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Artículos de revistas sobre el tema "Inference teaching"

Autor: Grafiati
Publicado: 2 de julio de 2021
Última modificación: 10 de febrero de 2022

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1

Murza, Kimberly A., Chad Nye, Jamie B. Schwartz, Barbara J. Ehren y Debbie L. Hahs-Vaughn. "A Randomized Controlled Trial of an Inference Generation Strategy Intervention for Adults With High-Functioning Autism Spectrum Disorder". American Journal of Speech-Language Pathology 23, n.º 3 (agosto de 2014): 461–73. http://dx.doi.org/10.1044/2014_ajslp-13-0012.

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PurposeThe present intervention study investigated the efficacy of the ACT & Check Strategy intervention to improve inference generation when reading, metacognitive ability, general reading comprehension, and social inference ability in adults with high-functioning autism spectrum disorder (HF-ASD).MethodTwenty-five adults with HF-ASD were randomly assigned to either a treatment or a control group. Treatment sessions were conducted in 1-hr sessions, twice a week, for a total of 6 weeks. Treatment focused on explicit instruction of components of inference generation, categories of inferences, and increasingly independent strategy use.ResultsThe treatment group demonstrated significantly superior performance on 1 of 2 measures of inference generation in reading and 1 measure of metacognitive ability compared with the control group. Significant differences between groups were not found on measures of reading comprehension or social inference ability.ConclusionThese findings suggest that the ACT & Check Strategy was effective in improving participants' ability to generate inferences in reading and certain metacognitive abilities, but the skills do not appear to generalize to other social communication contexts, such as social inference generation. This research provides a measure of support for explicitly teaching inference generation to address a reading inference deficit in adults with HF-ASD.
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2

Eflin, Juli. "Teaching 'Inference to the Best Explanation'". Teaching Philosophy 17, n.º 2 (1994): 151–60. http://dx.doi.org/10.5840/teachphil199417213.

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Tarlow, Kevin R. "Teaching principles of inference with ANOVA". Teaching Statistics 38, n.º 1 (24 de julio de 2015): 16–21. http://dx.doi.org/10.1111/test.12085.

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Prodromou, Theodosia. "Model-based Informal Inference". International Journal of Statistics and Probability 6, n.º 5 (28 de agosto de 2017): 140. http://dx.doi.org/10.5539/ijsp.v6n5p140.

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Following recent scholarly interest in teaching informal linear regression models, this study looks at teachers’ reasoning about informal lines of best fit and their role in pedagogy. The case results presented in this journal paper provide insights into the reasoning used when developing a simple informal linear model to best fit the available data. This study also suggests potential in specific aspects of bidirectional modelling to help foster the development of robust knowledge of the logic of inference for those investigating and coordinating relations between models developed during modelling exercises and informal inferences based on these models. These insights can inform refinement of instructional practices using simple linear models to support students’ learning of statistical inference, both formal and informal.
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Hall, Colby y Marcia A. Barnes. "Inference Instruction to Support Reading Comprehension for Elementary Students With Learning Disabilities". Intervention in School and Clinic 52, n.º 5 (5 de diciembre de 2016): 279–86. http://dx.doi.org/10.1177/1053451216676799.

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Making inferences during reading is a critical standards-based skill and is important for reading comprehension. This article supports the improvement of reading comprehension for students with learning disabilities (LD) in upper elementary grades by reviewing what is currently known about inference instruction for students with LD and providing detailed suggestions and a five-step process for teaching students to make text-connecting and knowledge-based inferences while reading. By bolstering this key reading comprehension skill in the upper elementary grades, teachers can better prepare students for the increased reading comprehension demands of middle school.
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Eflin, Juli T. y Mary E. Kite. "Teaching Scientific Reasoning through Attribution Theory". Teaching of Psychology 23, n.º 2 (abril de 1996): 87–91. http://dx.doi.org/10.1207/s15328023top2302_3.

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A teaching activity improves students' scientific reasoning by focusing attention on the causal, explanatory nature of psychological theories, The instructor's initial lecture emphasizes the argument form called Inference to the Best Explanation (IBE). Students construct IBE arguments by sorting a series of premises and conclusions based on correspondent inference theory (Jones & Davis, 1965), Discussion of argument strength follows, using the concepts presented in the lecture. Fifty-two introductory social psychology students evaluated the activity favorably, and results of a nonequivalent control group experiment showed that participation improved their reasoning ability. Suggestions for modifying the activity for other psychology courses are provided.
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Jarell, Stephen. "Teaching and Learning Statistical Inference by Experimentation (SIBEX". Social Science Microcomputer Review 4, n.º 4 (diciembre de 1986): 500–502. http://dx.doi.org/10.1177/089443938600400407.

