Feedback

1. Feedback Boston ACS Fall, 2007 Paper 384

2. Don Rosenthal Don was an important contributor to the early days of using computers in chemistry education -- breaking into new territory. It is an honor to have been asked to speak at this event.

3. Feedback We have been conducting several experiments on the use of computer assisted feedback in learning settings.

4. dwb4.unl.edu We serve the equivalent of about one 150-page book every minute, 24/7. Included are several tools.

5. dwb4.unl.edu/Stoich

8. Tools Conversion factors Atomic masses Molar mass calculator Chemical equation balancer Tutoring

9. Feedback • All a teacher really can do is to provide feedback. Feedback comes in many ways. John Burmeister, an excellent teacher of introductory chemistry, once said that the role of a lecturer is to ‘tell the students that parts of the book they are not responsible for.’ • Feedback is what teachers do. • Human learning comes from feedback.

10. Trained in mechanisms. A chemist always has a better understanding and ability to make predictions when s/he as a grasp of the mechanism involved. BUT, learning is very complex. How does the system work that we are providing feedback to? Also, it’s not like chemical mechanisms where you have often have a very concrete model in mind.

11. A proposal for the underlying mechanisms of human learning were set forth in: Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking innateness : A connectionist perspective on development. Cambridge, MA: MIT Press. Exciting recent related work: Vallabha, G. K., McClelland, J. L., Pons, F., Werker, J. F., & Amano, S. (2007). “Unsupervised learning of vowel categories from infant-directed speech.” Proceedings of the National Academy of Science of the United States of America, 104(33), 13273-13278.

12. Unified Learning Model Most important work to date. Driven by a 2004 Masters Thesis. Had the time needed due to family illness.

13. ICML

14. ICML Improving Chemistry Instruction Using an Interactive, Compensatory Model of Learning Schraw, G., Brooks, D. W. & Crippen, K. J. J. Chem. Educ. 2005, 82, 637-640.

16. Researchers describe two types of intelligence: Fluid intelligence, something not easily changed that can be applied to any problem, and Crystallized intelligence that can be changed through learning and experience. In the ICML, ability refers to fluid intelligence.

17. Doug Phelps

18. You can measure working memory capacity. One doesn’t have the same issues as are connected with measuring, say, IQ.

19. Long-term memory capacity is very, very large. Working memory is the amount of long term memory and/or input information you can activate at a given moment.

20. 4 or 5 chunks BUT chunks can grow (through learning and experience)

21. fluid intelligence involves number of chunks -- can’t change crystallized intelligence involves size of chunks -- can change through learning and experience

22. Perhaps the most difficult idea for chemists to overcome is that ability is paramount in chemistry learning. The expertise literature suggests that effort and experience matter more -- after a certain point.

24. Our contribution has been to integrate issues of motivation within exactly the same context as other issues.

25. Motivation is working memory allocation.

26. Junior high school teachers always talk about motivation. Textbooks about instructional design just about never mention motivation. Some colleges teachers spend considerable time on motivation. Others believe that motivation is the student’s problem, not the instructor's problem.

27. Experts have learned how to be motivated. You are motivated now, or you wouldn’t be here.

28. The reality is that a chemistry teacher, especially in a large, introductory, required class has few clues about motivation. We work on averages; we try not to aggravate too many. Consider acknowledging the best student scores on a quiz. Forty years ago, that was a reward; today it most often is a punishment.

29. “One Box”

30. Placing all of these entities (working memory, motivation, knowledge, ‘new’ learning) in the same “box” allows us to connect this model to neural changes, and these are the changes brought about be experience. In our model, all of these entites are at least partially ‘stored’ in the cerebral cortex. [Thinking, or talking to yourself, counts!]

31. Unified Learning Model Duane Shell & David Brooks Available on-line as a pdf 25 pages of text (including figures) 10 pages of references

32. The ultimate goal of all teaching and instruction is development of student expertise. • The primary function of teaching and instruction is directing how working memory is being allocated. • Teaching and instruction must be adapted to the current expertise of the student. • Maintaining student motivation is critical.

33. The first person I ever heard discuss working memory capacity at an ACS meeting was Alex Johnstone. As is happens, there is a deep literature about working memory limitations and learning that goes under the heading of cognitive load theory. John Sweller is a leader in this area.

34. Perhaps the biggest challenge to teachers is that of engaging students in active learning. If students are not working at learning, at best the process is inefficient and, at worst, it is a complete waste of time.

35. I used to think I had a good grasp of this topic with our paper on ‘performance related feedback.’ I’ve since decided that: • Experts can be actively engaged without feedback, which is what I hope is going on in your head right now • Some kids can be engaged in feedback while not really giving you what they have to give.

36. The best advice to be gleaned from this model is to advocate mastery strategies -- that have been around for a long time. These are the strategies that most emphasize the importance of prior knowledge to the success of new learning. Examples include Keller Plan, repeatable tests, on-line practice, course prerequisites.

37. A final puzzle I’ve had concerns certain types of laboratory and group activities. The students are engaged and enjoying themselves, and they are learning. BUT, maybe they are not learning much -- as is the case with much hands-on science, especially in P-9. It’s really difficult to participate in these activities without giving it one’s all. That is, there is full working memory allocation.

38. Flow Flow is a phenomenon where one is fully engaged. Athletes speak of this, as do artists. I’ve experienced flow often, especially when creating computer programs. After a couple of hours, a sensation that I really need to visit a different space -- and quickly so -- takes over. I’m quite literally unaware of most of my surroundings. Bottom line -- Flow feels good.

39. It is my sense that flow has confused several aspects of learning -- because student attitudes rather than student knowledge is often measured. Students can experience flow, be fully engaged, and report good feelings about their experiences, but not really be working at content that is difficult. That is, they are not moving to the next step.

40. For most students, keeping motivation high enough to achieve good performances is enough of a problem that including some ‘high flow’ activities might be appropriate in achieving an effective instructional mix. Keep in mind that, when doing do, learning efficiency usually suffers.

41. Final Reminder Just because you understand how learning works doesn’t make it easy to make it work for you. BUT, it IS possible to understand how it works.

42. Questions I’ll try to entertain questions. Since something is VERY likely to come up after the talk, please feel free to send me e-mail: dbrooks1@unl.edu http://dwb4.unl.edu