Bibliografías temáticas / Educational statistics – data processing

Literatura académica sobre el tema "Educational statistics – data processing"

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Publicado: 25 de julio de 2024

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Artículos de revistas sobre el tema "Educational statistics – data processing"

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Szreder, Mirosław. "Some new challenges and expectation of statistics". Wiadomości Statystyczne. The Polish Statistician 61, n.º 6 (28 de junio de 2016): 1–9. http://dx.doi.org/10.5604/01.3001.0014.1000.

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The article discusses recent research and educational challenges faced by the statistics in the world of fast-growing possibilities of collecting and processing data (Big Data) and the increasingly dominant in social and economic life numerical description of reality. These new phenomena and trends should encourage statisticians’ environment to take action of improving society statistical knowledge needed to critically evaluate and correct interpretation of the survey results.
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2

Nur Ghifari, Shafira Zahra, Zahid Mubarok y Sutisna Sutisna. "DIFFERENCES IN STUDENTS' ABILITY TO RECEIVE AND PROCESS INFORMATION ON EDUCATIONAL STATISTICS COURSES". JURNAL EDUSCIENCE 10, n.º 1 (28 de mayo de 2023): 357–70. http://dx.doi.org/10.36987/jes.v10i1.4115.

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The ability to receive and process information is included in cognitive abilities. This ability is a basic ability that everyone should have well because almost every day human life is surrounded by information. However, not all individuals can immediately understand the information provided to them, including students. For this reason, this study aims to find out how students' abilities in receiving and processing information in educational statistics courses. In addition, it is also to find out what causes differences in students' abilities in receiving and processing information in educational statistics courses. The method used is a qualitative method with a grounded theory approach. Data collection was carried out by observing, filling out questionnaires, and documentation. The research was conducted in Class PAI 5E FAI UIKA Bogor. The results showed that student's ability to receive and process information in educational statistics courses was quite good. This can be seen from the results of student learning which shows that the average is at A-B grades. The causes of differences in student abilities in receiving and processing information on educational statistics courses are caused by 3 things, namely interest and motivation to learn, focus on learning, and supporting facilities in learning these courses.Keywords: cognitive ability; learning outcomes; educational statistics AbstrakKemampuan menerima dan mengolah informasi termasuk dalam kemampuan kognitif. Kemampuan ini merupakan kemampuan dasar yang seharusnya dimiliki setiap orang dengan baik karena hampir setiap hari kehidupan manusia dikelilingi dengan adanya informasi. Akan tetapi tidak semua individu bisa langsung memahami informasi yang diberikan kepada mereka termasuk mahasiswa. Untuk itu, penelitian ini bertujuan untuk mengetahui bagaimana kemampuan mahasiswa dalam menerima dan mengolah informasi mata kuliah statistika pendidikan. Selain itu juga untuk mengetahui apa penyebab perbedaan kemampuan mahasiswa dalam menerima dan mengolah informasi mata kuliah statistika pendidikan. Metode yang digunakan yaitu metode kualitatif dengan pendekatan grounded theory. Pengambilan data dilakukan dengan melakukan observasi, pengisian kuesioner, dan dokumentasi. Penelitian dilakukan di Kelas PAI 5E FAI UIKA Bogor. Hasil penelitian menunjukkan bahwa kemampuan mahasiswa dalam menerima dan mengolah informasi mata kuliah statistika pendidikan dapat dikatakan cukup baik. Hal ini dapat dilihat dari hasil belajar mahasiswa yang menunjukkan rata-rata berada pada nilai A-B. Adapun penyebab perbedaan kemampuan mahasiswa dalam menerima dan mengolah informasi mata kuliah statistika pendidikan disebabkan oleh 3 hal yaitu minat dan motivasi belajar, fokus belajar, serta fasilitas penunjang dalam pembelajaran mata kuliah tersebut.Kata kunci:Kemampuan kognitif, hasil belajar, statistika pendidikan
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Pečiuliauskienė, Palmira. "Application of Statistical Methods in Education Sciences: the Didactical Principles in the Educational Means Prepared by Bronislovas Bitinas". Pedagogika 124, n.º 4 (2 de diciembre de 2016): 47–57. http://dx.doi.org/10.15823/p.2016.50.

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Based on works of professor B. Bitinas this article analyses educational materials on applying statistical methods in education sciences. The educational materials on applying statistical methods in education sciences can be classified into two groups: methodological group (the application of mathematical statistics in quantitative research) and technological group (computer software for processing quantitative research data). The article analyses application of didactical principles in the methodological group and the technological group. Didactical principles provide direction for learning process and govern contents, methods and organisation of learning. Different didactical principles can be used when preparing educational materials on the applying statistical methods in education as principle of the availability and principle of relation between the theory and practise. Application of statistical methods in education sciences is influenced by technological tools used for processing statistical data. These tools changed from electronic calculating machines (ECM) to modern computers with modern statistical programs. Technological change influences didactical principles applied for preparing educational materials on applying statistical methods in education sciences. The comparative analysis of learning materials on applying statistical methods in education sciences prepared by Professor B. Bitinas shows that the principle of availability was an important principle at the time of the processing statistical data using the ECM. This principle is less important when statistical data is processed using modern statistical computer programs. Didactical principle of the relation between theory and practice was important in the early (the eighth-ninth decade of XX century) and later stages (the twentyfirst century.) Professor B. Bitinas prepared both methodological (the usage of mathematical statistics into quantitative research) and technological (computer software for quantitative research data processing) learning means. The didactical principles in the methodological and technological learning means are different. The principle of the availability and the principle of relation between the theory and practise dominate in the methodological learning means. The principle of systematization and continuity is important in technological learning means.
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4

KRASNOZHON, O. B. y V. V. MATSIUK. "КОМП’ЮТЕРНО-ОРІЄНТОВАНІ ЕЛЕМЕНТИ НАВЧАННЯ МАТЕМАТИЧНИХ ДИСЦИПЛІН МАЙБУТНІХ УЧИТЕЛІВ МАТЕМАТИКИ". Scientific papers of Berdiansk State Pedagogical University Series Pedagogical sciences 1, n.º 2 (4 de octubre de 2021): 255–62. http://dx.doi.org/10.31494/2412-9208-2021-1-2-255-262.

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The article is devoted to the issues of constructing effective computer-oriented components of the methodological system of teaching the disciplines «Linear Algebra» and «Probability Theory with Elements of Mathematical Statistics» provided for in the educational and professional program «Secondary Education (Mathematics)» of the first level of higher education in the specialty 014 Secondary Education (Mathematics). The article analyzes the methodological aspects of the effective organization of computations when finding the angle between a given vector and a nonzero subspace of Euclidean space, as well as using the least squares method for processing experimental data. The theoretical and practical information known to students-mathematicians from the corresponding sections of these academic disciplines is briefly presented. Analyzed educational, methodological and scientific literature used in teaching linear algebra and probability theory with elements of mathematical statistics; the expediency of using computer-oriented elements of teaching mathematical disciplines of future mathematics teachers has been substantiated. The authors proposed the use of computer-oriented learning elements in the processing of the content of disciplines and the development of test tasks of different levels of complexity in linear algebra and probability theory with elements of mathematical statistics in order to objectively assess the level of students' knowledge and timely correct individual educational trajectories. The article provides examples of the application of computer-oriented elements of teaching linear algebra and probability theory with elements of mathematical statistics, and also analyzes the methodological features of the organization of calculations in the software mathematical environment Mathcad. The methodological and practical materials presented in the article can be useful for students to organize and activate independent scientific and pedagogical activities, teachers of secondary educational institutions, heads of optional and circle work of students, teachers of linear algebra and probability theory courses with elements of mathematical statistics of pedagogical higher educational institutions. Key words: methods of teaching mathematics, computer-oriented elements of teaching mathematics, linear algebra, probability theory, mathematical statistics, Euclidean space, non-zero subspace of Euclidean space, least squares method.
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5

Soroko, N. V. y M. A. Shynenko. "Monitoring electronic educational and scientific resources using Google Analytics". CTE Workshop Proceedings 1 (21 de marzo de 2013): 95–96. http://dx.doi.org/10.55056/cte.144.

