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Journal articles on the topic 'Intelligence testing'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2024

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1

Richardson, Theresa, and Erwin V. Johanningmeier. "Intelligence testing." International Journal of Educational Research 27, no. 8 (February 1998): 699–714. http://dx.doi.org/10.1016/s0883-0355(98)00007-x.

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2

Garner, J. Bradley. "Intelligence Testing or Testing Inteligently." School Psychology International 6, no. 4 (October 1985): 235–38. http://dx.doi.org/10.1177/0143034385064008.

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3

Braaten, Ellen B., and Dennis Norman. "Intelligence (IQ) Testing." Pediatrics in Review 27, no. 11 (November 2006): 403–8. http://dx.doi.org/10.1542/pir.27-11-403.

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4

Braaten, Ellen B., and Dennis Norman. "Intelligence (IQ) Testing." Pediatrics In Review 27, no. 11 (November 1, 2006): 403–8. http://dx.doi.org/10.1542/pir.27.11.403.

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5

&NA;. "Standardized Intelligence Testing." Journal of Developmental & Behavioral Pediatrics 16, no. 6 (December 1995): 425???427. http://dx.doi.org/10.1097/00004703-199512000-00006.

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6

Nicolas, Serge, and Zachary Levine. "Beyond Intelligence Testing." European Psychologist 17, no. 4 (January 1, 2012): 320–25. http://dx.doi.org/10.1027/1016-9040/a000117.

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Though Alfred Binet was a prolific writer, many of his 1893–1903 works are not well known. This is partly due to a lack of English translations of the many important papers and books that he and his collaborators created during this period. Binet’s insights into intelligence testing are widely celebrated, but the centennial of his death provides an occasion to reexamine his other psychological examinations. His studies included many diverse aspects of mental life, including memory research and the science of testimony. Indeed, Binet was a pioneer of psychology and produced important research on cognitive and experimental psychology, developmental psychology, social psychology, and applied psychology. This paper seeks to elucidate these aspects of his work.

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7

Kaufman, Melvin E., and Wayne L. Sengstock. "Intelligence testing of children." Postgraduate Medicine 81, no. 5 (April 1987): 249–55. http://dx.doi.org/10.1080/00325481.1987.11699799.

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8

Aftarini, Tari, Indawan Syahri, and Mulyadi Mulyadi. "How is the effect of Multiple Intelligences on Students’ Self-Confidence and English Daily Exam Scores?" Journal of Social Work and Science Education 4, no. 3 (June 24, 2023): 1208–29. http://dx.doi.org/10.52690/jswse.v4i3.667.

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The research aims to understand the impact of multiple intelligences toward students. Confidence and daily exam score of English Subject. This research was carried out at SMP Negeri 2 Abab, Penukal Abab Lematang Ilir Regency. This research used a quantitative approach. Data testing techniques were used in this research. 193 students were given a multiple intelligences test to find out what is their dominant intelligence and then used the data testing techniques to find out whether there was an impact or not toward the students’ confidence and daily exam score in English Subjects. The result of this research showed that (1) the student’s dominant intelligence did not significantly affect the English daily exam score. (2) some of the students’ dominant intelligence affected the students’ confidence in learning the English process, they were verbal intelligence, kinesthetic intelligence, interpersonal intelligence, intrapersonal intelligence, and naturalistic intelligence. So, it can be concluded that the students’ dominant intelligence didn’t have any impact on the student’s exam scores while some of the students’ dominant intelligence had an impact to the student’s self-confidence.

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9

R, Mysiuk, Yuzevych V, and Mysiuk I. "Api test automation of search functionality with artificial intelligence." Artificial Intelligence 27, jai2022.27(1) (June 20, 2022): 269–74. http://dx.doi.org/10.15407/jai2022.01.269.

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One of the steps in software development is to test the software product. With the development of technology, the testing process has improved to automated testing, which reduces the impact of the human factor on error and speeds up testing. The main software products for testing are considered to be web applications, web services, mobile applications and performance testing. According to the testing pyramid, when testing web services, you need to develop more test cases than when testing a web application. Because automation involves writing software code for testing, the use of ready-made tools will speed up the software development process. One of the most important test indicators is the coverage of search functionality. The search functionality of a web application or web service requires a large number of cases, as you need to provide many conditions for its operation through the free entry of any information on the web page. There is an approach to data-based testing, which involves working with a test data set through files such as CSV, XLS, JSON, XML and others. However, finding input for testing takes a lot of time when creating test cases and automated test scenarios. It is proposed to use artificial data set generators based on real values and popular queries on the website to form a test data set. In addition, it is possible to take into account the probable techniques of developing test cases. It is proposed to conditionally divide the software for testing into several layers: client, test, work with data, checks and reports. The Java programming language has a number of libraries for working at each of these levels. It is proposed to use Rest Assured as a Restful client, TestNG as a library for writing tests with checks, and Allure report for generating reports. It is noted that the proposed approach uses artificial intelligence for automated selection of test cases when creating a test to diversify test approaches and simulate human input and behavior to maximize the use of cases.