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Jameson, Daphne A. "Implication versus Inference". Business Communication Quarterly 67, n.º 4 (diciembre de 2004): 387–411. http://dx.doi.org/10.1177/1080569904270989.

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The narrative concepts of the implied author and implied reader elucidate how business texts represent writers and readers. It is important, though, to distinguish carefully between writers’ implications and readers’ inferences. Instructors should contrast implied versus inferred writers and readers, provide multiple ways to comprehend these concepts, and illustrate them with examples (e.g., those provided in this article from Citigroup, Andersen, and Martha Stewart Living Omnimedia). The meaning-as-event analytical method, from reader-response narrative theory, reveals specific language features through which business texts manifest readers and writers. To help writers plan and readers analyze such texts, instructors may use the teaching suggestions, sample assignments, prewriting heuristics, and evaluation criteria provided in this article.
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Moskowitz, Gordon B. y Irmak Olcaysoy Okten. "Teaching & Learning Guide for: Spontaneous Goal Inference (SGI)". Social and Personality Psychology Compass 10, n.º 3 (marzo de 2016): 164–68. http://dx.doi.org/10.1111/spc3.12235.

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DOERR, HELEN M., ROBERT DELMAS y KATIE MAKAR. "A MODELING APPROACH TO THE DEVELOPMENT OF STUDENTS’ INFORMAL INFERENTIAL REASONING". STATISTICS EDUCATION RESEARCH JOURNAL 16, n.º 2 (30 de noviembre de 2017): 86–115. http://dx.doi.org/10.52041/serj.v16i2.186.

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Teaching from an informal statistical inference perspective can address the challenge of teaching statistics in a coherent way. We argue that activities that promote model-based reasoning address two additional challenges: providing a coherent sequence of topics and promoting the application of knowledge to novel situations. We take a models and modeling perspective as a framework for designing and implementing an instructional sequence of model development tasks focused on developing primary students’ generalized models for drawing informal inferences when comparing two sets of data. This study was conducted with 26 Year 5 students (ages 10-11). Our study provides empirical evidence for how a modeling perspective can bring together lines of research that hold potential for the teaching and learning of inferential reasoning. First published November 2017 at Statistics Education Research Journal Archives
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11

Rubin, Donald B. "Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies". Journal of Educational and Behavioral Statistics 29, n.º 3 (septiembre de 2004): 343–67. http://dx.doi.org/10.3102/10769986029003343.

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Inference for causal effects is a critical activity in many branches of science and public policy. The field of statistics is the one field most suited to address such problems, whether from designed experiments or observational studies. Consequently, it is arguably essential that departments of statistics teach courses in causal inference to both graduate and undergraduate students. This article discusses an outline of such courses based on repeated experience over more than a decade.
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12

Kula, Fulya y Rüya Gökhan Koçer. "Why is it difficult to understand statistical inference? Reflections on the opposing directions of construction and application of inference framework". Teaching Mathematics and its Applications: An International Journal of the IMA 39, n.º 4 (20 de enero de 2020): 248–65. http://dx.doi.org/10.1093/teamat/hrz014.

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Abstract Difficulties in learning (and thus teaching) statistical inference are well reported in the literature. We argue the problem emanates not only from the way in which statistical inference is taught but also from what exactly is taught as statistical inference. What makes statistical inference difficult to understand is that it contains two logics that operate in opposite directions. There is a certain logic in the construction of the inference framework, and there is another in its application. The logic of construction commences from the population, reaches the sample through some steps and then comes back to the population by building and using the sampling distribution. The logic of application, on the other hand, starts from the sample and reaches the population by making use of the sampling distribution. The main problem in teaching statistical inference in our view is that students are taught the logic of application while the fundamental steps in the direction of construction are often overlooked. In this study, we examine and compare these two logics and argue that introductory statistical courses would benefit from using the direction of construction, which ensures that students internalize the way in which inference framework makes sense, rather than that of application.
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13

Rossman, Allan J. y Beth L. Chance. "Teaching the Reasoning of Statistical Inference: A "Top Ten" List". College Mathematics Journal 30, n.º 4 (septiembre de 1999): 297. http://dx.doi.org/10.2307/2687668.

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14

Rossman, Allan J. y Beth L. Chance. "Teaching the Reasoning of Statistical Inference: A “Top Ten” List". College Mathematics Journal 30, n.º 4 (septiembre de 1999): 297–305. http://dx.doi.org/10.1080/07468342.1999.11974074.

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15

Yusuf, Syamsul Bahri, Chairina Nasir y Chentenei Lapenia Ningsih Rohiman. "Using think-aloud method in teaching reading skill". Studies in English Language and Education 5, n.º 1 (1 de marzo de 2018): 148–59. http://dx.doi.org/10.24815/siele.v5i1.9898.