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Currently, many scientific resources are created electronically. Such resources include: documents; electronic publications; electronic catalogs; pictures and images in different formats; audio, video, animation; digital maps and cartographic materials; computer programs, etc. It becomes important to analyze their relevance and the need for the development of science and education. Therefore, the problem of monitoring these resources arises.Google Analytics is a product of Google, which is part of the services called "cloud computing" (Cloud Computing).Cloud Computing is a data processing technology in which a software tool is provided to the user as an Internet service. For example, Google Analytics is a free service offered to create detailed statistics of website visitors.
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Haryati, Haryati, Muhammad Nur Akbar Rasyid, Sitti Mania y Supardi Widodo. "Evaluasi Pembelajaran Statistik Pendidikan di STAI Al Khairaat Labuha dengan Model Evaluasi Discrepancy dan Kirkpatrick". PALAPA 11, n.º 1 (1 de mayo de 2023): 426–45. http://dx.doi.org/10.36088/palapa.v11i1.3251.

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This study is aimed to evaluate the learning of Educational Statistics Course at PAI Study Program of STAI Alkhairaat Labuha, Ternate. This is evaluation research, employing the combination of Discrepancy and Kirkpatrick Evaluation Model. The study employed mixed methods involving questionnaires, observation, and documentation. The case study method was used, which included total sampling of 14 students. The research results reveal several significant findings: To begin with, the learning gap for Educational Statistics Course for the 2022/2023 Academic Year is relatively small with 11.57%. Second, the lecturers have pedagogical competence and high professionalism in the student's perspectives. This is indicated by the findings that the gap of pedagogical and professional competence is extremely small with 8.35% and 4.20% respectively. Third, the use of Mastery Learning approach to teach Educational Statistics is considered effective and making Google forms and applications to collect data directly from respondents and data processing procedures provides the most effective learning experience for students. The researchers made the following recommendations: firstly, lecturers should design well-developed and structured lesson plans, and enhance student learning with practical skills to make learning more successful. Second, lecturers are encouraged to improve their pedagogical skills, particularly in the topics of material distribution, calculating material, and to increase learning for quartile material and research hypotheses. Third, lecturers should continue using the Mastery Learning approach in the teaching of Educational Statistics. Fourth, lecturers are urged to fully enhance students' cognitive, affective, and psychomotor through direct practices and peer collaboration.
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Ashofteh, Afshin y Jorge M. Bravo. "Data science training for official statistics: A new scientific paradigm of information and knowledge development in national statistical systems". Statistical Journal of the IAOS 37, n.º 3 (1 de septiembre de 2021): 771–89. http://dx.doi.org/10.3233/sji-210841.

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The ability to incorporate new and Big Data sources and to benefit from emerging technologies such as Web Technologies, Remote Data Collection methods, User Experience Platforms, and Trusted Smart Statistics will become increasingly important in producing and disseminating official statistics. The skills and competencies required to automate, analyse, and optimize such complex systems are often not part of the traditional skill set of most National Statistical Offices. The adoption of these technologies requires new knowledge, methodologies and the upgrading of the quality assurance framework, technology, security, privacy, and legal matters. However, there are methodological challenges and discussions among scholars about the diverse methodical confinement and the wide array of skills and competencies considered relevant for those working with big data at NSOs. This paper develops a Data Science Model for Official Statistics (DSMOS), graphically summarizing the role of data science in statistical business processes. The model combines data science, existing scientific paradigms, and trusted smart statistics, and develops around a restricted number of constructs. We considered a combination of statistical engineering, data engineering, data analysis, software engineering and soft skills such as statistical thinking, statistical literacy and specific knowledge of official statistics and dissemination of official statistics products as key requirements of data science in official statistics. We then analyse and discuss the educational requirements of the proposed model, clarifying their contribution, interactions, and current and future importance in official statistics. The DSMOS was validated through a quantitative method, using a survey addressed to experts working at the European statistical systems. The empirical results show that the core competencies considered relevant for the DSMOS include acquisition and processing capabilities related to Statistics, high-frequency data, spatial data, Big Data, and microdata/nano-data, in addition to problem-solving skills, Spatio-temporal modelling, machine learning, programming with R and SAS software, Data visualisation using novel technologies, Data and statistical literacy, Ethics in Official Statistics, New data methodologies, New data quality tools, standards and frameworks for official statistics. Some disadvantages and vulnerabilities are also addressed in the paper.
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Salsabila, Nilza Humaira, Ulfa Lu’luilmaknun, Dwi Novitasari, Ratna Yulis Tyaningsih y Riska Ayu Ardani. "GAME EDUKASI PADA PEMBELAJARAN MATEMATIKA: TANGGAPAN SISWA SMP BERDASARKAN GENDER". Mathematic Education And Aplication Journal (META) 2, n.º 1 (15 de octubre de 2020): 25–32. http://dx.doi.org/10.35334/meta.v2i1.1632.

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AbstractThe use of media in mathematics learning can help students to learn more easily. One of them is educational game media. The study aims to describe the responses of junior high school students based on gender to learn mathematics by using educational game media. The subjects of this study were 7thand 8thgrade of junior high school students in Mataram-West Nusa Tenggara, a total of 101 students. Study data were obtained from a questionnaire about the use of educational games in mathematics classes. Study data processing using descriptive statistics, qualitative methods. The results of the study show that there are differences in responses between male and female students, towards: fun learning and increased motivation using media games and missions in educational games.Keywords: Edukasi, Game, Gender, MathematicsAbstrakPenggunaan media pada pembelajaran matematika dapat membantu siswa untuk lebih mudah belajar. Salah satunya media game edukasi. Adapun studi ini bertujuan untuk mendeskripsikan tanggapan siswa SMP berdasarkan gender terhadap pembelajaran matematika dengan menggunakan media game edukasi. Subjek penelitianini adalah siswaSMP kelas 7 dan 8 di Mataram-Nusa Tenggara Barat sebanyak 101 siswa. Data studi diperoleh dari kuesioner tentang penggunaan game edukasi di kelas matematika.Pengolahan data studi menggunakan statistic deskriptif ,metode kualitatif. Hasil studi menunjukkan bahwa terdapat perbedaan tanggapan antara siswa laki-laki dan perempuan, terkait: pembelajaran yang menyenangkan dan motivasi yang meningkatdenganmenggunakan media game, serta misi pada game edukasi.Kata kunci: Education, Game, Gender, Matematika
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Susilawati, Sumarni. "Analysis of Motivation and Interests of Arabic Language Education Students at Unismuh Makassar in Educational Statistics Subject". Journal of Mathematics and Applied Statistics 1, n.º 1 (30 de mayo de 2023): 38–41. http://dx.doi.org/10.35914/mathstat.v1i1.41.

