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Evaluating Computer Science Professional Development for Teachers in the United States

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Published:18 November 2021Publication History

ABSTRACT

Teacher professional development (PD) is a key factor in enabling teachers to develop mindsets and skills that positively impact students. It is also a key step in building capacity for computer science (CS) education in K-12 schools. Successful CS PD meets primary learning goals and enable teachers to grow their self-efficacy, asset and equity mindset, and interest in teaching CS. As part of a larger study, we conducted a secondary analysis of CS PD evaluation instruments (). We found that instruments across providers were highly dissimilar with limited data collected for measures related to teacher learning, which has implications for future K-12 CS education. Likewise, the instruments were limited in being connected to student learning and academic growth. As a way to enable PD providers to construct measures that align with known impacting factors, we offer recommendations for collecting demographic data and measuring program satisfaction, content knowledge, pedagogical content knowledge, growth and equity mindset, and self-efficacy. We also highlight questions for PD providers to consider when constructing their evaluation, including reflecting community values, the goals of the PD, and how the data collected will be used to continually improve CS programs.

References

  1. Ahmet Oguz Akturk and Handan Saka Ozturk. 2019. Teachers’ TPACK levels and students’ self-efficacy as predictors of students’ academic achievement. International Journal of Research in Education and Science 5 (2019). Issue 1.Google ScholarGoogle Scholar
  2. Alicia C Alonzo. 2007. Challenges of simultaneously defining and measuring knowledge for teaching. (2007).Google ScholarGoogle Scholar
  3. Daniel Alston, Jeff. Marshall, and Andrew Tyminski. 2017. Convincing Science Teachers for Inquiry-Based Instruction: Guskey’s Staff Development Model Revisited.Science Educator 25(2017). Issue 2.Google ScholarGoogle Scholar
  4. Judy Anderson and Deborah Tully. 2020. Designing and Evaluating an Integrated STEM Professional Development Program for Secondary and Primary School Teachers in Australia. https://doi.org/10.1007/978-3-030-52229-2_22Google ScholarGoogle Scholar
  5. Nalline Baliram and Arthur K. Ellis. 2019. The impact of metacognitive practice and teacher feedback on academic achievement in mathematics. School Science and Mathematics 119 (2019). Issue 2. https://doi.org/10.1111/ssm.12317Google ScholarGoogle Scholar
  6. A. Bandura. 1997. Self-efficacy: The exercise of control.W H Freeman/Times Books/ Henry Holt & Co.Google ScholarGoogle Scholar
  7. Eric R Banilower, P Sean Smith, Kristen A Malzahn, Courtney L Plumley, Evelyn M Gordon, and Meredith L Hayes. 2018. Report of the 2018 NSSME+.Horizon Research, Inc.(2018).Google ScholarGoogle Scholar
  8. Jürgen Baumert and Mareike Kunter. 2013. The effect of content knowledge and pedagogical content knowledge on instructional quality and student achievement. In Cognitive activation in the mathematics classroom and professional competence of teachers. Springer, 175–205.Google ScholarGoogle Scholar
  9. Patricia Benner. 1982. From novice to expert. American Journal of nursing 82, 3 (1982), 402–407.Google ScholarGoogle Scholar
  10. Edward M Bettencourt, Maxwell H Gillett, Meredith Damien Gall, and Ray E Hull. 1983. Effects of teacher enthusiasm training on student on-task behavior and achievement. American educational research journal 20, 3 (1983), 435–450.Google ScholarGoogle Scholar
  11. Moiz Bhai and Irina Horoi. 2019. Teacher characteristics and academic achievement. Applied Economics 51(2019). Issue 44. https://doi.org/10.1080/00036846.2019.1597963Google ScholarGoogle Scholar
  12. David Blazar. 2016. Teacher and teaching effects on students’ academic performance, attitudes, and behaviors. Ph.D. Dissertation.Google ScholarGoogle Scholar
  13. Rosemary Callingham, Colin Carmichael, and Jane M Watson. 2016. Explaining student achievement: The influence of teachers’ pedagogical content knowledge in statistics. International Journal of Science and Mathematics Education 14, 7(2016), 1339–1357.Google ScholarGoogle ScholarCross RefCross Ref
  14. Computer Science Teachers Association. 2021. Quality PD Indicators. https://www.csteachers.org/Page/quality-pd-review-processGoogle ScholarGoogle Scholar
