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Computer Science Education Research in Israel

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Past, Present and Future of Computing Education Research
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Abstract

Computer science education has been researched in Israel for a few decades, both at the K-12 and the undergraduate levels. The rich variety of the investigated topics addressed from the very beginning issues beyond the introductory course and programming, including the nature of the discipline and its fundamental ideas and concepts, which are stable, unlike the more technological aspects. Understanding the nature of the discipline and mapping its fundamental ideas and concepts constitute the basis on which curricula stand. Therefore, we chose to organize this chapter around ideas and concepts of CS. In line with this perspective, we will discuss research of all age levels: K-12, undergraduate, and even the graduate level, as well as research relating to teachers. We will present design-based research, which accompanied the design of new curricula, as well as studies aiming at identifying phenomena, or investigating educational hypotheses. We will also point out current challenges and possible future directions.

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References

  1. ACM Curriculum Committee on Computer Science (1968). Curriculum 68: Recommendations for academic programs in computer science. Communications of the ACM 11(3), 151–197.

    Article  Google Scholar 

  2. Aharoni, D. (2000). Cogito, ergo sum! Cognitive processes of students dealing with data structures, In Proceedings of the 31st SIGCSE Technical Symposium on Computer Science Education, 26–30.

    Google Scholar 

  3. Aharoni, D., and Leron, U. (1997). Abstraction is hard in computer-science too. In Proceedings of the Conference of the International Group for the Psychology of Mathematics Education (PME), 2:9–16.

    Google Scholar 

  4. Alexandron, G., Armoni, M., Gordon, G., and Harel, D. (2012). The effect of previous programming experience on the learning of scenario-based programming. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research, 151–159.

    Chapter  Google Scholar 

  5. Alexandron, G., Armoni, M., Gordon, G., and Harel, D. (2014). Scenario-based programming, usability oriented perception. ACM Transactions on Computing Education 14(3), 21:1–23.

    Article  Google Scholar 

  6. Alexandron, G., Armoni, M., Gordon, M., and Harel, D. (2014). Scenario-based programming: reducing the cognitive load, fostering abstract thinking. In Proceedings of the 36th International Conference on Software Engineering (ICSE), 311–320.

    Google Scholar 

  7. Alexandron, G., Armoni, M., Gordon, G., and Harel, D. (2016). Teaching nondeterminism through programming. Informatics in Education 15(1), 1–23.

    Article  Google Scholar 

  8. Alexandron, G., Armoni, M., Gordon, M., and Harel, D. (2017). Teaching scenario-based programming: an additional paradigm for the high school computer science curriculum, Part 1. Computing in Science & Engineering 19(5), 58–67.

    Article  Google Scholar 

  9. Alexandron, G., Armoni, M., Gordon, M., and Harel, D. (2017). Teaching scenario-based programming: an additional paradigm for the high school computer science curriculum, Part 2. Computing in Science & Engineering 19(6), 64–71.

    Article  Google Scholar 

  10. Armoni, M. (2009). Reduction in CS: a (mostly) quantitative analysis of reductive solutions to algorithmic problems. Journal on Educational Resources in Computing 8(4), 11:1–30.

    Article  Google Scholar 

  11. Armoni, M. (2013). On teaching abstraction in computer science to novices. Journal of Computers in Mathematics and Science Teaching 32(3), 265–284.

    Google Scholar 

  12. Armoni, M., and Ben-Ari, M. (2009). The concept of nondeterminism: its development and implications for education. Science & Education 18(8), 1005–1030.

    Article  Google Scholar 

  13. Armoni, M., and Gal-Ezer, J. (2006). Reduction – an abstract thinking pattern: the case of the computational models course. In Proceedings of the 37th SIGCSE Technical Symposium on Computer Science Education, 389–393.

    Chapter  Google Scholar 

  14. Armoni, M., and Gal-Ezer, J. (2006). Introducing non-determinism. Journal of Computers in Mathematics and Science Teaching 25(4), 325–359.

    Google Scholar 

  15. Armoni, M., and Gal-Ezer, J. (2007). Non-determinism: an abstract concept in computer science studies. Computer Science Education 17(4), 243–262.

    Article  Google Scholar 

  16. Armoni, M., and Gal-Ezer, J. (2014). Early computing education – Why? What? When? How? ACM Inroads 5(4), 54–59.

    Article  Google Scholar 

  17. Armoni, M., and Ginat, D. (2008) Reversing: a fundamental idea in computer science. Computer Science Education 18(3), 213–230.