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Education statistics subject is one of the compulsory subjects for students majoring in education which is often avoided by many students. Students tend to have a negative attitude towards subjects related to numbers and calculations. Based on the observation results of Arabic Language Education student exam results, there are still many students who have not been able to describe or analyze the results of data processing. Thus, research was conducted regarding the interest and motivation of Arabic Language Education students in the Education Statistics subject. The research method used is the questionnaire method to measure aspects of interest and talent, aspects of learning readiness, and learning support facilities. So the results obtained from the study were that 85.7% of students liked the Education Statistics subject, and prepared the media to record material well. The presence of lecturers in the room also affects student enthusiasm for learning. It's just that the enthusiasm for student learning has not been supported by adequate facilities.
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Nursyabani, Akbar, Tajuddin Tajuddin y Nur Astuti Darmiyanti. "PENINGKATAN EDUCATIONAL QUALITY ASSURANCE (EQA) MELALUI SARANA DAN PRASARANA DI LEMBAGA PENDIDIKAN ISLAM MADRASAH TSANAWIYAH NEGERI 4 KARAWANG". Jurnal Pendidikan dan Pengajaran Guru Sekolah Dasar (JPPGuseda) 4, n.º 2 (14 de julio de 2021): 154–58. http://dx.doi.org/10.55215/jppguseda.v4i2.3618.

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IMPROVING EDUCATIONAL QUALITY ASSURANCE THROUGH FACILITIES AND INFRASTRUCTURE AT ISLAMIC EDUCATION INSTITUTIONS MADRASAH TSANAWIYAH NEGERI 4 KARAWANG Educational facilities and infrastructure are an important part of achieving a maximum quality of education, the minimum standard of facilities and infrastructure for MTs or SMP is contained in the Regulation of the Minister of National Education number 19 of 2005. This study aims to empirically determine the effect of management of facilities and infrastructure on quality of education services at Madrasah Tsanawiyah Negeri 4 Karawang. In this study, the authors used the quantitative method with statistical processing using the Spss V.18 software with multiple linear regression formulas, because the independent variables in this study were double or two. Collecting data using observation techniques, documentation and questionnaire instruments / questionnaire with a Likert scale method of 1 to 4. The population of this study were all teachers and staff, totaling 49 people, with a sample size of 44 respondents. Sampling using purposive sampling technique, while the data analysis technique using descriptive statistics, inferential statistics using multiple linear regression consulted using correlative statistics. The results of data processing can be concluded that simultaneously the management of facilities and infrastructure has a significant effect on the quality of education services with the results of the F test of 1228.07 which is interpreted to the F table of 3.2, then F count F table. While the correlation coefficient value obtained is 0.996, if interpreted to the guideline table for the correlation analysis scale, it has a very strong relationship level of 99.6%. Thus it can be argued that the management of facilities and infrastructure greatly affects the quality of education services at MTsN 4 Karawang in the 2020/2021 school year.
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Tesis sobre el tema "Educational statistics – data processing"

1

Hill, Rachelle Phelps. "A Case Study of the Impact of the Middle School Data Coach on Teacher Use of Educational Test Data to Change Instruction". Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc33164/.

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With the advent of No Child Left Behind (NCLB) legislation in 2002 and its attendant increases in accountability pressure, many districts and schools currently embrace data analysis as an essential part of the instructional decision making process. In their attempts to overcome low achievement on state-mandated tests, some districts have begun employing data coaches. The study reported here, which was set in three middle schools in a northeast Texas school district, assessed the influence of the campus data coach on a middle school mathematics teachers' use of analyzed data to make instructional decisions. It also examined the extent to which the Data Coach/teacher relationship resolved teacher concerns about data-driven decision making. Phenomenological interviews with data coaches were guided by Seidman's (2006) three-series interview. Measurement of teacher use of data to make decisions was based on the concerns-based adoption model's levels of use interview protocol, stages of concern questionnaire, and innovation configuration map. By the end of one school year, two out of the three teachers never used data to make instructional decisions, although the non-users both had moved closer toward employing the innovation in their classroom. Data indicated all teachers were aware of the innovation, but all three ended the study with high personal concerns, signifying that the minimal efforts made by the data coaches to resolve concerns were not successful. This study's small sample gave the research paradigm of data-based decision making an in-depth glimpse into the process of implementing data-based instructional decision making and the Data Coach position on three middle school campuses in one large northeast Texas district.
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2

Zhang, Zhidong 1957. "Cognitive assessment in a computer-based coaching environment in higher education : diagnostic assessment of development of knowledge and problem-solving skill in statistics". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102853.

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Diagnostic cognitive assessment (DCA) was explored using Bayesian networks and evidence-centred design (ECD) in a statistics learning domain (ANOVA). The assessment environment simulates problem solving activities that occurred in a web-based statistics learning environment. The assessment model is composed of assessment constructs, and evidence models. Assessment constructs correspond to components of knowledge and procedural skill in a cognitive domain model and are represented as explanatory variables in the assessment model. Explanatory variables represent specific aspects of student's performance of assessment problems. Bayesian networks are used to connect the explanatory variables to the evidence variables. These links enable the network to propagate evidential information to explanatory model variables in the assessment model. The purpose of DCA is to infer cognitive components of knowledge and skill that have been mastered by a student. These inferences are realized probabilistically using the Bayesian network to estimate the likelihood that a student has mastered specific components of knowledge or skill based on observations of features of the student's performance of an assessment task.
The objective of this study was to develop a Bayesian assessment model that implements DCA in a specific domain of statistics, and evaluate it in relation to its potential to achieve the objectives of DCA. This study applied a method for model development to the ANOVA score model domain to attain the objectives of the study. The results documented: (a) the process of model development in a specific domain; (b) the properties of the Bayesian assessment model; (c) the performance of the network in tracing students' progress towards mastery by using the model to successfully update the posterior probabilities; (d) the use of estimates of log odds ratios of likelihood of mastery as a measure of "progress toward mastery;" (e) the robustness of diagnostic inferences based on the network; and (f) the use of the Bayesian assessment model for diagnostic assessment with a sample of 20 students who completed the assessment tasks. The results indicated that the Bayesian assessment network provided valid diagnostic information about specific cognitive components, and was able to track development towards achieving mastery of learning goals.
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Mercier, Julien 1974. "Help seeking and use of tutor scaffolding by dyads learning with a computer tutor in statistics". Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85191.

