Academic literature on the topic 'Educational Measurement'

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Journal articles on the topic "Educational Measurement"

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Sayler, Michael. "Understanding Educational Measurement." Gifted and Talented International 9, no. 2 (September 1994): 81–82. http://dx.doi.org/10.1080/15332276.1994.11672803.

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Feuerstahler, Leah. "Educational and Psychological Measurement." Measurement: Interdisciplinary Research and Perspectives 20, no. 3 (July 3, 2022): 175–80. http://dx.doi.org/10.1080/15366367.2021.2018216.

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Gulliksen, Harold. "Perspective on Educational Measurement." Applied Psychological Measurement 10, no. 2 (June 1986): 109–32. http://dx.doi.org/10.1177/014662168601000201.

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Borsboom, Denny. "Educational Measurement (4th ed.)." Structural Equation Modeling: A Multidisciplinary Journal 16, no. 4 (October 6, 2009): 702–11. http://dx.doi.org/10.1080/10705510903206097.

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Cantley, Ian. "A Quantum Measurement Paradigm for Educational Predicates: Implications for validity in educational measurement." Educational Philosophy and Theory 49, no. 4 (May 22, 2015): 405–21. http://dx.doi.org/10.1080/00131857.2015.1048668.

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Goldstein, Harvey. "Validity, science and educational measurement." Assessment in Education: Principles, Policy & Practice 22, no. 2 (April 2015): 193–201. http://dx.doi.org/10.1080/0969594x.2015.1015402.

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Jiao, Hong. "Sociocognitive Foundations of Educational Measurement." Measurement: Interdisciplinary Research and Perspectives 18, no. 1 (January 2, 2020): 37–39. http://dx.doi.org/10.1080/15366367.2019.1705655.

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Isbell, Daniel. "Sociocognitive Foundations of Educational Measurement." Language Assessment Quarterly 17, no. 1 (October 14, 2019): 121–24. http://dx.doi.org/10.1080/15434303.2019.1677669.

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Morska, Liliya, and Tetiana Storchova. "CHALLENGES IN CREATIVITY DEFINITION AND MEASUREMENT: EDUCATIONAL IMPLICATIONS." Pedagogical Education:Theory and Practice 35 (March 21, 2024): 75–83. http://dx.doi.org/10.32626/2309-9763.2023-75-83.

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The article examines the complex nature of creativity with the goal to project implications for educational settings, starting from its historical evolution, and the challenges associated with its definition and measurement. The study aims at reviewing the main approaches to defining the concept of creativity with a particular focus on educational settings application. Creativity, once attributed to divine sources, is now recognized as a multifaceted human capacity encompassing cognitive, conative, and environmental factors. The research spans various definitions and assessments, including personality and biographical inventories, behavioral tests, and both objective and subjective evaluations of creative outputs. Creativity theories are highlighted, emphasizing creativity's presence across multiple domains. Despite the plethora of testing methods, the paper critiques the dichotomous view of creativity as simply present or absent, advocating for a nuanced understanding that encompasses its varied degrees and dimensions. The study further stresses the importance of considering individual traits and contextual factors as part of a dynamic interplay that influences creativity. For educators, these findings underscore the need for professional development that addresses the broad scope of creative competencies, encouraging a holistic approach to fostering creativity in students. The paper concludes with recommendations for developing clear operational definitions for creativity that can lead to more consistent and reliable assessments within educational research and practice.
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Karwowski, Maciej, and Bogusław Milerski. "Educational Rationality: Measurement, Correlates, and Consequences." Education Sciences 11, no. 4 (April 12, 2021): 182. http://dx.doi.org/10.3390/educsci11040182.