    Article  Google Scholar 

  18. Armoni, M., Gal-Ezer, J., and Tirosh, D. (2005). Solving problems reductively. Journal of Educational Computing Research 32(2), 113–129

    Article  Google Scholar 

  19. Armoni, M., Gal-Ezer, J., and Hazzan, O. (2006). Reductive thinking in computer science. Computer Science Education 16(4), 281–301.

    Article  Google Scholar 

  20. Armoni, M., Lewenstein, N., and Ben-Ari, M. (2008). Teaching students to think nondeterministically. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education, 4–8.

    Chapter  Google Scholar 

  21. Armoni, M., Meerbaum-Salant, O., and Ben-Ari, M. (2015). From Scratch to “real” programming. ACM Transactions on Computing Education 14(4), 25:1–15.

    Article  Google Scholar 

  22. Armoni, M., Gal-Ezer, J., and Ulmer, C. Professional development of primary school teachers participating in a pilot project on teaching computer science to fourth graders. In preparation.

    Google Scholar 

  23. Armoni, M., Gal-Ezer, J., Harel, D., Marelly, R., and Szekely, S. (In Press). Plethora of skills: a game-based platform for introducing and practicing computational problem solving, to be published in: H. Abelson & K. Siu-Cheung (Eds.) Computational Thinking Curricula in K-12: International Implementations. MIT Press. Cambridge, MA.

    Google Scholar 

  24. Ben-Ari, M., and Ben-David Kolikant, Y. (1999). Thinking parallel: the process of learning concurrency. In Proceedings of the 4th Annual SIGCSE/SIGCUE ITiCSE Conference on Innovation and Technology in Computer Science Education, 13–16.

    Chapter  Google Scholar 

  25. Benaya, T., and Zur, E. (2008). Understanding object oriented programming concepts in an advanced programming course. In Proceedings of the 2nd International Conference on Informatics in Secondary Schools: Evolution and Perspective (ISSEP), Lecture Notes in Computer Science (LNCS 5090), 161–170.

    Google Scholar 

  26. Ben-Bassat Levy, R., and Ben-Ari, M. (2007). We work so hard and they don’t use it: acceptance of software tools by teachers. In Proceedings of the 12th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE), 246–250.

    Google Scholar 

  27. Ben-David Kolikant, Y. (2001) Gardeners and cinema tickets: high school students’ preconceptions of concurrency. Computer Science Education 11(3), 221–245.

    Article  Google Scholar 

  28. Ben-David Kolikant, Y. (2004). Learning concurrency: evolution of students’ understanding of synchronization. International Journal of Human-Computer Studies 60(2), 243–268.

    Article  Google Scholar 

  29. Ben-David Kolikant, Y. (2005). Students’ alternative standards for correctness. In Proceedings of the 1st International workshop on Computing Education Research (ICER), 37–43.

    Google Scholar 

  30. Ben-David Kolikant, Y., and Ben Ari, M. (2008). Fertile zones of cultural encounter in computer science education. Journal of the Learning Science 18(1), 1–32.

    Article  Google Scholar 

  31. Ben-David Kolikant, Y., and Mussai, M. (2008) “So my program doesn’t run!” Definition, origins, and practical expressions of students’ (mis)conceptions of correctness. Computer Science Education 18(2), 135–151,

    Article  Google Scholar 

  32. Ben-David Kolikant, Y., and Pollack, S. (2004) Establishing computer science professional norms among high-school students. Computer Science Education 14(1), 21–35.

    Article  Google Scholar 

  33. Ben-David Kolikant, Y., Ben-Ari, M., and Pollack, S. (2000). The anthropology of semaphores. In Proceedings of the 5th Annual SIGCSE/SIGCUE ITiCSE Conference on Innovation and Technology in Computer Science Education, 21–24.

    Google Scholar 

  34. Biggs, J.B., and Collis, K.F. (1982). Evaluating the Quality of Learning: The SOLO Taxonomy (Structure of the Observed Learning Outcome). Academic Press.

    Google Scholar 

  35. Brandes, O., and Armoni, M. (2019). Using action research to distill research-based segments of pedagogical content knowledge of K-12 computer science teachers. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE), 485–491.