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Research on tutoring has shown that the student's interaction with the tutor determines the learning outcomes. In human tutoring, the responsibility of the interaction is shared between the tutor and the student. In the case of a computer coach such as the McGill Statistics Tutor, the control of the interaction is put entirely in the hands of the learners. Learners' ability to interact with the system productively therefore represents a critical aspect affecting the learning outcomes. This ability of help seeking (Nelson-LeGall, 1981) has not been well researched from a cognitive science point of view in the context of computer-supported learning (Aleven et al., 2003). The aims of the present work were to elaborate and test a cognitive model of help seeking and to examine the prevalence of help seeking in a problem-based computer-supported learning situation, as well as individual differences and the effect of the progression in a sequence of tasks with respect to help seeking.
Participants were 18 graduate students from a faculty of Education of a Canadian university. The seven-hour experiment involved working in pairs to solve a very challenging statistics problem for which students did not have sufficient background. A computer coach based on human tutoring, the McGill Statistics Tutor, was available to provide help with every aspect of the task.
Data consisted of two complementary sources. The main source was the dialogue between the participants as they worked on the statistics problem using the computer coach. The students' use of the computer coach and solutions to the tasks were also integrated into the database.
Data analysis consisted of statistical analyses using log-linear models. Conditional probability graphs were also constructed from the data.
The results were consistent with the help seeking model. Individual differences were found in terms of emphasis on certain help seeking activities. Effects of the progression in the sequence of tasks were also found. The quality of the solutions students elaborated corresponded to specific profiles of help seeking. The structure of help seeking episodes was established and corresponded to the model. These results have implications for the design of computer coaches and instructional situations.
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Thayne, Jeffrey L. "Making statistics matter| Self-data as a possible means to improve statistics learning". Thesis, Utah State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10250713.

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Research has demonstrated that well into their undergraduate and even graduate education, learners often struggle to understand basic statistical concepts, fail to see their relevance in their personal and professional lives, and often treat them as little more than mere mathematics exercises. Undergraduate learners often see statistical concepts as means to passing exams, completing required courses, and moving on with their degree, and not as instruments of inquiry that can illuminate their world in new and useful ways.

This study explored ways help learners in an undergraduate learning context to treat statistical inquiry as mattering in a practical research context, by inviting them to ask questions about and analyze large, real, messy datasets that they have collected about their own personal lives (i.e., self -data). This study examined the conditions under which such an intervention might (and might not) successfully lead to a greater sense of the relevance of statistics to undergraduate learners. The goal is to place learners in a context where their relationship with data analysis can more closely mimic that of disciplinary professionals than that of students with homework; that is, where they are illuminating something about their world that concerns them for reasons beyond the limited concerns of the classroom.

The study revealed five themes in the experiences of learners working with self-data that highlight contexts in which data-analysis can be made to matter to learners (and how self-data can make that more likely): learners must be able to form expectations of the data, whether based on their own experiences or external benchmarks; the data should have variation to account for; the learners should treat the ups and downs of the data as more or less preferable in some way; the data should address or related to ongoing projects or concerns of the learner; and finally, learners should be able to investigate quantitative or qualitative covariates of their data. In addition, narrative analysis revealed that learners using self-data treated data analysis as more than a mere classroom exercise, but as exercises in inquiry and with an invested engagement that mimicked (in some ways) that of a disciplinary professional.

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5

Tao, Yufei. "Indexing and query processing of spatio-temporal data /". View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20TAO.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.
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6

Andersson-Sunna, Josefin. "Large Scale Privacy-Centric Data Collection, Processing, and Presentation". Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-84930.

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It has become an important part of business development to collect statistical data from online sources. Information about users and how they interact with an online source can help improving the user experience and increasing sales of products. Collecting data about users has many benefits for the business owner, but it also raises privacy issues since more and more information about users are spread over the internet. Tools that collect statistical data from online sources exists, but using such tools gives away the control over the data collected. If a business implements its own analytics system, it is easier to make it more privacy centric and the control over the data collected is kept.  This thesis examines what techniques that are most suitable for a system whose purpose is to collect, store, process, and present large-scale privacy centric data. Research about what technique to use for collecting data and how to keep track of unique users in a privacy centric way has been made as well as research about what database to use that can handle many write requests and store large scale data. A prototype was implemented based on the research, where JavaScript tagging is used to collect data from several online sources and cookies is used to keep track of unique users. Cassandra was chosen as database for the prototype because of its high scalability and speed at write requests. Two versions of the processing of raw data into statistical reports was implemented to be able to evaluate if the data should be preprocessed or if the reports could be created when the user asks for it.   To evaluate the techniques used in the prototype, load tests of the prototype was made where the results showed that a bottleneck was reached after 45 seconds on a workload of 600 write requests per second. The tests also showed that the prototype managed to keep its performance at a workload of 500 write requests per second for one hour, where it completed 1 799 953 requests. Latency tests when processing raw data into statistical reports was also made to evaluate if the data should be preprocessed or processed when the user asks for the report. The result showed that it took around 30 seconds to process 1 200 000 rows of data from the database which is too long for a user to wait for the report. When investigating what part of the processing that increased the latency the most it showed that it was the retrieval of data from the database that increased the latency. It took around 25 seconds to retrieve the data and only around 5 seconds to process it into statistical reports. The tests showed that Cassandra is slow when retrieving many rows of data, but fast when writing data which is more important in this prototype.
Det har blivit en viktig del av affärsutvecklingen hos företag att samla in statistiska data från deras online-källor. Information om användare och hur de interagerar med en online-källa kan hjälpa till att förbättra användarupplevelsen och öka försäljningen av produkter. Att samla in data om användare har många fördelar för företagsägaren, men det väcker också integritetsfrågor eftersom mer och mer information om användare sprids över internet. Det finns redan verktyg som kan samla in statistiska data från online-källor, men när sådana verktyg används förloras kontrollen över den insamlade informationen. Om ett företag implementerar sitt eget analyssystem är det lättare att göra det mer integritetscentrerat och kontrollen över den insamlade informationen behålls. Detta arbete undersöker vilka tekniker som är mest lämpliga för ett system vars syfte är att samla in, lagra, bearbeta och presentera storskalig integritetscentrerad information. Teorier har undersökts om vilken teknik som ska användas för att samla in data och hur man kan hålla koll på unika användare på ett integritetscentrerat sätt, samt om vilken databas som ska användas som kan hantera många skrivförfrågningar och lagra storskaligdata. En prototyp implementerades baserat på teorierna, där JavaScript-taggning används som metod för att samla in data från flera online källor och cookies används för att hålla reda på unika användare. Cassandra valdes som databas för prototypen på grund av dess höga skalbarhet och snabbhet vid skrivförfrågningar. Två versioner av bearbetning av rådata till statistiska rapporter implementerades för att kunna utvärdera om data skulle bearbetas i förhand eller om rapporterna kunde skapas när användaren ber om den. För att utvärdera teknikerna som användes i prototypen gjordes belastningstester av prototypen där resultaten visade att en flaskhals nåddes efter 45 sekunder på en arbetsbelastning på 600 skrivförfrågningar per sekund. Testerna visade också att prototypen lyckades hålla prestandan med en arbetsbelastning på 500 skrivförfrågningar per sekund i en timme, där den slutförde 1 799 953 förfrågningar. Latenstest vid bearbetning av rådata till statistiska rapporter gjordes också för att utvärdera om data ska förbehandlas eller bearbetas när användaren ber om rapporten. Resultatet visade att det tog cirka 30 sekunder att bearbeta 1 200 000 rader med data från databasen vilket är för lång tid för en användare att vänta på rapporten. Vid undersökningar om vilken del av bearbetningen som ökade latensen mest visade det att det var hämtningen av data från databasen som ökade latensen. Det tog cirka 25 sekunder att hämta data och endast cirka 5 sekunder att bearbeta dem till statistiska rapporter. Testerna visade att Cassandra är långsam när man hämtar ut många rader med data, men är snabb på att skriva data vilket är viktigare i denna prototyp.
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Wong, Ka-yan y 王嘉欣. "Positioning patterns from multidimensional data and its applications in meteorology". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B39558630.

