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Статті в журналах з теми "Statistics and Computer Science"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 29 січня 2023

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1

Matshonisa Seeletse, Solly, Tsakani Violet Ndobe, Tichavasia Alex Dandadzi, and Taurai Hungwe. "Crowdsourcing benefits in postgraduate project supervision: Sefako Makgatho Health Sciences University statistics and computer science case study." Environmental Economics 7, no. 2 (June 3, 2016): 122–29. http://dx.doi.org/10.21511/ee.07(2).2016.13.

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Анотація:

The paper reports on the findings made on an experiential exercise of Bachelor of Science Honours in Statistics (BSc Hons Stat) in the Department of Statistics and Operations Research (SOR) of the Sefako Makgatho Health Sciences University (SMU) in South Africa. SOR is a small, understaffed department, which offers courses for degrees from Bachelor to Doctoral levels in the subfields of Artificial Intelligence, Data Mining, Operations Research, Statistics and related ones. On SMU campus, expertize in some of these fields is also available in the Department of Computer Science (DCS). In the 2015 academic year SOR admitted 20 BSc Hons Stat students beyond its staffing capacity. Then, SOR invited DCS in a crowdsourcing initiative to jointly supervise student projects in the various subfields mentioned. The challenges include conflict and limited experience. These are managed satisfactorily though, but mainly because they occur at low levels. This crowdsourcing arrangement nevertheless results in timely submissions of final projects, improved quality projects worthy of being published, innovation, quality teamwork, and some synergistic outcomes. Coordinators also learn and/or improved some project management skills

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2

Cowles, Mary Kathryn. "Probability and Statistics for Computer Science." American Statistician 60, no. 1 (February 2006): 98. http://dx.doi.org/10.1198/tas.2006.s37.

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3

LaLonde, Steven M. "Probability and Statistics for Computer Science." Technometrics 46, no. 4 (November 2004): 491–92. http://dx.doi.org/10.1198/tech.2004.s235.

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4

Cleveland, William S. "Learning from Data: Unifying Statistics and Computer Science." International Statistical Review 73, no. 2 (January 15, 2007): 217–21. http://dx.doi.org/10.1111/j.1751-5823.2005.tb00276.x.

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5

Tannehill, Robert. "Computer-Based Statistics." Science & Technology Libraries 6, no. 4 (July 3, 1986): 61–81. http://dx.doi.org/10.1300/j122v06n04_06.

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6

Baxter, Laurence A., and Arnold O. Allen. "Probability, Statistics, and Queueing Theory with Computer Science Applications." Technometrics 34, no. 2 (May 1992): 240. http://dx.doi.org/10.2307/1269262.

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7

Derringer, George C. "Statistics for the Engineering and Computer Sciences." Technometrics 31, no. 3 (August 1989): 387–88. http://dx.doi.org/10.1080/00401706.1989.10488570.

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8

McCool, John I. "Probability and Statistics With Reliability, Queuing and Computer Science Applications." Technometrics 45, no. 1 (February 2003): 107. http://dx.doi.org/10.1198/tech.2003.s25.

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9

Ratkó, I. "On special mathematical and computer science methods in medical sciences." Journal of Mathematical Sciences 92, no. 3 (November 1998): 3926–29. http://dx.doi.org/10.1007/bf02432365.

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10

Yuan Hwang Li. "Lord_DIF: A Computer Program to Compute Lord's DIF Statistics." Applied Psychological Measurement 19, no. 1 (March 1995): 72. http://dx.doi.org/10.1177/014662169501900108.

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11

Demir, Servet. "Welcome to the Journal of Research in Data Science." Journal of Research in Data Science 1, no. 1 (November 15, 2021): 1–2. http://dx.doi.org/10.51853/jorids/11125.

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Анотація:

Data science is defined as the collection of basic principles that support and lead the extraction of information and knowledge from data (Provost & Fawcett, 2013). It is an interdisciplinary field as it requires mathematics, statistics, computer sciences, natural sciences, journalists, sociology, psychology and other disciplines to afford knowledge from data (Igual & Seguí, 2017).

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12

Oliver, Jeffrey C., and Torbet McNeil. "Undergraduate data science degrees emphasize computer science and statistics but fall short in ethics training and domain-specific context." PeerJ Computer Science 7 (March 25, 2021): e441. http://dx.doi.org/10.7717/peerj-cs.441.