    Google Scholar 

  36. Breuer, S, Gal-Ezer, J., and Zwas, G. (1990). Microcomputer laboratories in mathematics education. Computers and Mathematics 19(3), 13–34.

    MATH  Google Scholar 

  37. Bruner, J.S. (1960). The Process of Education. Harvard University Press. Boston, MA.

    Google Scholar 

  38. Caspersen, M., Diethelm, I., Gal-Ezer, J., McGettrick, A., Nardelli, E., Passey, D., Rovan, B., and Webb, M. (2022). Informatics References Framework for School.https://www.informaticsforall.org/the-informatics-reference-framework-for-school-release-february-2022/

  39. Damm, W., and Harel, D. (2001). LSCs: Breathing life into message sequence charts. Formal Methods in System Design 19(1), 45–80.

    Article  MATH  Google Scholar 

  40. Dijkstra, E. W. (1975). Guarded commands, nondeterminacy and formal derivation of programs. Communications of the ACM 18(8), 453-457.

    Article  MathSciNet  MATH  Google Scholar 

  41. Ericson, B., Armoni, M., Gal-Ezer, J., Seehorn, D., Stephenson, C., and Tree, F. (2008). Ensuring Exemplary Teaching in an Essential Discipline: Addressing the Crisis in Computer Science Teacher Certification, Final Report of the CSTA Teacher Certification Task Force. ACM. New York, NY.

    Google Scholar 

  42. Floyd, R. W. (1967). Assigning meaning to programs. In Proceedings of Symposia in Applied Mathematics, American Mathematical Society 19, 19–32.

    Google Scholar 

  43. Friebroon-Yesharim, M., and Armoni, M. (2022). The tale of an intended CS curriculum for 4th graders, the case of abstraction. In Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE), pp. 623.

    Google Scholar 

  44. Gaber, I., Armoni, M., and Statter, D. (2021). Teaching reduction as an algorithmic problem solving strategy. In Proceedings of the 3rd International Conference on Computer Science and Technology in Education (CSTE), 19–26.

    Google Scholar 

  45. Gagliardi, F., Hankin, C., Gal-Ezer, J., McGettrick, A., and Meitern, M. (2016). Advancing Cybersecurity Research and Education in Europe: Major Drivers of Growth in the Digital Landscape. https://www.acm.org/binaries/content/assets/publipolicy/2016_euacm_cybersecurity_white_paper.pdf

  46. Gal-Ezer, J., and Harel, D. (1998). What (else) should CS educators know?, Communications of the ACM, 41(9), 77–84.

    Article  Google Scholar 

  47. Gal-Ezer, J., and Harel, D. (1999). Curriculum and course syllabi for high-school computer science program. Computer Science Education 9(2), 114–147.

    Article  Google Scholar 

  48. Gal-Ezer, J., and Trakhtenbrot, M. (2016): Identification and addressing reduction-related misconceptions, Computer Science Education 26(2–3), 80–103.

    Google Scholar 

  49. Gal-Ezer, J., and Zur, E. (2004). The efficiency of algorithms – misconceptions. Computers and Education 42(3), 215–226.

    Article  Google Scholar 

  50. Gal-Ezer, J., and Zwas, G. (1984). An Algorithmic Approach to Linear Systems. International. Journal of Mathematics Education in Science and Technology 15(4), 501–519.

    Article  MATH  Google Scholar 

  51. Gal-Ezer, J., Beeri, C., Harel, D., and Yehudai, A. (1995). A high-school program in computer science. Computer 28(10), 73–80.1

    Article  Google Scholar 

  52. Gal-Ezer, J., Vilner, T., and Zur, E. (2004). Teaching efficiency at CS1 level: a different approach. Computer Science Education 14(3), 235–248.

    Article  Google Scholar 

  53. Gal-Ezer, J. Vilner, T., and Zur, E. (2009). Has the paradigm shift in CS1 a harmful effect on data structures courses: a case study. In Proceedings of the 40th Technical Symposium on Computer Science Education (SIGCSE), 126–130.

    Google Scholar 

  54. Ginat, D. (2001). Early algorithm efficiency with design patterns. Computer Science Education 11(2), 89–109.

    Article  Google Scholar 

  55. Ginat, D. (2001). Loop invariants, exploration of regularities, and mathematical games. International Journal of Mathematical Education in Science and Technology 32(5), 635–651.