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Fathi, Salmi Meisam. "Processing Big Data in Main Memory and on GPU". The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1451992820.

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Gwaze, Arnold Rumosa. "A cox proportional hazard model for mid-point imputed interval censored data". Thesis, University of Fort Hare, 2011. http://hdl.handle.net/10353/385.

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There has been an increasing interest in survival analysis with interval-censored data, where the event of interest (such as infection with a disease) is not observed exactly but only known to happen between two examination times. However, because so much research has been focused on right-censored data, so many statistical tests and techniques are available for right-censoring methods, hence interval-censoring methods are not as abundant as those for right-censored data. In this study, right-censoring methods are used to fit a proportional hazards model to some interval-censored data. Transformation of the interval-censored observations was done using a method called mid-point imputation, a method which assumes that an event occurs at some midpoint of its recorded interval. Results obtained gave conservative regression estimates but a comparison with the conventional methods showed that the estimates were not significantly different. However, the censoring mechanism and interval lengths should be given serious consideration before deciding on using mid-point imputation on interval-censored data.
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Tsoi, Kit-hon y 徐傑漢. "Aspects of the statistics of condensation polymer networks". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38985433.

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Libros sobre el tema "Educational statistics – data processing"

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Stemmer, Paul M. Microcomputer programs for educational statistics: A review of popular programs. Princeton, N.J: ERIC Clearinghouse on Tests, Measurements, and Evaluation, Educational Testing Service, 1986.

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National Forum on Education Statistics (U.S.). National Education Statistics Agenda Committee. Improving the capacity of the national education data system to address equity issues: An addendum to A guide to improving the national education data system. Washington, DC: The Center, 1995.

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Fundação Centro Brasileiro de TV Educativa., ed. Um Relato do estado atual de informática no ensino no Brasil. [Rio de Janeiro]: Ministério da Educação e Cultura, Fundação Centro Brasileiro de TV Educativa, 1985.

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Holcomb, Edie L. Data-based decision making. 3a ed. Bloomington, IN: Solution Tree PRess, 2012.

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Abbott, Martin L. Understanding educational statistics using Microsoft Excel® and SPSS®. Hoboken, N.J: Wiley, 2011.

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Sheila, Heaviside, Educational Resources Information Center (U.S.) y National Center for Education Statistics., eds. Advanced telecommunications in U.S. public elementary and secondary schools, 1995: Fast Response Survey System. [Washington, DC]: U.S. Dept. of Education, Office of Educational Research and Improvement, Educational Resources Information Center, 1996.

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Yi, Chong-sŏng. Kyoyuk yŏnʾgu ŭi sŏlgye wa charyo punsŏk. 8a ed. Sŏul Tʻŭkpyŏlsi: Kyohak Yŏnʾgusa, 1996.

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Logunova, Oksana, Petr Romanov y Elena Il'ina. Processing of experimental data on a computer. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064882.

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The textbook provides information about the main methods and tools for automating computational processes used in data processing; methods for representing and generating models of experimental data; data models and classification of processing tasks; and the organization of the user interface in automated systems for processing experimental data. Contains structured chapters on the specifics of experimental research. The features of using software for processing experimental data are clearly and logically described. Theoretical material and basic algorithms for processing experimental data used in industrial statistics are presented. Examples of processing experimental data in the field of metallurgy and management in higher education are given. Meets the requirements of the Federal state educational standards of higher education of the latest generation. For students and postgraduates of higher educational institutions.
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National Center for Education Statistics. y Educational Resources Information Center (U.S.), eds. Evaluation of the 1996-97 nonfiscal common core of data surveys data collection, processing, and editing cycle. [Washington, DC]: U.S. Dept. of Education, Office of Educational Research and Improvement, National Center for Education Statistics, 1999.

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National Center for Education Statistics, ed. Integrated postsecondary education data system: Guide to surveys. [Washington, D.C.?]: U.S. Dept. of Education, Office of Educational Research and Improvement, National Center for Education Statistics, 1992.

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Capítulos de libros sobre el tema "Educational statistics – data processing"

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Fischer, Svenja y Andreas H. Schumann. "Data Processing". En Type-Based Flood Statistics, 73–95. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32711-7_6.

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Romero, Cristóbal, José Raúl Romero y Sebastián Ventura. "A Survey on Pre-Processing Educational Data". En Educational Data Mining, 29–64. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02738-8_2.

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Xue, Dingyü y Feng Pan. "Data Processing and Statistics". En MATLAB and Simulink in Action, 353–84. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-1176-9_12.

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Froeschl, K. A. "Semantic Metadata: Query Processing and Data Aggregation". En Computational Statistics, 357–62. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-642-48678-4_45.

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Wang, Jiachun, Jing Zhao, Shiliang Sun y Dongyu Shi. "Intelligent Educational Data Analysis with Gaussian Processes". En Neural Information Processing, 353–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04224-0_30.

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Kauffman, James M. y John Wills Lloyd. "Statistics, Data, and Special Educational Decisions". En Handbook of Special Education, 29–39. Second Edition. | New York : Routledge, 2017. | “First edition published by Routledge 2011”—T.p. verso.: Routledge, 2017. http://dx.doi.org/10.4324/9781315517698-4.

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Zhang, Qing, Germaine Uwimpuhwe, Dimitris Vallis, Akansha Singh, Tahani Coolen-Maturi y Jochen Einbeck. "Elicitation of Priors for Intervention Effects in Educational Trial Data". En Contributions to Statistics, 28–33. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65723-8_5.

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Froeschl, Karl A. "Application Case I: Labour Force Statistics — Harmonizing Official Statistics Data". En Metadata Management in Statistical Information Processing, 279–390. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-6856-1_4.

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Buscemi, Francesco. "Reverse Data-Processing Theorems and Computational Second Laws". En Springer Proceedings in Mathematics & Statistics, 135–59. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2487-1_6.

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Reboiro-Jato, Miguel, Daniel Glez-Peña y Hugo López-Fernández. "CHAPTER 5. Statistics, Data Mining and Modeling". En Processing Metabolomics and Proteomics Data with Open Software, 120–200. Cambridge: Royal Society of Chemistry, 2020. http://dx.doi.org/10.1039/9781788019880-00120.

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Actas de conferencias sobre el tema "Educational statistics – data processing"

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Ponomarenko, Alexey. "Reformatting statistical education in Russia: changes in classifications, standards, and programs". En Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17314.

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In the centrally planned economy the main function of official statistics was monitoring of plans’ execution. Hence, official statisticians had to be experts in economics and bookkeeping, like tax inspectors. Russian statistical education was oriented mostly to official needs and statistics was included in the same educational group as economics. Currently, professional requirements for statisticians have changed. Official statistics lost its control function, and the old reporting system is being replaced by sample surveys that are less onerous for respondents and also less expensive. The statistical agency needs more professionals in survey methodology, as well as statistical managers and mathematicians. In 2015, the new professional standard "statistician" was accepted in Russia in line with ISCO 2008. It consists of set of competences for professionals in data collection, processing, analysis and methodology in any field of activity, including business, finance, science, medicine. In 2016, the educational classification of statistics was changed to the same group as mathematics. A new educational standard for statistics was also accepted. This presentation describes part of the reconstructive process.
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Lee, Taerim. "A deep learning analytics to facilitate sustainability of statistics education". En Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19306.

