Teaching algorithms in upper secondary education: a study of teachers’ pedagogical content knowledge

References

• Aho, A. V. (2011). Ubiquity symposium: Computation and computational thinking. Ubiquity, 2011(January). https://doi.org/10.1145/1922681.1922682
   Google Scholar
• Alonzo, A. C., & Kim, J. (2016). Declarative and dynamic pedagogical content knowledge as elicited through two video-based interview methods. Journal of Research in Science Teaching, 53(8), 1259–1286. https://doi.org/10.1002/tea.21271
   Web of Science ®Google Scholar
• Armoni, M. (2013). On teaching abstraction in CS to novices. Journal of Computers in Mathematics and Science Teaching, 32(3), 265–284. https://www.learntechlib.org/primary/p/41271/
   Google Scholar
• Barendsen, E., Dagienė, V., Saeli, M., & Schulte, C. (2014). Eliciting computer science teachers’ PCK using the content representation format: Experiences and future directions. In Y. Gülbahar, E. Karatas, & M. Adnan (Eds.), Informatics in schools: Situation, evolution and perspectives (pp. 71–82). Ankara University Press.
   Google Scholar
• Barendsen, E., Grgurina, N., & Tolboom, J. (2016). A new informatics curriculum for secondary education in the Netherlands. In A. Brodnik & F. Tort (Eds.), Informatics in schools: Improvement of informatics knowledge and perceptions (pp. 105–117). Springer.
   Google Scholar
• Barendsen, E., & Henze, I. (2019). Relating teacher PCK and teacher practice using classroom observation. Research in Science Education, 49(5), 1141–1175. https://doi.org/10.1007/s11165-017-9637-z
   Web of Science ®Google Scholar
• Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54. https://doi.org/10.1145/1929887.1929905
   Google Scholar
• Basso, D., Fronza, I., Colombi, A., & Pahl, C. (2018). Improving assessment of computational thinking through a comprehensive framework. In Proceedings of the 18th Koli Calling International Conference on Computing Education Research (pp. 1–5). New York, NY: Association for Computing Machinery.
   Google Scholar
• Bayram-Jacobs, D., Henze, I., Evagorou, M., Shwartz, Y., Aschim, E. L., Alcaraz-Dominguez, S., Barajas, M., & Dagan, E. (2019). Science teachers’ pedagogical content knowledge development during enactment of socioscientific curriculum materials. Journal of Research in Science Teaching, 56(9), 1207–1233. https://doi.org/10.1002/tea.21550
   Web of Science ®Google Scholar
• Bell, T., & Vahrenhold, J. (2018). CS Unplugged – How is it used, and does it work? In H. J. Böckenhauer, D. Komm, & W. Unger (Eds.), Adventures between lower bounds and higher altitudes (pp. 497–521). Springer International Publishing.
   Google Scholar
• Bell, T., Alexander, J., Freeman, I., & Grimley, M. (2009). Computer science unplugged: School students doing real computing without computers. The New Zealand Journal of Applied Computing and Information Technology, 13(1), 20–29. https://researchportal.bath.ac.uk/en/publications/computer-science-unplugged-school-students-doing-real-computing-wdoi=“10.1007/978-3-319-98355-4_29„
   Google Scholar
• Black, J., Brodie, J., Curzon, P., Myketiak, C., McOwan, P. W., & Meagher, L. R. (2013). Making computing interesting to school students: Teachers’ perspectives. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (pp. 255–260). New York, NY: Association for Computing Machinery.
   Google Scholar
• Boeije, H. (2010). Analysis in qualitative research. Sage.
   Google Scholar
• Brandes, O., & 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 (pp. 485–491). New York, NY: Association for Computing Machinery.
   Google Scholar
• Brinkmann, S., & Kvale, S. (2015). InterViews - Learning the craft of qualitative research interviewing (3rd ed.). Sage.
   Google Scholar
• Charmaz, K. (2006). Constructing grounded theory – A practical guide through qualitative analysis. Sage Publications.
   Google Scholar
• Cohen, L., Manion, L., & Morrison, K. (2011). Research methods in education (7th ed.). Routledge.
   Google Scholar
• Computing at School Working Group. (2012). Computer science: A curriculum for schools. https://www.computingatschool.org.uk/cacfs
   Google Scholar
• Cormen, T., Leiserson, C., Rivest, R., & Stein, C. (2009). Introduction to algorithms (3rd ed.). The MIT Press.
   Google Scholar
• Corradini, I., Lodi, M., & Nardelli, E. (2017). Conceptions and misconceptions about computational thinking among Italian primary school teachers. In Proceedings of the 2017 ACM Conference on International Computing Education Research (ICER ‘ 17) (pp. 136–144). New York, NY: Association for Computing Machinery.
