1. 1 USC Information Sciences Institute Yolanda GilFebruary 2001 Knowledge Acquisition as Tutorial Dialogue: Some Ideas Yolanda Gil

2. 2 USC Information Sciences Institute Yolanda GilFebruary 2001 Exploring Possible Synergies Intelligent Tutoring System (ITS) Intelligent Studious System teaches ? ? ITS KA (RKF)

3. 3 USC Information Sciences Institute Yolanda GilFebruary 2001 Exploring Possible Synergies: Dialogue Intelligent Tutoring System (ITS) Intelligent Studious System (ISS) teaches ? ? Good tutoring strategies Good tutoring strategies

4. 4 USC Information Sciences Institute Yolanda GilFebruary 2001 What ITS community has Mountains of example tutoring dialogues  Can be analyzed for strategies, misconceptions, hints and help  E.g., http://www.pitt.edu/~circle/Archive.htm Many and diverse tutoring system have been built Raised grades by 1.0 standard deviation units  Best humans raise grades by 2.0

5. 5 USC Information Sciences Institute Yolanda GilFebruary 2001 Main Approaches to ITS Coached practice and review Socratic dialogue: questions discover student misconceptions, avoid telling students what they need to know Critiquing student solutions

6. 6 USC Information Sciences Institute Yolanda GilFebruary 2001 Model Tracing Tutors [Anderson et al. 85] Contain a model of the cognition designers want students to engage EXPERT MODEL HIGH BANDWITH INTERFACE PEDAGOGICAL MODULE XX√XX√ -----? --- ----? -----

7. 7 USC Information Sciences Institute Yolanda GilFebruary 2001 Model Tracing Tutors [Anderson et al. 85] Expert Model: how student should reason  simple, precise, complete problem solving strategies HB Interface: where student displays reasoning  goal trees, explicating Pedagogical Module: feedback and hints  immediate feedback, hint sequences with increasingly more help EXPERT MODEL HIGH BANDWITH INTERFACE PEDAGOGICAL MODULE XX√XX√ -----? --- ----? -----

8. 8 USC Information Sciences Institute Yolanda GilFebruary 2001 Key Research Projects CIRCLE Research Center @ CMU  PACT Geometry tutor, Ken Koedinger  Andes Physics tutor, Kurt VanLehn –Model tracing approach CST: CIRCSIM-Tutor, from Illinois Institute of Technology  Socratic dialogue approach  Domain: physiology  Used in classrooms in a non-experimental basis ACLS (& others) @ UMass  teaches a new concept when relevant during a simulation of ER Many, many others: NEOMYCIN, SIERRA, CASCADE, SOPHIE,...

9. 9 USC Information Sciences Institute Yolanda GilFebruary 2001 Interactive Directive Lines of Reasoning [Rose et al. 2000] Instead of mini-lessons, which require that students have prior knowledge and motivation Tutor starts by presenting student with a scenario and lesson overview (“advanced organizer”)  Useful to draw prior knowledge (e.g., stating an analogy)  Useful to detect missing prior knowledge  Useful to give context to the new knowledge Tutor asks detailed questions Once student provides the desired answers, tutor ends with a summary

10. 10 USC Information Sciences Institute Yolanda GilFebruary 2001 Interactive Directive Lines of Reasoning: An Example Tutor : Let’s think about the difference between speed and velocity. A closely related distinction is that of the difference between distance traveled and displacement from the origin. Take as an example a bumblebee flying from point A to point B by means of a curvy path. If you draw a vector from point A to point B, you will have drawn the bee’s displacement vector. What does the displacement vector represent? Student : The bee’s distance. […] Tutor : So the equation for speed is the length of the path traveled by the body divided by […], even if the path […]

11. 11 USC Information Sciences Institute Yolanda GilFebruary 2001 Fading and Deepening (I) [VanLehn et al. 2000] Human tutors start with lots of scaffolding that later fades, while ITS tools are quite rigid:  support one strategy –st mix steps from different strategies –st wonders what to do next, tool’s advice seems random (but he was!)  force students to enter information they hold in memory  provide too much scaffolding in detecting errors and hinting solns –st looked for the last hint in the sequence that says what to enter –hints are not bad, but may not make sense within student’s context

12. 12 USC Information Sciences Institute Yolanda GilFebruary 2001 Fading and Deepening (II) [VanLehn et al. 2000] Human tutors pursue deep learning At most two nested strategies e.g.: lesson on how acceleration opposes velocity when slowing down T: What is the definition of acceleration? S: Velocity divided by time T: Yes, it is the change of velocity divided by time S: It’s the derivation of time T: Well, forget about the definition of acceleration. Let’s try analogy. Suppose… Tutor’s strategy: derive from definition Almost right, tutor enters 2nd level strat. Student is even more confused Abandon top-level strategy for another one

13. 13 USC Information Sciences Institute Yolanda GilFebruary 2001 Fading and Deepening (III) [VanLehn et al. 2000] Deep learning through knowledge construction dialogues  Teach a domain principle –Three main KC types: from definition, analogy, contradiction  Teach to do right thing for right reasons (no guessing of actions) –Tutor should ask to justify actions  Teach domain language –Tutor should ask to say “I applied to because ”  Emphasize basic approach instead of details –Tutor should ask student to state basic approach  Qualitative skills, not just quantitative –Tutor should ask qualitative questions during lesson

14. 14 USC Information Sciences Institute Yolanda GilFebruary 2001 Why do only some tutorial events cause learning? [VanLehn et al. 98] Analysis of tutorial dialogues showed that depending on what is the rule being learned:  Students that make an error (reach impasse) tend to gain  Students that hear a generalization of a rule tend to gain  Students that produce incorrect equation gained when explained why it was wrong (though not when using calculus) Suggested strategies for ITS:  Tutors should let students make mistakes instead of avoiding that by giving them strong hints  Different rules may require different kinds of tutorial explanations (e.g., stating generalization, showing why wrong, etc.)

15. 15 USC Information Sciences Institute Yolanda GilFebruary 2001 Discussion: Differences ISS does not suffer lack of motivation ISS can be built with a lot more initiative and participation than a human student ISS does not need “cognitive tricks”:  Eg, incremental hints, they can just be given the solution

16. 16 USC Information Sciences Institute Yolanda GilFebruary 2001 Discussion: Opportunities Intelligent Student Systems  Student guides dialogue using good teaching strategies Training human tutors  Tutor uses ISS to learn good teaching strategies Simulated student colleagues  “I think the tutor meant …”