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Journal articles on the topic 'Computer programming'

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Published: 4 June 2021
Last updated: 30 July 2024

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1

Casey, Patrick J. "Computer Programming." Computers in the Schools 13, no. 1-2 (June 18, 1997): 41–51. http://dx.doi.org/10.1300/j025v13n01_05.

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2

Huston, Robert K., Andrea M. Markell, Elizabeth A. McCulley, Matthew J. Marcus, and Howard S. Cohen. "Computer Programming." Nutrition in Clinical Practice 28, no. 4 (June 10, 2013): 515–21. http://dx.doi.org/10.1177/0884533613490741.

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3

MALIK, MAREK. "Computer Programming." Pacing and Clinical Electrophysiology 15, no. 12 (December 1992): 2336–38. http://dx.doi.org/10.1111/j.1540-8159.1992.tb04175.x.

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4

MacGregor, S. Kim. "Computer Programming Instruction." Journal of Research on Computing in Education 21, no. 2 (December 1988): 155–64. http://dx.doi.org/10.1080/08886504.1988.10781868.

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5

Cuypers, L. "Computer music programming." Microprocessing and Microprogramming 25, no. 1-5 (January 1989): 65–69. http://dx.doi.org/10.1016/0165-6074(89)90175-0.

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6

Da Rosa, Evandro Chagas Ribeiro, and Rafael De Santiago. "Ket Quantum Programming." ACM Journal on Emerging Technologies in Computing Systems 18, no. 1 (January 31, 2022): 1–25. http://dx.doi.org/10.1145/3474224.

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Quantum programming languages (QPL) fill the gap between quantum mechanics and classical programming constructions, simplifying the development of quantum applications. However, most QPL addresses the inherent quantum programming problem, neglecting quantum computer implementation constraints. We present a runtime architecture for classical-quantum execution that mitigates the limitation of interaction between classical and quantum computers originated from the cloud-based model of quantum computation provided by several vendors, which implies a quantum computer processing in batch. In the proposed runtime architecture, we introduce (i) runtime quantum code generation to enable generic quantum programming and dynamic quantum execution; and (ii) the concept of futures to handle dynamic interaction between classical and quantum computers. To support our proposal, we have implemented the Ket Quantum Programming framework that features a Python-embedded classical-quantum programming language named Ket, the C++ quantum programming library Libket, and Ket Bitwise (quantum computing) Simulator. The last one improves over the bitwise representation, making the simulation time not dependent on the number of qubits but the amount of superposition and entanglement of simulation.
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7

Gong, Weiwei. "Database Programming Technology Based on Computer Software Engineering." Journal of Physics: Conference Series 2173, no. 1 (January 1, 2022): 012073. http://dx.doi.org/10.1088/1742-6596/2173/1/012073.

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Abstract Based on practical application, this paper further discusses the database programming technology in computer software engineering. Computer technology has developed to a certain extent and is still active in various fields. However, because the demand for computers in various industries and the requirements for technical performance are different, software engineers are required to develop software systems suitable for enterprises according to their own production characteristics. Because the efficiency and quality of computer software can not reach at present, the programming technology level of database may have a certain impact on the software system.In order for database programming technology to play a full role in various fields, it is necessary to increase investment in database programming technology.This paper analyzes the database technology of computer software engineering in detail. In the process of establishing the actual database programming system, we make full use of the file creation and file access of the database to improve the database programming technology in the current computer software engineering, and then improve the stability of computer software. This paper analyzes computer software engineering, summarizes database programming program, fully realizes the application value of program technology in actual production, and combines database programming technology with the design of computer software engineering project, so as to promote the continuous innovation and development of computer software technology in China.
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8

Bezvoda, Václav. "Geography and Teaching of Programming." Geografie 94, no. 1 (1989): 47–53. http://dx.doi.org/10.37040/geografie1989094010047.

