Adaptive Tutoring Systems M Sasikumar sasikumar@iitb.ac.in - PowerPoint PPT Presentation

Adaptive Tutoring Systems M Sasikumar sasikumar@iitb.ac.in thelittlesasi@gmail.com 1 Sasikumar M

Overview ● What is ATS ● What is ITS ● Knowing vs Teaching ● Course plan, assessments, etc ● Architecture, components and issues ● ...Case study of LCM tutor ● Case study of Stepmat & Language learning 2 Sasikumar M

ATS Direct customised instruction and/or feedback to learner. Adaptive tutoring systems one to one, adaptive => personalised Focus can be: instruction, problem solving instruction – personalised/adaptive instruction – tailor instruction as per the user problem solving – ITS – our main focus in the course. 3 Sasikumar M

ATS aspects Both needs a student model with different concerns. In 1, it is learner style and other relatively static aspects. In 2, it is more fine grained and very dynamic. Focus on design and development of systems in these categories, and review relevant educational aspects. 4 Sasikumar M

Personalise what? Anything! ● Curriculum ● Content – language, style, examples ● Pedagogy – definition-example-explanation? ● Learning style – visual, hands-on, auditory ● UI – what to display where and how ● Pace, Location and time ● Problems ● Explanations ● Feedback ● Intervention strategies ● 5 Sasikumar M

Why personalise? ● Traditional instruction – one size fits all – not effective enough ● Use of computer and Internet enables a solution! ● Learning is personal! ● E-learning has not much followers, despite claimed advantages. ● Negative factors ● Personalisation could alleviate them. 6 Sasikumar M

Not a new interest For years, researchers have debated ● Despite the lack of whether phonics or whole-word recognition is the best way to teach children how to technology, the concern is read. Various experts can be found who will advocate one approach or the other. many decades old. ● Programmed instruction, Bloom's mastery learning, Kellers personalised system of instruction, etc ● Listed among engineering grand challenges! 7 Sasikumar M

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Personalise? ● Matching content to learner style: good or bad? ● Learning theories recommend presenting information in multiple perspectives. ● Existence of mental representations of environment [Cognitivism]. ● Dewey's insight that learning process is of unique and individual nature. 9 Sasikumar M

Our scope ● Two streams in this course: ● Adaptive instruction ● Intelligent tutoring ● Important and difficult to realise in general. 10 Sasikumar M

ITS ● Names ● ICAI – intelligent computer aided instruction ● Knowledge based tutoring systems ● ITS name coined by JS Brown ● Early 70s start from AI angle. Peak period in 70- 80s. Human tutor as model. ● One-to-one tutoring demonstrated significant performance gain (Bloom, 84) – can we get even a part of that with ITS? ● Now re-activated due to growth of e-learning, web and high performance computing systems. 11 Sasikumar M

What is ITS? ● Intelligent ● Tutoring System ● In what way? ● Not teaching! ● Interaction ● One to one assumed ● Diagnosis ● Narrowly focussed, ● Pedagogy often a single type ● etc of problem. 12 Sasikumar M

ITS and e-learning ● Infinite repetitions of problem solving sessions at individual pace ● High degree of personalisation ● Studies show good performance improvement with such approaches. ● Particularly important in the increasing acceptance of constructivist view of learning. ● But: ● Hard to build: ITS is AI-complete ● Expensive in system capability 13 Sasikumar M

ITS as intelligent teaching ● Organisation of content ● Granularity ● Meta-data/annotations ● Selection of content for individual learner ● Goal ordering ● Remedial teaching ● Refer to specialised alternate resources. 14 Sasikumar M

ITS as tutoring ● Specific problem solving skills ● Tutoring depends on the nature of problem solving ● Equipment diagnosis/classification ● Iterative processes ● Processes ● System design and implementation critically dependent on this. 15 Sasikumar M

MYCIN to GUIDON ● MYCIN – among the earliest expert systems ● Very popular, and spurred most of the innovations in the field of expert systems. ● Domain: diagnosis of bacterial infections of blood, and suggest treatment. ● Evaluation showed performance comparable to that of experienced doctors. ● Why not use it to teach new doctors? 16 Sasikumar M