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Think-Aloud Method is believed to assist language learners in improving their reading comprehension. The objective of this study is to know whether students’ reading comprehension particularly in identifying main idea and supporting details, identifying reference, making inference and interpreting vocabulary. The sample was the students of class eight-5 of of SMP Negeri 17 Banda Aceh. This study was one group pre-test and post-test design. Based on the findings, the mean score of pre-test was 38.15and post-test was78.89. The critical value of t-table 0.05. The significant levels was1.71, the t-test value is higher than the critical value of t-table (4.75>2.05). The reading aspects that were improved the most were detailed information, main idea and inference. For the other aspects, such as vcabulary and reference were also improved, but not as much as the three aspects above. It means that the use of Think-Aloud Method could significantly improve the ability of the second grade students of SMPN 17 Banda Aceh in reading, especially in descriptive text. In conclusion, Think-Aloud Method can be used to improve the student’s ability in reading.
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16

Kobylnyk, T. "Methodological Aspects Of Teaching The Topic "Logical Inference With Unreliable Knowledge"". Physical and Mathematical Education 17, n.º 3 (noviembre de 2018): 57–60. http://dx.doi.org/10.31110/2413-1571-2018-017-3-010.

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17

Kang, Min Suk. "A study on types of pragmatic inference in teaching Korean listening". Journal of Korean Culture 45 (31 de mayo de 2019): 141–74. http://dx.doi.org/10.35821/jkc.2019.05.45.141.

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翟, 凤琦. "Application of Analogical Inference in Mathematics Teaching in Senior Middle School". Advances in Education 09, n.º 01 (2019): 43–46. http://dx.doi.org/10.12677/ae.2019.91010.

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19

Petrosky, Edward M. "Teaching Inference in Personality Test Interpretation: Verbal Abstract Reasoning and the Tat". Psychological Reports 102, n.º 1 (febrero de 2008): 83–94. http://dx.doi.org/10.2466/pr0.102.1.83-94.

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Instructors of personality assessment are challenged with teaching students how to execute the tacit thinking skills necessary to make sound test interpretations. The author provides a concrete tool for teaching students how to make interpretive inferences from verbal personality test data utilizing the construct of verbal abstract reasoning. Applied specifically to Thematic Apperception Test interpretation, the author discusses how the construct of verbal abstract reasoning can be utilized: as a model for providing explicit instruction on the implicit process of drawing inferences in test interpretation; for grounding students in data when constructing interpretations and avoiding making overpersonalized interpretations; as a means of basing interpretations on a convergence of data; as a means of gauging the level of confidence one can place in interpretations; and for understanding inferences drawn from other tests, such as the Rorschach.
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20

Huang, Wen-xue, Xin Gao, Ning Wang, Yan-chao Yang y Ying Yan. "Teaching Quality Assessment System Based on Support Vector Machine Technology". International Journal of Emerging Technologies in Learning (iJET) 11, n.º 11 (29 de noviembre de 2016): 4. http://dx.doi.org/10.3991/ijet.v11i11.6251.

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A scientific, objective and accurate assessment of teaching quality is helpful in finding the relevant problems. So a highly accurate, quick and easy-to-implement teaching quality assessment system is necessary to build. The index value of the pulsed GTAW pool dynamic process by support vector machine inference and support vector machine neural networks is implemented in the teaching quality evaluation system. The teaching quality assessment system was tested on 30 teachers in a college. The results show that the assessment system is increasingly evidence-based. And the system can improve teaching quality and teaching management implementation.
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21

May, Richard B. y Michael A. Hunter. "Interpreting Students' Interpretations of Research". Teaching of Psychology 15, n.º 3 (octubre de 1988): 156–58. http://dx.doi.org/10.1207/s15328023top1503_17.

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This article examines the role of random sampling and random assignment in the interpretation of research results. Students and faculty were asked two multiple-choice questions about research methods. When questioned about the role of random sampling, 75% of all subjects correctly associated it with generality inference (generalizability). When asked about the role of random assignment, however, only 27% of all subjects correctly associated it with causal inference. The remaining subjects allocated at least some part of the role of random assignment to generalizability. We suggest that these findings reflect a bias in psychologists' training due to teaching a statistical model that stresses random sampling and generality inference. We discuss the random assignment model, which focuses on causal inference, and recommend that both models be taught in introductory methods and statistics courses.
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22

Utami, Budi, Sulistyo Saputro, Ashadi Ashadi, Mohammad Masykuri, Riezky Maya Probosari y Alimin Sutanto. "Students' critical thinking skills profile: constructing best strategy in teaching chemistry". IJPTE : International Journal of Pedagogy and Teacher Education 2 (31 de enero de 2018): 63. http://dx.doi.org/10.20961/ijpte.v2i0.19768.

