The Effects of a Modeling and Computational Thinking Professional Development Program on STEM Educators’ Perceptions toward Teaching Science and Engineering Practices
Abstract
:1. Introduction
1.1. The Next Generation Science Standards
1.2. Science and Engineering Practices (SEPs)
- Asking questions and defining problems.
- Developing and using models.
- Planning and carrying out investigations.
- Analyzing and interpreting data.
- Using mathematics and computational thinking.
- Constructing explanations and designing solutions.
- Engaging in argument from evidence.
- Obtaining, evaluating, and communicating information.
1.3. Modeling and Computational Thinking SEPs
1.4. Professional Development and SEPs
2. Purpose and Research Questions
- How often did participants use SEPs in their teaching prior to attending the PD?
- How did PD attendance impact participants’ interest and confidence using SEPs?
- What factors are associated with participants’ post-PD confidence using SEPs?
- What factors are the most influential to participants’ use of SEPs?
- What barriers exist that limit participants’ use of SEPs?
- What benefits and recommendations do participants identify regarding the PD?
3. A Framework for Teacher Professional Development and the Nebraska STEM Education Conference
3.1. Content Focus
3.2. Active Learning
3.3. Coherence
3.4. Duration
3.5. Collective Participation
4. Methods
4.1. Conference Attendees and Study Participants
4.2. Instrumentation and Data Collection
4.3. Data Analysis
4.4. Validity and Reliability
5. Results
5.1. Participants’ Prior Use of SEPs in Teaching
5.2. Changes to Participants’ Interest and Confidence in Using SEPs as a Result of the PD
5.3. Factors Associated with Participants’ Post-PD Confidence in Using SEPs
5.4. Factors Most Influential to Using SEPs
5.5. Barriers to Incorporate SEPs into Teaching
5.6. Participants’ Perceived Benefits and Recommendations Regarding the PD
“It is important to think of other ways to implement and purposefully embed modeling and computational thinking into the learning environment… there is no one set way to teach modeling and computational thinking.”
“I am more aware of some resources that are out there. I will definitely try to use these things now that I am aware of them.”
“The strategies [used in the PD] to help get to know lots of people were great! I really appreciated making connections to other science teachers,”
“…this conference was very well done and allowed connections between secondary and higher education that were desperately need.”
6. Discussion
“I feel like I can now incorporate [SEPs] in my classroom so some of the barriers are lifted a bit.”
7. Conclusions and Recommendations
8. Study Limitations
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- National Center for Education Statistics. The Nation’s Report Card: 2015 Science at Grades 4, 8, and 12. Available online: https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2016162 (accessed on 18 February 2022).
- American Association for the Advancement of Science. Science for All Americans: Project 2061; Oxford University Press: Cary, NC, USA, 1990; Available online: http://www.project2061.org/publications/sfaa/online/sfaatoc.htm (accessed on 5 January 2022).
- National Research Council. Inquiry and the National Science Education Standards: A Guide for Teaching and Learning; National Academies Press: Washington, DC, USA, 2000; pp. 1–222. [Google Scholar] [CrossRef]
- National Research Council. A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas; National Academies Press: Washington, DC, USA, 2012; pp. 1–400. [Google Scholar] [CrossRef]
- Llewellyn, D. Inquiry within: Implementing Inquiry-Based Science Standards, 1st ed.; Corwin: Thousand Oaks, CA, USA, 2001; pp. 1–192. [Google Scholar]
- Minner, D.D.; Levy, A.J.; Century, J. Inquiry-based science instruction—What is it and does it matter? Results from a synthesis years 1984 to 2002. J. Res. Sci. Teach. 2010, 47, 474–496. [Google Scholar] [CrossRef]
- Schunk, D.H. Learning Theories: An Educational Perspective, 6th ed.; Merrill Education/Prentice Hall: Hoboken, NJ, USA, 2012; pp. 1–576. [Google Scholar]
- Cobern, W.W.; Schuster, D.; Adams, B.; Applegate, B.; Skjold, B.; Undreiu, A.; Loving, C.C.; Gobert, J.D. Experimental comparison of inquiry and direct instruction in science. Res. Sci. Technol. Educ. 2010, 28, 81–96. [Google Scholar] [CrossRef]
- Bybee, R.W.; Taylor, J.A.; Gardner, A.; Van Scotter, P.; Powell, J.C.; Westbrook, A.; Landes, N. The BSCS 5E Instructional Model: Origins and Effectiveness. Available online: https://media.bscs.org/bscsmw/5es/bscs_5e_full_report.pdf (accessed on 8 January 2022).
