The Teaching Model Sasikumar M Sasikumar M
Overview ● Concerned about “how to teach” ● Learning theory, nature of topic, nature of learner, etc all play a role ● We look at a few models and some example scenarios – They are not orthogonal ● No universal solutions on any aspect. Sasikumar M
How to teach ● Organisation of the lessons. ● Nature of lessons and expectations. ● How to select intervention. ● Keeping students motivated. ● Managing the cognitive load for the learner. ● Not about what to teach [Domain model] Sasikumar M
One view Actions Objects Navigation Explanation Summarize Go to step by step mode Example Describe Hints Move on Define Definition Stay here Show example Theorem Go back to prev Demonstrate Quiz topic Test Analogy Sasikumar M
Interventions and learning ● Significant impact ● Worked examples – reduces cognitive load of learner – More for low end students ● Feedback corrects inappropriate strategies and errors. – Reduces uncertainty on performance – Improves motivation – More specific feedback more beneficial Sasikumar M
Teaching models ● Based on human teaching – Apprenticeship training – Problem solving – Tutorial dialogue – Collaborative learning ● Based on learning theory – Socratic learning – Cognitive learning theory – Constructive learning theory – Situated learning ● Facilitated by technology – Animated agents – Virtual reality Sasikumar M
Apprenticeship training ● Model based on human tutor (in select scenarios). ● No explicit tutoring. ● Presence of an expert monitoring the learner. ● Hands-on active learning. ● Engage in real or simulated environment. ● May provide scaffold, fading it out gradually. Sasikumar M
… ● Provide a realistic simulation of the target environment for work. ● Good process models needed for apprenticeship systems – Covering structure and functioning – Model expert behaviour in a given situation – Involving students in situated knowledge – Must have epistemic fidelity (significant completeness) – enabling experimentation – Conventional simulation or stochastic models ● Provide only some quantitative aspects. ● Unable to explain any phenomena. Sasikumar M
Examples ● SOPHIE and SHERLOCK examples – Classic systems in ITS ● SOPHIE – Sophisticated instructional environment – Troubleshooting of broken electronic equipment – Introduced advanced communication and modelling features – Dialogue, with history – Almost NLP – but no real NLP used; semantic grammar and typical sentence patterns. Sasikumar M
SOPHIE Learner: Now what is the output? Sophie: The output voltage is 1.2 volts. Is that right? In a working circuit, the output voltage is 30.1 volts. That value is incorrect. What is the voltage between the current limiting transistor and the constant current source? The voltage at N4 is 2.48 volts. What could be wrong? Here are some faults which explain the measurements you observed: the beta of Q3 is low, the beta of Q4 is low or R22 has the wrong resistance. What would the output voltage be if the beta of Q3 were 8? Under the present setting, if the beta of Q3 were 8, the output voltage would be 10.5 volts. In a working circuit it is 19.9 volts. Is the emitter current of Q4 correct? The emitter current of Q4 is 61 milliamps. In a working circuit the emitter current is 49 millamps. That value seems incorrect. Could R22 be low? Is it ok if use 25 ohms for the resistance of R22? Yes . That fault agrees with all the measurements you have made. Sasikumar M
… SHERLOCK – Troubleshooting electronic diagnostic board (from F-15 aircraft) – Can place tools on relevant points in the circuit, and take measurements. – Review of solution at the end, vis a vis expert solution. ● Explains reason for each step Sasikumar M
Problem solving ● Maths, physics, programming, etc ● Quantitative problems requiring rigorous analytical reasoning ● No evidence this is a good strategy; lot of cognitive load! – May lead to trial and error, use of weak strategies, etc – But heavily used by all! ● Concern on detecting errors in the process and rectifying is the main focus. ● Example system: Andes, PAT, SQL-tutor, LISP tutor, etc. Sasikumar M
Learning theory based approaches ● Lot of work in learning theory in the context of ICT use ● No theory appropriate in all learning situations – Level of learner – Nature of topic, etc ● Cognitive learning theory for prescriptive solutions (algebra) ● Heuristic problem solving better with constructivist model Sasikumar M