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This paper introduces and empirically tests the model of tetragonal educational rationality that consists of four interrelated categories: hermeneutic, emancipatory, praxeological, and negational rationalities. Based on a large longitudinal study on primary and middle school students (total N = 1990), we investigated the psychometric properties of the Educational Rationalities Questionnaire (ERQ) and examined relevant correlates, antecedents and longitudinal consequences of the four rationalities. Confirmatory factor analysis corroborated the four-factor structure of the ERQ. Praxeological rationality was more prevalent than hermeneutic rationality, which was accepted more often than emancipatory and negational rationality. Structural equation modeling demonstrated that hermeneutic rationality was primarily driven by participants’ academic self-concept in their native language, as well as extraversion, neuroticism, and valuing creativity. Emancipatory rationality was linked to academic self-concept, valuing creativity, and agreeableness, while praxeological rationality was predicted by extraversion, school achievement, and valuing creativity. Finally, negational rationality was inversely linked with several individual characteristics: academic self-concept, agreeableness, and school achievement. Longitudinal analyses demonstrated that negational rationality was associated with lower grades and more negative emotions during lessons, while hermeneutic rationality was associated with school grades improvement. Emancipatory rationality was related to positive emotions felt during classes, yet negatively with grades. We discuss potential reasons and consequences of these findings.
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Dissertations / Theses on the topic "Educational Measurement"

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Marks, Lori J. "Curriculum-Based Measurement: Writing." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etsu-works/3680.

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Stein, Zachary. "Tipping the Scales: Social Justice and Educational Measurement." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13383548.

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In this work I address foundational concerns at the interface of educational measurement and social justice. Following John Rawls’s philosophical methods, I build and justify an ethical framework for guiding practices involving educational measurement. This framework demonstrates that educational measurement is critical to insuring, or inhibiting, just educational arrangements. It also clarifies a principled distinction between efficiency-oriented testing and justice-oriented testing. In order to explore the feasibility and utility of this proposed framework, I employ it to analyze several historical case studies that exemplify the ethical issues related to testing: (1) the widespread use of IQ-style testing in schools during the early decades of the 20th century; (2) the founding of the Educational Testing Service; and (3) the recent history of test-based accountability associated with No Child Left Behind. I conclude with a set of speculative design principles and arguments in favor of radically democratic school reforms, which address how the future of testing might be shaped to ensure justice for all.
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Wikström, Christina. "Criterion-referenced measurement for educational evaluation and selection." Doctoral thesis, Umeå universitet, Beteendevetenskapliga mätningar, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-492.

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In recent years, Sweden has adopted a criterion-referenced grading system, where the grade outcome is used for several purposes, but foremost for educational evaluation on student- and school levels as well as for selection to higher education. This thesis investigates the consequences of using criterion-referenced measurement for both educational evaluation and selection purposes. The thesis comprises an introduction and four papers that empirically investigate school grades and grading practices in Swedish upper secondary schools. The first paper investigates the effect of school competition on the school grades. The analysis focuses on how students in schools with and without competition are ranked, based on their grades and SweSAT scores. The results show that schools that are exposed to competition tend to grade their students higher than other schools. This effect is found to be related to the use of grades as quality indicators for the schools, which means that schools that compete for their students tend to be more lenient, hence inflating the grades. The second paper investigates grade averages over a six-year period, starting with the first cohort who graduated from upper secondary school with a GPA based on criterion-referenced grades. The results show that grades have increased every year since the new grading system was introduced, which cannot be explained by improved performances, selection effects or strategic course choices. The conclusion is that the increasing pressure for high grading has led to grade inflation over time. The third paper investigates if grading practices are related to school size. The study is based on a similar model as paper I, but with data from graduates over a six-year period, and with school size as the main focus. The results show small but significant size effects, suggesting that the smallest schools (<300 students) are higher grading than other schools, and that the largest schools (>1000 students) are lower grading than other schools. This is assumed to be an effect of varying assessment practices, in combination with external and internal pressure for high grading. The fourth and final paper investigates if grading practices differ among upper secondary programmes, and how the course compositions in the programmes affect how students are ranked in the process of selection to higher education. The results show that students in vocationally oriented programmes are higher graded than other students, and also favoured by their programmes’ course compositions, which have a positive effect on their competitive strength in the selection to higher education. In the introductory part of the thesis, these results are discussed from the perspective of a theoretical framework, with special attention to validity issues in a broad perspective. The conclusion is that the criterion-referenced grades, both in terms of being used for educational evaluation, and as an instrument for selection to higher education, are wanting both in reliability and in validity. This is related to the conflicting purposes of the instruments, in combination with few control mechanisms, which affects how grades are interpreted and used, hence leading to consequences for students, schools and society in general.
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Wikström, Christina. "Criterion-referenced measurement for educational evaluation and selection /." Umeå : Department of Educational Measurement, Umeå University, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-492.

