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Journal articles on the topic 'Programming science'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

COLLIS, D. "Programming Programming." Science 254, no. 5031 (1991): 589–90. http://dx.doi.org/10.1126/science.254.5031.589.

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2

Dutton, W. "Programming to Forget." Science 327, no. 5972 (2010): 1456. http://dx.doi.org/10.1126/science.1187723.

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3

Varol, Hacer, and Cihan Varol. "Improving Female Student Retention in Computer Science during the First Programming Course." International Journal of Information and Education Technology 4, no. 5 (2014): 394–98. http://dx.doi.org/10.7763/ijiet.2014.v4.437.

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4

Chung, Myung-Hoon. "Science Code .Net: Object-oriented programming for science." Science of Computer Programming 71, no. 3 (2008): 242–47. http://dx.doi.org/10.1016/j.scico.2008.01.003.

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5

Lavine, Marc S. "Programming programmable materials." Science 366, no. 6466 (2019): 703.6–704. http://dx.doi.org/10.1126/science.366.6466.703-f.

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6

Yin, P., R. F. Hariadi, S. Sahu, et al. "Programming DNA Tube Circumferences." Science 321, no. 5890 (2008): 824–26. http://dx.doi.org/10.1126/science.1157312.

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7

Barnett, Michael, and Phillip Windley. "Dysfunctional Programming: Teaching Programming Using Formal Methods to Noncomputer Science Majors." Computer Science Education 5, no. 1 (1994): 111–22. http://dx.doi.org/10.1080/0899340940050108.

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8

Susko, Alexander Q., and Zachary T. Brym. "An Introduction to R Statistical Computing for Horticultural Science." HortTechnology 26, no. 5 (2016): 588–91. http://dx.doi.org/10.21273/horttech03339-16.

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We present the format for a workshop on introductory computer programming, which was held at the 2015 American Society for Horticultural Science (ASHS) Annual Conference in New Orleans, LA. The main workshop objective was to familiarize attendees with basic computer programming, including data structures, data management, and data analysis. The workshop used the general programming language R, though the concepts and principles presented are transferable across programming languages. Given the increased importance of statistical analysis in the agricultural sciences, the workshop was well attended. Participants appreciated the opportunity to improve their computational literacy and supported follow-up workshops like this at future ASHS events. We have released the presentation and the companion R script online.

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9

Sengupta, Pratim, Amanda Dickes, Amy Voss Farris, Ashlyn Karan, David Martin, and Mason Wright. "Programming in K-12 science classrooms." Communications of the ACM 58, no. 11 (2015): 33–35. http://dx.doi.org/10.1145/2822517.

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10

Bhimd, L. L. "COMPUTER PROGRAMMING FOR SCIENCE AND ENGINEERS." Drying Technology 10, no. 1 (1992): 275–76. http://dx.doi.org/10.1080/07373939208916430.

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11

Voronkov, A. A. "Logic programming and ?-programming." Cybernetics 25, no. 1 (1989): 83–91. http://dx.doi.org/10.1007/bf01074888.

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12

WALDROP, M. MITCHELL. "Hypercube Breaks a Programming Barrier." Science 240, no. 4850 (1988): 286. http://dx.doi.org/10.1126/science.240.4850.286.

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13

BOBROW, D. G., and M. J. STEFIK. "Perspectives on Artificial Intelligence Programming." Science 231, no. 4741 (1986): 951–57. http://dx.doi.org/10.1126/science.231.4741.951.

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14

Azad M. San Ahmed, Rania, Sardasht M-Raouf Mahmood, Rebwar M. Nabi, and Dana L. Hussein. "The Impact of Teaching Materials on Learning Computer Programming Languages in Kurdistan Region Universities and Institutes." Kurdistan Journal of Applied Research 3, no. 1 (2018): 27–33. http://dx.doi.org/10.24017/science.2018.1.7.

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It is evident that learning and teaching computer programming are considered as one of the striking challenges in academic environments. Meanwhile, selecting the correct and appropriate materials can leave an enormous impact in learning computer programming languages. However, recently this argument has been put under scrutiny as to which types of materials motivate learners to learn computer programming languages as well as enhance learning outcomes. Therefore, the main objective of this study is to investigate the current teaching and learning materials of computer programming languages in Kurdistan region of Iraq universities. Additionally, another aim is to give a rigorous analysis of how materials help students to learn computer programming language. A further focus is to identify the difficulties of learning computer programming languages at undergraduate level which constitutes technical Diploma and Bachelor. The last but not the least, this paper examines new approaches to teaching programming languages as a cognitive model for programming education.