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Deep Learning Analytics uses predictive models that provide actionable information. It is a multidisciplinary approach based on data processing, AI technology-learning enhancement, educational data mining, and visualization. The problem is that embracing DLA(Deep Learning Analytics) in evaluating data in higher education diverts educators’ attention from clearly identifying methods, benefits, and challenges of using DLA in higher education. Predictive models including random forest (RF), support vector machines (SVM), logistic regression (logistic), and Deep Learning were trained and their performances compared. The predicted value of “source of sustainability” and selected input variables were utilized to predict the drop out of learner. Expected significant outcomes and impact is that using DLA we can find the optimal learning management model for supporting services for instructors significantly impact the quality of statistics education and for learners is necessary to support announcements from instructors, for providing appropriate learning environments.
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Marhoum, Adil. "Plenary lecture: the teaching of statistics in morocco l’enseignement de la statistique au maroc". En Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17103.

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Discussing higher education in Morocco, particularly about statistics, the talk reflects on the following points: • Interest of statistics • Place of statistics in education programs in Morocco • Difficulties in teaching statistics • Teachers of statistics and teaching methods • Suggestions for further increasing the penetration of statistics into curricula Various reforms of Moroccan education have consistently placed more emphasis on the teaching of statistics. This teaching is currently being introduced in most of the training streams, such as economics, management sciences, and agronomic sciences. Despite this positive development, there are a number of shortcomings in the teaching of statistics. There is an absence of statistical reasoning in students: the ability to analyze and interpret the results of calculations. Statistics courses are taught by teachers who have often been forced to teach it, usually mathematics teachers, who on the whole have a formal approach to statistics. The mathematician takes the place of the statistician, without considering more applied questions. In order to encourage better statistical understanding, we suggest the presence of experienced statisticians in any university or scientific research institution, in association if necessary; training seminars (Study visits, refresher courses, thematic networking of statisticians, etc.) in countries with more statistics education capacity; local training for researchers; participation in regional seminars; creation of courses of study more oriented towards statistical processing and data processing; and drafting and dissemination of guides to good statistical practice in experimentation, sampling and publication of results.
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Motoryn, Ruslan, Tetiana Motoryna y Kateryna Prykhodko. "Impact of big data on development of the curriculums of training statisticians in Ukrainian university". En Teaching Statistics in a Data Rich World. International Association for Statistical Education, 2017. http://dx.doi.org/10.52041/srap.17702.

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In the Soviet era in Ukraine, theoretical subjects dominated in the education curriculum. Since independence, there have been profound changes in the higher education system of Ukraine. The system has adapted to the market model, with an increasing number of applied disciplines such as data mining, software engineering and data visualization. Courses should cover both theoretical knowledge and practical skills. Recently, one of the top requested areas at the information technology market is the processing of large data sets (Big data). Many leading universities of Ukraine, including Taras Shevchenko National University of Kyiv and Kyiv-Mohyla Academy, plan to establish courses of data mining, statistics, and data visualization. For now, however, universities do not have enough teachers. Therefore, under the scope of the Ukrainian Distance Education Project "Prometheus", in 2017 an online course on the Big Data will be launched. This paper will summarize the curriculum developed by the authors, in courses on Business Analytics, Software Engineering, Data Analysis, and Data Visualization.
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Lee, Jung Jin y Gunseog Kang. "An Educational Software for the Design of Experiments". En Statistical Literacy- Material From Some of the Talks. International Association for Statistical Education, 2001. http://dx.doi.org/10.52041/srap.01105.

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This paper introduces a software called the Information Statistics Provider(ISP), which has many modules on teaching statistics as well as data processing. The ISP is a GUI oriented, interactive, and user-friendly system with multiple sheets. This paper focuses on modules of the Design of Experiments, which show the design sheets of an experiment and enable a post-analysis.
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Höper, Lukas, Susanne Podworny, Carsten Schulte y Daniel Frischemeier. "Exploration of Location Data: Real Data in the Context of Interaction with a Cellular Network". En IASE 2021 Satellite Conference: Statistics Education in the Era of Data Science. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.nkppy.

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Students are not aware and have little understanding of collecting and processing personal data in their everyday contexts of interaction with data-driven digital artifacts. To be aware of where, how and why data are collected and processed is important to be self-determined. Therefore, we develop and evaluate a teaching sequence to provide reasoning about data as a fundamental aspect of statistical literacy. This teaching sequences deals with the context of interaction with a cellular network where location data are collected. Students get real location data from an unknown person which can be explored with the aim to characterize the person. Students gain different insights by using different basic filters and explain how they achieve these. The results of the exploratory study indicate that students learned to gain insights by exploring given location data and that these insights may describe the person with detailed aspects that may not necessarily be true.
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Puchianu, Crenguta, Anata flavia Ionescu, Daniela Caprioara y Dorin mircea Popovici. "STATISTICAL SOFTWARE FOR EDUCATIONAL RESEARCHES". En eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-116.

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The importance of scientific research in the educational sciences is supported by the pace of change in society. The objectives of this research are radiography, knowledge, understanding and interpreting specific processes and phenomena in the field of education. To achieve its goals, research must comply with certain standards and principles of scientific quality. This paper presents a software application useful in the design, deployment and exploitation of scientific researches. The objective of the system development has been the acquisition and/or enhancement of skills of the students from Bachelor and Master in Education Sciences in the design, organization, development and exploitation of scientific research. The software system consists of four modules: the first one provides the base structure of the research designs; the second module manages questionnaires and their validation (testing the distribution of item answers and the questionnaire fidelity); the third one deals with statistical processing (by applying many statistical computations and tests) and interpretation of data; the last module provides the base structure of a scientific paper that contains the results obtained in the course of scientific research. The system provides RTF and Microsoft Word documents which are opened at the request of the user to see the current structure of the research project. In addition, the system displays several charts (histograms and pies) of the data. The software system has been tested by the students and professors of the Psychology and Educational Sciences faculty. We claim that this system is an original software tool useful to teachers in the design and development process of quality education.
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Wei, Yuan. "The training of researchers in the use of statistics in China". En Training Researchers in the Use if Statistics. International Association for Statistical Education, 2000. http://dx.doi.org/10.52041/srap.00404.

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The responsibility of training researchers in the use of statistics in China belongs to colleges, universities and research institutes. There is a National Statistical Education Association. Under the Association, the Higher Education Branch is an organisation of colleges, universities and research institutes who have a major in statistics or a statistics faculty. Since China has a population of 1.25 billion and more than 100 thousand official statisticians in the whole country, statistics training is a huge task. There are degree and non-degree training programs. In the degree program, there are undergraduate programs (colleges and universities) and graduate programs (colleges, universities and research institutes). In the non-degree training, different training programs have been used. Statistical methods are widely used in almost all the fields. The most important application areas are: official statistical work including sampling survey and data processing, micro-economic analysis, management and quality improvement, medical application, agriculture and industry experiment, etc. Most researchers in the above fields need to be trained. Many patterns have been used in training. Class teaching, group discussion, field training, TV, broadcasting programs and Internet are the main patterns.
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R. P, Arya y Anuja S. B. "Effectively Analysis and Predict Students Performance and Other Evaluation". En The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/gdhl6261/ngcesi23p2.