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The interdisciplinary field of data science, which applies techniques from computer science and statistics to address questions across domains, has enjoyed recent considerable growth and interest. This emergence also extends to undergraduate education, whereby a growing number of institutions now offer degree programs in data science. However, there is considerable variation in what the field actually entails and, by extension, differences in how undergraduate programs prepare students for data-intensive careers. We used two seminal frameworks for data science education to evaluate undergraduate data science programs at a subset of 4-year institutions in the United States; developing and applying a rubric, we assessed how well each program met the guidelines of each of the frameworks. Most programs scored high in statistics and computer science and low in domain-specific education, ethics, and areas of communication. Moreover, the academic unit administering the degree program significantly influenced the course-load distribution of computer science and statistics/mathematics courses. We conclude that current data science undergraduate programs provide solid grounding in computational and statistical approaches, yet may not deliver sufficient context in terms of domain knowledge and ethical considerations necessary for appropriate data science applications. Additional refinement of the expectations for undergraduate data science education is warranted.

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13

Jäntschi, Lorentz. "Detecting Extreme Values with Order Statistics in Samples from Continuous Distributions." Mathematics 8, no. 2 (February 8, 2020): 216. http://dx.doi.org/10.3390/math8020216.

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In the subject of statistics for engineering, physics, computer science, chemistry, and earth sciences, one of the sampling challenges is the accuracy, or, in other words, how representative the sample is of the population from which it was drawn. A series of statistics were developed to measure the departure between the population (theoretical) and the sample (observed) distributions. Another connected issue is the presence of extreme values—possible observations that may have been wrongly collected—which do not belong to the population selected for study. By subjecting those two issues to study, we hereby propose a new statistic for assessing the quality of sampling intended to be used for any continuous distribution. Depending on the sample size, the proposed statistic is operational for known distributions (with a known probability density function) and provides the risk of being in error while assuming that a certain sample has been drawn from a population. A strategy for sample analysis, by analyzing the information about quality of the sampling provided by the order statistics in use, is proposed. A case study was conducted assessing the quality of sampling for ten cases, the latter being used to provide a pattern analysis of the statistics.

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14

BILGIN, AYSE AYSIN, ANGELA POWELL, and DEBORAH RICHARDS. "WORK INTEGRATED LEARNING IN STATISTICS AND COMPUTER SCIENCE AND FAIR ASSESSMENT OF AUTHENTIC PROJECTS." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (July 4, 2022): 12. http://dx.doi.org/10.52041/serj.v21i2.26.

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Анотація:

Work integrated learning (WIL) has been the norm in disciplines such as medicine, teacher education and engineering , however it has not been implemented until recently in statistics and not for every student in computer science education. With the changed focus of universities, making graduates ‘job ready’ the collaboration of university-industry widened to encompass learning and teaching. Undoubtedly authentic problems coming from industry created opportunities for students to practice their future profession before graduation. However, this shift in the curriculum brought its challenges both for the students and their lecturers. In this paper, we will present assessment structures and case studies from statistics and computer science. Our approaches can be adopted or adapted by teachers of statistics and data science.

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15

Heiberger, Richard M., and Norman S. Matloff. "Probability Modeling and Computer Simulation: An Integrated Introduction With Applications to Engineering and Computer Science." Journal of the American Statistical Association 85, no. 409 (March 1990): 267. http://dx.doi.org/10.2307/2289577.

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16

Awofala, Adeneye Olarewaju, Oladiran Stephen Olabiyi, Awoyemi Abayomi Awofala, Abayomi A. Arigbabu, Alfred O. Fatade, and Uchenna Nkiruka Udeani. "Attitudes toward Computer, Computer Anxiety and Gender as determinants of Pre-service Science, Technology and Mathematics Teachers’ Computer Self-efficacy." Digital Education Review, no. 36 (December 31, 2019): 51–67. http://dx.doi.org/10.1344/der.2019.36.51-67.

