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Part I - Background
Published online by Cambridge University Press: 15 February 2019
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References
References
Abdul-Rahman, S. S., & du Boulay, B. (2014). Learning programming via worked-examples: Relation of learning styles to cognitive load. Computers in Human Behavior, 30, 286–298.Google Scholar
Abelson, H., & DiSessa, A. (1986). Turtle Geometry: The Computer as a Medium for Exploring Mathematics. Cambridge, MA: MIT Press.Google Scholar
Altadmri, A., & Brown, N. C. (2015). 37 million compilations: Investigating novice programming mistakes in large-scale student data. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 522–527). New York: ACM.CrossRefGoogle Scholar
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.Google Scholar
Anderson, R., Fincher, S. A., & Guzdial, M. (2005). Proceedings of the 1st International Computing Education Research Workshop, ICER 2005. New York: ACM.Google Scholar
Austin, H. (1976). Teaching Teachers Logo: The Lesley Experiments. Retrieved from http://hdl.handle.net/1721.1/6237Google Scholar
Barr, A., Beard, M., & Atkinson, R. C. (1976). The computer as a tutorial laboratory: The Stanford BIP project. International Journal of Man–Machine Studies, 8(5), 567–582.CrossRefGoogle Scholar
Barron, D. W. (1977). An Introduction to the Study of Programming Languages. Cambridge: Cambridge University Press.Google Scholar
Bednarik, R., & Tukiainen, M. (2006). An Eye-tracking Methodology for Characterizing Program Comprehension Processes. Paper presented at the Proceedings of the 2006 Symposium on Eye Tracking Research & Applications (ETRA ‘06), San Diego, CA.Google Scholar
Ben-Ari, M., Berglund, A., Booth, S., & Holmboe, C. (2004). What do we mean by theoretically sound research in computer science education? ACM SIGCSE Bulletin, 36, 230–231.Google Scholar
Berglund, A. (2002). Learning computer systems in a distributed course: Problematizing content and context. European Association for Research on Learning and Instruction (EARLI), SIG, 10, 1–22.Google Scholar
Berglund, A., & Pears, A. (2003). Students’ Understanding of Computer Networks in an Internationally Distributed Course. Paper presented at the 3rd IEEE International Conference on Advanced Learning Technologies (ICALT’03), Athens, Greece.Google Scholar
Blackwell, A. F., Rode, J. A., & Toye, E. F. (2009). How do we program the home? Gender, attention investment, and the psychology of programming at home. International Journal of Human-Computer Studies, 67(4), 324–341.Google Scholar
Booth, S. (1992). Learning to Program: A Phenomenographic Perspective (PhD thesis), University of Gothenburg.Google Scholar
Booth, S. (1993). A study of learning to program from an experiential perspective. Computers in Human Behavior, 9(2–3), 185–202.CrossRefGoogle Scholar
Bork, A. M. (1971). “Learning to Program for the Science Student.” Technical Report. University of California, Irvine. Physics Computer Science Project. Washington, DC: National Science Foundation.Google Scholar
Brna, P., du Boulay, B., & Pain, H. (Eds.). (1999). Learning to Build and Comprehend Complex Information Structures: Prolog as a Case Study. Stamford, CT: Ablex Publishing Corporation.Google Scholar
Brooks, R. (1977). Towards a theory of the cognitive processes in computer programming. International Journal of Man-Machine Studies, 9(6), 737–751.Google Scholar
Brooks, R. (1978). Using a behavioral theory of program comprehension in software engineering. In Proceedings of the 3rd International Conference on Software Engineering (pp. 196–201). Hoboken, NJ: IEEE Press.Google Scholar
Brooks, R. (1983). Towards a theory of the comprehension of computer programs. International Journal Man–Machine Studies, 18(6), 543–554.Google Scholar
Bryant, S., Romero, P., & du Boulay, B. (2008). Pair programming and the mysterious role of the navigator. International Journal of Human–Computer Studies, 66(7), 519–529.Google Scholar
Byrne, M. D., Catrambone, R., & Stasko, J. T. (1999). Evaluating animations as student aids in learning computer algorithms. Computers & Education, 33(4), 253–278.Google Scholar
Cannara, A. B. (1976). Experiments in Teaching Children Computer Programming (doctoral dissertation), School of Education, Stanford University.Google Scholar
Carver, S. M. (1986). Transfer of Logo Debugging Skill: Analysis, Instruction, and Assessment (PhD thesis), Carnegie-Mellon University.Google Scholar
Cheney, P. H. (1977). Teaching computer programming in an environment where collaboration is required. Journal of the Association for Educational Data Systems, 11(1), 1–5.Google Scholar