  15. National Research Council 2001. Classroom assessment and the national science education standards. National Academies Press.Google ScholarGoogle Scholar
  16. Linda Darling-Hammond, Maria E Hyler, and Madelyn Gardner. 2017. Effective Teacher Professional Development. Research Brief.Learning Policy Institute(2017).Google ScholarGoogle ScholarCross RefCross Ref
  17. Sloan Davis, Jason Ravitz, and Juliane Blazevski. 2018. Evaluating Computer Science Professional Development Models and Educator Outcomes to Ensure Equity. 2018 Research on Equity and Sustained Participation in Engineering, Computing, and Technology, RESPECT 2018 - Conference Proceedings. https://doi.org/10.1109/RESPECT.2018.8491716Google ScholarGoogle Scholar
  18. Yaron Doppelt, Christian D. Schunn, Eli M. Silk, Matthew M. Mehalik, Birdy Reynolds, and Erin Ward. 2009. Evaluating the impact of a facilitated learning community approach to professional development on teacher practice and student achievement. Research in Science and Technological Education 27 (2009). Issue 3. https://doi.org/10.1080/02635140903166026Google ScholarGoogle Scholar
  19. Stuart E Dreyfus. 2004. The five-stage model of adult skill acquisition. Bulletin of science, technology & society 24, 3 (2004), 177–181.Google ScholarGoogle Scholar
  20. David Dunning. 2011. The Dunning–Kruger effect: On being ignorant of one’s own ignorance. In Advances in experimental social psychology. Vol. 44. Elsevier, 247–296.Google ScholarGoogle Scholar
  21. Anna J Egalite and Brian Kisida. 2018. The effects of teacher match on students’ academic perceptions and attitudes. Educational Evaluation and Policy Analysis 40, 1 (2018), 59–81.Google ScholarGoogle ScholarCross RefCross Ref
  22. Roger D Goddard, Laura LoGerfo, and Wayne K Hoy. 2004. High school accountability: The role of perceived collective efficacy. Educational Policy 18, 3 (2004), 403–425.Google ScholarGoogle ScholarCross RefCross Ref
  23. Thomas R Guskey. 2003. What makes professional development effective?Phi delta kappan 84, 10 (2003), 748–750.Google ScholarGoogle Scholar
  24. Karla Hamlen, Nigamanth Sridhar, Lisa Bievenue, Debbie K. Jackson, and Anil Lalwani. 2018. Effects of teacher training in a computer science principles curriculum on teacher and student skills, confidence, and beliefs. SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education 2018-January. https://doi.org/10.1145/3159450.3159496Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Ronald H Heck, Terry J Larsen, and George A Marcoulides. 1990. Instructional leadership and school achievement: Validation of a causal model. Educational Administration Quarterly 26, 2 (1990), 94–125.Google ScholarGoogle ScholarCross RefCross Ref
  26. Wayne K Hoy and John W Hannum. 1997. Middle school climate: An empirical assessment of organizational health and student achievement. Educational Administration Quarterly 33, 3 (1997), 290–311.Google ScholarGoogle ScholarCross RefCross Ref
  27. Helen H Hu, Cecily Heiner, Thomas Gagne, and Carl Lyman. 2017. Building a statewide computer science teacher pipeline. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. 291–296.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Thomas J Kane, Daniel F McCaffrey, Trey Miller, and Douglas O Staiger. 2013. Have We Identified Effective Teachers? Validating Measures of Effective Teaching Using Random Assignment. Research Paper. MET Project.Bill & Melinda Gates Foundation(2013).Google ScholarGoogle Scholar
  29. Erdogan Kaya, Ezgi Yesilyurt, Anna Newley, and Hasan Deniz. 2019. Examining the impact of a computational thinking intervention on pre-service elementary science teachers’ computational thinking teaching efficacy beliefs, interest and confidence. Journal of Computers in Mathematics and Science Teaching 38, 4(2019), 385–392.Google ScholarGoogle Scholar
  30. Melanie M Keller, Knut Neumann, and Hans E Fischer. 2017. The impact of physics teachers’ pedagogical content knowledge and motivation on students’ achievement and interest. Journal of Research in Science Teaching 54, 5 (2017), 586–614.Google ScholarGoogle ScholarCross RefCross Ref
  31. Claudia Khourey-Bowers and Doris G Simonis. 2004. Longitudinal study of middle grades chemistry professional development: Enhancement of personal science teaching self-efficacy and outcome expectancy. Journal of Science Teacher Education 15, 3 (2004), 175–195.Google ScholarGoogle ScholarCross RefCross Ref