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15

MARCOPOULOS, ELIAS, and YUANLIN ZHANG. "onlineSPARC: A Programming Environment for Answer Set Programming." Theory and Practice of Logic Programming 19, no. 2 (2018): 262–89. http://dx.doi.org/10.1017/s1471068418000509.

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AbstractRecent progress in logic programming (e.g. the development of the answer set programming (ASP) paradigm) has made it possible to teach it to general undergraduate and even middle/high school students. Given the limited exposure of these students to computer science, the complexity of downloading, installing, and using tools for writing logic programs could be a major barrier for logic programming to reach a much wider audience. We developed onlineSPARC, an online ASP environment with a self-contained file system and a simple interface. It allows users to type/edit logic programs and perform several tasks over programs, including asking a query to a program, getting the answer sets of a program, and producing a drawing/animation based on the answer sets of a program.

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16

Weigend, Michael. "Mathematical Modeling and Programming in Science Education." Computer Tools in Education, no. 2 (June 28, 2019): 55–64. http://dx.doi.org/10.32603/2071-2340-2019-2-55-64.

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17

Broy, Manfred. "Editorial–Science of Computer Programming–25 years." Science of Computer Programming 66, no. 2 (2007): 103–4. http://dx.doi.org/10.1016/j.scico.2007.01.001.

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18

Werner, Linda L., Brian Hanks, and Charlie McDowell. "Pair-programming helps female computer science students." Journal on Educational Resources in Computing 4, no. 1 (2004): 4. http://dx.doi.org/10.1145/1060071.1060075.

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19

McCracken, Daniel D. "Programming languages in the computer science curriculum." ACM SIGCSE Bulletin 24, no. 1 (1992): 1–4. http://dx.doi.org/10.1145/135250.134511.

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20

Parberry, Ian, Max B. Kazemzadeh, and Timothy Roden. "The art and science of game programming." ACM SIGCSE Bulletin 38, no. 1 (2006): 510–14. http://dx.doi.org/10.1145/1124706.1121500.

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21

Shafto, Sylvia A. "Programming for learning in mathematics and science." ACM SIGCSE Bulletin 18, no. 1 (1986): 296–302. http://dx.doi.org/10.1145/953055.5635.

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22

Mariano, Diego, Pedro Martins, Lucianna Helene Santos, and Raquel Cardoso de Melo‐ Minardi. "Introducing Programming Skills for Life Science Students." Biochemistry and Molecular Biology Education 47, no. 3 (2019): 288–95. http://dx.doi.org/10.1002/bmb.21230.

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23

Weintrop, David. "Block-based programming in computer science education." Communications of the ACM 62, no. 8 (2019): 22–25. http://dx.doi.org/10.1145/3341221.

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24

Crawford, Albert L. "Functional programming for freshman computer science majors." ACM SIGCSE Bulletin 19, no. 1 (1987): 165–69. http://dx.doi.org/10.1145/31726.31753.

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25

Lebeck, Alvin R. "Cache conscious programming in undergraduate computer science." ACM SIGCSE Bulletin 31, no. 1 (1999): 247–51. http://dx.doi.org/10.1145/384266.299772.

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26

Bagert, Donald J. "Should computer science examinations contain “programming” problems?" ACM SIGCSE Bulletin 20, no. 1 (1988): 288–92. http://dx.doi.org/10.1145/52965.53036.

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27

Hajkova, P. "DEVELOPMENT: Enhanced: Programming the X Chromosome." Science 303, no. 5658 (2004): 633–34. http://dx.doi.org/10.1126/science.1094408.

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28

FELLEISEN, MATTHIAS, ROBERT BRUCE FINDLER, MATTHEW FLATT, and SHRIRAM KRISHNAMURTHI. "The structure and interpretation of the computer science curriculum." Journal of Functional Programming 14, no. 4 (2004): 365–78. http://dx.doi.org/10.1017/s0956796804005076.

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Twenty years ago Abelson and Sussman's Structure and Interpretation of Computer Programs radically changed the intellectual landscape of introductory computing courses. Instead of teaching some currently fashionable programming language, it employed Scheme and functional programming to teach important ideas. Introductory courses based on the book showed up around the world and made Scheme and functional programming popular. Unfortunately, these courses quickly disappeared again due to shortcomings of the book and the whimsies of Scheme. Worse, the experiment left people with a bad impression of Scheme and functional programming in general. In this pearl, we propose an alternative role for functional programming in the first-year curriculum. Specifically, we present a framework for discussing the first-year curriculum and, based on it, the design rationale for our book and course, dubbed How to Design Programs. The approach emphasizes the systematic design of programs. Experience shows that it works extremely well as a preparation for a course on object-oriented programming.