Clancy, M. J., & Linn, M. (1992). Designing Pascal Solutions: A Case Study Approach. New York: W.H. Freeman & Company.Google Scholar
diSessa, A. A. (1985). A principled design for an integrated computational environment. Human-Computer Interaction, 1(1), 1–47.Google Scholar
diSessa, A. A., & Abelson, H. (1986). Boxer: A reconstructible computational medium. Communications of the ACM, 29(9), 859–868.Google Scholar
diSessa, A. A., Hammer, D., Sherin, B. L., & Kolpakowski, T. (1991). Inventing graphing: Meta-representational expertise in children. Journal of Mathematical Behavior, 2, 117–160.Google Scholar
Druin, A., Knell, G., Soloway, E., Russell, D., Mynatt, E., & Rogers, Y. (2011). The future of child–computer interaction In CHI EA ‘11: CHI ‘11 Extended Abstracts on Human Factors in Computing Systems (pp. 693–696). New York: ACM.Google Scholar
du Boulay, B. (1986). Some difficulties of learning to program. Journal of Educational Computing Research, 2(1), 57–73.Google Scholar
du Boulay, B., & O’Shea, T. (1981). Teaching novices programming. In Coombs, M. J. & Alty, J. L. (Eds.), Computing Skills and the User Interface (pp. 147–200). Cambridge, MA: Academic Press.Google Scholar
du Boulay, B., O’Shea, T., & Monk, J. (1981). The black box inside the glass box: presenting computing concepts to novices. International Journal of Man–Machine Studies, 14(3), 237–249.Google Scholar
du Boulay, B., & Sothcott, C. (1987). Computers teaching programming: An introductory survey of the field. In Lawler, R. W. & Yazdani, M. (Eds.), Artificial Intelligence and Education (Vol. 1, pp. 345–372). Norwood, NJ: Ablex.Google Scholar
du Boulay, J. B. H. (1978). Learning Primary Mathematics through Computer Programming (PhD thesis), University of Edinburgh.Google Scholar
Eason, K. D. (1976). Understanding the naive computer user. The Computer Journal, 19(1), 3–7.Google Scholar
Eisenstadt, M. (1979). A friendly software environment for psychology students. In AISB Quarterly, 34, 589–593.Google Scholar
Eisenstadt, M., & Brayshaw, M. (1990). A fine-grained account of Prolog execution for teaching and debugging. Instructional Science, 19(4–5), 407–436.Google Scholar
Ensmenger, N. L. (2010). The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Ericson, B. J., Guzdial, M. J., & Morrison, B. B. (2015). Analysis of Interactive Features Designed to Enhance Learning in an Ebook. Paper presented at the Proceedings of the 11th Annual International Conference on International Computing Education Research, Omaha, NE, USA.Google Scholar
Feurzeig, W., Papert, S., Bloom, M., Grant, R., & Solomon, C. (1969). Programming-Languages as a Conceptual Framework for Teaching Mathematics. Final Report on the First Fifteen Months of the Logo Project. Retrieved from https://eric.ed.gov/?id=ED038034Google Scholar
Fincher, S., Lister, R., Clear, T., Robins, A., Tenenberg, J., & Petre, M. (2005). Multi-institutional, multi-national studies in CSEd Research: some design considerations and trade-offs. In ICER ‘05: Proceedings of the First International Workshop on Computing Education Research (pp. 111–121). New York: ACM.Google Scholar
Fincher, S., & Tenenberg, J. (2006). Using theory to inform capacity-building: Bootstrapping communities of practice in computer science education research. Journal of Engineering Education, 95(4), 265–277.Google Scholar
Friend, J. (1975). Programs Students Write. Retrieved from https://eric.ed.gov/?id=ED112861Google Scholar
Gannon, J. D. (1978). Characteristic Errors in Programming Languages. Paper presented at ACM ‘78, Washington, DC, USA.Google Scholar
Garner, S., Haden, P., & Robins, A. (2005). My Program is Correct but it Doesn’t Run: a Preliminary Investigation of Novice Programmers’ Problems. Paper presented at the 7th Australasian Conference on Computing Education–Newcastle, New South Wales, Australia.Google Scholar
Gegg-Harrison, T. S. (1991). Learning Prolog in a schema-based environment. Instructional Science, 20(2–3), 173–192.Google Scholar
Goldberg, A., & Kay, A. (1976). Smalltalk-72: Instruction Manual. Palo Alto, CA: Xerox Corporation.Google Scholar
Goldberg, A., & Robson, D. (1983). Smalltalk-80: The Language and Its Implementation. Boston, MA: Addison-Wesley Longman Publishing Co., Inc.Google Scholar
Good, J. (2011). Learners at the wheel: Novice programming environments come of age. International Journal of People-Oriented Programming (IJPOP), 1(1), 1–24.Google Scholar
Gould, J. D. (1975). Some psychological evidence on how people debug computer programs. International Journal Man–Machine Studies, 7(2), 171–182.Google Scholar
Gould, J. D., & Drongowski, P. (1974). An exploratory study of computer program debugging. Human Factors, 16(3), 258–277.Google Scholar