- Shernoff, D.J.; Sinha, S.; Bressler, D.M.; Schultz, D. Teacher perceptions of their curricular and pedagogical shifts: Outcomes of a project-based model of teacher professional development in the Next Generation Science Standards. Front. Psychol. 2017, 8, 989. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Duschl, R.A.; Bybee, R.W. Planning and carrying out investigations: An entry to learning and to teacher professional development around NGSS science and engineering practices. Int. J. STEM Educ. 2014, 1, 12. [Google Scholar] [CrossRef] [Green Version]
- National Research Council. Next Generation Science Standards: For States, by States; National Academies Press: Washington, DC, USA, 2013; pp. 1–532. [Google Scholar] [CrossRef]
- National Science Teaching Association. About the Next Generation Science Standards. Available online: https://ngss.nsta.org/about.aspx (accessed on 17 February 2022).
- Reiser, B.J. What Professional Development Strategies are Needed for Successful Implementation of the Next Generation Science Standards? In Proceedings of the Invitational Research Symposium on Science Assessment, Washington DC, USA, 24–25 September 2013; Available online: https://www.ets.org/research/policy_research_reports/publications/paper/2013/jvhf (accessed on 15 January 2022).
- National Science Teaching Association. Science and Engineering Practices. Available online: https://ngss.nsta.org/practicesfull.aspx (accessed on 15 January 2022).
- Garcia-Carmona, A. From inquiry-based science education to the approach based on scientific practices. Sci. Educ. 2020, 29, 443–463. [Google Scholar] [CrossRef]
- Brand, B.R. Integrating science and engineering practices: Outcomes from a collaborative professional development. Int. J. STEM Educ. 2020, 7, 13. [Google Scholar] [CrossRef]
- Christian, K.B.; Kelly, A.M.; Bugallo, M.F. NGSS-based teacher professional development to implement engineering practices in STEM instruction. Int. J. STEM Educ. 2021, 8, 21. [Google Scholar] [CrossRef]
- Singer, J.E.; Ross, J.M.; Jackson-Lee, Y. Professional development for the integration of engineering in high school STEM classrooms. J. Pre-Coll. Eng. Educ. Res. 2016, 6, 3. [Google Scholar] [CrossRef]
- Chiarello, F.; Belingheri, P.; Fantoni, G. Data science for engineering design: State of the art and future directions. Comput. Ind. 2021, 129, 103447. [Google Scholar] [CrossRef]
- Schmitz, P.; Yockel, S.; Mizumoto, C.; Cheatham, T.; Brunson, D. Advancing the workforce that supports computationally and data intensive research. Comput. Sci. Eng. 2021, 23, 19–27. [Google Scholar] [CrossRef]
- Momsen, J.; Speth, E.B.; Wyse, S.; Long, T. Using systems and systems thinking to unify biology education. CBE Life Sci. 2022, 21, es3. [Google Scholar] [CrossRef] [PubMed]