Socratic learning ● Enquiry based learning ● Ideas and answers are within – get right kind of questions to draw them out. ● Learner explores answers with what is known and reflection – Engages in dialogue with the teacher ● Example system WHY – Understanding rainfall in different regions Sasikumar M
WHY Why does the amazon jungle have heavy rainfall? Because it is near atlantic. Yes, Amazon jungle is near the equatorial atlantic. How does that affect the rainfall in the region? The water in current is warm. What happens after the moisture filled air reaches the land? I dont know. Do you think the Andes mountain range has any effect on the rainfall in the Amazon jungle? Yes . How? The prevailing wind hits the Andes and the air cools. How does the fact that “the prevailing wind hits the Andes” lead to air cooling? Sasikumar M
Cognitive Learning theory ● Communicate knowledge to the learner in an efficient and effective way. ● Comparison with existing knowledge. ● Presence of: sensory register, restricted short term memory, large long term memory. ● Relevant ideas: – Use of meaningful examples, etc – Serial position effects (first and last easier to remember) – Practice effects – Transfer effects (of knowledge from one task to another) – Interference from prior learning Sasikumar M
…ITS based on CLT ● Computers and human similar in information processing ● They are input-output equivalents of similar processes. – Functionally identical architectures. ● Need to delineate “chunks” of cognitive skills for modelling internally. ● Convert declarative knowledge into procedural production-rule form for use in problem solving. ● ACT based approach is a classic example – Area of model tracing tutors Sasikumar M
… ● Problem: feedback is as per the chunks in use; not necessarily appropriate always. – Some rules may be trivial from learner perspective. ● No student specific error messages – Not easy to remember history of errors, and other relevant errors – Can build mechanisms outside to provide this in some cases. ● Not easy to handle hypothetical scenarios during problem solving – Allow student to go with an erroneous path for self-realisation: difficult to do. Sasikumar M
Constructivist theory ● Lot of variants and ● Sensorimotor stage (0-2 yrs): motor actions, sense interpretations in this organisation thread ● Pre-operation period (3-7): ● Knowledge constructed by intuitive reasong the learner; we can only facilitate. ● Concrete operational stage (8-11): logical intelligence, ● Growth of learning concrete objects capabilities – choose right ● Formal operations (12-15): model for right age group. abstract thinking. Sasikumar M
… ● Assimilation and accommodation – Interpret with what is known – Revise to make sense of new things. ● Constantly involved in case based or inquiry learning ● Situated in realistic setting ● Testing integrated with tasks, not handled separately. Sasikumar M
… implementing ● Not many follow fully. ● Intelligence for Counter Terrorism tutor – Uses simulation exercises ● Outcomes not fully predetermined as in other models. ● Use of spiral organisation of topics. ● Presence of multiple answers. Sasikumar M
Situated learning ● Learning is a function of the activity, context and culture in which it occurs. ● Learners in a relevant “community” picks up the expertise naturally. – Social interaction – Community culture – Unintentional, not deliberate ● Attempt to provide realistic environment – Technologies like animated agents, VR, etc useful. – Provide manipulations and operations which are physically realistic. – More than the functional simulations often used. ● Similar to “experiential learning” of psychological theory. – People have a natural propensity to learn – Teacher sets right climate, provide resources, and share thoughts. Sasikumar M
… ● Many military applications ● VR models for manipulations in space. ● Steve: Soar training expert for virtual environments – Uses animated agents and VR to provide “situatedness” – Demo by such an agent of functions, more effective than descriptions. ● Tactical language tutor – Track activities rather than results. ● Cosmo: Escorting a “packet” through internet – to learn networking concepts. ● Such models may work better for ill-defined domains. Sasikumar M
ZPD ● Zone of proximal development [Vygotsky]. ● Difference between what is possible without help, with help, and not possible. – Help: peer, society, community – Attempt to reduce this distance with tutoring ● Social interaction plays a major role in development of cognition. ● ITS can be such a mentor, peer, etc. ● Well suited for apprenticeship model. Sasikumar M
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