Green, T. R. G., & Petre, M. (1992). When visual programs are harder to read than textual programs. In van der Veer, G. C., Tauber, M. J., Bagnarola, S., & Antavolits, M. (Eds.), Human–Computer Interaction: Tasks and Organisation, Proceedings EECE-6 (6th European Conference on Cognitive Ergonomics) (pp. 167–180). Rome, Italy: CUD.Google Scholar
Green, T. R. G., & Petre, M. (1996). Usability analysis of visual programming environments: a “cognitive dimensions” framework. Journal of Visual Languages & Computing, 7(2), 131–174.Google Scholar
Green, T. R. G., Petre, M., & Bellamy, R. K. E. (1991). Comprehensibility of visual and textual programs: A test of “superlativism” against the “match–mismatch” conjecture. In Koenemann-Belliveau, J., Moher, T., & Robertson, S. (Eds.), Empirical Studies of Programmers: Fourth Workshop (pp. 121–146). Norwood, NJ: Ablex.Google Scholar
Greenberger, M. (1962). Computers and the World of the Future. Cambridge, MA: MIT Press.Google Scholar
Guzdial, M. (2015). Learner-Centered Design of Computing Education: Research on Computing for Everyone. San Rafael, CA: Morgan & Claypool Publishers.Google Scholar
Habermann, A. N. (1973). Critical comments on the programming language Pascal. Acta Informatica, 3(1), 47–57.Google Scholar
Hasemer, T. (1983). An Empirically-based Debugging System for Novice Programmers (Open University Technical Report No. 6) (PhD thesis). The Open University.Google Scholar
Hoc, J.-M. (1977). Role of mental representation in learning a programming language. International Journal Man–Machine Studies, 9(1), 87–105.Google Scholar
Hoc, J.-M., Green, T. R. G., Samurcay, R., & Gilmore, D. J. (1990). Psychology of Programming. Cambridge, MA: Academic Press.Google Scholar
Howland, K., Good, J., & du Boulay, B. (2013). Narrative threads: A tool to support young people in creating their own narrative-based computer games. In Pan, Z., Cheok, A. D., Müller, W., Iurgel, I., Petta, P., & Urban, B. (Eds.), Transactions on Edutainment X (pp. 122–145). Berlin, Germany: Springer Berlin Heidelberg.CrossRefGoogle Scholar
Hundhausen, C. D., & Brown, J. L. (2007). An experimental study of the impact of visual semantic feedback on novice programming. Journal of Visual Languages and Computing, 18(6), 537–559.Google Scholar
Hundhausen, C. D., Douglas, S. H., & Stasko, J. T. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages and Computing, 13, 259–290.Google Scholar
Hundhausen, C. D., Farley, S., & Brown, J. L. (2006). Can Direct Manipulation Lower the Barriers to Programming and Promote Positive Transfer to Textual Programming? An Experimental Study. Paper presented at the Proceedings of the Visual Languages and Human-Centric Computing.Google Scholar
Hundhausen, C. D., Olivares, D. M., & Carter, A. S. (2017). IDE-based learning analytics for computing education: A process model, critical review, and research agenda. Transactions of Computing Education, 17(3), 1–26.Google Scholar
Johnson, W. L., & Soloway, E. (1987). Proust: An automatic debugger for Pascal programs. In Kearsley, G. P. (Ed.), Artificial Intelligence & Instruction: Applications and Methods (pp. 49–67). Reading, MA: Addison-Wesley Publishing.Google Scholar
Johnson, W. L., Soloway, E., Cutler, B., & Draper, S. (1983). Bug Catalogue 1 (286). Technical Report. New Haven, CT: Yale University.Google Scholar
Johnson-Laird, P. N. (1983). Mental Models: Towards a Cognitive Science of Language, Inference and Consciousness. Cambridge, MA: Harbard University Press.Google Scholar
Joni, S.-N. A., & Soloway, E. (1986). But my program runs! Discourse rules for novice programmers. Journal of Educational Computing Research, 2(1), 95–125.Google Scholar
Kahney, H. (1982). An In-Depth Study of the Cognitive Behaviour of Novice Programmers (Technical Report No. 5). Milton Keynes, UK: The Open University.Google Scholar
Katz, I. R., & Anderson, J. R. (1987). Debugging: An analysis of bug-location strategies. Human–Computer Interaction, 3(4), 351–399.Google Scholar
Kay, A., & Goldberg, A. (1977). Personal dynamic media. In IEEE Computer (pp. 31–41). Hoboken, NJ: IEEE Press.Google Scholar
Kay, A. C. (1972). A Personal Computer for Children of All Ages. Paper presented at the ACM Annual Conference, 1972. Boston, MA, USA.Google Scholar
Kay, A. C. (1993). The early history of Smalltalk. In The Second ACM SIGPLAN Conference on History of Programming Languages (pp. 69–95). New York: ACM.Google Scholar
Kehoe, C., Stasko, J., & Taylor, A. (2001). Rethinking the evaluation of algorithm animations as learning aids. International Journal of Human–Computer Studies, 54(2), 265–284.Google Scholar