- Taylor, S.; Calvo-Amodio, J.; Well, J. A method for measuring systems thinking learning. Systems 2020, 8, 11. [Google Scholar] [CrossRef] [Green Version]
- York, S.; Lavi, R.; Dori, Y.J.; Orgill, M. Applications of systems thinking in STEM education. J. Chem. Educ. 2019, 96, 2742–2751. [Google Scholar] [CrossRef]
- de Bem Machado, A.; Secinaro, S.; Calandra, D.; Lanzalonga, F. Knowledge management and digital transformation for industry 4.0: A structured literature review. Knowl. Manag. Res. Pract. 2022, 20, 320–338. [Google Scholar] [CrossRef]
- Mdoda, L.; Mdiya, L. Factors affecting the using of information and communication technologies (ICTs) by livestock farmers in the Eastern Cape province. Cogent Soc. Sci. 2022, 8, 2026017. [Google Scholar] [CrossRef]
- Khan, I.H.; Javaid, M. Role of internet of things (loT) in adoption of industry 4.0. J. Ind. Integr. Manag. 2021, 6, 1–19. [Google Scholar] [CrossRef]
- Marin Suelves, D.; Cuevas Monzonis, N.; Gabarda Mendez, V. Digital competence for citizen: Analysis of trends in education. Ried-Rev. Iberoam. Educ. Distancia 2021, 24, 329–349. [Google Scholar]
- Maharjan, M.; Dahal, N.; Pant, B.P. ICTs into mathematical instructions for meaningful teaching and learning. Adv. Mob. Learn. Educ. Res. 2022, 2, 341–350. [Google Scholar] [CrossRef]
- Saraswathy, R. Secondary school teachers’ and students’ level of utilization of ICT tools for teaching and learning mathematics. J. Posit. Sch. Psychol. 2022, 6, 10183–10187. [Google Scholar]
- Tsarava, K.; Moeller, K.; Roman-Gonzalez, M.; Golle, J.; Leifhet, L.; Butz, M.V.; Ninaus, M. A cognitive definition of computational thinking in primary education. Comput. Educ. 2022, 179, 104425. [Google Scholar] [CrossRef]
- Haas, A.; Grapin, S.E.; Wendel, D.; Llosa, L.; Lee, O. How fifth-grade English learners engage in systems thinking using computational models. Systems 2020, 8, 47. [Google Scholar] [CrossRef]
- Pierson, A.E.; Brady, C.E. Expanding opportunities for systems thinking, conceptual learning, and participation through embodied and computational modeling. Systems 2020, 8, 48. [Google Scholar] [CrossRef]
- Yoon, S.A.; Goh, S.; Park, M. Teaching and learning about complex systems in K-12 science education: A review of empirical studies 1995–2015. Rev. Educ. Res. 2018, 88, 285–325. [Google Scholar] [CrossRef] [Green Version]
- Banilower, E.R.; Smith, P.; Weiss, I.R.; Malzahn, K.A.; Campbell, K.M.; Weis, A.M. Report of the 2012 National Survey of Science and Mathematics Education; Horizon Research, Inc.: Chapel Hill, NC, USA, 2013; Available online: http://www.horizon-research.com/2012nssme/wp-content/uploads/2013/02/2012-NSSME-Full-Report1.pdf (accessed on 18 February 2022).