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The urgent need of computers in natural and social sciences will strongly influence the modification of the curricula at our universities and colleges. On the basis of an analysis of the history of application of computers at the Faculty of Natural Sciences of the Charles University, Prague and the situation in teaching mathematical programming and computer art, the paper formulates one of the most probable variants of teaching the above-mentioned subjects in geographical sciences. A special attention is paid to the role of microcomputers as the basic yet still problematic device in the computer art.
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Moole, Madhav, and Flavia Gonsalves. "Exploring the Application of Sanskrit in Computer Programming." International Journal of Science and Research (IJSR) 13, no. 6 (June 5, 2024): 594–98. http://dx.doi.org/10.21275/sr24608114347.

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10

Herwijnen, Eric Van. "New Books: Computer Programming." Physics Essays 11, no. 4 (December 1998): 613. http://dx.doi.org/10.4006/1.3025349.

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11

Robertson, David, and Fausto Giunchiglia. "Programming the social computer." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1987 (March 28, 2013): 20120379. http://dx.doi.org/10.1098/rsta.2012.0379.

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The aim of ‘programming the global computer’ was identified by Milner and others as one of the grand challenges of computing research. At the time this phrase was coined, it was natural to assume that this objective might be achieved primarily through extending programming and specification languages. The Internet, however, has brought with it a different style of computation that (although harnessing variants of traditional programming languages) operates in a style different to those with which we are familiar. The ‘computer’ on which we are running these computations is a social computer in the sense that many of the elementary functions of the computations it runs are performed by humans, and successful execution of a program often depends on properties of the human society over which the program operates. These sorts of programs are not programmed in a traditional way and may have to be understood in a way that is different from the traditional view of programming. This shift in perspective raises new challenges for the science of the Web and for computing in general.
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12

CHALMERS, A. "COMPUTER PROGRAMMING FOR GEOGRAPHERS." New Zealand Geographer 46, no. 1 (April 1990): 56. http://dx.doi.org/10.1111/j.1745-7939.1990.tb01956.x.

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13

Hayes, Brian. "Programming Your Quantum Computer." American Scientist 102, no. 1 (2014): 22. http://dx.doi.org/10.1511/2014.106.22.

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14

Sauter, Vicki L. "Predicting computer programming skill." Computers & Education 10, no. 2 (January 1986): 299–302. http://dx.doi.org/10.1016/0360-1315(86)90031-x.

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15

Norman, Victor T. "Beauty and computer programming." ACM Inroads 3, no. 1 (March 2012): 46–48. http://dx.doi.org/10.1145/2077808.2077824.

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16

Roy, Debdulal Dutta. "Computer Programming Job Analysis." Management and Labour Studies 27, no. 4 (October 2002): 255–62. http://dx.doi.org/10.1177/0258042x0202700403.

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This study investigated relative uses of computer programming job characteristics across different organizations and effects of different demographic variables on job analysis ratings. Data were collected from 201 computer programers of 6 different organizations through checklist. Principal component analysis noted four mostly used job characteristics as program writing and testing, human relations, data analysis and user satisfaction. Of them only data analysis differed among different organizations significantly. No significant main and interaction effects of the demographic variables on job analysis ratings were noted. Results were explained in terms of different human resource program design.
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17

Laskov, Lasko M. "Introduction to Computer Programming Through a System of Tasks." Mathematics and Informatics LXIV, no. 6 (December 31, 2021): 634–49. http://dx.doi.org/10.53656/math2021-6-7-int.

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Computer programming is a fundamental discipline in many academic programs, especially in the fields of informatics, applied mathematics, physics, and engineering. Despite its popularity, computer programming courses does not possess a widely-accepted methodology for its structure, and because of this reason, even introductory courses highly differ in their curriculum, approach, complexity, and even technical background. In this paper we propose a methodology for introductory computer programming course structure definition that is based on the concept of notion formation through a system of tasks. The approach is intended to be applied in the context of academic education, but it is also applicable in the last years of high-school courses.
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18

Toulouse. "Automatic Quantum Computer Programming: A Genetic Programming Approach." Genetic Programming and Evolvable Machines 7, no. 1 (2006): 125. http://dx.doi.org/10.1007/s10710-005-4866-8.

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19

Toulouse, Michel. "Automatic Quantum Computer Programming: A Genetic Programming Approach." Genetic Programming and Evolvable Machines 7, no. 1 (March 2006): 125–26. http://dx.doi.org/10.1007/s10710-006-4866-3.

