4, 2019): 63–74. http://dx.doi.org/10.1177/0731948719826296.

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Specific learning disabilities (SLD) identification has consistently been shown to be problematic; however, research has largely focused on SLD identification using test scores only. The present study, therefore, examined SLD identification decisions across identification methods and student evaluation data levels, including test scores, background information, and observations. Participants included 461 school psychologists who were randomly assigned to one of 12 conditions to make identification decisions. Results indicated that response to intervention (RtI) methods resulted in greater identification consistency than ability-achievement discrepancy while pattern of strengths and weaknesses (PSW) resulted in lower identification consistency than ability-achievement discrepancy. However, background information and observation data did not impact SLD identification consistency. Implications for practice and research are also discussed.

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O'Shea, Lawrence J., and Greg Valcante. "A Comparison over Time of Relative Discrepancy Scores of Low Achievers." Exceptional Children 53, no. 3 (November 1986): 253–59. http://dx.doi.org/10.1177/001440298605300309.

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The accurate differentiation of learning disabled (LD) students from other low achieving students and the provision of effective remedial services continue to be important issues in the field of learning disabilities. Despite numerous criticisms, the calculation of a discrepancy between ability and achievement is used widely in the identification of students with learning disabilities. In the present investigation, the stability of discrepancies for LD and non-LD, low achieving students was examined. A differential change in relative descrepancy scores was found between the two groups of students from grades two to five. LD and non-LD groups did not differ significantly in second grade but did differ significantly in fifth grade in terms of relative discrepancy. Results and the implications for service delivery models are discussed.

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Sudarwati, Ninik, and _. Rukminigsih. "Evaluating E-Learning as a Learning Media: A Case of Entrepreneurship E-Learning Using Schoology as Media." International Journal of Emerging Technologies in Learning (iJET) 13, no. 09 (September 29, 2018): 269. http://dx.doi.org/10.3991/ijet.v13i09.7783.

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This study aimed to describe and identify the effectiveness of e-learning strategy on entrepreneurship class at economic education program of STKIP PGRI Jombang. It is an evaluation study using discrepancy evaluation method. The data was from questionnaires and observation, and it would be analyzed descriptively. The result showed that the implementation of e-learning in STKIP PGRI Jombang corresponded to the standard of quality in all management aspects of e-learning with average percentage tile on 71% for all components, including: design of material, delivery methods of e-learning, learning interaction, and evaluation of e-learning activity. In general, the implementation of e-learning at entrepreneurship class in economic education program of STKIP PGRI Jombang is considered effective to improve the quality of learning entrepreneurship.

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Sujana, Ketut, Luh Putu Artini, and Ni Nyoman Padmadewi. "DISCREPANCY BETWEEN PERCEIVED AND OBSERVED TEACHING CREATIVITY OF ENGLISH TEACHERS." Acitya: Journal of Teaching and Education 3, no. 1 (January 30, 2021): 34–43. http://dx.doi.org/10.30650/ajte.v3i1.1410.

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This study was aimed to investigate the discrepancy that occurred between the teachers’ perception and the observed implementation in teaching creativity in the classroom. Embedded mixed method QUAL (quan) was used as the design of the study. The data corpus comprises primary and supportive data, in which the first refers to qualitative data and the later were the supportive data. SMA Negeri 1 Melaya was selected as the research setting and two English teachers were selected as the research subject. The questionnaires and in-depth interview were used as the instrument to collect the data. The data from the questionnaires were analysed quantitatively and qualitatively. Then, data reduction was used to analyse the data from in-depth interview. The result demonstrates that the discrepancy occur between teachers’ perception and the observed implementation in the classroom. The teachers perceive themselves as creative, however the observational set data indicate that their teaching mostly focus on using conventional media, deductive learning, teacher centre technique, monotonous teaching activity, using same media in every meeting, rarely conducting innovative activity and integrating the contextual problem as learning material in the class. Their teaching also mainly focused on offline learning and printed media. In addition, they were also hardly found to show effort to redesign old media to be unique and creating attractive activities in the class.

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Pravitayani, Luh Gede Gellyna, Nyoman Dantes, and I. Made Candiasa. "EVALUASI DISKREPANSI TERHADAP IMPLEMENTASI PENDEKATAN SAINTIFIK PADA MUATAN PELAJARAN MATEMATIKA TEMA ORGAN TUBUH MANUSIA DAN HEWAN KELAS V SD DI KECAMATAN DENPASAR BARAT." Jurnal Penelitian dan Evaluasi Pendidikan Indonesia 9, no. 1 (September 4, 2019): 25–34. http://dx.doi.org/10.23887/jpepi.v9i1.2805.