    Article  MathSciNet  MATH  Google Scholar 

  56. Ginat, D. (2002). Effective binary perspectives in algorithmic problem solving. Journal on Educational Resources in Computing 2(2), 4–12.

    Article  Google Scholar 

  57. Ginat, D. (2002). On various perspectives of problem decomposition. In Proceedings of the 33rd SIGCSE Technical Symposium on Computer Science Education, 331–335.

    Google Scholar 

  58. Ginat, D. (2003). Decomposition diversity in computer science—beyond the top-down icon. Journal of Computers in Mathematics and Science Teaching 22(4), 365–379.

    Google Scholar 

  59. Ginat, D., (2003). Seeking or skipping regularities? Novice tendencies and the role of invariants. Informatics in Education 2(2), 211–222.

    Article  Google Scholar 

  60. Ginat, D. (2003). The novice programmers’ syndrome of design-by-keyword. In Proceedings of the 8th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), 154–157.

    Google Scholar 

  61. Ginat, D. (2008). Design Disciplines and Non-specific Transfer. In Proceedings of the International Conference on Informatics in Secondary Schools: Evolution and Perspectives (ISSEP), Lecture Notes in Computer Science (LNCS, 5090), 87–98.

    Google Scholar 

  62. Ginat, D. (2009). On the non-modular design of on-the-fly computations. Inroads – SIGCSE bulletin 41(4), 35–39.

    Article  Google Scholar 

  63. Ginat, D. (2009). The overlooked don’t-care notion in algorithmic problem solving. Informatics in Education 8(2), 217–226.

    Article  Google Scholar 

  64. Ginat, D. (2010). The baffling CS notions of "as-if" and "don’t-care". In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (SIGCSE), 385–389.

    Google Scholar 

  65. Ginat, D. (2014). On Inductive Progress in Algorithmic Problem Solving. Olympiads in Informatics 8, 81–91.

    Google Scholar 

  66. Ginat, D. (2021). Abstraction, declarative observations and algorithmic problem solving. Informatics in Education 20(4), 567–582.

    Article  Google Scholar 

  67. Ginat, D., and Alankry R. (2012). Pseudo abstract composition: the case of language concatenation. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), 28–33.

    Google Scholar 

  68. Ginat, D., and Blau, Y. (2017). Multiple levels of abstraction in algorithmic problem solving. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. 237–242.

    Google Scholar 

  69. Ginat, D., and Menashe, E. (2015). SOLO taxonomy for assessing novices’ algorithmic design. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE). 452–457.

    Google Scholar 

  70. Ginat, D., Shifroni, E., and Menashe, E. (2011). Transfer, cognitive load, and program design difficulties. In Proceedings of The 5th International Conference on Informatics in Secondary Schools: Evolution and Perspective (ISSEP), Lecture Notes in Computer Science (LNCS 7013), 165–176.

    Google Scholar 

  71. Ginat, D., Menashe, E., and Taya, A. (2013). Novice Difficulties with Interleaved Pattern Composition. In Proceedings of the 5th International Conference on Informatics in Schools: Situation, Evolution and Perspective (ISSEP), Lecture Notes in Computer Science (LNCS 7780), 57–67.

    Google Scholar 

  72. Gordon, M., Marron, A., and Meerbaum-Salant, O. (2012). Spaghetti for the main course?: observations on the naturalness of scenario-based programming. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), 198–203.

    Google Scholar 

  73. Green, A., Armoni, M., and Ginat, D. Object-first vs. object-Second. In preparation.

    Google Scholar 

  74. Haberman, B. (2002). Frames and boxes – A pattern-based method for manipulating binary trees. Inroads – SIGCSE Bulletin 34(4), 60–64.

    Article  Google Scholar 

  75. Haberman, B. (2004). High-School students’ attitudes regarding procedural abstraction. Education and Information Technologies 9(2), 131–145.

    Article  Google Scholar 

  76. Haberman, B. (2008). Formal and practical aspects of implementing abstract data types in the prolog instruction. Informatica 19(1), 17–30.

    Article  MATH  Google Scholar 

  77. Haberman, B., and Muller, O. (2008). Teaching abstraction to novices: Pattern-based and ADT-based problem-solving processes. In Proceedings of the 38th Annual Frontiers in Education Conference (FIE), F1C:7–12.