   Google Scholar
• CSTA. (2017). K-12 computer science standards, Revised. https://csteachers.org/page/standards/
   Google Scholar
• Cutts, Q., Robertson, J., Donaldson, P., & O’Donnell, L. (2017). An evaluation of a professional learning network for computer science teachers. Computer Science Education, 27(1), 30–53. https://doi.org/10.1080/08993408.2017.1315958
   Web of Science ®Google Scholar
• Dagienė, V., & Jevsikova, T. (2012). Reasoning on the content of informatics education for beginners. Social Sciences, 78(4), 84–90. https://doi.org/10.5755/j01.ss.78.4.3233     
   Google Scholar
• Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39. https://doi.org/10.1145/2998438
   Web of Science ®Google Scholar
• Fenstermacher, G. D. (1994). The knower and the known: The nature of knowledge in research on teaching. Review of Research in Education, 20(1), 3–56. https://doi.org/10.3102/0091732X020001003
   Web of Science ®Google Scholar
• Forišek, M., & Steinová, M. (2012). Metaphors and analogies for teaching algorithms. In Proceedings of the 43rd ACM technical symposium on computer science education (pp. 15–20). New York, NY: Association for Computing Machinery.
   Google Scholar
• Futschek, G. (2006). Algorithmic thinking: The key for understanding computer science. In R. T. Mittermeir (Ed.), Informatics education – The bridge between using and understanding computers (pp. 159–168). Springer.
   Google Scholar
• Futschek, G., & Moschitz, J. (2010). Developing algorithmic thinking by inventing and playing algorithms. In Proceedings of the 2010 constructionist approaches to creative learning, thinking and education. http://publik.tuwien.ac.at/files/PubDat_187461.pdf
   Google Scholar
• Gal-Ezer, J., Beeri, C., Harel, D., & Yehudai, A. (1995). A high-school program in computer science. Computer, 28(10), 73–80. https://doi.org/10.1109/2.467599
   Web of Science ®Google Scholar
• Gal-Ezer, J., & Zur, E. (2002). The concept of ‘algorithm efficiency’ in the high school curriculum. In Proceedings of the 32nd ASEE/IEEE Frontiers in Education Conference. Boston, MA.
   Google Scholar
• Gess-Newsome, J. (1999). Pedagogical content knowledge: An introduction and orientation. In J. Gess-Newsome & N. G. Lederman (Eds.), Examining pedagogical content knowledge: The construct and its implications for science education (pp. 3–17). Springer Netherlands.
   Google Scholar
• Gibson, J. P. (2012). Teaching graph algorithms to children of all ages. In Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education (pp. 34–39). New York, NY: Association for Computing Machinery.
   Google Scholar
• Grgurina, N., Barendsen, E., Suhre, C., van Veen, K., & Zwaneveld, B. (2017). Investigating informatics teachers’ initial pedagogical content knowledge on modeling and simulation. In V. Dagienė & A. Hellas (Eds.), Informatics in schools: Focus on learning programming (pp. 65–76). Springer International Publishing.
   Google Scholar
• Grgurina, N., Tolboom, J., & Barendsen, E. (2018). The second decade of informatics in Dutch secondary education. In S. N. Pozdniakov & V. Dagienė (Eds.), Informatics in schools: Fundamentals of computer science and software engineering (pp. 271–282). Springer International Publishing.
   Google Scholar
• Grover, S. (2017). Assessing algorithmic and computational thinking in K-12: Lessons from a middle school classroom. In P. J. Rich & C. B. Hodges (Eds.), Emerging research, practice, and policy on computational thinking (pp. 269–288). Springer International Publishing.
   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. https://doi.org/10.3102/0013189X12463051
   Web of Science ®Google Scholar
• Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237. https://doi.org/10.1080/08993408.2015.1033142
   Web of Science ®Google Scholar
• Haberman, B., Averbuch, H., & Ginat, D. (2005). Is it really an algorithm: The need for explicit discourse. ACM SIGCSE Bulletin, 37(3), 74–78. https://doi.org/10.1145/1151954.1067469
   Google Scholar
• Hashweh, M. Z. (2005). Teacher pedagogical constructions: A reconfiguration of pedagogical content knowledge. Teachers and Teaching, 11(3), 273–292. https://doi.org/10.1080/13450600500105502
   Google Scholar
• Hasni, T. F., & Lodhi, F. (2011). Teaching problem solving effectively. ACM Inroads, 2(3), 58–62. https://doi.org/10.1145/2003616.2003636
   Google Scholar
• Hazzan, O., Lapidot, T., & Ragonis, N. (2014). Guide to teaching computer science (2nd ed.). Springer.
   Google Scholar
• Henze, I., & Van Driel, J. H. (2015). Toward a more comprehensive way to capture PCK in its complexity. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Re-examining pedagogical content knowledge in science education (pp. 120–134). Routledge.
