Cognitive Principles in Tutor e-Learning Design

1. Cognitive Principles in Tutor & e-Learning Design Ken Koedinger Human-Computer Interaction & Psychology Carnegie Mellon University CMU Director of the Pittsburgh Science of Learning Center

2. Lots of Lists of Principles 1 Cognitive Tutor Principles Koedinger, K. R. & Corbett, A. T. (2006). Cognitive Tutors: Technology bringing learning science to the classroom. Handbook of the Learning Sciences. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4 (2), 167-207. Multimedia & eLearning Principles Mayer, R. E. (2001). Multimedia Learning. Cambridge University Press. Clark, R. C., & Mayer, R. E. (2003). e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Jossey-Bass. How People Learn Principles Donovan, M. S., Bransford, J. D., & Pellegrino, J.W. (1999). How people learn: Bridging research and practice. Washington, D.C.: National Academy Press. Progressive Abstraction or “Bridging” Principles Koedinger, K. R. (2002). Toward evidence for instructional design principles: Examples from Cognitive Tutor Math 6. Invited paper in Proceedings of PME-NA. Other lists on the web … See learnlab.org/research/wiki

3. Principles on web: See learnlab.org/research/wiki

4. Overview Cognitive Tutor Principles Multimedia Principles Theoretical & Experimental evidence Instructional Bridging Principles Need empirical methods to apply PSLC Principles

5. Cognitive Tutor Principles Represent student competence as a production set Communicate the goal structure underlying the problem solving Provide instruction in the problem-solving context Promote an abstract understanding of the problem-solving knowledge Minimize working memory load Provide immediate feedback on errors

6. 1. Represent student competence as a production set Accurate model of target skill to: Inform design of Curriculum scope & sequence, interface, error feedback & hints, problem selection & promotion Interpret student actions in tutor Knowledge decomposition! Identify the components of learning

7. 6. Provide immediate feedback on errors Productions are learned from the examples that are the product of problem solving Benefits: Cuts down time students spend in error states Eases interpretation of student problem solving steps Evidence: LISP Tutor Smart delayed feedback can be helpful Excel Tutor

8. Feedback Studies in LISP Tutor (Corbett & Anderson, 1991)

9. Tutoring Self-Correction of Errors Recast delayed vs. immediate feedback debate as contrasting “model of desired performance” Expert Model Goal: students should not make errors Intelligent Novice Model Goal: students can make some errors, but recognize them & take action to self-correct Both provide immediate feedback Relative to different models of desired performance The mutually exclusive choice just described stems partly from the fact that much of the debate concerning when to provide feedback is cast in terms of feedback latency. We suggest that a more appropriate focus is on the underlying model of desired performance that serves as the basis for providing feedback to students. Currently feedback in cognitive tutors is based on what is broadly referred to as an Expert Model. Such a model characterizes the end-goal of the instructional process as error-free task execution. Feedback is structured so as to lead students towards such performance. An Expert Model based tutor focuses on the generative skills. An alternative model that could serve as the basis for feedback in cognitive tutors is that of an Intelligent Novice. The assumption underlying such a model is that an intelligent novice, while progressively getting skillful, is likely to make errors. Recognizing this possibility, the Intelligent Novice model incorporates both self-monitoring, error detection and error correction activities as part of the task. Feedback based on such a model would support the student in both the generative and evaluative aspects of a skill while preventing unproductive floundering. Immediate feedback with respect to such a model would resemble delayed feedback with respect to an Expert Model. FigureThe mutually exclusive choice just described stems partly from the fact that much of the debate concerning when to provide feedback is cast in terms of feedback latency. We suggest that a more appropriate focus is on the underlying model of desired performance that serves as the basis for providing feedback to students. Currently feedback in cognitive tutors is based on what is broadly referred to as an Expert Model. Such a model characterizes the end-goal of the instructional process as error-free task execution. Feedback is structured so as to lead students towards such performance. An Expert Model based tutor focuses on the generative skills. An alternative model that could serve as the basis for feedback in cognitive tutors is that of an Intelligent Novice. The assumption underlying such a model is that an intelligent novice, while progressively getting skillful, is likely to make errors. Recognizing this possibility, the Intelligent Novice model incorporates both self-monitoring, error detection and error correction activities as part of the task. Feedback based on such a model would support the student in both the generative and evaluative aspects of a skill while preventing unproductive floundering. Immediate feedback with respect to such a model would resemble delayed feedback with respect to an Expert Model. Figure