    Google Scholar 

  78. Haberman, B., and Ragonis, N. (2010). So different though so similar? Or vice versa? Exploration of the logic programming and the object-oriented programming paradigms. Issues in Informing Science and Information Technology 7, 393–402.

    Article  Google Scholar 

  79. Haberman, B., and Scherz, Z. (2009). Connectivity between abstraction layers in declarative ADT-based problem-solving processes. Informatics in Education 8(1), 3–16.

    Article  Google Scholar 

  80. Haberman, B., Shapiro, E., and Scherz, Z. (2002). Are black boxes transparent? High school students’ strategies of using abstract data types. Journal of Educational Computing Research 27(4), 411–436.

    Article  Google Scholar 

  81. Haberman, B., Lev, E., and Langley, D. (2003). Action research as a tool for promoting teacher awareness of students; conceptual understanding. In Proceedings of the 8th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), 144–148.

    Google Scholar 

  82. Haberman, B., Averbuch, H., and Ginat, D. (2005). Is it really an algorithm? The need for explicit discourse. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE), 74–78.

    Google Scholar 

  83. Hazzan, O. (2003). How students attempt to reduce abstraction in the learning of mathematics and in the learning of computer science. Computer Science Education 13(2), 95–122.

    Article  Google Scholar 

  84. Hazzan, O. (2003). Reducing abstraction when learning computability theory. Journal of Computers in Mathematics and Science Teaching 22(2), 95–117.

    Google Scholar 

  85. Hazzan, O., and Hadar, I. (2005). Reducing abstraction when learning Graph Theory. Journal of Computers in Mathematics and Science Teaching 24(3), 255–272.

    Google Scholar 

  86. Hazzan, O., Gal-Ezer, J., and Blum, L. (2008). A model for high school computer science education: the four key elements that make it! In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education, 281–285.

    Google Scholar 

  87. Hoare, C. A. R. (1969). An axiomatic basis for computer programming. Communications of the ACM 12(10), 576–580.

    Article  MATH  Google Scholar 

  88. Holz, H. J., Applin, A., Haberman, B., Joyce, D., Purchase, H., and Reed, C. (2006). Research methods in computing: what are they, and how should we teach them? In Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education (ITiCSE-WGR), 96–114.

    Google Scholar 

  89. Hubwieser, P., Armoni, M., Brinda, T., Dagiene, V., Diethelm, I., Giannakos, M. N., Knobelsdorf, M., Magenheim, J., Mittermeir, R., and Schubert, S. (2011). Computer science/informatics in secondary education. In Proceedings of the 16th Annual Conference Reports on Innovation and Technology in Computer Science Education – Working Group Reports (ITiCSE-WGR), 19–38.

    Google Scholar 

  90. Israel National Center for Computer Science Teachers (2002). "Machshava": the Israeli National Center for high school computer science teachers, In Proceedings of the 7th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), pp 234.

    Google Scholar 

  91. Lapidot, T., and Aharoni, D. (2007). The Israeli summer seminars for CS leading teachers. In Proceedings of the 12th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE), pp. 318.

    Google Scholar 

  92. Lapidot T., Levy D., and Paz T. (1999). Implementing constructivist ideas in a functional programming course for secondary school. In Proceedings of the Workshop on Functional and Declarative Programming in Education, 29–31.

    Google Scholar 

  93. Lapidot T., Levy D., and Paz T. (2000). Teaching functional programming to high school students. In Proceedings of the International Conference on Mathematics/Science Education and Technology (M/SET).

    Google Scholar 

  94. Lavy, I., Rashkovits, R., and Kouris, R. (2009). Coping with abstraction in object orientation with a special focus on interface classes. Computer Science Education 19(3), 155–177.

    Article  Google Scholar 

  95. Leron, U. (1985). Logo today: vision and reality. The Computing Teacher 12(5), 26-32.

    Google Scholar 

  96. Leron, U. (1987). Abstraction barriers in mathematics and computer-science. In Proceedings of the Third International Conference on LOGO and Mathematics Education (LME).

    Google Scholar 

  97. Leron, U., and Paz, T. (2014). Functions via everyday actions: Support or obstacle? The Journal of Mathematical Behavior 36, 126-134

    Article  Google Scholar 

  98. Levy, D., Lapidot, T., and Paz, T. (2001). ‘It’s just like the whole picture, but smaller’: Expressions of gradualism, selfsimilarity, and other pre-conceptions while classifying recursive phenomena. In Proceedings of the 13th Workshop of the Psychology of Programming Interest Group (PPIG), 249–262

    Google Scholar 

  99. Lieberman, N., Ben-David Kolikant, Y., and Beeri, C. (2011). Difficulties in learning inheritance and polymorphism. ACM Transactions on Computing Education 11(1), 4:1–23.