- Capps, D.K.; Crawford, B.A. Inquiry-based instruction and teaching about the nature of sciences: Are they happening? J. Sci. Teach. Educ. 2013, 24, 497–526. [Google Scholar] [CrossRef]
- Forbes, C.T.; Zint, M. Elementary teachers’ beliefs about, perceived competencies for, and reported use of scientific inquiry to promote student learning about and for the environment. J. Environ. Educ. 2010, 42, 30–42. [Google Scholar] [CrossRef]
- Smith, K.L.; Rayfield, J.; McKim, B.R. Effective practices in STEM integration: Describing teacher perceptions and instructional method use. J. Agric. Educ. 2015, 56, 182–201. [Google Scholar] [CrossRef]
- Forbes, C.T.; Davis, E. Exploring preservice elementary teachers’ critique and adaption of science curriculum materials in respect to socioscientific issues. Sci. Educ. 2008, 17, 829–854. [Google Scholar] [CrossRef]
- Kolbe, T.; Jorgenson, S. Meeting instructional standards for middle-level science: Which teachers are most prepared? Elem. Sch. J. 2018, 118, 549–577. [Google Scholar] [CrossRef]
- Smith, T.M.; Desimone, L.M.; Zeidner, T.L.; Dunn, A.C.; Bhatt, M.; Rumyantseva, N.L. Inquiry-oriented instruction in science: Who teaches that way? Educ. Eval. Pol. Anal. 2007, 29, 169–199. [Google Scholar] [CrossRef]
- Kolbe, T.; Steele, C.; White, B. Time to teach: Instructional time and science teachers’ use of inquiry-oriented instructional practices. Teach. Coll. Rec. 2020, 122, 1–54. [Google Scholar] [CrossRef]
- Fogleman, J.; McNeil, K.L.; Krajcik, J. Examining the effect of teachers’ adaptations of a middle school science inquiry-oriented curriculum unit on student learning. J. Res. Sci. Teach. 2011, 48, 149–169. [Google Scholar] [CrossRef] [Green Version]
- Peters-Burton, E.E.; Johnson, T. Cross-case analysis of engineering education experiences in inclusive STEM-focused high school in the United States. Int. J. Educ. Math. Sci. Tech. 2018, 6, 320–342. [Google Scholar] [CrossRef] [Green Version]
- Thatcher, W.; Meyer, H. Identifying initial conceptions of engineering and teaching engineering. Educ. Sci. 2017, 7, 88. [Google Scholar] [CrossRef] [Green Version]
- Kreifels, M.S.; Conner, N.; Reiling, B.A.; Stripling, C.T.; Balschweid, M.A. Teacher perceptions of facilitating inquiry-based instruction following a 12-month professional development experience. Adv. Agric. Dev. 2021, 2, 14–24. [Google Scholar] [CrossRef]
- Desimone, L.M. Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educ. Res. 2009, 38, 181–199. [Google Scholar] [CrossRef] [Green Version]
- Darling-Hammond, L.; Bransford, J.D. Preparing Teachers for a Changing World: What Teachers Should Learn and Be Able to Do, 1st ed.; Jossey-Bass: San Francisco, CA, USA, 2005; pp. 1–479. [Google Scholar]
- Kang, H.S.; Cha, J.; Ha, B. What should we consider in teachers’ professional development impact studies? Based on the conceptual framework of Desimone. Creat. Educ. 2013, 4, 11–18. [Google Scholar] [CrossRef] [Green Version]
- Desimone, L.M.; Garet, M.S. Best practices in teachers’ professional development in the United States. Psychol. Soc. Educ. 2015, 7, 252–262. [Google Scholar] [CrossRef] [Green Version]
- American Association for the Advancement of Science. Vision & Change in Undergraduate Biology Education: Unpacking a Movement and Sharing Lessons Learned. 2017. Available online: https://live-visionandchange.pantheonsite.io/wp-content/uploads/2018/09/VandC-2018-finrr.pdf (accessed on 10 January 2022).