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Анотація:

The study investigated attitudes towards computer and computer anxiety as determinants of computer self-efficacy among 2100 pre-service science, technology and mathematics (STM) teachers from the University of Lagos of Nigeria using the quantitative research method within the blueprint of the descriptive survey design. Data collected were analysed using the descriptive statistics of percentages, mean, and standard deviation and inferential statistics of independent samples t-test, Pearson product moment correlation coefficient and multiple regression analysis. Finding revealed significant correlations between computer attitudes, computer anxiety and computer self-efficacy. Gender differences in attitude toward computer, computer self-efficacy and computer anxiety among pre-service STM teachers were significant. Affective component, perceived control component, and perceived usefulness component, behavioural intention component, gender, and computer anxiety made statistically significant contributions to the variance in pre-service STM teachers’ computer self-efficacy. The study recommended among others that academic institutions should pay more attention to this computer anxiety and adopt proper ways of reducing the computer anxiety, so that positive e-learning experiences can be created for pre-service STM teachers.

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17

Andersson, Christina, and Doina Logofatu. "A Blended Learning Module in Statistics for Computer Science and Engineering Students Revisited." International Journal of Engineering Pedagogy (iJEP) 7, no. 4 (November 24, 2017): 66. http://dx.doi.org/10.3991/ijep.v7i4.7441.

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Анотація:

Teaching a statistics course for undergraduate computer science students can be very challenging: As statistics teachers we are usually faced with problems ranging from a complete disinterest in the subject to lack of basic knowledge in mathematics and anxiety for failing the exam, since statistics has the reputation of having high failure rates. In our case, we additionally struggle with difficulties in the timing of the lectures as well as often occurring absence of the students due to spare-time jobs or a long traveling time to the university. This paper reveals how these issues can be addressed by the introduction of a blended learning module in statistics. In the following, we describe an e-learning development process used to implement time- and location-independent learning in statistics. The study focuses on a six-step-approach for developing the blended learning module. In addition, the teaching framework for the blended module is presented, including suggestions for increasing the interest in learning the course. Furthermore, the first experimental in-class usage, including evaluation of the students’ expectations, has been completed and the outcome is discussed.

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18

Carvalho, Rui, and Michael Batty. "The geography of scientific productivity: scaling in US computer science." Journal of Statistical Mechanics: Theory and Experiment 2006, no. 10 (October 25, 2006): P10012. http://dx.doi.org/10.1088/1742-5468/2006/10/p10012.

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19

Pijacki, Roger J., Judith Rattenbury, Paula Pelletier, and Laura Klem. "Computer Processing of Social Science Data Using OSIRIS IV." Journal of the American Statistical Association 81, no. 393 (March 1986): 260. http://dx.doi.org/10.2307/2288015.

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20

Khalimsky, Efim. "Topological structures in computer science." Journal of Applied Mathematics and Simulation 1, no. 1 (January 1, 1987): 25–40. http://dx.doi.org/10.1155/s1048953388000036.

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Анотація:

Topologies of finite spaces and spaces with countably many points are investigated. It is proven, using the theory of ordered topological spaces, that any topology in connected ordered spaces, with finitely many points or in spaces similar to the set of all integers, is an interval-alternating topology. Integer and digital lines, arcs, and curves are considered. Topology of N-dimensional digital spaces is described. A digital analog of the intermediate value theorem is proven. The equivalence of connectedness and pathconnectedness in digital and integer spaces is also proven. It is shown here how methods of continuous mathematics, for example, topological methods, can be applied to objects, that used to be investigated only by methods of discrete mathematics. The significance of methods and ideas in digital image and picture processing, robotic vision, computer tomography and system's sciences presented here is well known.

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21

Shanmugam, Ramalingam. "The beauty of mathematics in computer science." Journal of Statistical Computation and Simulation 89, no. 13 (March 7, 2019): 2594. http://dx.doi.org/10.1080/00949655.2019.1589118.

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22

Khomyak, Maria, and Svitlana Yatsyuk. "COMPUTER ORIENTED TRAININGS FOR THEORY OF PROBABILITY AND MATHEMATICAL STATISTICS FOR FUTURE TEACHERS OF COMPUTER SCIENCE." Scientific bulletin of KRHPA, no. 14 (2022): 66–73. http://dx.doi.org/10.32782/2410-2075-2022-14.7.