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<p>The purpose of this study was to determine the profile of critical thinking skills of senior high school students. The paper-based essay questions and the questionnaires were used as the instruments to measure critical thinking skills in daily chemistry problems, which is based on Facione’s critical thinking aspects included interpretation, analysis, evaluation, inference, explanation, and self-regulation. The research was conducted in one of government senior high school in Central Java, Indonesia. The descriptive-qualitative method was used for the research method. The qualitative approach was used to analyze the data by using essay questions and questionnaires. The criteria for critical thinking skills were categorized as null, low, medium, and high. The result showed that most of the students have weak or low skill in critical thinking, especially in analysis, evaluation, explanation, and self-regulation whether their skill of interpretation and inference are medium. This finding can be used to develop a new strategy in teaching chemistry related to critical thinking skill.</p><p> </p>
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23

Malloy, Thomas E. "Difference to Inference: Teaching logical and statistical reasoning through on-line interactivity". Behavior Research Methods, Instruments, & Computers 33, n.º 2 (mayo de 2001): 270–73. http://dx.doi.org/10.3758/bf03195375.

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Reutzel, D. Ray y Paul M. Hollingsworth. "Highlighting Key Vocabulary: A Generative-Reciprocal Procedure for Teaching Selected Inference Types". Reading Research Quarterly 23, n.º 3 (1988): 358. http://dx.doi.org/10.2307/748047.

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Neogi, Amartya, Abhoy Chand Mondal y Soumitra Kumar Mandal. "A Cascaded Fuzzy Inference System for University Non-Teaching Staff Performance Appraisal". Journal of Information Processing Systems 7, n.º 4 (31 de diciembre de 2011): 595–612. http://dx.doi.org/10.3745/jips.2011.7.4.595.

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Kramer, Klaus-Dietrich, Annedore Söchting y Thomas Stolze. "Fuzzy Control Teaching Models". Issues in Informing Science and Information Technology 13 (2016): 225–33. http://dx.doi.org/10.28945/3490.

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Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tune the system behavior. In order to support the development process a graphical user interface is needed to display the fuzzy terms under real time conditions, especially with a debug system and trace functionality. The experiences with such a programming tool, the Fuzzy Control Design Tool (FHFCE Tool), and four fuzzy teaching models will be presented in this paper. The methodical and didactical objective in the utilization of these teaching models is to develop solution strategies using Computational Intelligence (CI) applications for Fuzzy Controllers in order to analyze different algorithms of inference or defuzzyfication and to verify and tune those systems efficiently.
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LANE-GETAZ, SHARON J. "IS THE P-VALUE REALLY DEAD? ASSESSING INFERENCE LEARNING OUTCOMES FOR SOCIAL SCIENCE STUDENTS IN AN INTRODUCTORY STATISTICS COURSE". STATISTICS EDUCATION RESEARCH JOURNAL 16, n.º 1 (31 de mayo de 2017): 357–99. http://dx.doi.org/10.52041/serj.v16i1.235.

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In reaction to misuses and misinterpretations of p-values and confidence intervals, a social science journal editor banned p-values from its pages. This study aimed to show that education could address misuse and abuse. This study examines inference-related learning outcomes for social science students in an introductory course supplemented with randomization and simulation content. Learning gains were measured across a suggested taxonomy of inference learning outcomes using the Reasoning about P-values and Statistical Significance (RPASS-10) scale. Three graphical comparisons of students’ Pretest and Posttest proportions were encoded by learning gain or loss, an inference learning outcome taxonomy, or if a correct concept or misconception was assessed. What students learned and the difficulties that persisted shape recommendations for teaching and future research. First published May 2017 at Statistics Education Research Journal Archives
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28

Peñaloza Figueroa, J. L. y C. Vargas Perez. "BIG-DATA and the Challenges for Statistical Inference and Economics Teaching and Learning". Multidisciplinary Journal for Education, Social and Technological Sciences 4, n.º 1 (10 de abril de 2017): 64. http://dx.doi.org/10.4995/muse.2017.6350.