Kurland, D. M., & Pea, R. D. (1985). Children’s mental models of recursive Logo programs. Journal of Educational Computing Research, 1(2), 235–243.Google Scholar
Kurland, D. M., Pea, R. D., Clement, C., & Mawby, R. (1986). A study of the development of programming ability and thinking skills in high school students. Journal of Educational Computing Research, 2(4), 429–458.Google Scholar
Ladner, R. E., & Israel, M. (2016). “For all” in “computer science for all”. Communications of the ACM, 59(9), 26–28.Google Scholar
Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208(4450), 1335–1342.Google Scholar
Lecarme, O., & Desjardins, P. (1975). More comments on the programming language Pascal. Acta Informatica, 4(3), 231–243.Google Scholar
Lemos, R. S. (1975). FORTRAN programming: An analysis of pedagogical alternatives. Journal of Educational Data Processing, 12(3), 21–29.Google Scholar
Lemos, R. S. (1978). Students’ attitudes towards programming: The effects of structured walk-throughs. Computers & Education, 2(4), 301–306.Google Scholar
Lemos, R. S. (1979). Teaching programming languages: A survey of approaches. ACM SIGCSE Bulletin – Proceedings of the 10th SIGCSE Symposium on Computer Science, 11(1), 174–181.Google Scholar
Lister, R. (2011). Ten years after the McCracken Working Group. ACM Inroads, 2(4), 18–19.Google Scholar
Lister, R., Box, I., Morrison, B., Tenenberg, J., & Westbrook, D. S. (2004). The dimensions of variation in the teaching of data structures. SIGCSE Bulletin, 36(3), 92–96.Google Scholar
Love, T. (1977). An Experimental Investigation of the Effect of Program Structure on Program Understanding. Paper presented at the ACM Conference on Language Design for Reliable Software, Raleigh, NC, USA.Google Scholar
Lukey, F. J. (1980). Understanding and debugging programs. International Journal of Man–Machine Studies, 12(2), 189–202.Google Scholar
Maloney, J., Burd, L., Kafai, Y., Rusk, N., Silverman, B., & Resnick, M. (2004). Scratch: a sneak preview. In C5 ‘04: Proceedings of the Second International Conference on Creating, Connecting and Collaborating through Computing (pp. 104–109). Washington, DC: IEEE Computer Society.Google Scholar
Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., & Rusk, N. (2008). Programming by Choice: Urban Youth Learning Programming with Scratch. Paper presented at SIGCSE ‘08: The 39th SIGCSE Technical Symposium on Computer Science Education, New York, NY, USA.Google Scholar
Margolis, J., & Fisher, A. (2002). Unlocking the Clubhouse: Women in Computing. Cambridge, MA: MIT Press.Google Scholar
Marton, F. (1981). Phenomenography – Describing conceptions of the world around us. Instructional Science, 10(2), 177–200.Google Scholar
Mateas, M. (2008). Procedural literacy: Educating the new media practitioner. In Drew, D. (Ed.), Beyond Fun (pp. 67–83). Pittsburgh, PA: Carnegie Mellon University: ETC Press.Google Scholar
Mayer, R. E. (1975). Different problem-solving competencies established in learning computer programming with and without meaningful models. Journal of Educational Psychology, 67(6), 725–734.Google Scholar
Mayer, R. E. (1976). Comprehension as affected by structure of problem representation. Memory and Cognition, 4(3), 249–255.Google Scholar
Mayer, R. E. (1979). A psychology of learning BASIC. Communications of the ACM, 22(11), 589–593.Google Scholar
McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B.-D., … Wilusz, T. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. ACM SIGCSE Bulletin, 33(4), 125–140.Google Scholar
McLuhan, M. H. (1962). The Gutenberg Galaxy: The Making of Typographic Man. Toronto, CA: University of Toronto Press.Google Scholar
McLuhan, M. H. (1964). Understanding Media: The Extensions of Man. Cambridge, MA: MIT Press.Google Scholar
McNerney, T. S. (2004). From turtles to tangible programming bricks: explorations in physical language design. Personal and Ubiquitous Computing, 8(5), 326–337.Google Scholar
Miller, L. A. (1974). Programming by non-programmers. International Journal of Man–Machine Studies, 6(2), 237–260.Google Scholar
Miller, L. A. (1975). Naive Programmer Problems with Specification of Transfer-of-control. Paper presented at the AFIPS ‘75 National Computer Conference, Anaheim, CA, USA.Google Scholar
Moreno-León, J., Robles, G., & Román-González, , , M. (2017). Towards data-driven learning paths to develop computational thinking with Scratch. IEEE Transactions on Emerging Topics in Computing, 99, 1.Google Scholar
Morrison, B. B., Dorn, B., & Guzdial, M. (2014). Measuring Cognitive Load in Introductory CS: Adaptation of an Instrument. Paper presented at the 10th Annual Conference on International Computing Education Research, Glasgow, UK.Google Scholar
