Student Model Sasikumar M IIT Bombay
Overview What is SM and why SM? Types of SM Relation to Domain models Ways to capture and update – Examples Open student model IIT Bombay
Why Student Model? Adaptive Tutoring Personalised Student Model IIT Bombay
... Student models needed for – Determining help during problem solving – Diagnosis of errors – Choice of teaching strategy – To intervene or not – To give hint or not – Choose type of hint – Etc... Note: SM depends on other modules... IIT Bombay
Purpose Short term immediate response Adapt media and resources Long term curriculum planning Enhance learner confidence Affective Provide a sense of challenge Information Build curiosity IIT Bombay
Contents of SM A SM can contain a wide variety of information, in general: – Related to knowledge of what is being taught – Other relevant background knowledge – Emotional profile (motivation, attention level, emotional state, etc) – Learner style – Cultural parameters (incl language) IIT Bombay
Issues Granularity Dependency on other modules – No point in collecting information you cannot use! – What you collect must be usable by others. Nature of representation Purpose and mode of use Change over time etc IIT Bombay
Student Model Short term vs long term model Single observation vs multiple observation Cost of building/updating vs utility – Time and complexity IIT Bombay
Classification Many classifications based on various aspects Based on type of knowledge used by the system – If-then rules – Semantic networks – Ontologies – Concept maps – And so on And type of task performed – Concept learning – Rules and constraints – Procedures SM can usually be seen as an annotation on the domain model.. IIT Bombay
Domain Types Problem solving domains – maths, physics, trouble shooting – Relatively well understood to design SM Analytic and unverifiable domains – law, ethics – Empirical verification not usually possible Design domains – architecture, music. – Most complex and ill-structured – Hardest to define SM IIT Bombay
Classification... Based on knowledge in SM – Models with course knowledge – Models with individual characteristic independent of course • Learning style • Personality traits • mood Most of the focus on “course knowledge” – Dynamic update needed – Some interest in learning style for adaptive instruction. IIT Bombay
Knowledge Models Just a number 0-100? (eg. Exam score in the subject) – A vector of numbers, for each major topic? Overlay for further granularity Bug libraries Genetic models Technologies – Bayesian networks – If-then rules – Semantic nets IIT Bombay
Overlay model Subject is a set of independent pieces. A value attached to each piece. Student knowledge subset_of expert knowledge – Hence “overlay”, “cover”, etc. IIT Bombay
Overlay model Expert knowledge Student Knowledge IIT Bombay
... problems But, student knowledge not a subset of teacher knowledge – Misconceptions – Wrong procedures – Dependencies Bug libraries – Perturbation models IIT Bombay
Bug libraries Concept of bug – Enumerated? – Whatever is not right? – Concept, step, application of a concept/rule – Full enumeration difficult except in very small domains. Perturbation approach – Perturb the right way at each node/step – The modifications – bugs IIT Bombay
Formulating bugs Bugs in rules – Wrong conditions – Wrong conclusions – Not used Bugs in component – Wrong component – Not connected properly – Not working correctly Bugs in procedure – Delete a part – Add incorrect part – Replace a correct part with an incorrect part. IIT Bombay
... Scoping required: – Bugs can arise in everything, everywhere – Error in reading, faulty meter, ... ?? Handcompile from analysis of thousands of case logs. Collect from systematic analysis of domains. Theoretical perturbations may not be seen much in practice. Prioritisation of bugs. IIT Bombay
... problems Student knowledge evolves with time – Levels of understanding – Refined with more precision and detail with time – Bug models change with these refinements Layered student models – Genetic graph models IIT Bombay
Genetic Graph Models Levels of knowledge, with own representation and models – Node: rules, facts, etc – Links: evolutionary relationships • Analogy • Simplification • Deviation • ... Levels interlinked to capture relationships and dependencies. Learner evolves through this! Curriculum Information Network (CIN) of DesignFirst. IIT Bombay