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The development of intelligent technologies gains popularity in the education field. Educational Data Mining (EDM) is a research field of data mining, which focuses on the application of data mining, machine learning and statistical methods. The clustering effect of K-means Algorithm is tested by discriminant analysis. K-means Algorithm improves the reliability of prediction results. The development of intelligent technologies gains popularity in the education field. The rapid growth of educational data indicates traditional processing methods may have limitations and distortion. Therefore, reconstructing the research technology of data mining in the education field has become increasingly prominent. In order to avoid unreasonable evaluation results and monitor the students’ future performance in advance, this paper comprehensively uses the relevant theories of clustering, discrimination and convolution neural network to analyze and predict students’ academic performance. Firstly, this paper proposes that the clustering-number determination is optimized by using a statistic which has never been used in the algorithm of K-means.
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Toledo, J. M., Thiago J. M. Moura y R. D. A. Timoteo. "BrStats: a socioeconomic statistics dataset of the Brazilian cities". En Dataset Showcase Workshop. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/dsw.2023.233621.

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Brazil is the fifth largest country in the world and is one of the most populous. These characteristics make it very important to summarize up-to-date data from all the Brazilian regions for decision-makers, in the public or private sectors. In this work, we obtain a unified dataset with statistical data for all cities in the country, integrating data related to population, economy, employment, education, and health. We show the process of extraction of the data from different public sources, the processing, and the generation of the dataset. We discuss the possible uses of the dataset, analyze the limitations of the proposed methodology, and discuss its possible evolutions.
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Informes sobre el tema "Educational statistics – data processing"

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Volkova, Nataliia P., Nina O. Rizun y Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], septiembre de 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.
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Mazorchuk, Mariia S., Tetyana S. Vakulenko, Anna O. Bychko, Olena H. Kuzminska y Oleksandr V. Prokhorov. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. [б. в.], junio de 2021. http://dx.doi.org/10.31812/123456789/4451.

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This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian localization and provides Ukrainian researchers with rapid access to well-structured PISA data. In particular, such an application should provide for data across the factorial features and indicators applied at the country level and demonstrate the Ukrainian indicators compared to the other countries’ results. This paper includes a description of the application core functionalities, architecture, and technologies used for development. The proposed solution leverages the shiny package available with R environment that allows implementing both the UI and server sides of the application. The technical implementation is a proven solution that allows for simplifying the access to PISA data for Ukrainian researchers and helping them utilize the calculation results on the key features without having to apply tools for processing statistical data.
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Vos, Rob. Educational Indicators: What's to Be Measured? Inter-American Development Bank, enero de 1996. http://dx.doi.org/10.18235/0011588.

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Currently, numerous strategies and new initiatives for improving quality of schooling at the primary and secondary levels are being considered and implemented, with strong support from multilateral agencies, including the World Bank and the IDB. These initiatives include increasing availability and quality of teaching materials, in-service training of teachers, improvement of teaching methods, supply of subsidized breakfast and lunches at school, etc. There is little dispute these are important and necessary interventions. However, the design, monitoring and evaluation of the cost-effectiveness of these programs and of the educational sector in general is hampered by persistent deficiencies in the quality and timely availability of educational statistics. The objective of the present paper is to provide a framework for the identification of relevant educational indicators. Which are relevant types of information depends on what one wishes to analyze for which policy need. Accordingly, the paper conceptualizes two typologies. The first identifies different types of operational educational indicators, distinguishing between input, access, output and outcome indicators to show that an appropriate information system requires to cover the whole process from supplying educational services, demand factors and accessibility, to results in terms of educational performance and externalities derived from enhanced human capital formation. The second typology distinguishes various types of policy-relevant analysis, such as the assessment of educational performance and needs, cost-effectiveness analysis of educational programs, impact evaluation and assessment of externalities. The informational needs in terms of indicators are specified for each type of policy analysis. The paper concludes with a specification of priority needs in data improvement.
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Stonaha, P. Development of a Data Acquisition Program for the Purpose of Monitoring Processing Statistics Throughout the BaBar Online Computing Infrastructure's Farm Machines. Office of Scientific and Technical Information (OSTI), septiembre de 2004. http://dx.doi.org/10.2172/833112.

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Cueto, Santiago. Empirical Information and the Development of Educational Policies in Latin America. Inter-American Development Bank, octubre de 2005. http://dx.doi.org/10.18235/0008713.

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This paper discusses the links between empirical information and the development of educational policies in Latin America. The data presented in this paper comes from responses to a questionnaire sent to Vice Ministers of Education in six countries of the region: Argentina, Brazil, Ecuador, Paraguay, Peru and Uruguay. The study's principal objectives were to describe what information was available and to understand how the information available in their countries proved useful in the design and evaluation of programs and policies. The results are presented with both statistics and qualitative analysis, and include formal evaluation mechanisms, achievement assessments on a national scale, and examples of policy development. This paper was prepared for the Hemispheric Meeting of the Regional Policy Dialogue held on November 7th and 8th, 2005 in Washington, DC.
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Murphy, Keire y Anne Sheridan. Annual report on migration and asylum 2022: Ireland. ESRI, noviembre de 2023. http://dx.doi.org/10.26504/sustat124.

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Annual Report on Migration and Asylum gives overview of statistics and developments in migration in 2022. The European Migration Network (EMN) Ireland within the ESRI has published its annual review of migration and asylum in Ireland. The EMN is an EU network that provides objective, comparable policy-relevant information on migration and international protection. EMN Ireland is located in the ESRI and is funded by the European Union and the Department of Justice. With an overview of the latest data as well as policy and operational developments, research, and case law from 2022, this report is a comprehensive reference that gives an opportunity to view the entire migration landscape in Ireland. The report shows that many forms of migration are recovering quickly from COVID-19 travel restrictions. It also shows that migration is being impacted by shortages in the labour market and the Russian invasion of Ukraine. As a result of these developments and others, Ireland saw a significant increase in immigration, with 141,600 people arriving in the year leading up to April 2023, according to CSO figures. This represents a 31% increase from the year to April 2022. However, emigration also increased, with 64,000 individuals leaving Ireland during the same period, marking a 14% increase from the previous year. 2022 saw a significant increase in first residence permits (which are granted to migrants from outside the EEA) from 2021. 85,793 permits were issued in 2022, with education the most common reason for permits (48%). Partially reflecting changes to eligible occupations for employment permits, the number of employment permits issued was the highest in the last 10 years. 39,995 employment permits were issued, with the information and communication sector the largest recipient of permits. Key developments in this area highlighted by the report include discussions on and progress with the Employment Permits Bill, changes to the Atypical Working Scheme, plans for a single application procedure for employment permits and immigration permissions, and changes to employment permits occupation lists to respond to labour market shortages. The report analyses international protection, showing significant increases in international protection applications as well as details of applications, decisions made, and statuses awarded. It shows an expansion of decision-making in response to increased applications. Looking at the broader EU situation, the report shows that applications for international protection in Ireland accounted for 1.3% of the EU total in 2022. The report also details the pressure on the reception and accommodation system for international protection applicants and beneficiaries of temporary protection, as well as the extraordinary measures taken to scale these up. It highlights measures taken to implement the White Paper to End Direct Provision and informs on a review of timelines of the plan. It discusses changes made by the International Protection Office to speed up processing, and criticism of these measures by NGOs, as well as details of the regularisation scheme for undocumented migrants and the humanitarian admission of Afghans. The Temporary Protection Directive – an EU Directive that creates an exceptional measure to provide immediate and temporary protection in the event of a mass influx of displaced persons – was triggered for the first time in March 2022, following the Russian invasion of Ukraine. As a result, the report includes a dedicated chapter with statistics relating to arrivals and a detailed overview of Ireland’s response to displaced persons from Ukraine. It also gives a comprehensive overview of other areas of migration, as well as research and case law from 2022, providing a crucial reference text for anyone working in the area.
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Shabelnyk, Tetiana V., Serhii V. Krivenko, Nataliia Yu Rotanova, Oksana F. Diachenko, Iryna B. Tymofieieva y Arnold E. Kiv. Integration of chatbots into the system of professional training of Masters. [б. в.], junio de 2021. http://dx.doi.org/10.31812/123456789/4439.