10. Intelligent Novice Condition Learns More

11. Learning Curves: Difference Between Conditions Emerges Early Number of attempts at a step by opportunities to apply a production rule

12. Overview Cognitive Tutor Principles Multimedia Principles Theoretical & Experimental evidence Instructional Bridging Principles Need empirical methods to apply PSLC Principles

13. Media Element Principles of E-Learning 1. Multimedia 2. Contiguity 3. Coherence 4. Modality 5. Redundancy 6. Personalization

14. Cognitive Processing of Instructional Materials Instructional material is: Processed by our eyes or ears Stored in corresponding working memory (WM) Must be integrated to develop an understanding Stored in long term memory

15. Multimedia Principle Which is better for student learning? A. Learning from words and pictures B. Learning from words alone Example: Description of how lightning works with or without a graphic A. Words & pictures Why? Students can mentally build both a verbal & pictorial model & then make connections between them

16. Coherence Principle Which is better for student learning? A. When extraneous, entertaining material is included B. When extraneous, entertaining material is excluded Example: Including a picture of an airplane being struck by lightning B. Excluded Why? Extraneous material competes for cognitive resources in working memory and diverts attention from the important material

17. Modality Principle Which is better for student learning? A. Spoken narration & animation B. On-screen text & animation Example: Verbal description of lightning process is presented either in audio or text A. Spoken narration & animation Why? Presenting text & animation at the same time can overload visual working memory & leaves auditory working memory unused.

18. Working Memory Explanation of Modality When visual information is being explained, better to present words as audio narration than onscreen text

19. Scientific Evidence That Principles Really Work

20. Summary of Media Element Principles of E-Learning Multimedia: Present both words & pictures Contiguity: Present words within picture near relevant objects Coherence: Exclude extraneous material Modality: Use spoken narration rather than written text along with pictures Redundancy: Do not include text & spoken narration along with pictures Personalization: Use a conversational rather than a formal style of instruction

21. Overview Cognitive Tutor Principles Multimedia Principles Theoretical & Experimental evidence Instructional Bridging Principles Need empirical methods to apply PSLC Principles

22. How People Learn Principles How People Learn book Build on prior knowledge Connect facts & procedures with concepts Support meta-cognition

23. But: What prior knowledge do students have?How can instruction best build on this knowledge?

24. Instructional Bridging Principles 1. Situation-Abstraction Concrete situational <-> abstract symbolic reps 2. Action-GeneralizationDoing with instances <-> explaining with generalizations 3. Visual-VerbalVisual/pictorial <-> verbal/symbolic reps 4. Conceptual-ProceduralConceptual <-> procedural

25. Dimensions of Building on Prior Knowledge Situation (candy bar) vs. Abstraction (content-free) Visual (pictures) vs. Verbal (words, numbers) Conceptual (fraction equiv) vs. Procedural (fraction add)

26. Which is easier, situation or analogous abstract problem?

27. Which is easier, situation or analogous abstract problem?

28. Key Point:Design principles require empirical methods to successfully implement

29. Overview Cognitive Tutor Principles Multimedia Principles Theoretical & Experimental evidence Instructional Bridging Principles Need empirical methods to apply PSLC Principles

30. PSLC Theory Wiki: Instructional principle & study pages … demo

37. … more in demo …

38. External validity of example-rule coordination Same principle tested across different background contexts (b’s) different courses

39. Cross-Cluster Theoretical Integration: Assistance Dilemma “How should learning environments balance information or assistance giving and withholding to achieve optimal student learning?” Koedinger & Aleven, 2007

40. Exploring dimensions of instructional assistance I like the tell vs. elicit distinction but would suggest give vs. ask as the terms. “Ask” because it is simpler language than “elicit”. “Give” because it is inclusive of both “tell” and “show” (in the case of an example you show or give it, not tell it) .For the cell values, Kurt had High and Low and these seemed confusing with high and low assistance. We could say “high learning” and “low learning”, but perhaps “better learning” and “some learning” are better.I like the tell vs. elicit distinction but would suggest give vs. ask as the terms. “Ask” because it is simpler language than “elicit”. “Give” because it is inclusive of both “tell” and “show” (in the case of an example you show or give it, not tell it) .For the cell values, Kurt had High and Low and these seemed confusing with high and low assistance. We could say “high learning” and “low learning”, but perhaps “better learning” and “some learning” are better.