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23

FARHADI, ALI, and MEHRDAD SHAHSHAHANI. "Higher Order Statistics in Computer Vision." Annals of the New York Academy of Sciences 980, no. 1 (December 2002): 152–67. http://dx.doi.org/10.1111/j.1749-6632.2002.tb04895.x.

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24

Ziegel, Eric. "Computer Science and Statistics: Proceedings of the 17th Symposium on the Interface." Technometrics 31, no. 1 (February 1989): 130. http://dx.doi.org/10.1080/00401706.1989.10488505.

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25

Mitchell, J. M. O., and L. Billard. "Computer Science and Statistics: Proceedings of the Sixteenth Symposium on the Interface." Journal of the Royal Statistical Society. Series A (General) 149, no. 3 (1986): 272. http://dx.doi.org/10.2307/2981559.

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26

Pemberton, J., and D. M. Allen. "Computer Science and Statistics: Proceedings of the Seventeenth Symposium on the Interface." Journal of the Royal Statistical Society. Series A (General) 150, no. 4 (1987): 396. http://dx.doi.org/10.2307/2982046.

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27

Maltby, J. "Learning statistics by computer software is cheating." Journal of Computer Assisted Learning 17, no. 3 (September 2001): 329–30. http://dx.doi.org/10.1046/j.0266-4909.2001.00188.x.

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28

Clifton, Jesse, and Eric Laber. "Q-Learning: Theory and Applications." Annual Review of Statistics and Its Application 7, no. 1 (March 9, 2020): 279–301. http://dx.doi.org/10.1146/annurev-statistics-031219-041220.

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Анотація:

Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we ( a) review the history of Q-learning in computer science and statistics, ( b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and ( c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods.

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29

Wang, Yazhen, and Hongzhi Liu. "Quantum Computing in a Statistical Context." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 479–504. http://dx.doi.org/10.1146/annurev-statistics-042720-024040.

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Quantum computing is widely considered a frontier of interdisciplinary research and involves fields ranging from computer science to physics and from chemistry to engineering. On the one hand, the stochastic essence of quantum physics results in the random nature of quantum computing; thus, there is an important role for statistics to play in the development of quantum computing. On the other hand, quantum computing has great potential to revolutionize computational statistics and data science. This article provides an overview of the statistical aspect of quantum computing. We review the basic concepts of quantum computing and introduce quantum research topics such as quantum annealing and quantum machine learning, which require statistics to be understood.

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30

Gürsakal, Necmi, Ecem Ozkan, Fırat Melih Yılmaz, and Deniz Oktay. "How Should Data Science Education Be?" International Journal of Energy Optimization and Engineering 9, no. 2 (April 2020): 25–36. http://dx.doi.org/10.4018/ijeoe.2020040103.

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The interest in data science is increasing in recent years. Data science, including mathematics, statistics, big data, machine learning, and deep learning, can be considered as the intersection of statistics, mathematics and computer science. Although the debate continues about the core area of data science, the subject is a huge hit. Universities have a high demand for data science. They are trying to live up to this demand by opening postgraduate and doctoral programs. Since the subject is a new field, there are significant differences between the programs given by universities in data science. Besides, since the subject is close to statistics, most of the time, data science programs are opened in the statistics departments, and this also causes differences between the programs. In this article, we will summarize the data science education developments in the world and in Turkey specifically and how data science education should be at the graduate level.

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31

Deshmukh, Omkar Madhukar. "Computer Vision." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1237–39. http://dx.doi.org/10.22214/ijraset.2021.35926.

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Анотація:

Computer vision may be a field of computer science that trains computers to interpret and perceive the visual world. exploitation digital pictures from cameras and videos and deep learning models, machines will accurately determine and classify objects — and so react to what they "see.”. Computer vision is Associate in Nursing knowledge domain scientific field that deals with however computers will gain high-level understanding from digital pictures or videos. From the angle of engineering, it seeks to grasp and alter tasks that the human sensory system will do. Computer vision tasks embrace strategies for exploit, processing, analyzing and understanding digital pictures, and extraction of high-dimensional knowledge from the important world so as to supply numerical or symbolic info, e.g. within the styles of selections. Understanding during this context suggests that the transformation of visual pictures (the input of the retina) into descriptions of the planet that be to thought processes and might elicit acceptable action. This image understanding will be seen because the disentangling of symbolic info from image knowledge mistreatment models created with the help of pure mathematics, physics, statistics, and learning theory.