  32. Lisa E Kim, Ilan Dar-Nimrod, and Carolyn MacCann. 2018. Teacher personality and teacher effectiveness in secondary school: Personality predicts teacher support and student self-efficacy but not academic achievement.Journal of Educational Psychology 110, 3 (2018), 309.Google ScholarGoogle Scholar
  33. Robert M Klassen and Ming Ming Chiu. 2010. Effects on teachers’ self-efficacy and job satisfaction: Teacher gender, years of experience, and job stress.Journal of educational Psychology 102, 3 (2010), 741.Google ScholarGoogle Scholar
  34. Kim Lange, Thilo Kleickmann, and Kornelia Möller. 2011. Elementary teachers’ pedagogical content knowledge and student achievement in science education. In ESERA-Conference, Lyon, France. 5–9.Google ScholarGoogle Scholar
  35. Jihyun Lee and Valerie J Shute. 2010. Personal and social-contextual factors in K–12 academic performance: An integrative perspective on student learning. Educational psychologist 45, 3 (2010), 185–202.Google ScholarGoogle Scholar
  36. Monica M McGill, Leigh Ann DeLyser, Karen Brennan, Baker Franke, Errol Kaylor, Eric Mayhew, Kelly Mills, and Aman Yadav. 2020. Evaluation and assessment for improving CS teacher effectiveness. ACM Inroads 11, 4 (2020), 35–41.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Muhsin Menekse. 2015. Computer science teacher professional development in the United States: a review of studies published between 2004 and 2014. Computer Science Education 25, 4 (2015), 325–350.Google ScholarGoogle ScholarCross RefCross Ref
  38. Bismark Mensah and Eric Koomson. 2020. Linking Teacher-Student Relationship to Academic Achievement of Senior High School Students. Social Education Research(2020). https://doi.org/10.37256/ser.122020140Google ScholarGoogle Scholar
  39. Emmelien Merchie, Melissa Tuytens, Geert Devos, and Ruben Vanderlinde. 2018. Evaluating teachers’ professional development initiatives: towards an extended evaluative framework. Research Papers in Education 33 (2018). Issue 2. https://doi.org/10.1080/02671522.2016.1271003Google ScholarGoogle Scholar
  40. Ahmad Mojavezi and Marzieh Poodineh Tamiz. 2012. The Impact of Teacher Self-efficacy on the Students’ Motivation and Achievement.Theory & Practice in Language Studies 2, 3 (2012).Google ScholarGoogle Scholar
  41. National Center for Education Statistics. 2019. Digest of Education Statistics. https://nces.ed.gov/programs/digest/d20/tables/dt20_214.10.aspGoogle ScholarGoogle Scholar
  42. Isaac M. Opper. 2019. Teachers Matter: Understanding Teachers’ Impact on Student Achievement. RAND Corporation, Santa Monica, CA. https://doi.org/10.7249/RR4312Google ScholarGoogle Scholar
  43. Shivaun O’Brien, Gerry McNamara, Joe O’Hara, and Martin Brown. 2020. Learning by doing: evaluating the key features of a professional development intervention for teachers in data-use, as part of whole school self-evaluation process. Professional Development in Education(2020). https://doi.org/10.1080/19415257.2020.1720778Google ScholarGoogle Scholar
  44. Ursula Pieper and Jan Vahrenhold. 2020. Critical Incidents in K-12 Computer Science Classrooms-Towards Vignettes for Computer Science Teacher Training. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education. 978–984.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Jason Ravitz, Chris Stephenson, Karen Parker, and Juliane Blazevski. 2017. Early lessons from evaluation of computer science teacher professional development in Google’s CS4HS program. ACM Transactions on Computing Education (TOCE) 17, 4 (2017), 1–16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Susan J Rosenholtz. 1989. Workplace conditions that affect teacher quality and commitment: Implications for teacher induction programs. The Elementary School Journal 89, 4 (1989), 421–439.Google ScholarGoogle ScholarCross RefCross Ref
  47. Ronny Scherer and Fazilat Siddiq. 2015. Revisiting teachers’ computer self-efficacy: A differentiated view on gender differences. Computers in Human Behavior 53 (2015), 48–57.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Alan H Schoenfeld. 2007. The complexities of assessing teacher knowledge. (2007).Google ScholarGoogle Scholar
  49. Khurram Shahzad and Sajida Naureen. 2017. Impact of Teacher Self-Efficacy on Secondary School Students’ Academic Achievement.Journal of Education and Educational Development 4, 1(2017), 48–72.Google ScholarGoogle Scholar
  50. Roger C Shouse. 1998. Restructuring’s impact on student achievement: Contrasts by school urbanicity. Educational Administration Quarterly 34, 1_suppl (1998), 677–699.Google ScholarGoogle Scholar
  51. Lee Shulman. 1987. Knowledge and teaching: Foundations of the new reform. Harvard educational review 57, 1 (1987), 1–23.Google ScholarGoogle Scholar