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29

Ray, L. B. "Programming circuitry for synthetic biology." Science 352, no. 6281 (2016): 48–50. http://dx.doi.org/10.1126/science.352.6281.48-l.

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30

Hurtley, Stella M. "Metabolic programming of tissue APCs." Science 357, no. 6355 (2017): 1011.10–1013. http://dx.doi.org/10.1126/science.357.6355.1011-j.

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31

Mueller, K. L. "Genetic programming for self-renewal." Science 351, no. 6274 (2016): 676–78. http://dx.doi.org/10.1126/science.351.6274.676-l.

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32

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 (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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33

Saracco, Benjamin H. "Data Science and Predictive Analytics: Biomedical and Health Applications Using R." Journal of the Medical Library Association 108, no. 2 (2020): 334. http://dx.doi.org/10.5195/jmla.2020.901.

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Ivo D. Dinov’s Data Science and Predictive Analytics: Biomedical and Health Applications Using R is a comprehensive twenty-three-chapter text and online course for burgeoning or seasoned biomedical and/or health sciences professionals who analyze data sets using the R programming language.

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34

Cooper, Stephen, and Wanda Dann. "Programming." ACM Inroads 6, no. 1 (2015): 50–54. http://dx.doi.org/10.1145/2723169.

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35

Lukkarinen, Aleksi, Lauri Malmi, and Lassi Haaranen. "Event-driven Programming in Programming Education." ACM Transactions on Computing Education 21, no. 1 (2021): 1–31. http://dx.doi.org/10.1145/3423956.

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During the past two decades, event-driven programming (EDP) has emerged as a central and almost ubiquitous concept in modern software development: Graphical user interfaces are self-evident in most mobile and web-based applications, as well as in many embedded systems, and they are most often based on reacting to events. To facilitate both teaching practice and research in programming education, this mapping review seeks to give an overview of the related knowledge that is already available in conference papers and journal articles. Starting from early works of the 1990s, we identified 105 papers that address teaching practices, present learning resources, software tools or libraries to support learning, and empirical studies related to EDP. We summarize the publications, their main content, and findings. While most studies focus on bachelor’s level education in universities, there has been substantial work in K-12 level, as well. Few courses address EDP as their main content—rather it is most often integrated with CS1, CS2, or computer graphics courses. The most common programming languages and environments addressed are Java, App Inventor, and Scratch. Moreover, very little of deliberate experimental scientific research has been carried out to explicitly address teaching and learning EDP. Consequently, while so-called experience reports, tool papers, and anecdotal evidence have been published, this theme offers a wide arena for empirical research in the future. At the end of the article, we suggest a number of directions for future research.

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36

Govender, Desmond Wesley, and Sujit Kumar Basak. "An investigation of factors related to self-efficacy for Java programming among computer science education students." Journal of Governance and Regulation 4, no. 4 (2015): 612–19. http://dx.doi.org/10.22495/jgr_v4_i4_c5_p6.

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Students usually perceived computer programming courses as one of the most difficult courses since learning to program is perceived as a difficult task. Quite often students’ negative perceptions on computer programming results in poor results and high drop-out rates. The purpose of this study is to examine the impact of factors that affect computer science education students’ Java programming self-efficacy and the relationship between Java programming self-efficacy and students’ age and gender. A questionnaire was used to gather data. A scale with thirty-two items assessing Java programming self-efficacy was adapted from Askar and Davenport’s (2009) computer programming self-efficacy scale. A total of twenty students from a Computer Science Education Discipline participated in this study. Collected data were analysed using SPSS version 22.0. Descriptive statistics, reliability test, mean, standard deviation, and rotated component matrix were utilized to analyze the resulting data. Results indicated that there is not much difference between males (45%) and females (55%) Java programming self-efficacy. Furthermore, the results also indicated that programming skills and Java constructs have higher influence on the self-efficacy for Java programming among computer science education students followed by non-complexity, time consciousness, ability to recode for better understanding and self-motivation.

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37

La Torre, Davide. "Preface: Multiple criteria optimization and goal programming in science, engineering, and social sciences." Annals of Operations Research 251, no. 1-2 (2017): 1–5. http://dx.doi.org/10.1007/s10479-017-2443-1.