    Google Scholar 

  100. McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Ben-David Kolikant, Y., Laxer, C., Thomas, L., Utting, I., and Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. In Working Group Reports from ITiCSE on Innovation and Technology in Computer Science Education (ITiCSE-WGR), 125–180.

    Google Scholar 

  101. Meerbaum-Salant, O., Armoni, M., and Ben-Ari, M. (2011). Habits of programming in Scratch. In Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education (ITiCSE), 168–172.

    Google Scholar 

  102. Meerbaum-Salant, O., Armoni, M., and Ben-Ari, M. (2013). Learning computer science concepts with Scratch. Computer Science Education 23(3), 239–264.

    Article  Google Scholar 

  103. Muller, O. (2005). Pattern oriented instruction and the enhancement of analogical reasoning. In Proceedings of the 1st International workshop on Computing Education Research (ICER). 57–67.

    Google Scholar 

  104. Muller, O., and Haberman, B. (2008). Supporting abstraction processes in problem-solving through pattern-oriented-instruction. Computer Science Education, 18(3), 187–212.

    Article  Google Scholar 

  105. Muller, O., and Haberman, B. (2009). A course dedicated to developing algorithmic problem solving skills – Design and experiment. In Proceedings of 21st Annual Workshop of the Psychology of Programming Interest Group (PPIG), 9:1–9.

    Google Scholar 

  106. Nakar, L., and Armoni, M. (2022). Pattern-oriented instruction and students’ abstraction skills. In Proceedings of the 27th ACM Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), pp. 613.

    Google Scholar 

  107. Omar, A., Hadar, I., and Leron, U. (2017). Investigating the under-usage of code decomposition and reuse among high school students: the case of functions. Lecture Notes in Business Information Processing 286, 92–98.

    Article  Google Scholar 

  108. Or-Bach, R., and Lavy, I. (2004). Cognitive activities of abstraction in object orientation: an empirical study. Inroads – the SIGCSE Bulletin 36(2), 82–86.

    Article  Google Scholar 

  109. Paz, T., and Lapidot, T. (2004). Emergence of automated assignment conceptions in a functional programming course. In Proceedings of the 9th Annual SIGCSE Conference on Innovations and Technology in Computer Science Education (ITiCSE), 181–185.

    Google Scholar 

  110. Paz, T., and Leron, U. (2009). The slippery road from actions on objects to functions and variables. Journal for Research in Mathematics Education 40(1), 18–39.

    Article  Google Scholar 

  111. Perrenet, J., Groot, J.F., and Kaasebrood, E. (2005). Exploring students’ understanding of the concept of algorithm: levels of abstraction. In Proceedings of the 10th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education (ITiCSE), 64–68.

    Google Scholar 

  112. Ragonis, N. (2010). A pedagogical approach to discussing fundamental object-oriented programming principles using the ADT SET. ACM Inroads 1(2), 42–52.

    Article  Google Scholar 

  113. Ragonis, N. (2012). Integrating the teaching of algorithmic patterns into computer science teacher preparation programs. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), 339–344.

    Google Scholar 

  114. Ragonis, N., and Ben-Ari, M. (2005). A long-term investigation of the comprehension of OOP concepts by novices. Computer Science Education 15(3), 203–221.

    Article  Google Scholar 

  115. Ragonis, N., and Hazzan, O. (2009). Integrating a tutoring model into the training of prospective Computer Science teachers. The Journal of Computers in Mathematics and Science Teaching 28(3), 309–339.

    Google Scholar 

  116. Rubinstein, A., and Chor, B. (2014). Computational thinking in life science education. PLOS Computational Biology 10(11), 1–5.

    Article  Google Scholar 

  117. Sakhnini, V., and Hazzan, O. (2008). Reducing abstraction in high school computer science education: The case of definition, implementation and use of abstract data types. ACM Journal on Educational Resources in Computing 8(2), 5:1–13.

    Google Scholar 

  118. Schocken, S., Nisan, N., and Armoni, M. (2009). A synthesis course in hardware architecture, compilers, and software engineering. In Proceedings of the 40th ACM Technical Symposium on Computer Science Education (SIGCSE), 443–447.