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20

Saka, Owodunni Adewale. "Learning to Write Programs using Think-Pair-Share Programming Strategy: What are the Students’ Perceptions and Experiences?" Journal of Educational Sciences 4, no. 4 (October 24, 2020): 705. http://dx.doi.org/10.31258/jes.4.4.p.705-717.

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The need to learn programming to solve many complex problems facing humankind necessitated this study. The purpose of the study was to collect information about students’ perceptions and experiences after exposure to the think-pair-share programming strategy. The sample consisted of 12 senior secondary school two students offering computer studies in Ijebu zone, Ogun State, Nigeria purposively selected from the two experimental groups. The data were collected through one-on-one in-depth interviews of the respondents using a Student Interview Guide (SIG). The data analysis was through thematic content analysis procedure. The study found that the respondents perceived the think-pair-share programming strategy helpful to learn programming concepts with or without computers. The study also found that the use of computer was more useful for the acquisition of programming skills than without the use of computers. Moreover, the study found that programming without computers was perceived to improve thinking. Therefore, the study argued that teachers should adopt the use of think-pair-share programming strategy for learning how to write programs notwithstanding the availability of computers due to its ability to aid knowledge retention.
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21

Chen, Chen, Stuart Jeckel, Gerhard Sonnert, and Philip M. Sadler. "“Cowboy” and “Cowgirl” Programming: The Effects of Precollege Programming Experiences on Success in College Computer Science." International Journal of Computer Science Education in Schools 2, no. 4 (January 31, 2019): 22–40. http://dx.doi.org/10.21585/ijcses.v2i4.34.

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This study examines the relationship between students' pre-college experience with computers and their later success in introductory computer science classes in college. Data were drawn from a nationally representative sample of 10,197 students enrolled in computer science at 118 colleges and universities in the United States. We found that students taking introductory college computer science classes who had programmed on their own prior to college had a more positive attitude toward computer science, lower odds of dropping out, and earned higher grades, compared with students who had learned to program in a pre-college class, but had never programmed on own, or those who had never learned programming before college. Moreover, nearly half of the effect on final grades was mediated by a positive attitude toward computing.
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22

Clement, Catherine A., D. Midian Kurland, Ronald Mawby, and Roy D. Pea. "Analogical Reasoning and Computer Programming." Journal of Educational Computing Research 2, no. 4 (November 1986): 473–86. http://dx.doi.org/10.2190/dfh5-e0pg-1ml4-m34j.

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Investigations of the cognitive demands of programming can inform teaching and validate claims that important cognitive skills are inherent in programming. Given reports of experts' use of analogical problem solving in programming, the study reported here related analogical reasoning to Logo programming mastery among high school students. Correlational analyses related pretests of analogical reasoning to posttests of programming mastery. As predicted, a significant correlation was found between analogical reasoning and the ability to write subprocedures which can be reused for several different programs. This sophisticated programming skill requires recognition of structural similarities among distinct programming tasks. A final, general discussion considers analogical reasoning skill as a cognitive demand and consequence of programming.
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23

Bradbury, Amanda, Eric Wiebe, Jessica Vandenberg, Jennifer Tsan, Collin Lynch, and Kristy Boyer. "The Interface Design of a Collaborative Computer Science Learning Environment for Elementary Aged Students." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 493–97. http://dx.doi.org/10.1177/1071181319631155.

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There is a currently a shortage of computer science professionals and this shortage is projected to continue into the foreseeable future as not enough students are selecting computer science majors. Researchers and policy-makers agree that development of this career pipeline starts in elementary school. Our study examined which collaborative programming setup, pair programming (two students collaborate on one computer) or side-by-side programming (two students collaborate on the same program from two computers), fifth-grade students preferred. We also sought to understand why students preferred one method over the other and explored ideas on how to effectively design a collaborative programming environment for this age group. Our study had participants first engage in five instructional days, alternating between pair and side-by-side programming, and then conducted focus groups. We found that students overwhelmingly preferred side-by-side programming. We explain this using self-determination theory which states that behavior is motivated by three psychological needs: autonomy, competence, and psychological relatedness which side-by-side programming was better able to meet.
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24

Olusola, Ogunlade Bamidele, Benedict Samaila Bahago, and Aderemi Sunday Ogunmodede. "Strengthen First-Computer-Based Computer-Based Student Programming Skills." European Journal of Education and Pedagogy 2, no. 6 (December 3, 2021): 68–74. http://dx.doi.org/10.24018/ejedu.2021.2.6.207.

