Mathew “Sandy” Martinuk, Rachel Moll*, and Andrzej Kotlicki Department of Physics and Astronomy, University of British Columbia, Vancouver BC V6T 1Z1 *Department of Curriculum and Pedagogy, University of British Columbia, Vancouver BC V6T 1Z4 Research Projects in Introductory Physics: Impacts on Student Learning and Attitudes Impacts on Student Learning References and Acknowledgments 1.Heller et al. “Teaching problem solving through cooperative grouping. Part 2: Designing problems and structuring groups.” Am. J. Phys 60, 637 (1992) 2.Adams et al. “New instrument for measuring student beliefs about physics and learning physics: The Colorado Learning Attitudes about Science Survey.” Physical Review Special Topics-Physics Education Research 2 (1), (2006) This work was supported by the Carl Wieman Science Education Initiative and the UBC Teaching and Learning Enhancement Fund. We would also like to thank Fei Zhou, Georg Rieger, and Joss Ives. Introduction and Background UBC’s Physics 100 course has recently been changed in an attempt to improve student attitudes and ability to transfer physics knowledge to novel real-world problems The course is now taught with a focus on real world contexts, and uses extensive environmental examples. One major change was the introduction of final research project (see sidebar to the right) This poster describes research aimed to measure the impact of the final projects on students’ abilities and their attitudes towards the relevance of physics. Physics UBC Algebra based, introductory course. Three sections, ~750 students Includes 1.5 hr/wk laboratory, 1 hr/wk context-rich 1 problem sessions Real-world and environmental curriculum focus (e.g. thermal physics is taught using contexts of home heating and the Earth’s energy balance) Final Projects Students work in groups to quantitatively model and estimate an answer to a scientific question Example project topic: Energy and greenhouse gas savings from using LEDs for city lighting Students present results to their peers Project is worth 8% of course grade. Transfer Problem A - Administered as a pre-test in week 7 and a post-test in week 13 of the term. You are hunting for an apartment with your friends. You have found two apartments with the same rent that both require you to pay your own utilities (i.e. electricity bill, gas bill, heating, TV, internet, phone). You want to estimate: which apartment will be cheaper? What information would you need to know in order to solve this problem? Describe briefly how you would figure out each piece of information that you specified above. List the physics principles or concepts that you would need to use in order to determine which choice will be more efficient. Once you had this information, what would you do with it? Describe briefly in point form how you would use this information to make the comparison. Discussion The majority of students mention Conduction but not Radiation in their answers (Figure 1). These topics had equal class time but conduction was discussed in the context of home heating. Significant differences were observed between Pre and Post groups’ use of three models (Figure 1). Conduction and Thermal Energy Balance models both had a decrease in the number of Strong models and an increase in the number of NR scores, while Direct Estimate shows the opposite. Chi-squared analysis shows that students with a Strong Direct Estimate model are more likely to have use that model exclusively in the post-test. This suggests the students are specializing. Conclusions: After completing the final project students are more likely to interpret Transfer Problem A in terms of Direct Estimates of power consumption alone. This suggests that the final project has not significantly improved students’ tendency to apply broad physical principles to everyday problems. However it seems there was improvement in conducting simpler (rate)*(time) calculations, a useful strategy for approaching many everyday problems. Transfer Problem A was administered to test students’ ability to apply physics to novel real-world problems. The pre-test was administered in week 7 to 199 students, and the post- test was administered in week 13 to 188 different students. Students were randomly divided by section, were given 10 minutes to complete each test and received 0.6% of total course grade for each. Measurement of Learning Students referenced four main models to answer the question: Conduction, Radiation, Thermal Energy Balance and Direct Estimate (summarized in Table 1 below). Each submission was scored as Strong, Weak or NR (no reference) for each of the four models. Coding was conducted by one researcher after 82% inter-rater reliability was achieved. Impacts on Student Attitudes Measurement of Attitudes Student attitudes were measured after completing the final project with an anonymous survey given in all problem-solving sessions (N=533 students). The survey included four 5-point multiple-choice questions (see Figure 2 for Q1-4) and one open ended question: Q5: What did you learn from working on this project? Responses to questions 1-4 are summarized in Figure 2 below. Responses to question 5 were coded and analyzed for emergent themes (see Table 2). Discussion Responses to Q1-4 show majority of students reported neutral or positive changes in attitudes after working on the final project (see Figure 2). Responses to Q5 “What did you learn from working on the final project?” showed students felt the project offered a wide variety of learning, much of which was related to the everyday relevance of physics (see Table 2). An increased perception of the real-world connections in physics is supported by the CLASS survey 2 results for the course: attitudes towards Real-World Connections in physics shifted by +2% towards more favorable attitudes, rather than the negative shift in this course prior to transformation. Conclusions: Results of these surveys indicate that the final project is a rich learning experience which significantly improves students attitudes in accordance with the course goals. Table 2: Top responses to attitudes Question 5: “What did you learn from working on the final project?” Coded by general category of knowledge cited in student comment. Table 1: Scoring for Transfer Problem A Each problem receives a score for each model of Strong, Weak, or No Reference. The Scoring Criteria summarize elements required for a Strong score. Table 1: Scoring for Transfer Problem A Each problem receives a score for each model of Strong, Weak, or No Reference. The Scoring Criteria summarize elements required for a Strong score. ModelKey ConceptsCriteria for Strong Score Conduction Heat loss through apartment walls and windows via conduction Explicit mention of thermal conduction with mention of majority of relevant variables. (area, thickness, conductivity) Radiation Heat loss and gain through apartment windows via radiation Explicit mention of both heat loss via radiation through windows and heat gain via solar exposure or other radiative source Thermal Energy Balance Consideration of both energy losses and gains to determine how much energy is needed from heating system Mention of sources of heat loss and heat gain, where heat loss is replaced by heat gain Direct Estimate Calculation of energy consumption via appliance / heater power rating and estimate of time on Mention of multiple appliances, power ratings, and estimates of usage times. Figure 2: Summary of responses (N=533) to Questions 1-4 of student attitude survey. Category A: Nature of Physics (N=231) Category B: Knowledge (N=296) learning about [a topic] Category C: Skills (N=239) learning how to do [a skill] CodesN%*CodesN%*CodesN%* Physics applies to everyday life 11349% Specific results of my / another project 20068% How to apply physics 6326% Real-world physics is complex 3314% Nothing 269% Teamwork skills 4117% Physics applies to environmental problems 2812% Specific concepts (e.g. specific heat, static friction) 238% Problem Solving skills 3013% Physics calculations can be simplified 156% Ways to save money 227% How to make assumptions 2611% Total number of responses is 535. Note that each response may be coded in multiple categories. The average is 1.5 codes per response. * % of the total number of codes in that category (column) Figure 1: Score distributions for Transfer Problem A, pre-test (N pre =199) and post-test (N post =188). *χ 2 analysis shows pre/post differences to p<0.05 Figure 1: Score distributions for Transfer Problem A, pre-test (N pre =199) and post-test (N post =188). *χ 2 analysis shows pre/post differences to p<0.05