Murphy, L., Lewandowski, G., McCauley, R., Simon, B., Thomas, L., & Zander, C. (2008). Debugging: The good, the bad, and the quirky – A qualitative analysis of novices’ strategies. ACM SIGCSE Bulletin, 40(1), 163–167.Google Scholar
Newell, A., & Simon, H. A. (1972). Human Problem Solving. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Ni, L., Tew, A. E., Guzdial, M. J., & McKlin, T. (2011). A Regional Professional Development Program for Computing Teachers: The Disciplinary Commons for Computing Educators. Paper presented at the 2011 Annual Meeting of the American Educational Research Association, New Orleans, LA, USA.Google Scholar
Nievergelt, J., Frei, H. P., Burkhart, H., Jacobi, C., Pattner, B., Sugaya, H., Weibel, B., & Weydert, J. (1978). XS-0: A self-explanatory school computer. ACM SIGCSE Bulletin, 10(4), 66–69.Google Scholar
Palumbo, D. J. (1990). Programming language/problem-solving research: A review of relevant issues. Review of Educational Research, 60(1), 65–89.Google Scholar
Palumbo, D. J., & Reed, M. W. (1991). The effect of basic programming language instruction on high school students’ problem solving and computer anxiety. Journal of Research on Computing in Education, 23(3), 343–372.Google Scholar
Papert, S. (1972). Teaching children to be mathematicians versus teaching about mathematics. International Journal of Mathematical Education in Science and Technology, 3(3), 249–262.Google Scholar
Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. New York: Basic Books.Google Scholar
Papert, S. (1987). Information technology and education: Computer criticism vs. technocentric thinking. Educational Researcher, 16(1), 22–30.Google Scholar
Papert, S. A., & Solomon, C. (1971). Twenty Things to do With a Computer. Retrieved from https://dspace.mit.edu/handle/1721.1/5836Google Scholar
Pea, R. D. (1986). Language-independent conceptual “bugs” in novice programming. Journal of Educational Computing Research, 2(1), 25–36.Google Scholar
Pea, R. D. (1987). The aims of software criticism: Reply to professor papert. Educational Researcher, 16(5), 4–8.Google Scholar
Pea, R. D., & Kurland, D. M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2(2), 137–168.Google Scholar
Pea, R. D., Kurland, D. M., & Hawkins, J. (1985). Logo and the development of thinking skills. In Chen, M. & Paisley, W. (Eds.), Children and Microcomputers: Research on the Newest Medium (pp. 193–317). Thousand Oaks, CA: SAGE.Google Scholar
Pears, A., Seidman, S., Malmi, L., Mannila, L., Adams, E., Bennedsen, J., & Paterson, J. (2007). A survey of literature on the teaching of introductory programming. ACM SIGCSE Bulletin, 39(4), 204–223.Google Scholar
Petre, M. (1995). Why looking isn’t always seeing: Readership skills and graphical programming. Communications of the ACM, 38(6), 33–44.Google Scholar
Pugh, J., & Simpson, D. (1979). Pascal errors – Empirical evidence. Computer Bulletin, 2, 26–28.Google Scholar
Resnick, M. (1990). MultiLogo: A study of children and concurrent programming. Interactive Learning Environments, 1(3), 153–170.Google Scholar
Resnick, M., Martin, F., Sargent, R., & Silverman, B. (1996). Programmable bricks: Toys to think with. IBM Systems Journal, 35(3–4), 443–452.Google Scholar
Ripley, G. D., & Druseikis, F. C. (1978). A statistical analysis of syntax errors. Computer Languages, 3(4), 227–240.Google Scholar
Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137–172.Google Scholar
Roman-Gonzalez, M., Perez-Gonzalez, J.-C., & Jimenez-Fernandez, C. (2016). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691.Google Scholar
Romero, P., du Boulay, B., Robertson, J., Good, J., & Howland, K. (2009). Is Embodied Interaction Beneficial When Learning Programming? In VMR ‘09 Proceedings of the 3rd International Conference on Virtual and Mixed Reality: Held as Part of HCI International 2009 (pp. 97–105). Berlin, Germany: Springer-Verlag.Google Scholar
Shapiro, S. C., & Witmer, D. P. (1974). Interactive visual simulators for beginning programming students. ACM SIGCSE Bulletin – Proceedings of the 4th SIGCSE Symposium on Computer Science Education, 6(1), 11–14.Google Scholar
Sherin, B. L. (2001). A comparison of programming languages and algebraic notation as expressive languages for physics. International Journal of Computers for Mathematical Learning, 6, 1–61.Google Scholar
Sherman, L., Druin, A., Montemayor, J., Farber, A., Platner, M., Simms, S., Porteous, J., Alborzi, H., Best, J., Hammer, J., & Kruskal, A. (2001). StoryKit: Tools for children to build room-sized interactive experiences. In CHI’01 Extended Abstracts on Human Factors in Computing Systems (pp. 197–198). New York: ACM.Google Scholar