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10

Deary, Ian J., Elizabeth J. Austin, and Peter G. Caryl. "Testing versus understanding human intelligence." Psychology, Public Policy, and Law 6, no. 1 (2000): 180–90. http://dx.doi.org/10.1037/1076-8971.6.1.180.

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11

Goldstein, Sam. "The Science of Intelligence Testing." Journal of Psychoeducational Assessment 31, no. 2 (March 21, 2013): 132–37. http://dx.doi.org/10.1177/0734282913478033.

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12

Daniel, Mark H. "Intelligence testing: Status and trends." American Psychologist 52, no. 10 (October 1997): 1038–45. http://dx.doi.org/10.1037/0003-066x.52.10.1038.

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13

Laosa, Luis M. "Intelligence testing and social policy." Journal of Applied Developmental Psychology 17, no. 2 (April 1996): 155–73. http://dx.doi.org/10.1016/s0193-3973(96)90023-4.

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14

Bleichrodt, Nico, René A. C. Hoksbergen, and Usha Khire. "Cross-Cultural Testing of Intelligence." Cross-Cultural Research 33, no. 1 (February 1999): 3–25. http://dx.doi.org/10.1177/106939719903300101.

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15

Sternberg, Robert J. "The Future of Intelligence Testing." Educational Measurement: Issues and Practice 5, no. 3 (September 1986): 19–22. http://dx.doi.org/10.1111/j.1745-3992.1986.tb00482.x.

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16

Laosa, Luis M. "INTELLIGENCE TESTING AND SOCIAL POLICY." ETS Research Report Series 1995, no. 2 (December 1995): i—38. http://dx.doi.org/10.1002/j.2333-8504.1995.tb01667.x.

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17

Kovalchuk, Nadiia. "Brief history of intelligence testing." Education and Development of Gifted Personality, no. 4(91) (2023): 121–23. http://dx.doi.org/10.32405/2309-3935-2023-4(91)-121-123.

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18

Damirchi, Qader Vazifeh, Gholamreza Rahimi, and Hossein Seyyedi. "THE ROLE OF WORKER'S MULTIPLE INTELLIGENCES ON THEIR PRODUCTIVITY IN CULTURAL INSTITUTIONS OF MOGHAN." Australian Journal of Business and Management Research 01, no. 07 (February 10, 2012): 42–50. http://dx.doi.org/10.52283/nswrca.ajbmr.20110107a04.

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The main focus of this study is Cultural Institutions of Moghan region in Iran. The theory of multiple intelligences was developed in 1983 by Howard Gardner. He suggests that the traditional notion of intelligence, based on I.Q. testing, is far too limited. Instead, Dr. Gardner proposes eight different intelligences to account for a broader range of human potential in children and adults. These intelligences are linguistic intelligence, logical intelligence, spatial intelligence, bodily intelligence, musical intelligence, interpersonal intelligence, intrapersonal intelligence and Naturalist intelligence. The purpose of this research is surveying of relationship between multiple intelligences and productivity of Cultural Institutions workers in Moghan. The methodology of study is descriptive and analytical study. Data collection instrument was a questionnaire that its reliability was confirmed by Crohn Bach’s alpha and library studies. The results show that, there is a relationship between multiple intelligences and productivity of Cultural Institutions workers in Moghan.

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19

McCredie, Hugh. "Pioneers and landmarks in intelligence testingChallenging the hierarchical model of intelligence." Assessment and Development Matters 11, no. 1 (2019): 27–30. http://dx.doi.org/10.53841/bpsadm.2019.11.1.27.

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Key digested messageSo far in this series we have tracked the view that factoring known measures of intelligence leads to a hierarchical model with general intelligence, g, transcending successively narrower abilities. However, most admit that the intellectual measures on which such factor analysis was based do not cover the full bandwidth of intelligent behaviour.The next two articles (articles 9 and 10 in the series) will look at authors who challenged this hierarchical model. The first, Robert Sternberg, extended the existing body of understanding in three different directions. The second, Howard Gardner, featured in the next article, offered an entirely non-hierarchical alternative model of intelligences.