- Supovitz, J.A.; Turner, H.M. The effects of professional development on science teaching practices and classroom culture. J. Res. Sci. Teach. 2000, 37, 963–980. [Google Scholar] [CrossRef]
- Hill, H.C. Learning in the teaching workforce. Future Child. 2007, 17, 111–127. [Google Scholar] [CrossRef]
- Cantrell, P. Traditional vs. retrospective pretests for measuring science teaching efficacy beliefs in preservice teachers. Sch. Sci. Math. 2010, 103, 177–185. [Google Scholar] [CrossRef]
- Little, T.D.; Chang, R.; Gorrall, B.K.; Waggenspack, L.; Fukuda, E.; Allen, P.J.; Noam, G.G. The retrospective pretest-posttest design redux: On its validity as an alternative to traditional pretest–posttest measurement. Int. J. Behav. Dev. 2019, 44, 175–183. [Google Scholar] [CrossRef]
- Young, J.; Kallemeyn, L. Testing the retrospective pretest with high school youth in out-of-school programs. J. Youth Dev. 2019, 14, 216–229. [Google Scholar] [CrossRef] [Green Version]
- Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; Lawrence Erlbaum: Mahwah, NJ, USA, 1988; pp. 1–579. [Google Scholar]
- Ary, D.; Jacobs, L.C.; Sorensen, C. Introduction to Research in Education, 8th ed.; Wadsworth Cengage Learning: Belmont, CA, USA, 2010; pp. 1–6888. [Google Scholar]
- Field, A. Discovering Statistics Using IBM SPSS, 4th ed.; Sage Publications: Thousand Oaks, CA, USA, 2013; pp. 1–856. [Google Scholar]
- Christian, K.B.; Kelly, A.M.; Bugallo, M.F.; Sheppard, L. University-based Training of High School Science Teachers to Implement the Next Generation Science Standards. In Proceedings of the American Society for Engineering Education Annual Conference and Exposition, Salt Lake City, UT, USA, 24–27 June 2018. [Google Scholar]
- Bybee, R.W. Science and engineering practices in K-12 classroom: Understanding “A Framework for K-12 Science Education”. Sci. Teach. 2011, 78, 34–40. Available online: https://eric.ed.gov/?id=EJ960316 (accessed on 15 January 2022).
- Kimmel, H.; Carpinelli, J.; Burr-Alexander, L.; Rockland, R. Bringing Engineering into K-12 schools: A Problem Looking for Solutions? In Proceedings of the American Society for Engineering Education Annual Conference and Exposition, Chicago, IL, USA, 18–21 June 2006. [Google Scholar]
- Smith, P.S. Obstacles to and Progress toward the Vision of the NGSS; Horizon Research, Inc.: Chapel Hill, NC, USA, 2020; Available online: http://horizon-research.com/NSSME/wp-content/uploads/2020/04/NGSS-Obstacles-and-Progress.pdf (accessed on 5 January 2022).
- Firman, M.A.; Ertikanto, C.; Abdurrahman, A. Description of meta-analysis of inquiry-based learning in science in improving students’ inquiry skills. J. Phys. Conf. 2019, 115, 1–7. [Google Scholar] [CrossRef]
- Wang, P.; Wu, P.; Yu, K.; Lin, Y. Influence of implementing inquiry-based instruction on science learning motivation and interest: A perspective of comparison. Procedia—Soc. Behav. Sci. 2015, 174, 1292–1299. [Google Scholar] [CrossRef] [Green Version]
Participant Characteristics | Frequency (n) | Percentage (%) |
---|---|---|
Gender | ||
Male | 12 | 26.7% |
Female | 33 | 73.3% |
Age | ||
19–25 | 3 | 6.7% |
26–35 | 9 | 20.0% |
36–45 | 13 | 28.9% |
46–55 | 16 | 35.6% |
56–65 | 4 | 8.9% |
Education Level | ||
Bachelors | 6 | 13.3% |