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AbstrakPenelitian ini bertujuan untuk mengetahui diskrepansi pendekatan saintifik terkait implementasi pendekatan saintifik pada muatan pelajaran matematika tema organ tubuh manusia dan hewan kelas V SD di Kecamatan Denpasar Barat. Penelitian ini merupakan penelitian evaluatif dengan model diskrepansi. Populasi dalam penelitian ini adalah seluruh guru kelas V SD Negeri di Kecamatan Denpasar Barat yang terdiri dari 8 gugus dan terdapat 55 sekolah. Sampel ditentukan dengan teknik Multistage Random Sampling dalam 3 tahap. Data dikumpulkan dengan menggunakan kuesioner persepsi guru, lembar observasi pembelajaran berpendekatan saintifik, dan dokumentasi untuk mengetahui hasil belajar matematika. Data dianalisis dengan menggunakan teknik analisis deskripyif kuantitatif dan analisis korelasi product moment. Hasil penelitian ini menunjukan bahwa : (1) diskrepansi yang terjadi terkait perencanaan pembelajaran berpendekatan saintifik sebesar 32,28 tergolong kategori kecil; (2) diskrepansi yang terjadi terkait pelaksanaan pembelajaran berpendekatan saintifik sebesar 32,79 tergolong kategori kecil; (3) diskrepansi yang terjadi terkait penilaian pembelajaran berpendekatan saintifik sebesar 34,74 tergolong kategori kecil; (4) diskrepansi yang terjadi terkait implementasi pembelajaran berpendekatan saintifik sebesar 33,27 tergolong kategori kecil; (5) persepsi guru tentang pendekatan saintifik sebesar 74,71 dengan kategori baik; (6) rata-rata nilai matematika tema organ tubuh manusia dan hewan sebesar 67 dengan kategori baik, dan; (7) tidak terdapat kontribusi antara kualitas pengelolaan proses pembelajaran berpendekatan saintifik dengan hasil belajar matematika tema organ tubuh manusia dan hewan kelas V SD di Kecamatan Denpasar Barat, dengan kategori korelasi sangat rendah.Kata Kunci: evaluasi, diskrepansi, pendekatan saintifik AbstractThis study aims to determined the magnitude of discrepancy scientific approach on the mathematics subjects of human and animal organs theme at grade 5th elementary school in West Denpasar Sub-district. This research was evaluative research with discrepancy model. The population in this research was the grade 5th (fifth) teachers of elementary school in West Denpasar Sub-district which cinsist of 8 clusters and 55 school. The sample were determined by Multistage Random Sampling technique in three stages. Data were collected by using teacher perception questionnaire, observation sheet of the scientific approach, and documentation to find out the results of mathematics learning. Data were analyzed by using quantitative descriptive analysis technique and product moment correlation analysis. The result of this study indicated indicated (1) the discrepancy thatoccurs related to the learning plan of scientific approaches is about 32,28 with small category; (2) discrepancy that occurs related to implementation of science-based learning is about 32,79 with small category; (3) discrepancy related to the assessment of the scientific approach learning amouted to 34,74 in the small category; (4) the discrepancy that occured related to the implementation of science based learning approaches amouted to 33,27 to small category; (5) the teachers perception abaout the scientific approach is 74,71 with good category; (6) the average mathematical value on the theme of human and animal organs of 67 in the medium category, and; (7) there is no contribution between the quality of management learning process of scientific approach with the result of mathematics learning on the theme human and animal organs in West Denpasar Sub-distric, with very low correlation categories. Keywords: Evaluation, discrepancy, scientific approach

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Gonzalez, Juan E. Jimenez, and Ana I. Garcia Espinel. "Is IQ-Achievement Discrepancy Relevant in the Definition of Arithmetic Learning Disabilities?" Learning Disability Quarterly 22, no. 4 (1999): 291. http://dx.doi.org/10.2307/1511263.

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Taylor, W. Pat, Jeremy Miciak, Jack M. Fletcher, and David J. Francis. "Cognitive discrepancy models for specific learning disabilities identification: Simulations of psychometric limitations." Psychological Assessment 29, no. 4 (April 2017): 446–57. http://dx.doi.org/10.1037/pas0000356.

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Maehler, Claudia, and Kirsten Schuchardt. "Working Memory in Children with Learning Disabilities: Rethinking the criterion of discrepancy." International Journal of Disability, Development and Education 58, no. 1 (March 2011): 5–17. http://dx.doi.org/10.1080/1034912x.2011.547335.

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Cervellera, Cristiano, and Danilo Maccio. "Local Linear Regression for Function Learning: An Analysis Based on Sample Discrepancy." IEEE Transactions on Neural Networks and Learning Systems 25, no. 11 (November 2014): 2086–98. http://dx.doi.org/10.1109/tnnls.2014.2305193.

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Gardner, P., T. J. Rogers, C. Lord, and R. J. Barthorpe. "Learning of model discrepancy for structural dynamics applications using Bayesian history matching." Journal of Physics: Conference Series 1264 (July 2019): 012052. http://dx.doi.org/10.1088/1742-6596/1264/1/012052.

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Zheng, Qiang, Sphoorti Shellikeri, Hao Huang, Misun Hwang, and Raymond W. Sze. "Deep Learning Measurement of Leg Length Discrepancy in Children Based on Radiographs." Radiology 296, no. 1 (July 2020): 152–58. http://dx.doi.org/10.1148/radiol.2020192003.

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Peterson, Kristin M. H., and Mark R. Shinn. "Severe Discrepancy Models: Which Best Explains School Identification Practices for Learning Disabilities?" School Psychology Review 31, no. 4 (December 1, 2002): 459–76. http://dx.doi.org/10.1080/02796015.2002.12086168.

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Dorri, Fatemeh, and Ali Ghodsi. "Minimizing the Discrepancy Between Source and Target Domains by Learning Adapting Components." Journal of Computer Science and Technology 29, no. 1 (January 2014): 105–15. http://dx.doi.org/10.1007/s11390-014-1415-z.

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Mishra, Siddhartha, and T. Konstantin Rusch. "Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences." SIAM Journal on Numerical Analysis 59, no. 3 (January 2021): 1811–34. http://dx.doi.org/10.1137/20m1344883.

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