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Shauchenka, Hanna. "Affective quality of educational services measurement in the context of higher education marketing." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/3525.

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Educational marketing has become an increasingly important area within Higher Education as the competition for students has intensified. Being able to measure and understand the quality of educational services – a key factor in the decision making process for a prospective student – is an incredibly challenging problem as it involves the quantitative measurement of factors such as emotions and affections towards an Institution or programme, which themselves tend to be intangible. The application of total quality management philosophy and methodology in the context of higher education today is fully acknowledged and widely used. These conditions have defined the main task of this research: to develop a methodology for quantitative measurement of the affective quality of educational services for marketing management analysis. In other words offered research investigates how to measure things that have often been considered immeasurable. It was hypothesized that availability of a methodology for quantitative estimation of the affective quality of educational services provides additional important information that ensures an effective decision-making process in the marketing department in higher education institutions. Kansei engineering formalizes such concepts as affections and emotions and highlights their role in the purchase decision-making process. Our KanMar (short for Kansei Marketing) approach is aimed on the implementation of the main Kansei engineering ideas in the context of educational marketing and provides the framework for the quantitative measurement of educational services’ affective quality. KanMar enables the formalization of the affective quality of educational services for its marketing analysis: comparison, prediction, control, etc. The results of such an analysis help to position own services in today’s competitive market more effectively. Data obtained using KanMar methodology enables to find out the stakeholders’ implicit motivations or attitudes. So, for example, data obtained during the conducted survey has indirectly confirmed the students’ orientation to the practical activity. This orientation is typical for the Universities of Applied Sciences and the respondents for this survey have all been students at one of them. KanMar approach also addresses major gaps of existing instruments based on SERVQUAL methodology aimed to measure service quality in education. The hypothesis was tested and partly confirmed using case study that illustrates the application of the KanMar approach.
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Kruse, Lance M. "Item-Reduction Methodologies for Complex Educational Assessments: A Comparative Methodological Exploration." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1576175496892792.

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Gascon, Gregg Manley. "An application of theory-driven evaluation in educational measurement." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1165331507.

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Gascon, Gregg Manley. "An application of theory-driven evaluation in educational measurement." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1165331507.

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Carter, Lacy. "Data Envelopment Analysis: Measurement of Educational Efficiency in Texas." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149569/.

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The purpose of this study was to examine the efficiency of Texas public school districts through Data Envelopment Analysis. The Data Envelopment Analysis estimation method calculated and assigned efficiency scores to each of the 931 school districts considered in the study. The efficiency scores were utilized in two phases. First, the school district efficiency scores were combined with school district student achievement to evaluate effectiveness with efficiency. A framework was constructed to graph the scores on an x-axis of student achievement scores and a y-axis of efficiency scores to further illustrate the data. The framework was evaluated with the full statewide sample and with school districts categorized into similar peer groups. Then, using variables selected from related scholarly literature, a regression analysis identified which factors impacted school district efficiency statewide. The non-discretionary variables included in the study were total student enrollment, the percentage of non-white students and the percentage of economically disadvantaged students. The discretionary variables selected included the teacher-to-student ratio, teachers’ average years of experience, the percentage of teachers with master’s degrees and the average teacher base salary. Amongst the seven factors selected for regression analysis, five statistically significant variables were identified as impacting statewide school district efficiency. All three non-discretionary variables were identified as statistically significant on efficiency and included total student enrollment, the percentage of non-white students and the percentage of economically disadvantaged students. Two discretionary factors showed statistically significant effects on efficiency which included teachers’ average years of experience and the percentage of teachers with master’s degrees. The teacher-to-student ratio and the average teacher base salary were ineffective in predicting efficiency. This study contributed to the understanding on educational efficiency. Data Envelopment Analysis has been employed mainly in the private sector to analyze efficiency in economics and business organizations. This study added to the educational research on selecting Data Envelopment Analysis as a primary estimation method for analyzing the efficiency of school systems.
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Wang, Yixi. "Application of Item Response Tree (IRTree) Models on Testing Data: Comparing Its Performance with Binary and Polytomous Item Response Models." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587481533999313.