Bachelors + 18 or more credits | 8 | 17.8% |
Masters | 9 | 20.0% |
Masters + 18 or more credits | 11 | 24.4% |
Doctorate | 11 | 24.4% |
Years of Teaching Experience | ||
0–2 Years | 4 | 9.1% |
3–5 Years | 9 | 20.5% |
6–10 Years | 5 | 11.4% |
11–15 Years | 6 | 13.6% |
More than 15 Years | 20 | 45.5% |
Teaching Position | ||
Middle School Teacher | 5 | 11.1% |
High School Teacher | 24 | 53.3% |
College or University Teacher | 8 | 17.8% |
Other | 8 | 17.8% |
SEP | Rarely or Never | A Few Times during the Course | Once Every 2 to 3 Weeks | About Once a Week | More Than Once per Week |
---|---|---|---|---|---|
Asking questions and defining problems | 0 (0.0%) | 6 (14.0%) | 15 (34.9%) | 10 (23.3%) | 12 (27.9%) |
Planning and carrying out investigations | 1 (2.3%) | 11 (25.6%) | 18 (41.9%) | 10 (23.3%) | 3 (7%) |
Analyzing and interpreting data | 0 (0.0%) | 4 (9.3%) | 12 (27.9%) | 19 (44.2%) | 8 (18.6%) |
Developing and using models | 1 (2.3%) | 7 (16.3%) | 12 (27.9%) | 17 (39.5%) | 6 (14.0%) |
Using mathematics and computational thinking | 1 (2.3%) | 7 (14.0%) | 17 (39.5%) | 10 (23.3%) | 9 (20.9%) |
Item | Very Disinterested | Somewhat Disinterested | Neither Interested nor Disinterested | Somewhat Interested | Very Interested | M (SD) | Paired t-Test | |
---|---|---|---|---|---|---|---|---|
Asking questions and defining problems | Before | 0 (0.0%) | 2 (4.7%) | 5 (11.6%) | 12 (27.9%) | 24 (55.8%) | 4.35 (0.87) | t = −2.60 p = 0.013 * |
After | 0 (0.0%) | 0 (0.0%) | 2 (4.7%) | 12 (27.9%) | 29 (67.4%) | 4.63 (0.58) | ||
Planning and carrying out investigations | Before | 0 (0.0%) | 0 (0.0%) | 7 (16.3%) | 13 (30.2%) | 23 (53.5%) | 4.37 (0.76) | t = −2.35 p = 0.024 * |
After | 0 (0.0%) | 0 (0.0%) | 3 (7.0%) | 11 (25.6%) | 29 (67.4%) | 4.60 (0.62) | ||
Analyzing and interpreting data | Before | 1 (2.3%) | 0 (0.0%) | 6 (14.0%) | 15 (34.9%) | 21 (48.8%) | 4.28 (0.88) | t = −3.18 p = 0.003 * |
After | 0 (0.0%) | 0 (0.0%) | 3 (7.0%) | 10 (23.3%) | 30 (69.8%) | 4.63 (0.62) | ||
Developing and using models | Before | 0 (0.0%) | 4 (9.3%) | 4 (9.3%) | 16 (37.2%) | 19 (44.2%) | 4.16 (0.95) | t = −4.06 p < 0.001 * |
After | 0 (0.0%) | 0 (0.0%) | 2 (4.7%) | 10 (23.3%) | 31 (72.1%) | 4.67 (0.57) | ||
Using math and computational thinking | Before | 0 (0.0%) | 3 (7.0%) | 11 (25.6%) | 14 (32.6%) | 15 (34.9%) | 3.95 (0.95) | t = −4.39 p < 0.001 * |
After | 0 (0.0%) | 0 (0.0%) | 3 (7.0%) | 13 (30.2%) | 27 (62.8%) | 4.56 (0.63) | ||
SEP Interest Scale | Before | 4.22 (0.72) | t = −4.34 p < 0.001 * | |||||
After | 4.62 (0.54) |
SEP | Not at All Confident | Slightly Confident | Moderately Confident | Extremely Confident | M (SD) | Paired t-Test | |
---|---|---|---|---|---|---|---|
Asking questions and defining problems | Before | 1 (2.3%) | 14 (32.6%) | 19 (44.2%) | 9 (20.9%) | 2.84 (0.79) | t = −5.1 p = < 0.001 * |
After | 0 (0.0%) | 2 (4.7%) | 25 (58.1%) | 16 (37.2%) | 3.33 (0.57) | ||
Planning and carrying out investigations | Before | 2 (4.7%) | 14 (32.6%) | 17 (39.5%) | 10 (23.3%) | 2.81 (0.85) | t = −4.9 p = < 0.001 * |
After | 0 (0.0%) | 4 (9.3%) | 24 (55.8%) | 15 (34.9%) | 3.26 (0.62) | ||
Analyzing and interpreting data | Before | 2 (4.7%) | 14 (32.6%) | 20 (46.5%) | 7 (16.3%) | 2.74 (0.79) | t = −5.2 p = < 0.001 * |