41. A step toward resolving assistance dilemma … with very broad impact implications!

42. Summary of Learning Principles Lots of lists of principles … 6 Cognitive Tutor Principles 6 Multimedia Principles See PSLC wiki for others … Should be Based on both: Cognitive theory Experimental studies Need Cognitive Task Analysis to apply Domain general principles are not enough Need to study details of how students think & learn in the domain you are teaching

43. EXTRAS

44. 2. Communicate the goal structure underlying the problem solving Successful problem solving involves breaking a problem down into subgoals “Reification” = “making thinking visible” Make goals explicit in interface

45. ANGLE: Tutor for geometry proof

46. Common Error in ITS Design New notation = new learning burden Examples: Goal trees, visual programming languages Alternative: Use existing interfaces as scaffolds Adding columns in a spreadsheet Used in Geometry Cognitive Tutor Writing an outline for a final report Used in Statistics OLI course

47. Principle 2a: Create “transfer appropriate” goal scaffolding interfaces Avoid creating new interfaces to scaffold goals Worth it when: Alternative: Find an existing interface to use for goal scaffolding “Transfer appropriate” because interface is useful outside of tutor Cost of learning interface is low or pays off

48. 3. Provide instruction in the problem-solving context Research: context-specificity of learning This is how students learn the critical “if-part” of the production rule! Does not address exactly when to provide instruction Before class, b/f each problem, during? LISP tutor: Before each tutor section when a new production is introduced And as-needed during problem solving Cognitive Tutor Algebra course: Instruction via guided discovery & as demanded by students’ needs or when they do not discover on their own

49. 3a. Use “transfer appropriate” problem solving contexts Use authentic problems that “make sense” to kids Intrinsically interesting, like puzzles Relevant to employment, probs about $ Relevant to citizens, rate of deforestation Why? Motivation: Inspire interest Cognitive: So students learn the goal structures & planning that will transfer outside of the classroom

50. 4. Promote an abstract understanding of the problem-solving knowledge To maximize transfer, want those variables in if-parts of productions to be as general as possible! Reinforce generalization through the language of hint & feedback messages Cannot be simply & directly applied Trade-off between concrete specifics vs. abstract terms students may not understand

51. 5. Minimize working memory load ACT-R analogy requires info to be active in working memory to learn new production rules Minimize info presentation to only what is relevant to current problem-solving step Impacts curriculum design as well as declarative instruction Sequence problems so prior productions are mastered before introducing new productions Supported by research on “Cognitive Load” Theory Eliminate extraneous load, leave intrinsic load, optimize germane load

52. Contiguity Principle Which is better for student learning? A. When corresponding words & pictures are presented far from each other on the page or screen B. When corresponding words & pictures are presented near each other on the page or screen Example: “Ice crystals” label in text off to the side of the picture or next to cloud image in the picture B. Near Why? Students do not have to use limited mental resources to visually search the page. They are more likely to hold both corresponding words & pictures in working memory & process them at the same time to make connections.

53. Redundancy Principle Which is better for student learning? A. Animation & narration B. Animation, narration, & text Example: The description of lightning occurs in pictures, is spoken. Text with the same words is present or not. A. Animation & narration Why? Text & animation are both processed in visual working memory and may overload it. Further, students eyes may be looking at the text when they should be looking at the animation. So, leave out the text which is redundant.

54. Personalization Principle Which is better for student learning? A. Formal style of instruction. B. Conversational style of instruction Example: “Exercise caution when opening containers that contain pyrotechnics” vs. “You should be very careful if you open any containers with pyrotechnics” B. Conversational Why? Humans strive to make sense of presented material by applying appropriate processes. Conversational instruction better primes appropriate processes because when people feel they are in a conversation they work harder to understand material. Note:Recent in vivo learning experiment in Chemistry LearnLab did not replicate this principle.