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Books on the topic "Educational Measurement"

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L, Linn Robert, National Council on Measurement in Education., and American Council on Education, eds. Educational measurement. Phoenix, AZ: Oryx Press, 1993.

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L, Linn Robert, National Council on Measurement in Education., and American Council on Education, eds. Educational measurement. 3rd ed. New York: American Council on Education, 1988.

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L, Brennan Robert, National Council on Measurement in Education, and American Council on Education, eds. Educational measurement. 4th ed. Westport, CT: Praeger Publishers, 2006.

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Ebel, Robert L. Essentialsof educational measurement. 5th ed. Englewood Cliffs: Prentice Hall, 1991.

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Noll, Victor Herbert. Introduction to educational measurement. Lanham: University Press of America, 1989.

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Ebel, Robert L. Essentials of educational measurement. 5th ed. Englewood Cliffs, N.J: Prentice Hall, 1991.

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Wiersma, William. Educational measurement and testing. 2nd ed. Boston: Allyn and Bacon, 1990.

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Wiersma, William. Educational measurement and testing. 2nd ed. Boston: Allyn and Bacon, 1990.

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Ebel, Robert L. Essentials of educational measurement. 5th ed. Englewood Cliffs, N.J: Prentice Hall, 1991.

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Ebel, Robert L. Essentials of educational measurement. 4th ed. Englewood Cliffs, N.J: Prentice-Hall, 1986.

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Book chapters on the topic "Educational Measurement"

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Plake, Barbara S., and Gerald J. Melican. "Future of Educational Measurement." In Historical Foundations of Educational Psychology, 373–91. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4899-3620-2_17.

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Carroll, John B. "Measurement and Educational Psychology." In Historical Foundations of Educational Psychology, 89–106. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4899-3620-2_5.

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Kauffman, James M., Daniel P. Hallahan, Paige C. Pullen, and Jeanmarie Badar. "Measurement of Educational Performance." In Special Education, 22–38. New York, NY: Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315211831-2.

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Ball, Samuel. "Evaluating Educational Programs." In Methodology of Educational Measurement and Assessment, 341–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58689-2_11.

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Wu, Margaret, Hak Ping Tam, and Tsung-Hau Jen. "What Is Measurement?" In Educational Measurement for Applied Researchers, 1–18. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3302-5_1.

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Wu, Margaret, Hak Ping Tam, and Tsung-Hau Jen. "An Ideal Measurement." In Educational Measurement for Applied Researchers, 91–107. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3302-5_6.

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Deterding, David. "Issues in the Acoustic Measurement of Rhythm." In Educational Linguistics, 9–24. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3883-6_2.

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Mislevy, Robert J. "On Measurement in Educational Assessment." In Handbook on Measurement, Assessment, and Evaluation in Higher Education, 11–31. Second edition. | New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315709307-3.

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Blossfeld, Hans-Peter, Gwendolin Josephine Blossfeld, and Sabine Weinert. "Introduction – Education, Competence Development and Career Trajectories." In Methodology of Educational Measurement and Assessment, 1–24. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27007-9_1.

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AbstractA lifespan perspective on development and education and corresponding longitudinal studies have long been proposed scientifically in many disciplines. However, even in the 2000s, little was known about education as a lifelong process or about the cumulative and interactive effects of learning that takes place in different educational settings across the lifespan and comprehensive longitudinal studies were rare. The German National Educational Panel Study (NEPS) was therefore designed to provide longitudinal data on educational trajectories and competence development from infancy to old age, and to make it possible to examine inter- and intraindividual changes and pathways in relation to family, educational institutions, workplaces, private life, and individual characteristics of the target persons. The data also addresses the institutions of the German educational system such as crèches and preschools, primary and secondary schools, vocational training, tertiary education, and opportunities of further learning in adulthood. It further enables the analysis of relevant monetary and non-monetary returns to education over the life course. The chapter briefly introduces important features of the multi-cohort sequence design of the NEPS and discusses the relevance of longitudinal data. Furthermore, it outlines Germany as a special case by introducing specific features of the German education system as well as some international comparative findings. Finally, an overview of the following chapters is given.
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Almond, Russell G., Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, and David M. Williamson. "The Biomass Measurement Model." In Bayesian Networks in Educational Assessment, 549–82. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2125-6_15.