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32

Zurita, Gustavo, José M. Merigó, Valeria Lobos-Ossandón, and Carles Mulet-Forteza. "Bibliometrics in computer science: An institution ranking." Journal of Intelligent & Fuzzy Systems 38, no. 5 (May 29, 2020): 5441–53. http://dx.doi.org/10.3233/jifs-179636.

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33

Grier, David Alan. "Integrating Computer Science with Statistical Analysis." Computer Science Education 2, no. 1 (January 1991): 31–44. http://dx.doi.org/10.1080/0899340910020103.

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34

Matshonisa Seeletse, Solly. "Information and communication technology as a primary tool for Sefako Makgatho Health Sciences University’s statistics and operations research business." Problems and Perspectives in Management 14, no. 3 (July 29, 2016): 115–22. http://dx.doi.org/10.21511/ppm.14(3).2016.12.

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Анотація:

The Department of Statistics and Operations Reasearch (SOR) at the Sefako Makgatho Health Sciences University (SMU) in South Africa desires to increase its research output, as well as to provide high quality teaching and learning. Most SOR lecturers want to embrace technology and innovations, and also be competitive both regionally and globally. This can be achieved more effectively if they are trained in computer applications. Thus, they should be developed into critical citizens of the digital world. They should also be prepared to use information and communication technology (ICT) as a teaching and learning resource, as well as a research and community engagement backing. An innovation in academia should be backed by the lecturer. Thus, the main concern of this paper is to explore use of ICT as a business tool in SOR. Methodologies of the study were case study and thematic content analysis, and the data collection tool was a questionnaire. The study found that SOR was understaffed and could not provide full statistics (stats) training mainly in the statistical packages. The lecturers were all trained in ICT and the packages. They were all willing to use ICT in SOR activities. The computer laboratories were adequate for the student numbers at the time, even though some computers were not working. These laboratories showed to be poorly adequate for the envisaged growth of SOR. SOR would also need more lecturers for the future growth. The study recommends growth of SOR in lecturers and ICT facilities, at the least

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35

Gierl, Mark J., and Jiawen Zhou. "Computer Adaptive-Attribute Testing." Zeitschrift für Psychologie / Journal of Psychology 216, no. 1 (January 2008): 29–39. http://dx.doi.org/10.1027/0044-3409.216.1.29.

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Анотація:

The influence of interdisciplinary forces stemming from developments in cognitive science, mathematical statistics, educational psychology, and computing science are beginning to appear in educational and psychological assessment. Computer adaptive-attribute testing (CA-AT) is one example. The concepts and procedures in CA-AT can be found at the intersection between computer adaptive testing and cognitive diagnostic assessment. CA-AT allows us to fuse the administrative benefits of computer adaptive testing with the psychological benefits of cognitive diagnostic assessment to produce an innovative psychologically-based adaptive testing approach. We describe the concepts behind CA-AT as well as illustrate how it can be used to promote formative, computer-based, classroom assessment.

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36

Holmes, Susan P., and Dan Gusfield. "Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology." Journal of the American Statistical Association 94, no. 447 (September 1999): 989. http://dx.doi.org/10.2307/2670026.

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37

PODWORNY, SUSANNE, SVEN HÜSING, and CARSTEN SCHULTE. "A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING." STATISTICS EDUCATION RESEARCH JOURNAL 21, no. 2 (July 4, 2022): 6. http://dx.doi.org/10.52041/serj.v21i2.46.

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Анотація:

Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.

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38

Kouvatsos, D. D. "Probability & Statistics with Reliability, Queueing and Computer Science Applications-K. S. Trivedi." IEEE Transactions on Education 28, no. 2 (1985): 116. http://dx.doi.org/10.1109/te.1985.4321753.

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39

Hudak, Mary A., and David E. Anderson. "Formal Operations and Learning Style Predict Success in Statistics and Computer Science Courses." Teaching of Psychology 17, no. 4 (December 1990): 231–34. http://dx.doi.org/10.1207/s15328023top1704_4.

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40

Atnip, Gilbert W. "Teaching the Use of Computers: A Case Study." Teaching of Psychology 12, no. 3 (October 1985): 171–72. http://dx.doi.org/10.1207/s15328023top1203_18.