Shneiderman, B. (1977a). Measuring computer program quality and comprehension. International Journal Man-Machine Studies, 9(4), 465–478.Google Scholar
Shneiderman, B. (1977b). Teaching programming: A spiral approach to syntax and semantics. Computers and Education, 1(4), 193–197.Google Scholar
Shneiderman, B., Mayer, R. E., McKay, D., & Heller, P. (1977). Experimental investigations of the utility of detailed flowcharts in programming. Communications of the ACM, 20(6), 373–381.Google Scholar
Sime, M. E., Arblaster, A. T., & Green, T. R. G. (1977a). Reducing programming errors in nested conditionals by prescribing a writing procedure. International Journal Man–Machine Studies, 9(1), 119–126.Google Scholar
Sime, M. E., Arblaster, A. T., & Green, T. R. G. (1977b). Structuring the programmer’s task. Journal of Occupational Psychology, 50(3), 205–216.Google Scholar
Sime, M. E., Green, T. R. G., & Guest, D. J. (1977). Scope marking in computer conditionals – A psychological evaluation. International Journal Man-Machine Studies, 9(1), 107–118.Google Scholar
Sleeman, D. (1986). The challenges of teaching computer programming. Communications of the ACM, 29(9), 840–841.Google Scholar
Sleeman, D., Putnam, R. T., Baxter, J., & Kuspa, L. (1986). Pascal and high school students: A study of errors. Journal of Educational Computing Research, 2(1), 5–23.Google Scholar
Smith, D. C. (1975). PYGMALION: A Creative Programming Environment (PhD thesis), Stanford University.Google Scholar
Soloway, E., & Ehrlich, K. (1984). Empirical studies of programming knowledge. IEEE Transactions on Software Engineering, SE-10, 5(5), 595–609.Google Scholar
Soloway, E., & Iyengar, S. (Eds.) (1986). Empirical Studies of Programmers: Papers Presented at the First Workshop on Empirical Studies of Programmers. Norwood, NJ: Ablex.Google Scholar
Sorva, J. (2012). Visual Program Simulation in Introductory Programming Education (Doctor of Science in Technology thesis), Aalto University School of Science.Google Scholar
Sorva, J. (2013). Notional machines and introductory programming education. Transactions in Computing Education, 13(2), 8:1–8:31.Google Scholar
Sorva, J., & Sirkiä, T. (2010). UUhistle: A Software Tool for Visual Program Simulation. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research (pp. 49–54). New York: ACM.Google Scholar
Spohrer, J. C., Pope, E., Lipman, M., Sack, W., Freiman, S., Littman, D., & Soloway, E. (1985). Bug Catalogue: II, III, IV (Technical Report 386). New Haven, CT: Yale University Press.Google Scholar
Stasko, J. T. (1997a). Supporting Student-built Algorithm Animation as a Pedagogical Tool. Paper presented at the CHI ‘97 Extended Abstracts on Human Factors in Computing Systems, Atlanta, GA, USA.Google Scholar
Stasko, J. T. (1997b). Using Student-built Algorithm Animations as Learning Aids. Paper presented at the 28th SIGCSE Technical Symposium on Computer Science Education, San Jose, CA, USA.Google Scholar
Statz, J. A. (1973). The Development of Computer Programming Concepts and Problem-solving Abilities Among Ten-year-olds Learning Logo (PhD thesis), Syracuse University.Google Scholar
Taylor, J., & du Boulay, B. (1987). Why novices may find programming in Prolog hard. In Rutkowska, J. C. & Crook, C. (Eds.), Computers, Cognition and Development: Issues for Psychology and Education (pp. 153–176). New York: Wiley.Google Scholar
Utting, I., Tew, A. E., McCracken, M., Thomas, L., Bouvier, D., Frye, R., & 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 (pp. 15–32). New York: ACM.Google Scholar
Weinberg, G. M. (1971). The Psychology of Computer Programming. New York: Van Nostrand/Reinhold.Google Scholar
Weintrop, D., & Wilensky, U. (2015). Using Commutative Assessments to Compare Conceptual Understanding in Blocks-based and Text-based Programs. Paper presented at the 11th Annual International Conference on International Computing Education Research, Omaha, NE, USA.Google Scholar
Welsh, J., Sneeringer, W. J., & Hoare, C. A. R. (1977). Ambiguities and insecurities in Pascal. Software: Practice and Experience, 7(6), 685–696.Google Scholar
Weyer, S. A., & Cannara, A. B. (1975). Children Learning Computer Programming: Experiments with Languages, Curricula and Programmable Devices (Technical Report 250). Stanford, CA: Institute for Mathematical Studies in Social Science.Google Scholar
Wiedenbeck, S. (1986). Beacons in computer program comprehension. International Journal of Man–Machine Studies, 25(6), 697–709.Google Scholar
Wolf, M. (2007). Proust and the Squid: The Story and Science of the Reading Brain. New York: Harper Collins.Google Scholar