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Conference papers on the topic "Educational Measurement"

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Kamensky, M., E. Kralikova, J. Cervenova, and K. Kovac. "Interfacing of measuring devices in educational software system for remote access." In 2017 11th International Conference on Measurement. IEEE, 2017. http://dx.doi.org/10.23919/measurement.2017.7983564.

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Mircea Popa. "Educational Virtual Instrumentation Application for System Identification." In 2006 IEEE Instrumentation and Measurement Technology. IEEE, 2006. http://dx.doi.org/10.1109/imtc.2006.236390.

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Popa, Mircea, Raul Ionel, Voicu Groza, and Marius Marcu. "Educational Virtual Instrumentation Application for System Identification." In 2006 IEEE Instrumentation and Measurement Technology. IEEE, 2006. http://dx.doi.org/10.1109/imtc.2006.328215.

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"Educational Measurement and Educational Evaluation of Decision Making based on IRT." In 2017 International Conference on Advanced Education, Psychology and Sports Science. Francis Academic Press, 2017. http://dx.doi.org/10.25236/aepss.2017.024.

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Adamo, F., F. Attivissimo, G. Cavone, and N. Giaquinto. "SCADA/HMI Systems in Advanced Educational Courses." In Proceedings of the IEEE Instrumentation and Measurement Technology Conference. IEEE, 2005. http://dx.doi.org/10.1109/imtc.2005.1604312.

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Haiyan, Qi, and Chen Huaxi. "Theories of Educational Measurement Deviation and Their Analysis." In 2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). IEEE, 2017. http://dx.doi.org/10.1109/icmtma.2017.0036.

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Györök, György, Margit Makó, and Olga Shvets. "Educational Universal Analog Circuit Measurement Laboratory with FPAA." In 2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES). IEEE, 2023. http://dx.doi.org/10.1109/ines59282.2023.10297677.

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Mahamood, Mohd Nor Mamat, and Siti Fatahiyah Mahamood. "Measurement of Rubrical Essay-based Test Using Rasch Model." In ICEEPSY 2016 International Conference on Education and Educational Conference. Cognitive-crcs, 2016. http://dx.doi.org/10.15405/epsbs.2016.11.80.

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Imai, Shotaro, Michiyo Shimamura, and Kazuhiko Terasawa. "Measurement of Educational Effect Based on Behavioral Change in a Trans-Graduate Educational Program." In 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2018. http://dx.doi.org/10.1109/iiai-aai.2018.00085.

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Yani, Ahmad, and Enok Maryani. "Geographic Skills Measurement for Geography Education Students." In 1st International Conference on Educational Sciences. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0007041003540360.

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Reports on the topic "Educational Measurement"

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Vo, Thao, Susan Lyons, Felice Levine, Nathan Bell, and Ye Tong. State of the Field: Gender and Racial Equity in Educational Measurement. American Educational Research Association, April 2024. http://dx.doi.org/10.3102/aera20241.

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This report is the result of a collaborative initiative of the American Educational Research Association (AERA), the National Council on Measurement in Education (NCME), and Women in Measurement (WIM) aimed at understanding the current state of gender and racial equity within the educational measurement field. Based on a survey of professionals’ experiences and perceptions, the study documents disparities across racial and gender groups and offers recommendations for creating a more diverse, equitable, and inclusive profession.
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Frandsen, Martin, Jakob Vind Madsen, Rasmus Lund Jensen, and Michal Zbigniew Pomianowski. Domestic water measurement in two Danish office and educational buildings - Data set description. Aalborg University, August 2022. http://dx.doi.org/10.54337/aau481810642.