    Google Scholar 

  119. Schwarz, S., and Ben-Ari, M. (2006). Why don’t they do what we want them to do? In Proceedings of the 18th Annual Workshop of the Psychology of Programming Interest Group (PPIG), 266–274.

    Google Scholar 

  120. Schwill, A. (1994). Fundamental ideas of computer science. Bulletin-European Association for Theoretical Computer Science 53, 274–295.

    MATH  Google Scholar 

  121. Sfard, A. (1991). On the dual nature of mathematical conceptions: Reflections on processes and objects as different sides of the same coin. Educational Studies in Mathematics 22, 1–36.

    Article  Google Scholar 

  122. Shmallo, R., and Ragonis, N. (2020). What is "this"? Difficulties and misconceptions regard the "this" reference. Journal of Education and Information Technologies 26(1), 733–762.

    Article  Google Scholar 

  123. Statter, D., and Armoni, M. (2017). Learning abstraction in computer science: a gender perspective. In Proceedings of the 12th Workshop in Primary and Secondary Computing Education (WiPSCE), 5–14.

    Google Scholar 

  124. Statter, D., and Armoni, M. (2020). Teaching Abstraction in Computer Science to 7th Grade Students. ACM Transactions on Computing Education 20(1), 8:1–37.

    Article  Google Scholar 

  125. Stavy, R., and Tirosh, D. (2000). How Students (mis-)Understand Science and Mathematics: Intuitive Rules. Teachers College Press. New York, NY.

    Google Scholar 

  126. Stephenson, C., Gal-Ezer, J., Haberman, B., and Verno, A. (2005). The New Educational Imperative: Improving High School Computer Science Education, Final report of the CSTA Curriculum Improvement Task Force. ACM. New York, NY.

    Google Scholar 

  127. Stolin, Y., and Hazzan, O. (2007). Students’ understanding of computer science soft ideas: the case of programming paradigm. Inroads – the SIGCSE Bulletin 39(2), 65–69.

    Article  Google Scholar 

  128. Taub, R., Armoni, M., and Ben-Ari, M. (2014). Abstraction as a bridging concept between computer science and physics. In Proceedings of the 9th Workshop in Primary and Secondary Computing Education (WiPSCE), 16–19.

    Google Scholar 

  129. Teif, M., and Hazzan, O. (2006). Partonomy and taxonomy in object-oriented thinking: Junior high school students’ perceptions of object-oriented basic concepts. Inroads – the SIGCSE Bulletin 38(4), 55–60.

    Article  Google Scholar 

  130. Utting, I., Tew, A. E., McCracken, M. E., Thomas, L., Bouvier, D., Frye, R., Paterson, J., Caspersen, M., Ben-David Kolikant, Y., Sorva, J., and Wilusz, T. (2013). A fresh look at novice programmers’ performance and their teachers’ expectations. In Proceedings of the ITiCSE Working Group Reports Conference on Innovation and Technology in Computer Science Education – Working Group Reports (ITiCSE-WGR), 15–32.

    Google Scholar 

  131. Vahrenhold, J., Nardelli, E., Pereira, C., Berry, G., Caspersen, M. E., Gal-Ezer, J., Kölling, M., McGettrick, A., and Westermeier, M. (2017). Informatics Education in Europe: Are We All in the Same Boat? ACM. New York, NY.

    Google Scholar 

  132. Vilner, T., Zur, E., and Gal-Ezer, J. (2007). Fundamental concepts of CS1: procedural vs. object oriented paradigm – a case study. In Proceedings of the 12th Annual ITiCSE Conference on Innovation and Technology in Computer Science Education, 171–175.

    Google Scholar 

  133. Zur-Bargury, I., (2012). A new curriculum for junior-high in computer science. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), 204–208.

    Google Scholar 

  134. Zur-Bargury, I., Pârv, B., and Lanzberg, D. (2013). A nationwide exam as a tool for improving a new curriculum. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE), 267–272.

    Google Scholar 

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Armoni, M., Gal-Ezer, J. (2023). Computer Science Education Research in Israel. In: Apiola, M., López-Pernas, S., Saqr, M. (eds) Past, Present and Future of Computing Education Research . Springer, Cham. https://doi.org/10.1007/978-3-031-25336-2_18

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