Youngs, E. A. (1974). Human errors in programming. International Journal of Man–Machine Studies, 6(3), 361–376.Google Scholar
References
Begel, A., & Simon, B. (2008a). Novice software developers, all over again. In Proceedings of the Fourth International Workshop on Computing Education Research (pp. 3–14). New York: ACM.Google Scholar
Begel, A., & Simon, B. (2008b). Struggles of new college graduates in their first software development job. In Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education (pp. 226–230). New York: ACM.Google Scholar
Fincher, S., & Petre, M. (2004). Computer Science Education Research. Abingdon, UK: Routledge.Google Scholar
Lave, J. (1988). Cognition in Practice: Mind, Mathematics and Culture in Everyday Life. Cambridge, UK: Cambridge University Press.Google Scholar
Malmi, L., Sheard, J., Simon, Bednarik, Helminen, R., Kinnunen, J., Korhonen, P., Myller, A., Sorva, N., , J., & Taherkhani, A. (2014). Theoretical underpinnings of computing education research: What is the evidence? In Proceedings of the Tenth Annual Conference on International Computing Education Research (pp. 27–34). New York: ACM.Google Scholar
Malmi, L., Sheard, J., Simon, Bednarik, Helminen, R., Korhonen, J., Myller, A., Sorva, N., , J., & Taherkhani, A. (2010). Characterizing research in computing education: A preliminary analysis of the literature. In Proceedings of the Sixth International Workshop on Computing Education Research (pp. 3–12). New York: ACM.Google Scholar
Pears, A., Seidman, S., Eney, C., Kinnunen, P., & Malmi, L. (2005). Constructing a core literature for computing education research. SIGCSE Bulletin, 37(4), 152–161.Google Scholar
Randolph, J. J. (2007). Findings from a methodological review of the computer science education research: 2000–2005. SIGCSE Bulletin, 39(4), 130.Google Scholar
Randolph, J. J. (2008). A methodological review of computer science education research. Journal of Information Technology Education, 7, 135–162.Google Scholar
Randolph, J. J., Julnes, G., Bednarik, R., & Sutinen, E. (2007). A comparison of the methodological quality of articles in computer science education journals and conference proceedings. Computer Science Education, 17(4), 263–274.Google Scholar
Randolph, J. J., Julnes, G. & Sutinen, E. (2009). Trends, tribes, and territories in computer science education research. Journal for Computing Teachers, 1(1), 1–19.Google Scholar
Scribner, S. (1984). Studying working intelligence. In Rogoff, B. & Lave, J. (Eds.), Everyday Cognition: Its Development in Social Context (pp. 9–44). Cambridge, MA: Harvard University Press.Google Scholar
Simon, (2016). A picture of the growing ICER community. In Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 153–159). New York: ACM.Google Scholar
Simon, Carbone, de Raadt, A., Lister, M., Hamilton, R., , M., & Sheard, J. (2008a). Classifying computing education papers: Process and results. In Proceedings of the Fourth International Workshop on Computing Education Research (pp. 61–172). New York: ACM.Google Scholar
Simon, , Sheard, J., Carbone, A., de Raadt, M., Hamilton, M., Lister, R., & Thompson, E. (2008). Eight years of computing education papers at NACCQ. In 21st Annual Conference of the National Advisory Committee on Computing Qualifications (NACCQ 2008) (pp. 101–107). Auckland, New Zealand: National Advisory Committee on Computing Qualifications.Google Scholar
Valentine, D. W. (2004). CS educational research: A meta-analysis of SIGCSE technical symposium proceedings. In Proceedings of the 35th SIGCSE Technical Symposium on Computer Science Education (pp. 255–259). New York: ACM.Google Scholar
Vansledright, B. (2010). What does it mean to think historically and how do you teach it? In Parker, W. (Ed.), Social Studies Today: Research and Practice (pp. 113–120). New York: Routledge.Google Scholar
References
Berland, M., Martin, T., & Benton, T. (2013). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564–599.Google Scholar
Blikstein, P. (2011). Using learning analytics to assess students’ behavior in open-ended programming tasks. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge – LAK 2011 (pp. 110–116). New York: ACM.Google Scholar
Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming pluralism: Using learning analytics to detect patterns in novices’ learning of computer programming. Journal of the Learning Sciences, 23(4), 561–599.Google Scholar
Blikstein, P. (2013). Digital Fabrication and ’Making’ in Education: The Democratization of Invention. In J. Walter-Herrmann & C. Büching (Eds.). FabLabs: Of Machines, Makers and Inventors (pp. 203–221). Bielefeld: Transcript Publishers.Google Scholar