   Google Scholar
• Henze, I., Van Driel, J. H., & Verloop, N. (2008). Development of experienced science teachers’ pedagogical content knowledge of models of the solar system and the universe. International Journal of Science Education, 30(10), 1321–1342. https://doi.org/10.1080/09500690802187017
   Web of Science ®Google Scholar
• Hromkovič, J., & Lacher, R. (2017). The computer science way of thinking in human history and consequences for the design of computer science curricula. In V. Dagienė & A. Hellas (Eds.), Informatics in schools: Focus on learning programming (pp. 3–11). Springer International Publishing.
   Google Scholar
• Hubbard, A. (2018). Pedagogical content knowledge in computing education: A review of the research literature. Computer Science Education, 28(2), 117–135. https://doi.org/10.1080/08993408.2018.1509580
   Web of Science ®Google Scholar
• Lessner, D. (2013). The role of algorithm in general secondary education revisited. In I. Diethelm, J. Arndt, M. Dünnebier, & J. Syrbe (Eds.), Informatics in Schools: Local Proceedings of the 6th International Conference ISSEP 2013 – Selected Papers (pp. 99–110). Universitätsverlag Potsdam.
   Google Scholar
• Leyzberg, D., & Moretti, C. (2017). Teaching CS to CS teachers: Addressing the need for advanced content in K–12 professional development. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’17) (pp. 369–374). New York, NY: Association for Computing Machinery.
   Google Scholar
• Lincoln, Y. S., & Guba, E. (1985). Establishing trustworthiness. In naturalistic inquiry (Chapter 11). Sage Publications.
   Google Scholar
• Liu, S. (2013). Pedagogical content knowledge: A case study of ESL teacher educator. English Language Teaching, 6(7), 128–138. https://doi.org/10.5539/elt.v6n7p128
   Google Scholar
• Loughran, J., Mulhall, P., & Berry, A. (2004). In search of pedagogical content knowledge in science: Developing ways of articulating and documenting professional practice. Journal of Research in Science Teaching, 41(4), 370–391. https://doi.org/10.1002/tea.20007
   Web of Science ®Google Scholar
• Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K–12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/j.chb.2014.09.012
   Web of Science ®Google Scholar
• Magnusson, S., Krajcik, J., & Borko, H. (1999). Nature, sources, and development of pedagogical content knowledge for science teaching. In J. Gess-Newsome & N. Lederman (Eds.), Examining pedagogical content knowledge (pp. 95–132). Springer.
   Google Scholar
• Mason, M. (2010). Sample size and saturation in PhD studies using qualitative interviews. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 11(3), Art 8. https://doi.org/10.17169/fqs-11.3.1428     
   Google Scholar
• McDonald, N., Schoenebeck, S., & Forte, A. (2019). Reliability and inter-rater reliability in qualitative research: Norms and guidelines for CSCW and HCI Practice. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 23. https://doi.org/10.1145/3359174
   Google Scholar
• Meijer, P. C., Verloop, N., & Beijaard, D. (1999). Exploring language teachers’ practical knowledge about teaching reading comprehension. Teaching and Teacher Education, 15(1), 59–84. https://doi.org/10.1016/S0742-051X(98)00045-6
   Web of Science ®Google Scholar
• Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis: A method sourcebook. Sage Publications.
   Google Scholar
• Nijenhuis-Voogt, J., Bayram-Jacobs, D., Meijer, P. C., & Barendsen, E. (2021). Omnipresent yet elusive: Teachers’ views on contexts for teaching algorithms in secondary education. Computer Science Education, 31(1), 30–59. https://doi.org/10.1080/08993408.2020.1783149
   Web of Science ®Google Scholar
• Park, S., & Chen, Y.-C. (2012). Mapping out the integration of the components of pedagogical content knowledge (PCK): Examples from high school biology classrooms. Journal of Research in Science Teaching, 49(7), 922–941. https://doi.org/10.1002/tea.21022
   Web of Science ®Google Scholar
• Park, S., & Oliver, J. S. (2008). Revisiting the conceptualisation of pedagogical content knowledge (PCK): PCK as a conceptual tool to understand teachers as professionals. Research in Science Education, 38(3), 261–284. https://doi.org/10.1007/s11165-007-9049-6
   Web of Science ®Google Scholar
• Perrenet, J., Groote, J. F., & Kaasenbrood, E. (2005). Exploring students’ understanding of the concept of algorithm: Levels of abstraction. Annual Joint Conference Integrating Technology into Computer Science Education, 37(3), 64–68. DOI: 10.1145/1151954.1067467
   Google Scholar
• Rahimi, E., Barendsen, E., & Henze, I. (2016). Typifying informatics teachers’ PCK of designing digital artefacts in Dutch upper secondary education. In Brodnik, Andrej and Tort, Françoise (Eds.), Informatics in schools: Situation, evolution, and perspectives (pp. 65–77). Springer.