After | 0 (0.0%) | 3 (7.0%) | 28 (65.1%) | 12 (27.9%) | 3.21 (0.56) | ||
Developing and using models | Before | 6 (14.0%) | 15 (34.9%) | 12 (27.9%) | 10 (23.3%) | 2.60 (1.00) | t = −6.5 p < 0.001 * |
After | 1 (2.3%) | 3 (7.0%) | 12 (51.2%) | 17 (39.5%) | 3.28 (0.70) | ||
Using math and computational thinking | Before | 7 (16.3%) | 17 (39.5%) | 13 (30.2%) | 6 (14.0%) | 2.42 (0.93) | t = −5.9 p <.001 * |
After | 3 (7.0%) | 4 (9.3%) | 23 (53.5%) | 13 (30.2%) | 3.07 (0.83) | ||
SEP Confidence Scale | Before | 2.68 (0.71) | t = −7.11 p < 0.001 * | ||||
After | 3.23 (0.51) |
Predictor Variable | B (Coefficient) | SEB | β (Standardized Coefficient) | t | p-Value |
---|---|---|---|---|---|
Constant | 1.488 | 0.715 | 2.081 | 0.044 * | |
Years of Teaching Experience | −0.083 | 0.061 | −2.41 | −1.355 | 0.184 |
Educational Attainment | 0.096 | 0.046 | 0.388 | 2.094 | 0.043 * |
Post-PD SEP Interest | 0.237 | 0.137 | 0.251 | 1.724 | 0.093 |
Prior SEP Frequency | 0.153 | 0.106 | 0.221 | 1.443 | 0.157 |
Rank | Factor | Mean 1 | SD |
---|---|---|---|
1 | Student learning outcomes | 3.20 | 2.50 |
2 | Student interest | 3.42 | 2.12 |
3 | The current topic | 4.44 | 2.67 |
4 | Time to plan lessons to incorporate SEPs | 4.53 | 2.12 |
5 | Student ability | 4.96 | 2.29 |
6 | Time in class relative to other activities | 5.07 | 2.02 |
7 | Previous experience using SEPs | 5.51 | 2.46 |
8 | The encouragement or experience of peers | 6.29 | 1.89 |
9 | Administrative mandates or recommendations | 7.58 | 2.22 |
Rank | Barrier | n |
---|---|---|
1 | Lack of knowledge and low perceived ability to effectively implement the SEPs | 22 |
2 | Limited access to resources and materials on the SEPs | 17 |
3 | Lack of time to adequately plan lessons that integrate the SEPs | 16 |
4 | Student limitations (e.g., low ability of students) | 11 |
5 | The social environment (e.g., social norms, reluctance of peers, etc.) | 7 |
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Colclasure, B.C.; Durham Brooks, T.; Helikar, T.; King, S.J.; Webb, A. The Effects of a Modeling and Computational Thinking Professional Development Program on STEM Educators’ Perceptions toward Teaching Science and Engineering Practices. Educ. Sci. 2022, 12, 570. https://doi.org/10.3390/educsci12080570
Colclasure BC, Durham Brooks T, Helikar T, King SJ, Webb A. The Effects of a Modeling and Computational Thinking Professional Development Program on STEM Educators’ Perceptions toward Teaching Science and Engineering Practices. Education Sciences. 2022; 12(8):570. https://doi.org/10.3390/educsci12080570
Chicago/Turabian StyleColclasure, Blake C., Tessa Durham Brooks, Tomáš Helikar, Scott J. King, and Audrey Webb. 2022. "The Effects of a Modeling and Computational Thinking Professional Development Program on STEM Educators’ Perceptions toward Teaching Science and Engineering Practices" Education Sciences 12, no. 8: 570. https://doi.org/10.3390/educsci12080570
APA StyleColclasure, B. C., Durham Brooks, T., Helikar, T., King, S. J., & Webb, A. (2022). The Effects of a Modeling and Computational Thinking Professional Development Program on STEM Educators’ Perceptions toward Teaching Science and Engineering Practices. Education Sciences, 12(8), 570. https://doi.org/10.3390/educsci12080570