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<p>The increasing automation in data collection, either in structured or</p><p>unstructured formats, as well as the development of reading, concatenation and comparison algorithms and the growing analytical skills which characterize the era of Big Data, cannot not only be considered a technological achievement, but an organizational, methodological and analytical challenge for knowledge as well, which is necessary to generate opportunities and added value.</p><p>In fact, exploiting the potential of Big-Data includes all fields of community activity; and given its ability to extract behaviour patterns, we are interested in the challenges for the field of teaching and learning, particularly in the field of statistical inference and economic theory.</p><p>Big-Data can improve the understanding of concepts, models and techniques used in both statistical inference and economic theory, and it can also generate reliable and robust short and long term predictions. These facts have led to the demand for analytical capabilities, which in turn encourages teachers and students to demand access to massive information produced by individuals, companies and public and private organizations in their transactions and inter- relationships.</p><p>Mass data (Big Data) is changing the way people access, understand and organize knowledge, which in turn is causing a shift in the approach to statistics and economics teaching, considering them as a real way of thinking rather than just operational and technical disciplines. Hence, the question is how teachers can use automated collection and analytical skills to their advantage when teaching statistics and economics; and whether it will lead to a change in what is taught and how it is taught.</p>
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Jacobs, RM. "Ten-year study of strategies for teaching clinical inference in predoctoral orthodontic education". Journal of Dental Education 52, n.º 5 (mayo de 1988): 235–44. http://dx.doi.org/10.1002/j.0022-0337.1988.52.5.tb02199.x.

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Sowey, Eric R. "From a logical point of view: an illuminating perspective in teaching statistical inference". International Journal of Mathematical Education in Science and Technology 36, n.º 7 (15 de octubre de 2005): 801–11. http://dx.doi.org/10.1080/00207390500271529.

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Mihajlov Carević, Miroslava, Milena Petrović, Nebojša Denić y Aleksandra Mitrović. "Computing Support in Statistical Evaluation of Mathematics Teaching Effectiveness: Development of Students’ Constructive Thinking". Technium: Romanian Journal of Applied Sciences and Technology 2, n.º 4 (15 de junio de 2020): 109–15. http://dx.doi.org/10.47577/technium.v2i4.996.

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One of the main tasks in teaching mathematics is to develop students’ constructive thinking. In order to effectively accomplish this task, it is necessary to make a good selection of instructional materials and teaching aids. In order to make good selection and improve the teaching of mathematics, it is, also, necessary to include a statistical analysis of the certain factors’ impact that affect mathematics curriculum. For the purpose of this research, we used the software computational approach ANFIS (adaptive neuro fuzzy inference system) to determine the qualitative impact of several factors on improving students’ ability to create constructive thinking.
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McKenzie, Patrick F. y Deren A. R. Eaton. "ipcoal: an interactive Python package for simulating and analyzing genealogies and sequences on a species tree or network". Bioinformatics 36, n.º 14 (12 de mayo de 2020): 4193–96. http://dx.doi.org/10.1093/bioinformatics/btaa486.

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Abstract Summary ipcoal is a free and open source Python package for simulating and analyzing genealogies and sequences. It automates the task of describing complex demographic models (e.g. with divergence times, effective population sizes, migration events) to the msprime coalescent simulator by parsing a user-supplied species tree or network. Genealogies, sequences and metadata are returned in tabular format allowing for easy downstream analyses. ipcoal includes phylogenetic inference tools to automate gene tree inference from simulated sequence data, and visualization tools for analyzing results and verifying model accuracy. The ipcoal package is a powerful tool for posterior predictive data analysis, for methods validation and for teaching coalescent methods in an interactive and visual environment. Availability and implementation Source code is available from the GitHub repository (https://github.com/pmckenz1/ipcoal/) and is distributed for packaged installation with conda. Complete documentation and interactive notebooks prepared for teaching purposes, including an empirical example, are available at https://ipcoal.readthedocs.io/. Contact p.mckenzie@columbia.edu
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33

MANOR BRAHAM, HANA y DANI BEN-ZVI. "STUDENTS’ EMERGENT ARTICULATIONS OF STATISTICAL MODELS AND MODELING IN MAKING INFORMAL STATISTICAL INFERENCES". STATISTICS EDUCATION RESEARCH JOURNAL 16, n.º 2 (30 de noviembre de 2017): 116–43. http://dx.doi.org/10.52041/serj.v16i2.187.

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A fundamental aspect of statistical inference is representation of real-world data using statistical models. This article analyzes students’ articulations of statistical models and modeling during their first steps in making informal statistical inferences. An integrated modeling approach (IMA) was designed and implemented to help students understand the relationship between sample and population, as well as reasoning with models and modeling. We explore the articulations of a pair of primary school students, who had previously participated in the Connections Project exploratory data analysis (EDA) activities, and suggest an emergent conceptual framework for reasoning with statistical models and modeling. We shed light on ideas of statistical models and modeling that can emerge among primary students and how they articulate those ideas. Implications for teaching and research are discussed. First published November 2017 at Statistics Education Research Journal Archives
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Dimitrova, Ganka, Caroline Hodges Persell y Richard Maisel. "Using and Evaluating ISEE, a New Computer Program for Teaching Sampling and Statistical Inference". Teaching Sociology 21, n.º 4 (octubre de 1993): 341. http://dx.doi.org/10.2307/1319083.

