Priscilla Laws Dickinson College

1. Engaging Students with Research-Validated Uses of Technology: Computer Data Acquisition, Video Analysis, Personal Response Systems and Distance Learning David Sokoloff University of Oregon USA USP São Carlos January 23, 2017

2. Priscilla LawsDickinson College Nearly 31 years of physics education research, development and dissemination. Winner of the American Physical Society’s 2010 Excellence in Physics Education Award Ronald Thornton Tufts University David Sokoloff University of Oregon

3. Thank you U.S. Department of EducationFUND FOR THE IMPROVEMENT OF POST-SECONDARY EDUCATION (FIPSE) For supporting this research and curriculum development.

4. Outline • Characteristics of active learning environments for university and high school introductory physics. • Characteristics of technologies for active learning. • Computer-based data acquisition tools (MBL). • Research-validated use of clickers. • Video analysis. • IOLab—computer-based data acquisition for distance learning.

5. The Problem: PER evidence shows that students taught in a traditional manner do not learn physics concepts! I will not learn concepts in physics class I will not learn concepts in physics class I will not learn concepts in physics class I will not learn concepts in physics class I will not learn concepts in physics class I will not learn concepts in physics class

6. How can they possibly not learn from my perfectly logical, sublimely entertaining lectures!?

7. The Proposed Solution not replacing, but complementing more quantitative work. Active Learning Environments . . .

8. Characteristics of Active vs. Passive Learning Environments.

9. Characteristics of Active vs. Passive Learning Environments.

10. Characteristics of Active vs. Passive Learning Environments.

11. Characteristics of Active vs. Passive Learning Environments.

12. Characteristics of Active vs. Passive Learning Environments.

13. At times there seems to be some confusion . . . Active learning requires much, much more than hands-on! Examples: Doing the most fun, exciting and compelling lab experiment is hands-on, but is not active learning if the students are not engaged by predictions and discussion. Eric Mazur’s Peer Instruction involves no hands-on, yet it is active learning.

14. Characteristics of Technologies that Enhance Active Learning. • Easy to use—don’t require a long learning curve. Tools for learning! • Flexible and versatile—independent of the experiments. • Relatively high accuracy. • Results displayed in clear, understandable ways—often in real time. • Students can easily appeal to displayed results to justify their conclusions. Enable students to observe physical phenomena directly and clearly, and to learn from their observations.

15. Computer-based data acquisition tools (MBL) for active learning in lab and lecture Interface Motion Detector Force Sensor Temperature Sensor Voltage Sensor Current Sensor

17. Active Learning in Lab: RealTimePhysics (RTP) Labs • Use computer-based tools to help students construct important concepts while acquiring vital laboratory skills. • Guide students to construct models from real observations of the physical world. • Sequenced—build upon previous knowledge. • Fit within the traditional structure of the introductory course. • Include pre-lab preparation sheets to help students prepare. • Include homework—to reinforce concepts and skills. • Usable with most computer-based data acquisition systems. • Instructor’s guides available.

18. published by John Wiley Module 1: Mechanics Module 2: Heat and Thermodynamics Module 3: Electricity and Magnetism Module 4: Light and Optics

19. Example from RTP Module 1, Lab 2

20. Note: Students do real experiments. I did not want to carry the equipment, so I will show you videos or photos of the experiments.

22. Positive direction

25. Active Learning in Lecture: Interactive Lecture Demonstrations (ILDs) “Prof. Sokoloff, may I be excused? My brain is full!”

26. Example ofILDsin Mechanics I will show you demonstrations and ask you to make INDIVIDUAL predictions on a Prediction Sheet. Note: Predictions are NEVER graded, but points may be awarded for attendance and participation. Then I will ask you to discuss your predictions with your nearest neighbor(s). See if your small group can reach a consensus on the correct prediction. Finally, I will do the demonstrations with the results displayed. I will ask you to discuss what you observe with the whole group. Normally, the demonstrations would be done live in class. Again, I will show you movies or photos of the demonstrations instead.

27. Prediction SheetIncludes observations like ones with a fan cart

28. Demonstration #8 Please just make your prediction on a sheet of paper.

29. Motion sensor

31. Interactive Lecture Demonstrations (ILDs) • Describe the demonstration and do it for the class without results displayed. 2. Ask students to record individual predictions on the Prediction Sheet. 3. Have the class engage in small group discussions. • Elicit common student predictions from the whole class. • Ask each student to record final prediction on the Prediction Sheet (which will be collected). 6. Carry out the demonstration and display the results. 7. Ask a few students to describe the results and discuss them in the context of the demonstration. Students may fill out the Results Sheet. 8. If appropriate, discuss analogous physical situations with different "surface" features.. This procedure is followed for each of the short lecture demonstrations in each ILD sequence.

32. I will show more details of ILDs in my short course tomorrow morning.

33. What Does the Technology Add to RTP and ILDs? • Clearly displayed observations of the physical world, often in real time. • Many of these observations cannot be made w/o computer-based tools. • Enables the active learning pedagogy that helps students to learn from their observations and from each other.

34. Is learning improved?

35. Action Research on Student Understanding of Mechanics Concepts Force and Motion Conceptual Evaluation (FMCE) • Uses multiple choice questions based on previous research. • Questions asked in a number of different forms and contexts. • Makes possible tracking of student progress and persistence of learning during a course. • More information will be presented in my short course tomorrow morning.

36. Comparison of Pre and Post FMCE Results Post-RTP • 88% gain • compared to pre-test Post ILDs Post-ILDs 74% gain compared to pre-test 74% Gain Post TraditionalInstruction8% gain Pre-Instruction

37. Characteristics of the Curricula that Make Them Effective Making predictions requires students to consider their beliefs before making observations of the physical world. We build upon the knowledge that students bring into the course. The process of prediction, defending the prediction in a small group, and writing down the prediction engages students. They want to know the result. The disequilibrium set up by the difference between prediction and observation inspires effective learning opportunities. Student knowledge is constructed from observations of the physical world, thus building students’ confidence as scientists.

38. Research-validated Use of Clickers for Active Learning in Lecture

39. Just about every physics department in the U.S. has a personal response (clicker) system! The question is, what are effective (research validated) ways of using clickers to improve learning?

40. Example of Low-TechILDs on Image Formation with High-Tech Clickers • PER shows that students don't understand that an infinite number of rays emanate from each point on an object. • For a perfect lens, all rays from a single point on an object that are incident on the lens will be focused to the same point on the image. • In these ILDs, two miniature light bulbs are used as two discrete object point sources of light. • Here’s how these ILDs look with clickers.

43. Image location

44. Modified ILD Procedure for Use with Clickers • Describe the demonstration and do it for the class without results displayed. 2. Ask students to record individual predictions with the clickers. Do not show histogram of class’s predictions. 3. Have the class engage in small group discussions. • Ask students to record individual predictions again with the clickers. Display the histogram of class’s predictions. • Carry out the demonstration and display the results. • Ask a few students to describe the results and discuss them in the context of the demonstration. Students may make notes on the demonstration in their notebooks. • If appropriate, discuss analogous physical situations with different "surface" features..

46. Block half of lens Whole image, dimmer

47. Block half of object Half of image not formed

48. Clicker Results After Traditional Instruction (First Histogram) D

49. Results After Small Group Discussions D . . . note effect of peer discussion

50. Do students learn concepts from Image Formation ILDs? The Light and Optics Conceptual Evaluation (LOCE) Same type of multiple choice conceptual evaluation as the FMCE.