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Programming is a difficult task that requires the creation of new ideas, thinking, and creative skills. The study, therefore, aimed to strengthen BASIC's programming skills to guide and mentor students at Veritas University Abuja, Nigeria. The researchers assessed students' interest in computer usage and gender differences. The study adopted the group that controls most, the experimental structure. 100 and 200 level undergraduate students are selected on a well-equipped computer and existing Computer lecturers are deliberately chosen at the University. Two non-static classes with 50 students are periodically assigned to the Experimental (25) and Control (25) groups. The treatment lasted six weeks. The instruments used were descendants of Undergraduate Students ’graduates towards the Question nailing Usage Questionnaire and Programming Skills Performance with the reliable coefficient of 0.78 and 0.88, respectively. Data were obtained from covariance analysis, with a significance level of 0.05. More than half of the participants were female (62%). Treatment had a significant impact on students in the language of BASIC Programming F = 36.385; p < 0.05. Also, students' interest in computer use has little effect on students F = 2.109; p > 0.05. Performance BASIC programming skills F = 0.021; p > 0.05. A computer-based teaching strategy has been developed to improve student performance and revitalize student interest in computer use and programming at Veritas University Abuja. Computer educators should adopt a computer-based teaching strategy in teaching computer programming in ICT education to enhance students' writing skills. Also, the design emphasizes the need to continue to use new techniques such as computer teaching techniques to simplify and improve the delivery of instruction.
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25

Petosa, Rita L. "The Benefits of Computer Programming in Developing Algorithmic Thinking." Mathematics Teacher 78, no. 2 (February 1985): 128–30. http://dx.doi.org/10.5951/mt.78.2.0128.

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Our school has not adopted a computer literacy course. Instead, we have opted to direct students’ interest in computers toward the study of mathematics. That is, we have infused an ongoing component of instruction in computer programming into our mathematics curriculum with interesting results.
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26

Yuschenko, Yu O. "Invention of a computer "Kyiv" architecture using a concept of Addressed Programming Language." PROBLEMS IN PROGRAMMING, no. 4 (December 2021): 103–18. http://dx.doi.org/10.15407/pp2021.04.103.

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The article is devoted to the history of the origin of high-level programming in Ukraine. The transition from calculations by arithmometers and logarithmic rulers to solving problems on the computer "Kyiv" using pointers and tree-like formats (abstract data types are analogous) is described. The factors that contributed to this transition include: the experience of providing instructions for calculations by arithmometers, and the experience of programming on MESM. As a result a computer "Kyiv" has been developed with a hardware-implemented possibility of high-level programming, invention of the Addressed Programming Language with indirect addressing (pointers), tree formats and declarative capabilities. Hardware-realized demining of pointers in the computer "Kyiv" is one of the outstanding inventions of Ukrainian engineers and mathematicians at the initial stage of the information technologies development. At that time it was significantly ahead of the world technologies. Programming in computer "Kyiv", unlike Plankalkül, could identify and process complex structures. The paper describes the individual applications of the Address Programming Language, which was implemented on many Soviet-made computers and has been used by programmers for more than 20 years. Due to the so-called "Iron Curtain", scientists in the field of programming outside the post-socialist world still do not know about the invention of pointers by Kiev scientists. A textbook describing the Addressed Programming Language was translated into many languages. A monograph with a description of the computer architecture "Kyiv" and of the Addressed Programming Language was translated into English and published in the United States in 1966.
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27

Chen, Woei-Kae, and Yu Chin Cheng. "Teaching Object-Oriented Programming Laboratory With Computer Game Programming." IEEE Transactions on Education 50, no. 3 (August 2007): 197–203. http://dx.doi.org/10.1109/te.2007.900026.