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20

Sternberg, Robert J. "Successful intelligence: A model for testing intelligence beyond IQ tests." European Journal of Education and Psychology 8, no. 2 (December 2015): 76–84. http://dx.doi.org/10.1016/j.ejeps.2015.09.004.

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21

Deary, Ian J. "Intelligence, destiny and education: The ideological roots of intelligence testing." Intelligence 34, no. 6 (November 2006): 621–22. http://dx.doi.org/10.1016/j.intell.2006.03.002.

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22

Ueno, Maomi, Kazuma Fuchimoto, and Emiko Tsutsumi. "e-Testing from artificial intelligence approach." Behaviormetrika 48, no. 2 (July 2021): 409–24. http://dx.doi.org/10.1007/s41237-021-00143-x.

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23

McPherson, Karla S. "On intelligence testing and immigration legislation." American Psychologist 40, no. 2 (1985): 242–43. http://dx.doi.org/10.1037/0003-066x.40.2.242.

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24

Armitage, Roseanne, and Robert M. Stelmack. "Book Review EEG and Intelligence Testing." Journal of Clinical and Experimental Neuropsychology 10, no. 2 (March 1988): 297–98. http://dx.doi.org/10.1080/01688638808408243.

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25

Furnham, Adrian, and Alastair McClelland. "Word frequency effects and intelligence testing." Personality and Individual Differences 48, no. 5 (April 2010): 544–46. http://dx.doi.org/10.1016/j.paid.2009.12.001.

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26

Cronbach, Lee J. "Signs of Optimism for Intelligence Testing." Educational Measurement: Issues and Practice 5, no. 3 (September 1986): 23–24. http://dx.doi.org/10.1111/j.1745-3992.1986.tb00483.x.

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27

Fehlmann, Thomas, and Eberhard Kranich. "Testing Artificial Intelligence by Customers’ Needs." ATHENS JOURNAL OF SCIENCES 6, no. 4 (November 18, 2019): 265–86. http://dx.doi.org/10.30958/ajs.6-4-3.

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28

Richards, T. "Website of the week: Testing intelligence." BMJ 323, no. 7308 (August 11, 2001): 348. http://dx.doi.org/10.1136/bmj.323.7308.348/a.

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29

Dobrovskaya, L. N., G. �. Mikailov, O. L. Perevozchikova, and S. D. Fishchuk. "Tools for intelligence testing of children." Cybernetics and Systems Analysis 29, no. 2 (1993): 282–86. http://dx.doi.org/10.1007/bf01132791.

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30

Bishirjian, Richard J. "Quantification and Intelligence Testing: A Reassessment." Humanitas 22, no. 1 (2009): 185–92. http://dx.doi.org/10.5840/humanitas2009221/213.

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31

Hiermeier, Ursula Maria, and Sarah Jane Verity. "‘Race’ and racism in intelligence testing." Clinical Psychology Forum 1, no. 352 (April 2022): 30–34. http://dx.doi.org/10.53841/bpscpf.2022.1.352.30.

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There are well-documented difficulties with inequity in neuropsychological assessment. The article discusses the misuse of ‘race’ in intelligence testing and highlights shortcomings in test norms, standardisation samples, within- and between-group differences. We share our standpoint epistemology in writing this paper, and share our current understanding as a call for personal, professional, and collective change.

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32

Ryabinin, A. E. "Education Testing System by Artificial Intelligence." Philosophical Problems of IT & Cyberspace (PhilIT&C), no. 2 (December 18, 2023): 90–107. http://dx.doi.org/10.17726/philit.2023.2.7.

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The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses the applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills.

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33

McCredie, Hugh. "Pioneers and landmarks in intelligence testing." Assessment and Development Matters 10, no. 3 (2018): 35–39. http://dx.doi.org/10.53841/bpsadm.2018.10.3.35.

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Key digested messageIn this article, the seventh in the series, the author takes a close look at developments in intelligence testing following the Second World War. Some rapprochement was achieved between the American and British positions regarding the existence of a general abilityg, whilst Vernon and Cattell postulated differing maps of the space between the more general and specific abilities

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34

Smith, Adam Leon. "Artificial Intelligence." ITNOW 64, no. 2 (May 12, 2022): 65. http://dx.doi.org/10.1093/itnow/bwac065.

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35

Ivashkevych, Eduard, and Yuliia Danchuk. "Dependence of the Success of Testing Pupils on Social Intelligence of Teachers." Collection of Research Papers "Problems of Modern Psychology", no. 56 (May 30, 2022): 45–63. http://dx.doi.org/10.32626/2227-6246.2022-56.45-63.