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Measurement data of domestic water in non-residential buildings is sparse. More data is needed to describe the non-residential buildings’ domestic hot water (DHW) demand and to be able to design more efficient DHW systems. This DCE Technical Report aims to present and describe the data set: “Data set - Domestic water at CREATE and TMV23”. This data set contains measurement data from two office and educational buildings in Aalborg, Denmark. The measurements in the building CREATE are conducted from October 2018 to January 2019 and in the building TMV23 from April 2021 to May 2021. The data set consists of water flow rates and temperatures for the domestic cold water, domestic hot water, district heating for DHW production, and DHW circulation circuit.
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Masters, Geofferey N., and Robert J. Mislevy. New Views of Student Learning: Implications for Educational Measurement. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada235285.

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Johnston, David, Carol Propper, Stephen Pudney, and Michael Shields. Child mental health and educational attainment: multiple observers and the measurement error problem. Institute for Fiscal Studies, July 2011. http://dx.doi.org/10.1920/wp.cem.2011.2711.

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Burstein, Jill. Duolingo English Test Responsible AI Standards. Duolingo, March 2023. http://dx.doi.org/10.46999/vcae5025.

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Artificial intelligence (AI) is now instantiated in digital learning and assessment platforms. Many sectors, including tech, government, legal, and military sectors, now have used formalized principles to develop responsible AI standards. While there is a substantial literature around responsible AI more generally (e.g., Fjeld et al., 2020; Gianni et al., 2022; and, NIST, 20231 ), traditional validity frameworks (such as, Xi, 2010a; Chapelle et al., 2008; Kunnan, 2000; and, Kane, 1992) pre-date AI advances, and do not provide formal standards for the use of AI in assessment. The AERA/APA/NCME Standards (2014) pre-date modern AI advances, and include limited discussion about the use of AI and technology in educational measurement. Some research discusses AI application in terms of validity (such as Huggins-Manley et al., 2022, Williamson et al., 2012, and Xi, 2010b). In earlier work, Aiken and Epstein (2000) discuss ethical considerations for AI in education. More recently, Dignum (2021) proposed a high-level vision for responsible AI for education, and Dieterle et al (2022) and OECD (2023) discuss guidelines and issues associated with AI in testing. The Duolingo English Test (DET)’s Responsible AI Standards were informed by the ATP (2021) and ITC-ATP (2022) guidelines, which provide comprehensive and relevant guidelines about AI and technology use for assessment. New guidelines for responsible AI are continually being developed (Department for Science, Technology & Innovation, 2023).
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Cloney, Dan, David Jeffries, Stephanie Templeton, Ursula Schwantner, Juliette Mendelovits, Amy Berry, Syeda Kashfee Ahmed, Nicola Andrews, and Bethany Andrews. Monitoring learning in the early years. A review of early childhood assessments to support global monitoring. Australian Council for Educational Research, 2024. http://dx.doi.org/10.37517/978-1-74286-746-5.

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Sustainable Development Goal 4 (SDG 4) aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”. In relation to Early Childhood Education (ECE), SDG Target 4.2 aims to “ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary education”. To operationalise this goal, the primary Indicator 4.2.1 sets out to measure the “proportion of children aged 24–59 months who are developmentally on track in health, learning and psychosocial well-being, by sex. The United Nations Children's Fund (UNICEF) is the custodian of SDG Target 4.2. As such, UNICEF has coordinated efforts to develop an assessment that “captures the achievement of key developmental milestones of children between the ages of 24 and 59 months”. This assessment is called the Early Childhood Development Index 2030, or ECDI2030. The ECDI2030 has been in use since March 2020. While the tool provides a quick snapshot for reporting, there is room for improvement, considering the breadth of skills that are developing as children grow. This review of existing ECE assessments has been motivated by the aim of the Global Education Monitoring (GEM) Centre at the Australian Council for Educational Research (ACER) to develop appropriate definitions and to contribute to the objective measurement of skills and attributes in early childhood education. The report reviews a range of assessments, including ECDI2030, with a view to identifying how they can be strengthened. The report is intended to benefit global stakeholders in the SDG 4 agenda, national governments, practitioners, and ultimately, children.
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7

Ham, Andrés, Angela Guarin, and Juanita Ruiz. How Accurately are Household Surveys Measuring the Size and Inequalities for the LGBT Population in Bogota, Colombia? Evidence from a List Experiment. Inter-American Development Bank, February 2023. http://dx.doi.org/10.18235/0004721.