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28

Yao, Neng. "The Computer-aided Programming System - A Friendly Programming Environment." IEEE Micro 5, no. 2 (April 1985): 9–19. http://dx.doi.org/10.1109/mm.1985.304451.

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29

Tsai, Meng-Jung, Ching-Yeh Wang, and Po-Fen Hsu. "Developing the Computer Programming Self-Efficacy Scale for Computer Literacy Education." Journal of Educational Computing Research 56, no. 8 (January 16, 2018): 1345–60. http://dx.doi.org/10.1177/0735633117746747.

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Computer programming has been gradually emphasized in recent computer literacy education and regarded as a requirement for all middle school students in some countries. To understand young students’ perceptions about their own learning in computer programming, this study aimed to develop an instrument, Computer Programming Self-Efficacy Scale (CPSES), for all students above middle school levels. Based on Berland and Lee’s computational thinking framework, this study developed the CPSES items at a literacy level and finally the instrument included the five subscales: Logical Thinking, Algorithm, Debug, Control, and Cooperation. An exploratory factor analysis and reliability tests were conducted in this study. The reliability alpha was .96 for the overall scale, and ranged from .84 to .96 for the subscales. This study also confirmed the positive correlation between computer programming experience and computer programming self-efficacy. In addition, for low- and middle-experienced learners, significant gender differences were found in two subscales: Algorithm and Debug. The CPSES can be applied as an evaluation tool in computer education, robotics education, as well as integrated STEM or STEAM education in which computer programming was regarded as a part of computer literacy.
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30

Chen, Milton. "Gender and Computers: The Beneficial Effects of Experience on Attitudes." Journal of Educational Computing Research 2, no. 3 (August 1986): 265–82. http://dx.doi.org/10.2190/wdry-9k0f-vcp6-jccd.

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This study examines gender differences in computer attitudes and experiences of adolescents. A sample of students from five Bay Area high schools was surveyed for their uses of computers before and during their high school years, in both formal instruction and informal settings. Adolescent males had greater total exposure to computers, based primarily on higher enrollments in computer programming classes and participation in voluntary experiences, such as home computer use. Fewer gender differences were found in enrollment in classes using computers for purposes other than programming. Overall, males held more positive attitudes of interest in and confidence with computers than did females. Controlling for amount of computer experience, however, males and females responded with similar levels of interest. Social influences, especially those among peer groups, are explored as important factors for differential rates of participation in computer activities.
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31

Zhang, Hongxin. "Optimization Strategies for Mathematical Algorithms in Computer Programming." Journal of Big Data and Computing 1, no. 1 (March 2023): 16–19. http://dx.doi.org/10.62517/jbdc.202301104.

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Computer programming is an important part of computing information technology, mathematical operation is one of the main modules of computer programming, through the optimization of mathematical operation to simple computer programming algorithm, can improve the efficiency of computer software. Therefore, in order to improve the efficiency of computer operation, it is particularly important to optimize the mathematical algorithms. Based on this, this paper studies the optimization strategy of mathematical algorithm in computer programming. Firstly, a brief overview of mathematical algorithm and computer programming is made, secondly, the role of mathematical algorithm in computer programming is analyzed, and finally, the optimization strategy of mathematical algorithm in computer programming is given.
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32

O. Osho, Lauretta, Francisca Ogwueleka, and Oluwafemi Osho. "Axiomatic Basis for Computer Programming." Universal Journal of Computational Mathematics 1, no. 3 (October 2013): 67–72. http://dx.doi.org/10.13189/ujcmj.2013.010301.

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33

Novaes, Luiza, and João Bonelli. "Teaching Computer Programming for Designers." Design Principles and Practices: An International Journal—Annual Review 9, no. 1 (2016): 1–13. http://dx.doi.org/10.18848/1833-1874/cgp/1-13.

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34

Prichard, Mary Kim. "Mathematical Iteration through Computer Programming." Mathematics Teacher 86, no. 2 (February 1993): 150–56. http://dx.doi.org/10.5951/mt.86.2.0150.