  52. Scott R Sweetland and Wayne K Hoy. 2000. School characteristics and educational outcomes: Toward an organizational model of student achievement in middle schools. Educational Administration Quarterly 36, 5 (2000), 703–729.Google ScholarGoogle ScholarCross RefCross Ref
  53. Seçil Bal Taştan, Seyed Mehdi Mousavi Davoudi, Alfiya R. Masalimova, Alexandr S. Bersanov, Rashad A. Kurbanov, Anna V. Boiarchuk, and Andrey A. Pavlushin. 2018. The impacts of teacher’s efficacy and motivation on student’s academic achievement in science education among secondary and high school students. Eurasia Journal of Mathematics, Science and Technology Education 14 (2018). Issue 6. https://doi.org/10.29333/ejmste/89579Google ScholarGoogle Scholar
  54. Bruce W Tuckman and Thomas L Sexton. 1991. The effect of teacher encouragement on student self-efficacy and motivation for self-regulated performance. Journal of Social Behavior and Personality 6, 1 (1991), 137.Google ScholarGoogle Scholar
  55. Cynthia L Uline, Daniel M Miller, and Megan Tschannen-Moran. 1998. School effectiveness: The underlying dimensions. Educational Administration Quarterly 34, 4 (1998), 462–483.Google ScholarGoogle ScholarCross RefCross Ref
  56. Mucella Ulug, Melis Seray Ozden, and Ahu Eryilmaz. 2011. The Effects of Teachers’ Attitudes on Students’ Personality and Performance. Procedia - Social and Behavioral Sciences 30 (2011), 738–742. https://doi.org/10.1016/j.sbspro.2011.10.144 2nd World Conference on Psychology, Counselling and Guidance - 2011.Google ScholarGoogle ScholarCross RefCross Ref
  57. University of Southern California - Center for Urban Education. 2021. Equity Mindset. https://cue.usc.edu/about/equity/equity-mindedness/Google ScholarGoogle Scholar
  58. U.S. Census Bureau. 2019. Census Bureau Reports Nearly 77 Million Students Enrolled in U.S. Schools. https://www.census.gov/newsroom/press-releases/2019/school-enrollment.htmlGoogle ScholarGoogle Scholar
  59. Beverly Vaillancourt and Emmanuel Schanzer. 2018. In Search of Quality Professional Development. https://drive.google.com/file/d/1ztko8ePIVo7I-QQzPuUrCIB4-qItPc88/viewGoogle ScholarGoogle Scholar
  60. Rebecca Vivian and Katrina Falkner. 2018. A survey of Australian teachers’ self-efficacy and assessment approaches for the K-12 digital technologies curriculum. Proceedings of the 13th Workshop in Primary and Secondary Computing Education (2018).Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Herbert Ware and Anastasia Kitsantas. 2007. Teacher and collective efficacy beliefs as predictors of professional commitment. The journal of educational research 100, 5 (2007), 303–310.Google ScholarGoogle Scholar
  62. Aman Yadav and Marc Berges. 2019. Computer science pedagogical content knowledge: Characterizing teacher performance. ACM Transactions on Computing Education (TOCE) 19, 3 (2019), 1–24.Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Aman Yadav, Marc Berges, Phil Sands, and Jon Good. 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. 92–95.Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. Aman Yadav, Alex Lishinski, and Phil Sands. 2021. Self-efficacy Profiles for Computer Science Teachers. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. 302–308.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. Marjolein Zee and Helma MY Koomen. 2016. Teacher self-efficacy and its effects on classroom processes, student academic adjustment, and teacher well-being: A synthesis of 40 years of research. Review of Educational research 86, 4 (2016), 981–1015.Google ScholarGoogle ScholarCross RefCross Ref
  66. Albert Zeggelaar, Marjan Vermeulen, and Wim Jochems. 2020. Evaluating effective professional development. Professional Development in Education(2020). https://doi.org/10.1080/19415257.2020.1744686Google ScholarGoogle Scholar
  67. Ninger Zhou, Ha Nguyen, Christian Fischer, Debra Richardson, and Mark Warschauer. 2020. High School Teachers’ Self-efficacy in Teaching Computer Science. ACM Transactions on Computing Education (TOCE) 20, 3 (2020), 1–18.Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

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    Koli Calling '21: Proceedings of the 21st Koli Calling International Conference on Computing Education Research
    November 2021
    287 pages
    ISBN:9781450384889
    DOI:10.1145/3488042

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    • Published: 18 November 2021

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