.. ● MYCIN to be the 'expertise' provider ● A teacher module will: ● Pick a patient to be diagnosed ● Learner pretends to be the doctor, ask for symptoms. ● Asks for investigations (e.g. blood count) and observations (nature of work, etc) ● Compares these with what MYCIN would do for the same case ● Uses difference to “instruct” the learner. 17 Sasikumar M

... ● MYCIN had the 'usual' explanation facilities ● Why do you want to know? – I am trying to see if you have this disease ● How did you infer x – I used rule 213 which says the following. ● These could be 'adapted' to instruct the learner of what is wrong ● So it seemed! 18 Sasikumar M

... ● MYCIN had rules to diagnose, not explicit justifications and strategies. ● When learner deviates from the MYCIN route, it was hard to know if it is right or wrong. ● Rules encoded different types of knowledge, not distinguishable. 19 Sasikumar M

... ● If person is less than 8 years, do not prescribe tetracycline. ● Significance of 8 years? ● What is the effect, if this is violated? ● Are there situations where this can be broken? 20 Sasikumar M

... ● If person is female ● And age is between 15 and 50 ● And pregnant ● Then swelling of knees could be due to pregnancy ● An attempt to make MYCIN look smart! ● But confusing to a learner as to the purpose of the rule. 21 Sasikumar M

... ● Similarly rules embodying definitions, common norms (age vs alcohol use), priorities, etc. ● A thorough revamp of the knowledge base was needed to make it 'respectable' as a tutor. ● Clancey documents the journey in his seminal papers – worth a read. 22 Sasikumar M

Course Plan ● Contact sessions: Tue and Fri, 2.00 to 3.30pm ● Assessment: ● 4 assignments, mid-term test, final exam (standard weightage) ● Along the lines of LCM, build one simple ITS. Choose a domain and stick to it till end. ● Build a student model and problem selection for assignment 1. ● Implement an ITS using CTAT framework. ● Implement a pedagogy model for the assignment 2. 23 Sasikumar M

Broad Scope... Tutor/ Learning Learner Pedagogy Theories Styles Domain Model Model Student Model Validation Adapt ITS Of Systems Instn Case Studies Knowledge Machine Development Representation Learning Tools CTAT Python 24 Sasikumar M

Resources ● Building Intelligent Interactive Tutors, Beverly Park Wolf, Morgan Kaufman, 2009 ● Most of the aspects covered in the course can be seen here. ● A lot of papers and articles ● Almost all will be available through the Moodle portal ● You are welcome to suggest relevant articles, resources, etc ● Post on Moodle; will review and add to the pool. 25 Sasikumar M

ITS components Student Model UI Tutoring model Domain Model 26 Sasikumar M

Student model ● A number of aspects can be included here ● Learner background [maths, language, science, etc] ● Learning style ● Psychological profile [motivation, initiative, collaboration, etc] ● Knowledge wrt what is to be learned ● Interaction history with the system so far ● etc. ● Selection of what to include, depends on other models 27 Sasikumar M

Domain model ● The space of learning ● Contents depend on what is to be taught/ learned. ● Facts ● Concepts ● Processes ● Relationships ● Strategies ● .... 28 Sasikumar M

...Domain model ● With each, we need ● Relevant content to explain it – For different student profiles? ● Dependent units ● How to test if learned ● General misconceptions ● Common errors ● How to detect them ● How to correct them ● Usually results in a lot of hard-coded system 29 Sasikumar M

... Domain model ● As far as possible to be externalised to make it visible to tutor model, and link with student model ● If-then rules with a generic interpreter ● Databases with a semantic layer ● Ontologic representation/concept map ● Some tradeoff in flexibility... 30 Sasikumar M

... Domain model [bugs] ● Tutoring requires understanding of correct and otherwise behaviour ● What makes a learner take a wrong/different path ● Misconceptions in a relation ● Wrong relationship/formula ● Buggy steps in a process ● Externalised domain knowledge needed to capture these effectively. 31 Sasikumar M

... Domain model [process] ● Set of sub-processes ● Variability in ordering ● What is permitted when? ● All else could be “errors”. ● e.g. Subtraction of 2-digit numbers: column ordering ● e.g. Different investigation sequence in a diagnosis task 32 Sasikumar M