Knowledge Acquisition as Tutorial Dialogue: Some Ideas

1. Knowledge Acquisition as Tutorial Dialogue:Some Ideas Yolanda Gil

2. Exploring Possible Synergies ? Intelligent Tutoring System (ITS) teaches ITS Intelligent Studious System KA (RKF) teaches ?

3. Exploring Possible Synergies: Dialogue ? Intelligent Tutoring System (ITS) Good tutoring strategies teaches Intelligent Studious System (ISS) Good tutoring strategies teaches ?

4. 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. 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. 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 X X √

7. Model Tracing Tutors [Anderson et al. 85] EXPERT MODEL • 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 HIGH BANDWITH INTERFACE -----? --- ----? ----- PEDAGOGICAL MODULE X X √

8. 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. 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. 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. Fading and Deepening (I) [VanLehn et al. 2000] • Human tutors start with lots of scaffolding that later fades, whileITS 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. 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… Fading and Deepening (II) [VanLehn et al. 2000] • Human tutors pursue deep learning • At most two nested strategies 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. 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 <principle> to <objs> because <goal>” • 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. 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. 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. 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 …”