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Анотація:

A course on the use of computers in psychology and the other social sciences is described. The course included an introduction to computers and computing and units on word processing, data analysis, data acquisition, artificial intelligence, and computer-assisted instruction, simulation, and modeling. Each unit incorporated the application of an appropriate program. Students conducted independent research projects using the computer. They evaluated the course very positively, as did the instructor. The major problems encountered in teaching the course related to the diversity of students' backgrounds in computers and in statistics, and to the difficulty of separating technique from content in assigning grades.

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41

Lallmahomed, Naguib. "Probability and Statistics for Computer Scientists." Journal of the Royal Statistical Society: Series A (Statistics in Society) 171, no. 1 (January 10, 2008): 312. http://dx.doi.org/10.1111/j.1467-985x.2007.00521_2.x.

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42

Maindonald, John H. "Probability with R: An Introduction with Computer Science Applications by Jane Horgan." International Statistical Review 78, no. 1 (April 2010): 146. http://dx.doi.org/10.1111/j.1751-5823.2010.00109_13.x.

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Schoonjans, F., A. Zalata, C. E. Depuydt, and F. H. Comhaire. "MedCalc: a new computer program for medical statistics." Computer Methods and Programs in Biomedicine 48, no. 3 (December 1995): 257–62. http://dx.doi.org/10.1016/0169-2607(95)01703-8.

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McElduff, Fiona. "Probability with R: an Introduction with Computer Science Applications." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 3 (January 4, 2010): 695. http://dx.doi.org/10.1111/j.1467-985x.2010.00646_8.x.

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Lunn, Stephanie, Leila Zahedi, Monique Ross, and Matthew Ohland. "Exploration of Intersectionality and Computer Science Demographics." ACM Transactions on Computing Education 21, no. 2 (June 2021): 1–30. http://dx.doi.org/10.1145/3445985.

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Анотація:

Although computing occupations have some of the greatest projected growth rates, there remains a deficit of graduates in these fields. The struggle to engage enough students to meet demands is particularly pronounced for groups already underrepresented in computing, specifically, individuals that self-identify as a woman, or as Black, Hispanic/Latinx, or Native American. Prior studies have begun to examine issues surrounding engagement and retention, but more understanding is needed to close the gap, and to broaden participation. In this research, we provide quantitative evidence from the Multiple-Institution Database for Investigating Engineering Longitudinal Development—a longitudinal, multi-institutional database to describe participation trends of marginalized groups in computer science. Using descriptive statistics, we present the enrollment and graduation rates for those situated at the intersection of race/ethnicity and gender between 1987 and 2018. In this work, we observed periods of significant flux for Black men and women, and White women in particular, and consistently low participation of Hispanic/Latinx and Native American men and women, and Asian women. To provide framing for the evident peaks and valleys in participation, we applied historical context analysis to describe the political, economic, and social factors and events that may have impacted each group. These results put a spotlight on populations largely overlooked in statistical work and have the potential to inform educators, administrators, and researchers about how enrollments and graduation rates have changed over time in computing fields. In addition, they offer insight into potential causes for the vicissitudes, to encourage more equal access for all students going forward.

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Sherif Sirajudeen, Mohamed, and Shaikhji Saad Mohamed Siddik. "Knowledge of Computer Ergonomics among Computer Science Engineering and Information Technology Students in Karnataka, India." Asian Journal of Pharmaceutical Research and Health Care 9, no. 2 (April 10, 2017): 64. http://dx.doi.org/10.18311/ajprhc/2017/11023.

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Ergonomics is the science of designing the job to fit the worker. Neglect of ergonomic principles results in inefficiency and pain in the workplace. The objective of this research is to assess the knowledge of Computer Ergonomics among Computer Science Engineering and Information Technology Students in Karnataka. In this Cross-sectional study, 177 Computer Science Engineering and Information Technology Students were recruited. A questionnaire is used to gather details regarding Personal characteristics, Computer Usage and Knowledge of Ergonomics. Descriptive statistics was produced for Personal characteristics and Computer usage. The distribution of responses to the items related to Ergonomic knowledge was presented by percentage of the subjects who answered correctly. The results shows that Majority of the subjects were unaware of ergonomics (32.8% correct responses), cumulative trauma disorders (18.6% correct responses), healthy postures related to elbow (34.4% correct responses), wrist & hand (39.5% correct responses), Level of Monitor (35% correct responses), Position of mouse (47.4% correct responses) and Mini breaks (42.9% correct responses). This research highlighted the necessity of Ergonomic training regarding healthy postures and the measures to reduce the risk of musculoskeletal disorders for the students.