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the purpose of our article is: to describe the most effective means of a cognitive control in the learning process of a foreign language, to describe three main stages of a test creation, to identify final or intermediate (thematic) tests; to show the dependence of the success of testing on social intelligence of teachers.methods of the research. The following theoretical methods of the research were used to solve the tasks formulated in the article: a categorical method, structural and functional methods, the methods of the analysis, systematization, modeling, generalization. Also in our research we used empirical methods of the statement study and molding experiment. With the aim to diagnose the level of the development of social intelligence of teachers we used the test of J. Gilford and M. O’Sullivan “Research of Social Intelligence”, adapted by O.S. Mykhailova (Гилфорд & О’Салливен, 2021).the results of the research. According to our research it is proved that the structure of social intelligence of a teacher had four components, such as: com-municative potential, characteristics of selfawareness, social perception and energy characteristics of the individual. The outlined characteristics determine a teacher’s prediction of the development of interpersonal situations, the ability to interpret the information he/she has heard or seen and the behavior of others, readiness for effective social interaction and decisionmaking. Thus, social intel-ligence of a teacher contains individual inclinations, abilities, characteristics that facilitate the acquisition of individual personal experience, skills and abilities of social actions and contacts.conclusions. Studying the social intelligence of the teacher is defined as the ability to perceive the surrounding and inner world, which allowed professionals at different levels to easily navigate in interpersonal interaction, harmonize their behavior. The structure of social intelligence of the teacher includes social sensitivity, empathy, selfknowledge and selfregulation. Social intelligence of a teacher is identified with certain psychological mechanisms. Social intelligence includes social thinking and wisdom. In such a way we analyze the concept of “social thinking” as a structural component of social intelligence. Social think-ing is defined as the ability of the subject to understand and to operate with perceived information. A high level of the development of social thinking allows a teacher to solve problems effectively that are arisen in the process of interper-sonal interaction; efficiency means speed, flexibility and accuracy in dealing with the facts of social reality.

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36

Christie, Anne, Peter Jordan, Ashlea Troth, and Sandra Lawrence. "Testing the links between emotional intelligence and motivation." Journal of Management & Organization 13, no. 3 (September 2007): 212–26. http://dx.doi.org/10.1017/s1833367200003709.

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AbstractThe emotional intelligence construct is still the focus of substantial controversy. In this paper we contribute to the current debate regarding the factors that comprise the emotional intelligence construct. While some authors argue that emotional intelligence consists of a number of social and emotional competencies including self-motivation (Goleman 1995; 1998), others maintain emotional intelligence abilities are restricted to abilities that directly link emotions to cognition (Mayer & Salovey 1997). This latter view does not include motivation as a factor, but acknowledges it as a separate, related function. We conducted an empirical study to test these two different conceptualisations. One hundred and thirteen individuals completed measures of emotional intelligence and McClelland's (1987) three motivational needs. Structural equation analyses clearly supported Mayer and Salovey's (1997) conceptualisation that motivation is a factor related to emotional intelligence but is not a component part of the emotional intelligence construct. Implications are discussed.

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37

Christie, Anne, Peter Jordan, Ashlea Troth, and Sandra Lawrence. "Testing the links between emotional intelligence and motivation." Journal of Management & Organization 13, no. 3 (September 2007): 212–26. http://dx.doi.org/10.5172/jmo.2007.13.3.212.

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AbstractThe emotional intelligence construct is still the focus of substantial controversy. In this paper we contribute to the current debate regarding the factors that comprise the emotional intelligence construct. While some authors argue that emotional intelligence consists of a number of social and emotional competencies including self-motivation (Goleman 1995; 1998), others maintain emotional intelligence abilities are restricted to abilities that directly link emotions to cognition (Mayer & Salovey 1997). This latter view does not include motivation as a factor, but acknowledges it as a separate, related function. We conducted an empirical study to test these two different conceptualisations. One hundred and thirteen individuals completed measures of emotional intelligence and McClelland's (1987) three motivational needs. Structural equation analyses clearly supported Mayer and Salovey's (1997) conceptualisation that motivation is a factor related to emotional intelligence but is not a component part of the emotional intelligence construct. Implications are discussed.

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38

Sharma, R., and S. Singh. "Swarm Intelligence Based Automated Testing for MTAAS." International Journal of Computer Sciences and Engineering 6, no. 8 (August 31, 2018): 146–50. http://dx.doi.org/10.26438/ijcse/v6i8.146150.