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This paper studies whether household surveys precisely identify the LGBT population and are suitable to measure labor market discrimination in Colombia. We first quantify the size of the LGBT population and estimate labor market inequalities from these data, highlighting potential pitfalls from using this approach. We then present findings from a list experiment in Bogotá, Colombia. Results show that household surveys underestimate the size of the LGBT population and may yield biased estimates of labor market inequalities. While survey estimates range between 1-4%, we find that LGBT people constitutes around 12-22% of the total population. We find heterogeneous reporting by sex, age groups, educational attainment, and marital status. Our findings suggest that while current measurement practices are a step forward for the LGBT populations statistical visibility, additional steps are required before household surveys may be used to consistently estimate discrimination and guide policy responses to protect this population.
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Mahat, Marian, Guy Morrow, Brian Long, Siew Fang Law, Amy Gullickson, and Chengxin Guo. Developing an impact framework for Science Gallery Network: Final report. University of Melbourne, 2022. http://dx.doi.org/10.46580/124372.

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The aim of this project was to develop an impact framework for the Science Gallery Network (SGN). This work was commissioned by the Science Gallery International (SGI). The SGN has eight member organisations across four continents: Dublin, London, Melbourne, Bengaluru, Detroit, Rotterdam, Atlanta and Berlin. Whilst the network consistently sees unprecedented levels of accomplishment by its members, a testimony to their capacity, innovation and vision, the SGN does not have a systematic way to measure and monitor this impact. An impact framework that can assist with understanding and reporting the value of this impact will provide important recognition that the SGN has achieved what it sets out to do— bringing science, art, technology and design together to deliver world-class educational and cultural experiences for young people. This report details the robust consultation approach that was undertaken by the University of Melbourne’s project team—one that included a desktop review, focus group discussions, surveys and interviews—to ensure multiple perspectives were gathered on what could be considered a multi-faceted concept. The desktop review provided a thorough review and an environmental scan of the impact literature and its measurement. In addition, the focus group discussions and interviews provided a rich understanding of what ‘good impact’ means for the SGN and the implications of this to the measurement of impact outcomes. Five key recommendations are provided and summarised. Note that these key recommendations should be taken as a point of departure for further in-depth consultation throughout the wider SGN.
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9

Jacob, Brian, and Lars Lefgren. Principals as Agents: Subjective Performance Measurement in Education. Cambridge, MA: National Bureau of Economic Research, July 2005. http://dx.doi.org/10.3386/w11463.

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10

Kaffenberger, Michelle, Lant Pritchett, and Martina Viarengo. Towards a Right to Learn: Concepts and Measurement of Global Education Poverty. Research on Improving Systems of Education (RISE), December 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/085.

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The idea that children have a “right to education” has been widely accepted since the Universal Declaration of Human Rights in 1948 (United Nations, 1948) and periodically reinforced since. The “right to education” has always, explicitly or implicitly, encompassed a “right to learn.” Measures of schooling alone, such as enrollment or grade attainment, without reference to skills, capabilities, and competencies acquired, are inadequate for defining education or education poverty. Because of education’s cumulative and dynamic nature, education poverty needs an “early” standard (e.g., Grade 3 or 4 or age 8 or 10) and a “late” standard (e.g., Grade 10 or 12 or ages 15 and older). Further, as with all international poverty definitions, there needs to be a low, extreme standard, which is found almost exclusively in low- and middle-income countries and can inform prioritization and action, and a higher “global” standard, against which even some children in high income countries would be considered education poor but which is considered a reasonable aspiration for all children. As assessed against any proposed standard, we show there is a massive learning crisis: students spend many years in school and yet do not reach an early standard of mastery of foundational skills nor do they reach any reasonable global minimum standard by the time they emerge from school. The overwhelming obstacle to addressing education poverty today is not enrollment/grade attainment nor inequality in learning achievement, but the fact that the typical learning profile is just too shallow for children to reach minimum standards.
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