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38

Cane, D. "Polyketide biosynthesis: molecular recognition or genetic programming?" Science 263, no. 5145 (1994): 338–40. http://dx.doi.org/10.1126/science.8278806.

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39

Aho, A. V. "Software and the Future of Programming Languages." Science 303, no. 5662 (2004): 1331–33. http://dx.doi.org/10.1126/science.1096169.

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40

Guzdial, Mark, and Susan Landau. "Programming programming languages, and analyzing Facebook's failure." Communications of the ACM 61, no. 6 (2018): 8–9. http://dx.doi.org/10.1145/3204443.

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41

Repenning, Alexander, and Corrina Perrone. "Programming by example: programming by analogous examples." Communications of the ACM 43, no. 3 (2000): 90–97. http://dx.doi.org/10.1145/330534.330546.

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42

Bergin, Susan, and Ronan Reilly. "Programming." ACM SIGCSE Bulletin 37, no. 1 (2005): 411–15. http://dx.doi.org/10.1145/1047124.1047480.

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43

Lazebna, Nataliia, Yuliya Fedorova, and Mariia Kuznetsova. "SCRATCH LANGUAGE OF PROGRAMMING VS ENGLISH LANGUAGE: COMPARING MATHEMATICAL AND LINGUISTIC FEATURES." EUREKA: Physics and Engineering 6 (November 30, 2019): 34–42. http://dx.doi.org/10.21303/2461-4262.2019.00982.

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This paper focuses on Scratch language of programming and traces its math and linguistic features. From a complex consideration about Scratch language programming in linguistic paradigm, focusing on structural, semantic and syntactic features and logic of its narration, this research attempts to clarify specifics of the language and correlate it with the English language features. Global integration of ideas and sciences underline the crucial importance of programming and language conglomerate. Human-computer interfaces, software systems, and development of various programming languages depend on well-balanced structure, shape, logic, and appearance of the actual code. Dynamic characteristics of the Scratch programming environment sustain the creation of interactive and media-rich projects. Ad expansion of Scratch for mediation of animated stories, music videos, science projects, tutorials, and other contents necessitates multifaceted analysis of this programming environment and evokes the interest of researching Scratch from the math and linguistic perspective as one possible projection on various aspects of the considered programming language.

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44

Shiau, Liejune. "Exploring Quasi-Concurrency in Introductory Computer Science." Journal of Educational Computing Research 15, no. 1 (1996): 53–66. http://dx.doi.org/10.2190/7ldf-va2r-vk66-qq8d.

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Most programming courses taught today are focused on managing batch-oriented problems. It is primarily because parallel computers are not commonly available, therefore problems with concurrent nature could not be explored. This consequence, at the same time, causes student's under preparation to meet the challenge of modern multi-process computation technologies. This article demonstrates an easy solution for implementing concurrent programming projects in computer labs. This solution does not require special hardware support or special programming languages. The goal is to facilitate a means to deal with the concept and usefulness of multi-process software systems in the early stage of computer science curriculum. We also include detailed descriptions on a few creative and interesting concurrent examples to illustrate this idea.

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45

Kiberstis, Paula A. "Some (re)programming notes on cancer." Science 362, no. 6410 (2018): 41.15–43. http://dx.doi.org/10.1126/science.362.6410.41-o.

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46

Orchard, Dominic, and Andrew Rice. "A Computational Science Agenda for Programming Language Research." Procedia Computer Science 29 (2014): 713–27. http://dx.doi.org/10.1016/j.procs.2014.05.064.

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47

Bottino, Rosa Maria, Paola Forcheri, and Maria Teresa Molfino. "Teaching computer science through a logic programming approach." Education and Computing 4, no. 2 (1988): 71–76. http://dx.doi.org/10.1016/s0167-9287(88)90535-3.

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48

Kataoka, Joy C., and Robin Lack. "Whales and Hermit Crabs Integrated Programming and Science." TEACHING Exceptional Children 27, no. 4 (1995): 17–21. http://dx.doi.org/10.1177/004005999502700405.

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49

Stewart-Gardiner, Carolee, David G. Kay, Joyce Currie Little, et al. "Collaboration vs plagiarism in computer science programming courses." ACM SIGCSE Bulletin 33, no. 1 (2001): 406–7. http://dx.doi.org/10.1145/366413.364790.

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50

Durham, A. L., and I. M. Adcock. "Basic science: Epigenetic programming and the respiratory system." Breathe 9, no. 4 (2013): 278–88. http://dx.doi.org/10.1183/20734735.000413.

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