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39

Shi, Tianyi, Wei Huang, Man Zhang, and Jingyue Wu. "Application of Artificial Intelligence in Material Testing." Highlights in Science, Engineering and Technology 1 (June 14, 2022): 171–74. http://dx.doi.org/10.54097/hset.v1i.445.

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Under the development of artificial intelligence technology, the technical level of machine learning and machine vision has been significantly improved. For testing, machine vision can input the characteristics of the inspected object into the computer, while machine learning ability enables the computer to better analyze the characteristics of the inspected object and make the testing conclusion. Compared with traditional testing methods, this process has the characteristics of high accuracy and high speed, and its excellent performance can be used in all aspects of material testing.

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40

McShane, John, and Kirill Kultinov. "CAN Bus Fuzz Testing with Artificial Intelligence." ATZelectronics worldwide 16, no. 11 (November 2021): 62–64. http://dx.doi.org/10.1007/s38314-021-0690-z.

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41

Duckworth, A. L., P. D. Quinn, D. R. Lynam, R. Loeber, and M. Stouthamer-Loeber. "Role of test motivation in intelligence testing." Proceedings of the National Academy of Sciences 108, no. 19 (April 25, 2011): 7716–20. http://dx.doi.org/10.1073/pnas.1018601108.

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42

Super, John T. "Intelligence testing and officer certification examination standing." Journal of Criminal Justice 25, no. 1 (January 1997): 43–47. http://dx.doi.org/10.1016/s0047-2352(90)00050-w.

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43

Gardner, Eric F. "Aptitude (Intelligence) Testing: Past, Present, and Future." Educational Measurement: Issues and Practice 5, no. 3 (September 1986): 6. http://dx.doi.org/10.1111/j.1745-3992.1986.tb00479.x.

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44

Thirumalaraju, P., C. L. Bormann, M. Kanakasabapathy, F. Doshi, I. Souter, I. Dimitriadis, and H. Shafiee. "Automated sperm morpshology testing using artificial intelligence." Fertility and Sterility 110, no. 4 (September 2018): e432. http://dx.doi.org/10.1016/j.fertnstert.2018.08.039.

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45

Perleth, Christoph, Ursula Hofmann, Susanne Schauer, and Hans Wernberger. "Intelligence Testing in a Bavarian Comprehensive School." School Psychology International 15, no. 3 (August 1994): 261–75. http://dx.doi.org/10.1177/0143034394153005.

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46

CHÁÁVEZ-GARCÍÍA, MIROSLAVA. "Intelligence Testing at Whittier School, 1890-1920." Pacific Historical Review 76, no. 2 (May 1, 2007): 193–228. http://dx.doi.org/10.1525/phr.2007.76.2.193.

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This article examines the intersections of youth, race, and science in early twentieth-century California. It explores how scientific researchers, reform school administrators, and social reformers at Whittier State School advocated the use of intelligence tests to determine the causes of delinquency. Through the process of testing, they identified a disproportionate number of delinquent boys of color-Mexicans, Mexican Americans, and African Americans-as mentally deficient or "feebleminded." As the evidence reveals, intelligence, race, heredity, and criminality became inextricably linked as the basis for segregating and removing youth of color from the reformatory. The records indicate that, despite officials' recommendations to send feebleminded boys to state hospitals that routinely sterilized their wards, as allowed by a 1909 state law, they sent the majority of youth to the Preston School of Industry, a reform school for older boys. In this instance, expediency in creating a premier institution at Whittier State School took precedence over larger eugenicists designs.

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47

McCredie, Hugh. "Pioneers and landmarks in intelligence testing: Introduction." Assessment and Development Matters 9, no. 1 (2017): 16–17. http://dx.doi.org/10.53841/bpsadm.2017.9.1.16.

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48

Schneider, William H. "After Binet: French intelligence testing, 1900–1950." Journal of the History of the Behavioral Sciences 28, no. 2 (April 1992): 111–32. http://dx.doi.org/10.1002/1520-6696(199204)28:2<111::aid-jhbs2300280202>3.0.co;2-w.

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49

Sulaiman, Nour, and safwan hasson. "Using intelligence techniques to automate Oracle testing." AL-Rafidain Journal of Computer Sciences and Mathematics 17, no. 1 (June 1, 2023): 91–97. http://dx.doi.org/10.33899/csmj.2023.179485.

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50

Khan, Adil I., Mazeeya Khan, and Raheeb Khan. "Artificial Intelligence in Point-of-Care Testing." Annals of Laboratory Medicine 43, no. 5 (April 21, 2023): 401–7. http://dx.doi.org/10.3343/alm.2023.43.5.401.

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