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Koparan, Timur y Gül Kaleli Yilmaz. "The Effect of Simulation-based Learning on Prospective Teachers' Inference Skills in Teaching Probability". Universal Journal of Educational Research 3, n.º 11 (noviembre de 2015): 775–86. http://dx.doi.org/10.13189/ujer.2015.031101.

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Okamoto, Toshio. "Representation of teaching-world knowledge and the method of the inference for intelligent cai". Systems and Computers in Japan 20, n.º 2 (1989): 25–37. http://dx.doi.org/10.1002/scj.4690200203.

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Wright, Heather Harris y Marilyn Newhoff. "Narration Abilities of Children With Language-Learning Disabilities in Response to Oral and Written Stimuli". American Journal of Speech-Language Pathology 10, n.º 3 (agosto de 2001): 308–19. http://dx.doi.org/10.1044/1058-0360(2001/027).

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The purpose of the present investigation was to examine story retelling and inference abilities in children with language-learning disabilities (LLD). There were 10 children in the LLD group and 20 who showed normal acquisition of language, 10 of whom were matched for chronological age (NACA) and 10 of whom were matched for language ability (NALA) to an LLD child. Stimuli were both orally presented (Heard Condition) and read silently (Read Condition) by the participants. Four stories were presented in each of these conditions. After each story, participants completed two tasks: retelling the story and answering questions that were either factual or that required inferencing. Generally, results indicated that children with LLD and NALA retold stories and drew inferences more poorly than NACA children regardless of stimulus presentation mode; children with LLD drew inferences best with orally presented stimuli; and children with NACA and NALA drew inferences best with stimuli presented in writing. A number of possible explanations for the differences between groups are discussed, including working memory and attending to relevant information in text, both of which are skills needed for reading comprehension. These children's difficulties in making inferences were attributed to impairments in cognitive functions. Clinically, then, teaching children with LLD to make inferences from both text and oral narratives would address impaired cognitive functions and reading comprehension abilities.
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Machfauzia, Ayu Niza. "Self-management Strategies of Music Teacher in Musical Interpretation Teaching". International Journal of Creative and Arts Studies 1, n.º 2 (17 de abril de 2017): 1. http://dx.doi.org/10.24821/ijcas.v1i2.1556.

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In this paper, presented the results of a qualitative study of case studies of self- management strategies music teachers in interpretation of music teaching. The purpose of this research is to reveal the self-management strategies that teachers use in interpretation of music teaching. Subjects in this study were teachers who teach musical instrument practice as many as 12 people. In this study subject determined by purposive sampling technique. Interviews, observation, and documentation techniques are used for data collection. The data have been collected from being validated by using triangulation techniques and triangulation time, and analyzed by means of data reduction, data presentation, and inference data. The results showed that components of self-management strategies used by teachers which include motivation, teaching method,and monitoring performance.
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39

Webster, Ray y Fay Sudweeks. "Personalization and learning: innovative approaches to teaching for e-learning". Psicologia Escolar e Educacional 11, spe (diciembre de 2007): 93–101. http://dx.doi.org/10.1590/s1413-85572007000300009.

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This paper suggests that for academics to be good teachers, especially in the context of e-Learning, they need to understand learning. This is especially important with the associated changes in higher education as we move towards the knowledge society. E-Learning is embedded in learning and, without an understanding of what learning encompasses, it can be difficult for academics to develop into good teachers. It is suggested that, although this may appear to be a simple aim, it is not necessarily understood or applied by university academics in their teaching. One inference is that university teachers need to develop a theory of learning and teaching. Academics may have a 'philosophy of teaching', but in many cases even this may not be consciously held or successfully implemented. A program for promoting conceptual change in academics' approaches to teaching is outlined.
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40

Wang, Mei Li, Long Long Li y Dong Jian He. "A Teaching Evaluation Model Based on Fuzzy Multiple Attribute Decision Making". Applied Mechanics and Materials 333-335 (julio de 2013): 2197–201. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.2197.

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Practical decision making, such as fuzzy multiple attribute decision making, involves many uncertainties with respect to all elements of the basic decision making model. Soft-computing approaches such as fuzzy inference and theories of fuzzy mathematics have been widely developed to achieve practical decision making. This paper proposes to select a representative scheme among a large set of available options by applying fuzzy extremum analysis. According to the objective function, the method standardizes an input vector through the intuitionistic fuzzy set, from which we construct the conversion relationship tables and finally calculate the optimal solution by fuzzy membership functions. The proposed method has been tested on teaching evaluation of Shaanxi Polytechnic Institute.
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41

Stock, James H. "The Other Transformation in Econometric Practice: Robust Tools for Inference". Journal of Economic Perspectives 24, n.º 2 (1 de mayo de 2010): 83–94. http://dx.doi.org/10.1257/jep.24.2.83.