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The article presents and describes innovative technologies of training in the professional training of Masters. For high-quality training of students of technical specialties, it becomes necessary to rethink the purpose, results of studying and means of teaching professional disciplines in modern educational conditions. The experience of implementing the chatbot tool in teaching the discipline “Mathematical modeling of socio-economic systems” in the educational and professional program 124 System Analysis is described. The characteristics of the generalized structure of the chatbot information system for investment analysis are presented and given: input information, information processing system, output information, which creates a closed cycle (system) of direct and feedback interaction. The information processing system is represented by accounting and analytical data management blocks. The investment analysis chatbot will help masters of the specialty system analysis to manage the investment process efficiently based on making the right decisions, understanding investment analysis in the extensive structure of financial management and optimizing risks in these systems using a working mobile application. Also, the chatbot will allow you to systematically assess the disadvantages and advantages of investment projects or the direction of activity of a system analyst, while increasing interest in performing practical tasks. A set of software for developing a chatbot integrated into training is installed: Kotlin programming, a library for network interaction Retrofit, receiving and transmitting data, linking processes using the HTTP API. Based on the results of the study, it is noted that the impact of integrating a chatbot into the training of Masters ensures the development of their professional activities, which gives them the opportunity to be competent specialists and contributes to the organization of high-quality training.
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Kelly, Elish y Bertrand Maître. Identification Of Skills Gaps Among Persons With Disabilities And Their Employment Prospects. ESRI, septiembre de 2021. http://dx.doi.org/10.26504/sustat107.

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In 2013, the Irish Government published its National Disability Strategy Implementation Plan 2013-2015. As part of this strategy, a number of goals were set around participation, including job access for people with disabilities. This specific objective recommended a number of actions, including the commitment to publish a comprehensive employment strategy (CES) for people with disabilities. This strategy, the Comprehensive Employment Strategy for People with Disabilities, was published in 2015 for the period 2015-2024. The objective of this strategy, which is cross-governmental, is to support people with disabilities in accessing the labour market. Building on existing actions in the CES, the National Disability Authority (NDA) commissioned the Economic and Social Research Institute (ESRI) to undertake quantitative research into the workplace skills and abilities of persons with disabilities. To undertake this research, a number of nationally representative data sources were utilised to characterise the skills/educational endowments and gaps among persons with disabilities compared to those without. Specifically, data from the Survey on Income and Living Conditions (SILC), the Programme for the International Assessment of Adult Competencies (PIAAC), the Census of Population, and the European Union Statistics on Income and Living Conditions (EU-SILC) were examined. Where the data permitted, we differentiated those with a disability by type, everyday difficulties (e.g. difficulty dressing), and/or severity level. We also examined the employment characteristics of people with disabilities compared to persons without, and investigated the impact of having a disability on an individual’s employment prospects. What follows is a summary of the principal findings from this research, including some discussion on future directions.
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Kelly, Elish y Bertrand Maître. Identification Of Skills Gaps Among Persons With Disabilities And Their Employment Prospects. ESRI, septiembre de 2021. http://dx.doi.org/10.26504/sustat107.

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In 2013, the Irish Government published its National Disability Strategy Implementation Plan 2013-2015. As part of this strategy, a number of goals were set around participation, including job access for people with disabilities. This specific objective recommended a number of actions, including the commitment to publish a comprehensive employment strategy (CES) for people with disabilities. This strategy, the Comprehensive Employment Strategy for People with Disabilities, was published in 2015 for the period 2015-2024. The objective of this strategy, which is cross-governmental, is to support people with disabilities in accessing the labour market. Building on existing actions in the CES, the National Disability Authority (NDA) commissioned the Economic and Social Research Institute (ESRI) to undertake quantitative research into the workplace skills and abilities of persons with disabilities. To undertake this research, a number of nationally representative data sources were utilised to characterise the skills/educational endowments and gaps among persons with disabilities compared to those without. Specifically, data from the Survey on Income and Living Conditions (SILC), the Programme for the International Assessment of Adult Competencies (PIAAC), the Census of Population, and the European Union Statistics on Income and Living Conditions (EU-SILC) were examined. Where the data permitted, we differentiated those with a disability by type, everyday difficulties (e.g. difficulty dressing), and/or severity level. We also examined the employment characteristics of people with disabilities compared to persons without, and investigated the impact of having a disability on an individual’s employment prospects. What follows is a summary of the principal findings from this research, including some discussion on future directions.
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Atuhurra, Julius, Rastee Chaudhry y Michelle Kaffenberger. Conducting Surveys of Enacted Curriculum Studies in Low- and Middle-Income Countries: A Toolkit for Policymakers, Researchers, and Education Practitioners. Research on Improving Systems of Education (RISE), marzo de 2023. http://dx.doi.org/10.35489/bsg-rise-misc_2023/13.

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The Surveys of Enacted Curriculum (SEC) approach is used to analyse and report on the academic content embedded in education instructional components such as curriculum standards, assessments, and teachers' classroom instruction. Through a partnership between the RISE Programme and the Center for Curriculum Analysis, the approach has been used to analyse educational alignment in low- and middle-income country education systems, including in Kenya, Nepal, Nigeria, Tanzania, and Uganda. The SEC approach has many applications including content and alignment analysis for curriculum standards, assessments, and instructional materials; curriculum reform design and implementation support; and teacher professional development and support. This document provides a comprehensive toolkit for conducting an SEC study in LMICs. Following the introduction and background, Section 2 gives an overview of the SEC approach and provides a brief description of sequential steps involved in conducting an SEC study: (i) generating data; (ii) inputting data; (iii) processing and analysing data; and (iv) interpretating results. Section 3 then gives detailed, step-by-step instructions for implementing an SEC study. Section 4 shares lessons learned from conducting SEC studies in LMICs. The document then closes with an Appendix that provides a detailed overview of the SEC tools and other resources provided with this toolkit. These appendices [.zip] are available for download.
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