where students design their ships and can analyze data likeweight and center of gravity.The designers of FLEET ensure every aspect of the game is authentic to the work of engineers.As shown in Figure 2, the flow of the FLEET interface reflects the cyclical nature of engineeringdesign processes. Students first receive an overview of the mission, then design a ship in thedrydock to meet the mission requirements and objectives. Students proceed to test their shipeither in the full mission or in shorter tests focused on different aspects of ship capability. Testsand missions end with a summary screen giving data on ship performance, such as time spent,number of collisions, and points scored. Students use this data to improve their ship design
various engineering fundamentals and concepts through hands-on, activelearning, the 18-day collaborative research phase focused on project-based learning. By modelingand reflecting an authentic research setting, this approach engaged teachers in significant self-directed learning and collaboration with fellow researchers. As evidenced from [36], active,collaborative, and problem-based learning are found to improve student engagement, facilitatelonger retention of information, and positively influence learner’s attitudes and study habits.On three days during the six-week PD, teachers participated in a lesson plan developmentworkshop conducted by teachers and researchers of a robotics PD program, also being conductedat NYU Tandon, to explore the 3D
qualitative data explicitly asked about whichfactors strongly influence their career choice. Immediate family and friends came in the top 10strongest factors, with immediate family coming in at number 2.This aligns with the findings of Yun et al. who concluded that parents are the front line withregards to the education of their children, and are important agents in the development andeducational achievement of their child in a formal setting [17].ConclusionsThere were a variety of very influential factors found in the study that impact male and femalestudents’ desire to pursue a career in STEM. The most influential factor found in the qualitativedata for both male and female students was Career Plans. This was also reflected in thequantitative data
-going professional mentoringprovides crucial advice and moral support to help the students persist and succeed in thefield. Together, these activities not only help students develop better self-confidenceand persist in cybersecurity but also provide them with educational experiences thatleverage them into cybersecurity related fields in college.ACKNOWLEDGEMENTSThis material is based upon work supported by Google CS4HS and NCWIT. Anyopinions, findings, and conclusions or recommendations expressed in this material arethose of the authors and do not necessarily reflect the views of Google or NCWIT.REFERENCES[1] Bureau of Labor Statistics, U.S. Department of Labor, “Occupational Outlook Handbook, Information Security Analysts.” 2018. [Online
-point scale. DoS Domain DoS Category DoS Scores (n=4) Average Range Activity Engagement Participation 3.25 2-4 Purposeful Activities 3.75 3-4 Engagement with STEM 3.25 3-4 STEM Knowledge and STEM Content Learning 3.5 3-4 Practices Inquiry 3.5 3-4 Reflection 3.25 2-4The classroom used at ECSU allowed informal
, andconclusions or recommendations expressed in this material are those of the author(s) and do notnecessarily reflect the views of the National Science Foundation. ReferencesBarr, 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.Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada.Computer Science Teacher Association (CSTA), & International Society for Technology in
to adopt best teaching practices in theclassroom is essential [17] for their success. According to [18], there are ten practices consideredthe best for teaching math and science. These include: use of manipulatives and hands-on learning;cooperative learning; discussion and inquiry; questioning and conjectures; justification ofthinking; writing for reflection and problem solving; use of problem-solving approach; integrationof technology; teacher as a facilitator; and use of assessment as a part of instruction. In addition,understanding students’ misconceptions also supports teachers’ pedagogy [10,19].The research literature indicates that providing effective technology PD to STEM teachers has apositive effect on teacher and student learning
circumplexare grouped together as negative affective states, and ‘Neutral’ affective state forms its owncategory. We observe that there was a net change (n = 5, 6.49%) from negative and neutralemotions to positive after the robotics-enhanced lesson was implemented. Plots showing thechange in affective states for individual classrooms are presented in Appendix A.The TOSRA (robotics enjoyment) data was scored, and descriptive statistics, such as mean,median, and mode were calculated. Mean TOSRA (robotics enjoyment) score was 25.39 (standarddeviation = 7.18) on a scale from 0 to 40, with 0 reflecting the most negative attitudes towardsrobotics and 40 the most positive. Overall, students displayed a positive attitude towards robotics-enhanced classes (mean
asked simply do you know any engineers and if so who. More thanhalf (56%) reported not knowing any engineers. These results are interesting, because the stateof Michigan as a whole has one of the largest per-capita populations of engineers in the country.Yet, the greater Lansing metropolitan area is a bit of an exception to that trend, with very fewtechnology based employers. That is reflected in this data, with only 14 students reporting thattheir parents are engineers. The majority of those responding that they knew an engineer tendedto cite a more distant relationship both in terms of bloodline and geography.Many of the survey questions focused on student perceptions of what do engineers do, and whatdoes it take to become an engineer. These
balanced to prevent overrepresentation ofstudents from a single high school or program to reflect the demographics of New York City.Students typically had a grade point average of 87-93 out of 100. Scholarships were providedbased on family income after the student was accepted.Survey LogisticsAn entry (presurvey) and exit (postsurvey) questionnaire pair for 2018 was designed to evaluatestudent development through the use of Likert scale, checkbox, and open-ended questions,approved by the Cooper Union Institutional Review Board. The questions and selectableresponses to the presurvey are recorded in Appendix B, while those to the postsurvey arerecorded in Appendix C. Participants were students in the summer STEM program, with studentand parent
conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the views of the NationalScience Foundation. We would like to acknowledge the family who participated in the study. References[1] Farenga, P. (1999). John Holt and the Origins of Contemporary Homeschooling. Paths of Learning: Options for Families & Communities, 1, 8–13.[2] U. S. Department of Education. (2014, October 5). Statistics About Non-Public Education in the United States [Information Analyses]. http://www2.ed.gov/about/offices/list/oii/nonpublic/statistics.html - homeschl[3] Wing J. M., “Computational Thinking,” Commun. Assoc. Comput. Mach., 2006.[4] Cuny, J