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Angrist and Pischke highlight one aspect of the research that has positively transformed econometric practice and teaching. They emphasize the rise of experiments and quasi-experiments as credible sources of identification in microeconometric studies, which they usefully term “design-based research.” But in so doing, they miss an important part of the story: a second research strand aimed at developing tools for inference that are robust to subsidiary modeling assumptions. My first aim in these remarks therefore is to highlight some key developments in this area. I then turn to Angrist and Pischke's call for adopting experiments and quasi-experiments in macroeconometrics; while sympathetic, I suspect the scope for such studies is limited. I conclude with some observations on the current debate about whether experimental methods have gone too far in abandoning economic theory.
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42

Nosofsky, Robert M. y Mark A. McDaniel. "Recommendations From Cognitive Psychology for Enhancing the Teaching of Natural-Science Categories". Policy Insights from the Behavioral and Brain Sciences 6, n.º 1 (marzo de 2019): 21–28. http://dx.doi.org/10.1177/2372732218814861.

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Because of their complex structures, many natural-science categories are difficult to learn. Yet achieving accuracy in classification is crucial to scientific inference and reasoning. Thus, an emerging theme in cognitive-psychology and cognitive-science research has been to investigate better ways to instruct about categories. This article briefly reviews major findings that will help inform policies for teaching categories in the science classroom. Many of the examples come from our specific project that examines teaching rock classifications in the geologic sciences. This project uses formal models of human category learning—developed in cognitive psychology—to search for optimal teaching procedures. The model-suggested category-teaching procedures often lead to better learning outcomes than do alternative procedures motivated by teachers’ and students’ intuitive judgments. In addition to reviewing these enhanced procedures for teaching natural-science categories, the article points to recent broader efforts for fostering collaborations between cognitive-science researchers and education researchers.
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43

Lodatko, Ye A. "Definition of a didactically expedient continuum in informatics for future primary school teachers". Informatics in school, n.º 2 (27 de abril de 2021): 5–8. http://dx.doi.org/10.32517/2221-1993-2021-20-2-5-8.

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The article analyzes modern preconditions that determine specific features of a future primary school teacher's Informatics training, which is necessary for teaching Informatics to younger students. An approach to determining the content of a future primary school teacher's Informatics training, focused on understanding the socio-cultural significance of modern digital technologies for sustainable development of society and sufficient for the assimilation of Informatics concepts and procedures that are directly related to the content of the school Informatics course and its methodological support is substantiates in the article. The necessity of focusing the attention of future primary school teachers on information technologies that are widespread in everyday life and significantly simplify the achievement of program learning outcomes. The necessity of mastering by teachers the skills of handling algorithmic procedures, the use of block diagrams for the visual presentation of algorithmic procedures, the use of simple categorical syllogisms and inference rules in the construction of inferences, reference to evaluative procedures and legal norms of information interaction is argued.
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44

Kim, Somin. "A Case Study of a Lesson on the Sample Mean for Prospective Mathematics Teachers". Mathematics 8, n.º 10 (3 de octubre de 2020): 1706. http://dx.doi.org/10.3390/math8101706.

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In statistical inference, many students have a difficult time learning the sample mean, sampling distribution, and the central limit theorem, even though these are key concepts in statistics. This study aimed to identify and correct prospective mathematics teachers’ misconceptions about the sample mean and sampling distribution during the statistical inference process in an introductory statistics course at two Korean universities. It also aimed to develop an exploratory lesson by applying Keller’s ARCS (attention, relevance, confidence, and satisfaction) model so that the prospective teachers could better understand the sample mean and sampling distribution and correct their misconceptions. The exploratory lesson was implemented, observed, and analyzed. The findings showed that the exploratory lesson had positive effects on prospective teachers’ attention, relevance, confidence, and satisfaction. In addition, through communication and discussion with their peers, they could better understand the concepts, discover new facts, and correct their misconceptions in the exploration process of the lesson. Thus, this study provided empirical evidence to show that an exploratory statistics lesson using Keller’s ARCS model can be an effective lesson model for teaching statistical inference.
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45

Eun, Eun-Suk. "Teaching-Learning Model of Structure-Constructivism Based on Piagetian Propositional Logic and Bayesian Causational Inference". Journal of the New Korean Philosophical Association 99 (31 de enero de 2020): 191–217. http://dx.doi.org/10.20433/jnkpa.2020.01.99.191.

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46

Tran, Dang, Long Khuong, Tram Huynh, Hong Le y Tuan Vo. "Directed Acyclic Graphs: Alternative tool for causal inference in epidemiology and biostatistics research and teaching". MedPharmRes 2, n.º 3 (27 de octubre de 2018): 12–16. http://dx.doi.org/10.32895/ump.mpr.2.3.12.