   Google Scholar
• Saeli, M., Perrenet, J., Jochems, W. M. G., & Zwaneveld, B. (2011). Teaching programming in secondary school: A pedagogical content knowledge perspective. Informatics in Education, 10(1), 73–88. https://doi.org/10.15388/infedu.2011.06
   Google Scholar
• Schwill, A. (1994). Fundamental ideas of computer science. Bulletin - European Association for Theoretical Computer Science, 53, 274-295. http://juniorstudium.cs.uni-potsdam.de/Forschung/Schriften/EATCS.pdf
   Google Scholar
• Schwill, A. (1997). Computer science education based on fundamental ideas. In D. Passey & B. Samways (Eds.), Information technology. IFIP advances in information and communication technology (pp. 285–291). Springer.
   Google Scholar
• Selby, C., & Woollard, J. (2013). Computational thinking: The developing definition. https://eprints.soton.ac.uk/356481/
   Google Scholar
• Sentance, S., & Csizmadia, A. (2017). Computing in the curriculum: Challenges and strategies from a teacher’s perspective. Education and Information Technologies, 22(2), 469–495. https://doi.org/10.1007/s10639-016-9482-0
   Web of Science ®Google Scholar
• Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14. https://doi.org/10.3102/0013189X015002004
   Google Scholar
• Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1–23. https://doi.org/10.17763/haer.57.1.j463w79r56455411
   Web of Science ®Google Scholar
• Solomon, J. (2007). Putting the science into computer science: Treating introductory computer science as the study of algorithms. SIGCSE Bulletin, 39(2), 46–49. https://doi.org/10.1145/1272848.1272882
   Google Scholar
• Statter, D., & Armoni, M. (2020). Teaching abstraction in computer science to 7th grade students. ACM Transactions on Computing Education, 20(1), 8:1–8: 37. https://doi.org/10.1145/3372143
   Google Scholar
• Taylor, S. J., & Bogdan, R. (1984). Introduction to qualitative research methods: The search for meanings (3rd ed.). John Wiley & Sons, Ltd.
   Google Scholar
• Verloop, N., Van Driel, J., & Meijer, P. (2001). Teacher knowledge and the knowledge base of teaching. International Journal of Educational Research, 35(5), 441–461. https://doi.org/10.1016/S0883-0355(02)00003-4
   Google Scholar
• Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press.
   Google Scholar
• Wangenheim, C. G., Hauck, J. C., Demetrio, M. F., Pelle, R., Da Cruz Alves, N., Barbosa, H., & Azevedo, L. F. (2018). CodeMater - Automatic assessment and grading of App Inventor and Snap! Programs. Informatics in Education, 17(1), 117–150. https://doi.org/10.15388/infedu.2018.08
   Web of Science ®Google Scholar
• Webb, M., Davis, N., Bell, T., Katz, Y. J., Reynolds, N., Chambers, D. P., & Sysło, M. M. (2017). Computer science in K–12 school curricula of the 2lst century: Why, what and when? Education and Information Technologies, 22(2), 445–468. https://doi.org/10.1007/s10639-016-9493-x
   Web of Science ®Google Scholar
• Werner, L., Denner, J., Campe, S., & Kawamoto, D. C. (2012). The fairy performance assessment: Measuring computational thinking in middle school. In Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (pp. 215–220). New York, NY: Association for Computing Machinery.
   Google Scholar
• Yadav, A., & Berges, M. (2019). Computer science pedagogical content knowledge: Characterizing teacher performance. ACM Transactions on Computing Education, 19(3), 29:1—-29: 24. https://doi.org/10.1145/3303770
   Web of Science ®Google Scholar
• Yadav, A., Berges, M., Sands, P., & Good, J. (2016). Measuring computer science pedagogical content knowledge: An exploratory analysis of teaching vignettes to measure teacher knowledge. In Proceedings of the 11th Workshop in Primary and Secondary Computing Education (pp. 92–95). New York, NY: Association for Computing Machinery.
   Google Scholar
• Yadav, A., Gretter, S., Hambrusch, S., & Sands, P. (2016). Expanding computer science education in schools: Understanding teacher experiences and challenges. Computer Science Education, 26(4), 235–254. https://doi.org/10.1080/08993408.2016.1257418
   Web of Science ®Google Scholar
• Yadav, A., Sands, P., Good, J., & Lishinki, A. (2018). Computer science and computational thinking in the curriculum: Research and practice. In Voogt, Joke and Knezek, Gerald and Christensen, Rhonda and Lai, Kwok-Wing (Eds.), Second Handbook of information technology in primary and secondary education (pp. 89–106). Springer International Publishing. 1 0.1 007/97 8-3-319-71054-9_6
   Google Scholar