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Mathematical iteration is a process for generating a sequence in which one or more initial terms are given and each subsequent term is determined from its predecessors in the same way. An equation that describes the relationship between a term and its predecessors is called a recurrence relation. Arithmetic and geometric sequences, common topics in high school algebra courses, are examples of iterative processes. Arithmetic sequences are generated iteratively from an initial term, a1, a common difference, d, and a recurrence relation, an+1, = an+ d. Geometric sequences are generated from an initial term, a1 a common ratio, r, and a recurrence relation, an+1 =an •r. Mathematical iteration is used in many other mathematical situations and algorithms, such as the Fibonacci sequence, the Euclidean algorithm, and Newton's method for solving equations.
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Powell, Norman, David Moore, John Gray, Janet Finlay, and John Reaney. "Dyslexia and learning computer programming." ACM SIGCSE Bulletin 36, no. 3 (September 2004): 242. http://dx.doi.org/10.1145/1026487.1008072.

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36

Hillis, W. Daniel, and Joshua Barnes. "Programming a highly parallel computer." Nature 326, no. 6108 (March 1987): 27–30. http://dx.doi.org/10.1038/326027a0.

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37

Kaufman, J. "Technical writing and computer programming." IEEE Transactions on Professional Communication 31, no. 4 (1988): 171–74. http://dx.doi.org/10.1109/47.9219.

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38

Papatriantafyllou, Maria. "Computer programming with mammalian cells." Nature Reviews Molecular Cell Biology 13, no. 7 (June 22, 2012): 408. http://dx.doi.org/10.1038/nrm3389.

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Mole, Richard. "Computer programming for management students." British Journal of Educational Technology 19, no. 3 (October 1988): 164–71. http://dx.doi.org/10.1111/j.1467-8535.1988.tb00010.x.

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40

Jansson, Lars C., Harvey D. Williams, and Robert J. Collens. "Computer Programming and Logical Reasoning." School Science and Mathematics 87, no. 5 (May 6, 1987): 371–79. http://dx.doi.org/10.1111/j.1949-8594.1987.tb11722.x.

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41

Lockard, James. "Computer Programming in the Schools:." Computers in the Schools 2, no. 4 (January 20, 1986): 105–14. http://dx.doi.org/10.1300/j025v02n04_14.

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42

Maddux, Cleborne D. "Computer Programming in Special Education:." Computers in the Schools 3, no. 3-4 (February 2, 1987): 159–72. http://dx.doi.org/10.1300/j025v03n03_17.

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43

Breneman, G. L., and O. J. Parker. "Computer programming in general chemistry." Journal of Chemical Education 64, no. 7 (July 1987): 584. http://dx.doi.org/10.1021/ed064p584.

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Powell, Norman, David Moore, John Gray, Janet Finlay, and John Reaney. "Dyslexia and learning computer programming." Innovation in Teaching and Learning in Information and Computer Sciences 3, no. 2 (June 2004): 1–12. http://dx.doi.org/10.11120/ital.2004.03020005.

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Spyropoulou, Natalia, Gerasimoula Demopoulou, Christos Pierrakeas, Ioannis Koutsonikos, and Achilles Kameas. "Developing a Computer Programming MOOC." Procedia Computer Science 65 (2015): 182–91. http://dx.doi.org/10.1016/j.procs.2015.09.107.

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46

Reeves, A. P. "Parallel programming for computer vision." IEEE Software 8, no. 6 (November 1991): 51–59. http://dx.doi.org/10.1109/52.103577.

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47

Randell, B. "The origins of computer programming." IEEE Annals of the History of Computing 16, no. 4 (1994): 6–14. http://dx.doi.org/10.1109/85.329752.

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48

Veness, Chris. "Programming principles in computer graphics." Computer-Aided Design 18, no. 9 (November 1986): 502. http://dx.doi.org/10.1016/0010-4485(86)90008-4.

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49

Keudell, C. J. "Computer Technology & Fitness Programming." Recreational Sports Journal 21, no. 3 (May 1997): 30–32. http://dx.doi.org/10.1177/155886619702100310.

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Nodelman, Vladimir. "Learning computer graphics by programming." ACM SIGCSE Bulletin 35, no. 3 (September 2003): 261. http://dx.doi.org/10.1145/961290.961622.

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