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The issue of causation is one of the major challenges for epidemiologists who aim to understand the association between an exposure and an outcome to explain disease patterns and potentially provide a basis for intervention. Suitably designed experimental studies can offer robust evidence of the causal relationships. The experimental studies, however, are not popular, difficult or even unethical and impossible to conduct; it would be desirable if there is a methodology for reducing bias or strengthening the causal inferences drawn from observational studies. The traditional approach of estimating causal effects in such studies is to adjust for a set of variables judged to be confounders by including them in a multiple regression. However, which variables should be adjusted for as confounders in a regression model has long been a controversial issue in epidemiology. From my observation, the adjustments using only "statistical artifacts" methods such as the p-value<0.2 in univariate analysis, stepwise (forward/backward) are widely used in research and teaching in Epidemiology and Statistics but without appropriated notice on the biological or clinical relationships between exposure and outcome which may induce the bias in estimating causal effects. In this mini-review, we introduce an interesting method, namely Directed Acyclic Graphs (DAGs), which can be used to reduce the bias in estimating causal effects; it is also a good application for Epidemiology and Biostatistics teaching.
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47

Saldanha, Luis A. y Patrick W. Thompson. "Conceptual issues in understanding the inner logic of statistical inference: Insights from two teaching experiments". Journal of Mathematical Behavior 35 (septiembre de 2014): 1–30. http://dx.doi.org/10.1016/j.jmathb.2014.03.001.

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48

Buchanan, John. "Telling Tales Out of School: Exploring Why Former Teachers are Not Returning to the Classroom". Australian Journal of Education 56, n.º 2 (agosto de 2012): 205–17. http://dx.doi.org/10.1177/000494411205600207.

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Teacher attrition is a cost to the community and, often, to the teachers concerned. One ready potential source of teachers is those having left the profession, particularly recently, and who may be willing to return. For this article, 22 former teachers were interviewed about their journey into and out of teaching. Understanding what made teachers leave may inform us on what it might take to get them back. This paper reports on these former teachers' professional intentions in light of their changing constructs of teaching through time, comparing, where possible, their former and current constructs of teaching and of their teacher-selves. The study constitutes a series of ‘before and after’ pictures, providing stark contrast at times. The findings generate theory on factors leading to these changes. One emerging inference is that the teaching profession fails to apply some of its corporate pedagogical knowledge to its own newcomers.
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49

FORBES, SHARLEEN, JEANETTE CHAPMAN, JOHN HARRAWAY, DOUG STIRLING y CHRIS WILD. "USE OF DATA VISUALISATION IN THE TEACHING OF STATISTICS: A NEW ZEALAND PERSPECTIVE". STATISTICS EDUCATION RESEARCH JOURNAL 13, n.º 2 (28 de noviembre de 2014): 187–201. http://dx.doi.org/10.52041/serj.v13i2.290.

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For many years, students have been taught to visualise data by drawing graphs. Recently, there has been a growing trend to teach statistics, particularly statistical concepts, using interactive and dynamic visualisation tools. Free down-loadable teaching and simulation software designed specifically for schools, and more general data visualisation tools are increasingly being used in New Zealand classrooms. This paper discusses four examples: the use of GenStat for Teaching and Learning Schools and Undergraduate (GTL); Auckland University’s iNZight and VIT (Visual Inference Tools) for teaching bootstrapping and randomisation; the CAST e-books, and the use of data visualisation tools to assist learning concepts in official statistics. All these tools are publically available and several are already being used internationally. First published November 2014 at Statistics Education Research Journal Archives
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DOLOR, JASON y JENNIFER NOLL. "USING GUIDED REINVENTION TO DEVELOP TEACHERS’ UNDERSTANDING OF HYPOTHESIS TESTING CONCEPTS". STATISTICS EDUCATION RESEARCH JOURNAL 14, n.º 1 (29 de mayo de 2015): 60–89. http://dx.doi.org/10.52041/serj.v14i1.269.

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Statistics education reform efforts emphasize the importance of informal inference in the learning of statistics. Research suggests statistics teachers experience similar difficulties understanding statistical inference concepts as students and how teacher knowledge can impact student learning. This study investigates how teachers reinvented an informal hypothesis test for categorical data through the framework of guided reinvention. We describe how notions of variability help bridge the development from informal to formal understandings of empirical sampling distributions and procedures for constructing statistics and critical values for conducting hypothesis tests. A product of this paper is a hypothetical learning trajectory that statistics educators could utilize as both a framework for research and as an instructional tool to improve the teaching of hypothesis testing. First published May 2015 at Statistics Education Research Journal Archives
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