More detailed SM ... Build simulations to capture more behaviour Type of tutor – Model tracing tutor – Constraint based model AI techniques – Formal logic – Bayesian networks – Expert system models IIT Bombay
Model tracing tutor Model tracing tutors traces through internal model to keep pace with the learner. Mostly for procedural problem solving tasks. Create a solution path, and augment with “wrong paths”. CTAT is a framework on this notion. IIT Bombay
... If-then rules or a graph as the knowledge representation. Probabilistic rule and bayesian network used for SM. Model's step need to be induced from the student's actions. Probabilities induced from student behaviour. If student is following steps correctly, he knows all the rules along that path. IIT Bombay
... Rules and misconceptions If the goal is to solve a(bx+c) = d, then a) rewrite as bx+c = d/a b) rewrite as abx + ac = d c) rewrite as abx + c = d IIT Bombay
... For each rule, count: – Correct use – needed and used – Wrong use – not needed, used – Needed, and not used Used to define “knowledge level” on the rule. IIT Bombay
Constraint based Model Set of constraints is the knowledge. Solution must not violate any; probabilities on the constraint. Ordered pair: relevance and satisfaction -- relevance: is the constraint applicable -- satisfaction: what is the constraint When adding fractions (a/b, c/d), numerators can be added, when the denominators are same (b == d). Driven by Ohlsson – error based learning theory. -- People learn when they make mistakes. IIT Bombay
.... CBM evaluates knowledge of the student, and does not generate. Hence no need to induce strategy of student. Student can take any path, as long as constraints are maintained. It is pedagogy neutral – not dependent on any strategy. Easier to build SM; but feedback may not be as sharp. When multiple solution paths are possible, partial solution correctness not easy to check. IIT Bombay
CBM for knowledge state Constraint help to detect nature of error and provide feedback. To estimate knowledge of topic, a constraint-concept tree is made. Constraints are leaf nodes; concepts, sub- topics, and topics as we go up. All children “known”, parent “known”. IIT Bombay
Types of knowledge Domain knowledge – propositions, concepts Reasoning knowledge – rules etc allowing to connect the pieces in DK. Monitoring knowledge – when to use the RK; strategy dependent. Reflective knowledge – problem solving process itself. Use of hints, etc IIT Bombay
Technologies for SM If then rules Bayesian networks Fuzzy rules Logic programming IIT Bombay
... if then rules Rules as pieces of knowledge Can also be “wrong” knowledge – misconceptions Tag rules as “known”, “unknown”, etc If rules are not well defined, explaining error may be hard [Guidon] Generating aggregate status? IIT Bombay
... Bayesian networks Model based on probability theory, – Specifically Bayes' theorem Nodes are knowledge units, links indicate causality. Joint probability distribution as product of conditional probability of all nodes – Dependent on causal nodes Formal model Impact of prior probability – Some studies “not critical”, difference (change in prob) important. [Detailed talk later...] IIT Bombay
Fuzzy rules Knowledge level as a fuzzy variable StatLady uses: remedial, intermediate, and mastered. – Each in turn has low and high mastered(x) – fuzzy variable Rules to increase and decrease membership value. IIT Bombay
Logic programming Domain knowledge + student model -> student behaviour +: as modified by Where student model has info, that over- rides what is in the DK. IIT Bombay
Blame assignment Finding responsible knowlege piece for a wrong answer is difficult. – When answer is correct, all contributors get “+” support. – When answer is wrong, reduce all? • Too much noise – Use domain knowledge and relations among pieces to narrow down choices • Worked well in BIP-II, WISOR, etc IIT Bombay
Change over time State of knowledge changes with time – Forgetting, weakening – Due to time change, due to arrival of new knowledge (confusion) Most systems do not address this aspect. In SQL-tutor, last four logs only checked to evaluate knowledge. IIT Bombay
Wrapping up... Student model key to effectiveness of ITS in most cases and scenarios Various approaches with associated + and -. Must match and go with Domain model. Little work on monitoring and reflective knowledge. ...[A few related topics to be covered later]. IIT Bombay
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