Blikstein, P. (2018). Pre-College Computer Science Education: A Survey of the Field. Mountain View, CA: Google LLC. Retrieved on 1 November 2018 from https://goo.gl/gmS1VmGoogle Scholar
Brennan, K. (2013). Learning computing through creating and connecting. Computer, 46(9), 52–59.Google Scholar
Buechley, L., & Eisenberg, M. (2008). The LilyPad Arduino: Toward wearable engineering for everyone. IEEE Pervasive Computing, 7(2), 12–15.Google Scholar
Clements, D. H. (1990). Metacomponential development in a LOGO programming environment. Journal of Educational Psychology, 82(1), 141.Google Scholar
De Corte, E., & Verschaffel, L. (1989). Logo: A vehicle for thinking. In Greer, B. & Mulhern, G. (Eds.), New Directions in Mathematics Education (pp. 63–81). London/New York: Routledge.Google Scholar
diSessa, A. (2000). Changing Minds: Computers, Learning, and Literacy. Cambridge, MA: MIT Press.Google Scholar
diSessa, A. (2018). Computational literacy and “the big picture” concerning computers in mathematics education. Mathematical Thinking and Learning, 20(1), 3–31.Google Scholar
Google LLC. & Gallup Inc. (2016). Diversity gaps in computer science: Exploring the underrepresentation of girls, Blacks and Hispanics. Retrieved from http://goo.gl/PG34aHGoogle Scholar
Graham, P. (2004). Hackers & Painters: Big Ideas from the Computer Age. Sebastopol, CA: O’Reilly Media.Google Scholar
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.Google Scholar
K–12 Computer Science Framework Steering Committee (2016). K–12 Computer Science Framework (978-1-4503-5278-9). Retrieved from http://k12cs.org/wp-content/uploads/2016/09/K%E2%80%9312-Computer-Science-Framework.pdfGoogle Scholar
Liu, M. (1997). The effects of HyperCard programming on teacher education students’ problem-solving ability and computer anxiety. Journal of Research on Computing in Education, 29(3), 248–262.Google Scholar
Maltese, A., & Tai, R. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among US students. Science Education, 95(5), 877–907.Google Scholar
National Research Council (2006). America’s Lab Report: Investigations in High School Science. Washington, DC: National Academies Press.Google Scholar
National Research Council (2012). A Framework for K–12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. Washington, DC: National Academies Press.Google Scholar
Noonan, R. (2017). STEM Jobs: 2017 Update (ESA Issue Brief # 02-17). Retrieved from www.esa.gov/reports/stem-jobs-2017-updateGoogle Scholar
Palumbo, D. (1990). Programming language/problem-solving research: A review of relevant issues. Review of Educational Research, 60(1), 65–89.Google Scholar
Papert, S. (1980). Mindstorms: Children, Computers and Powerful Ideas. New York: Basic Books.Google Scholar
Pears, A., Seidman, S., Malmi, L., Mannila, L., Adams, E., Bennedsen, J., & Paterson, J. (2007). A survey of literature on the teaching of introductory programming. ACM SIGCSE Bulletin, 39(4), 204–223.Google Scholar
Seehorn, D., Carey, S., Fuschetto, B., Lee, I., Moix, D., O’Grady-Cunniff, D., …, Verno, A. (2011). CSTA K–12 Computer Science Standards: Revised 2017. Retrieved from www.csteachers.org/page/standardsGoogle Scholar
Sherin, B. L. (2001). A comparison of programming languages and algebraic notation as expressive languages for physics. International Journal of Computers for Mathematical Learning, 6(1), 1–61.Google Scholar
Turkle, S., & Papert, S. (1990). Epistemological pluralism: Styles and voices within the computer culture. Signs: Journal of Women in Culture and Society, 16(1), 128–157.Google Scholar
US Department of Labor (2007). The STEM workforce challenge: The role of the public workforce system in a national solution for a competitive science, technology, engineering, and mathematics (STEM) workforce. Retrieved from https://digitalcommons.ilr.cornell.edu/key_workplace/637/Google Scholar
Vogel, S., Santo, R., & Ching, D. (2017). Visions of computer science education: Unpacking arguments for and projected impacts of CS4All initiatives. In Proceedings of the 48th ACM Technical Symposium on Computer Science Education – SIGCSE 2017 (pp. 609–614). New York: ACM.Google Scholar
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.CrossRefGoogle Scholar
Wilensky, U. (1999, updated 2006, 2017). NetLogo [Computer software] (Version 6). Evanston, IL: Center for Connected Learning and Computer-Based Modeling. Retrieved from http://ccl.northwestern.edu/netlogoGoogle Scholar
Wilensky, U., & Papert, S. (2010). Restructurations: Reformulating knowledge disciplines through new representational forms. In Proceedings of Constructionism 2010 Paris (p. 15). Paris, France: American University of Paris.Google Scholar