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Nuriakhmetov, R. R. "VISUALIZATION OF DATA AND RESULTS AS А METHODOLOGICAL BASIS OF APPLIED STATISTICS TEACHING". Bulletin of Siberian Medicine 13, № 4 (28 серпня 2014): 81–88. http://dx.doi.org/10.20538/1682-0363-2014-4-81-88.

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Traditional methods of teaching in medical high school of informatics as computer sciences and statistics as a section of high mathematics contradict to requirements of modern applied medicine and a medical science. A research objective is revealing of the reasons of the given discrepancy and its elimination ways. Similar discrepancy was revealed earlier by foreign researchers studying efficiency of the statistic school programs. The revealed laws appeared to be extended to a technique of teaching of statistics in a high medical school. Pursuing this aim the tests of educational achievements developed by the author were applied on the students of medical and biologic department of the Siberian State Medical Universirty that trained on specialities of “biophysics" and “biochemistry". The fundamental problem of statistical education is that symbols used by these science concern to the objects, which students still have to design. As a substantiation of this conclusion serves the ontosemiotical approach to working out of the maintenance of a course. In the article there are considered the approaches to the permission of the given contradiction, based on the experience of teaching of statistics in foreign schools and on the workings out of the author. In particular the conclusion about necessity of revision the tradition of using professional statistical packages and introduction of a special educational software. To working out the maintenance of a learning course it is offered to more widely apply the historical approach which concrete definition is represented by a principle of a guided reinvention.

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Kim, Yoon Jeon, José A. Ruipérez Valiente, Dirk Ifenthaler, Erik Harpstead, and Elizabeth Rowe. "Analytics for Game-Based Learning." Journal of Learning Analytics 9, no. 3 (December 16, 2022): 8–10. http://dx.doi.org/10.18608/jla.2022.7929.

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The purpose of this special section is to collect in one place how data in game-based learning environments may be turned into valuable analytics for student assessment, support of learning, and/or improvement of the game, using existing or emerging empirical research methodologies from various fields, including computer science, software engineering, educational data mining, learning analytics, learning sciences, statistics, and information visualization. Four contributions form this special section, which will inspire future high-quality research studies and contribute to the growing knowledge base of learning analytics and game-based learning research and practice.

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PARK, TAESUNG, KIWOONG KIM, SUNG-GON YI, JIN HYUK KIM, YONG-SUNG LEE, and SEUNGYEOUN LEE. "SPOT INTENSITY RATIO STATISTICS IN TWO-CHANNEL MICROARRAY EXPERIMENTS." Journal of Bioinformatics and Computational Biology 05, no. 04 (August 2007): 865–73. http://dx.doi.org/10.1142/s0219720007002928.

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In two-channel microarray experiments, the image analysis extracts red and green fluorescence intensities. The ratio of the two fluorescence intensities represents the relative abundance of the corresponding DNA sequence. The subsequent analysis is performed by taking a log-transformation of this ratio. Therefore, the statistical analyses depend on accuracy of the ratios calculated from the image analysis. However, not many studies have been proposed for developing more reliable ratio statistics. In this paper, we consider a new type of log-transformed ratio statistic. We compare the new ratio statistic with the conventional ratio statistic commonly used in two-channel microarray experiments. First, under the specific log-normal distributional assumption, we compare analytically the new statistics with the conventional ratio statistic. Second, we compare those ratio statistics using a two-channel microarray data obtained by hybridizing a mixture of mouse RNA and yeast in vitro transcript (IVT). Both comparisons show that the proposed ratio statistic performs better than the conventional one.

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Raymondo, James C., and James R. Garrett. "Assessing the Introduction of a Computer Laboratory Experience into a Behavioral Science Statistics Course." Teaching Sociology 26, no. 1 (January 1998): 29. http://dx.doi.org/10.2307/1318677.

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