Case Studies Sasikumar M Overview Set of internal case studies - - PowerPoint PPT Presentation

Case Studies

Sasikumar M

Overview

• Set of internal case studies
• Marathi Tutor
• SQL tutor
• Simultaneous equation tutor

Marathi tutor (Abhijit Joshi)

• Attempt to follow first language learning of children
• Key points:

– Controlled repetition – Relevant feedback

• Controlled repetition

– Generate similar sentences by learner and teacher – Teacher to review and correct, if not correctly formed

• No significant use of linguistic terms or ideas
• Let the learner evolve the knowlege.

Inflection handling

• Verbs and nouns undergo inflection generally.
• Verb controlled by GNP of subject and tense

– is/am/are going – has/have left – went

• Noun as indirect or direct object, and as

adjective inflects

– Particularly pronoun – She/her/He/him/his/we/our/us/etc

...

• Rules for transformation
• Pronoun forms

– mee:First:M:Ekavachana

• Make plural form

– 2:M:aa:e – For masculine nouns replace 'aa' with e

• Case inflection

– Convert to base form – Then convert to suitable suffix

• A number of knowledge bases for exceptions, different

types and their transformations

...

• Similar case with verbs also
• Tense modifier, and then inflections

– 1:First:M:Ekavachana:Vartamaana:to – Chalaa -> chalaato

Verb Form- ation

Pro-noun

Sentence generation

• Need to generate sentences which are

grammatically correct,

• ... and more-or-less meaningful.
• Fixed repository is difficult to manage.
• Open grammar can produce silly sentences

– Table ate banana.

Templates

<sentence id=1 type = “simple”> <subject> <noun type = “person” tag=”x” gender=”any” number=”any” person=”first”/> </subject> <adjective type=”time”> <object> <noun type = “edible”> </object> <verb dhatoo=”eat” tense = “present”/> </sentence>

Sentence generation

Tutor

• Formal models of tutoring and student model to be

done.

• System shows many variants on the same structure

using template

• Learner can specify parts, and ask system to fill in.
• System shows partial sentence and asks learner to

fill in.

• A dialogue structure to help correct errors

User-driven generation

End of case study 1

Case study 2

• SQL tutor
• Scaffolding, domain model, diagnosis

mechanism,etc

Acharya: SQL tutor

• Similar to SQL-tutor of CBM
• But done much earlier!
• Teach how to write good SQL queries
• Select name, age, salary from faculty where

salary > 10000

• Issues: problem selection, error finding,

feedback

Problem selection

• A repository of problems

– Find age of all faculty members whose monthly salary

exceeds 1lac.

– How many staff not promoted in the last 3 years. – Tabularise people who joined in the last 3 years in terms of

their base degree.

• Generation of problems difficult

– Variations possible

• Database need to be included.

– A number of common schemas built and kept.

User Interface

• SQL queries have a fixed structure.
• Cannot impose an order in the way you write

queries.

• So validate “when asked”.
• Structure can be used to make a scaffolding to

avoid parsing, or even handling incomplete parts.

GUI

Content and Student model

• SQL organised as a set of concepts with some

dependency.

• Problems attached with each concept.
• Correctly solving them decides 'confidence' in

the concept

• Simple CF model for confidence

– Student Model

Correctness

• How to check correct answer?

– Run query against a real database? – Need sufficiently richly populated database – Getting right set of tuples retrieved => correct

query?

• Check for efficiency, good practices?
• How to find what went wrong?
• Must validate by “analysing” query

...

• Use of an ideal answer
• Check response part by part

– Select items – Table names – Where clauses – Groupby, etc

• Issues?

...

• Multiple correct combinations
• Select items in any order...

– Use sorted order for matching

• All relevant fields and only the relevant fields retrieved?

– If order specified in problem, check for the order after

this.

• Table names

– Order not relevant – User defined name ignored now

...

• Where clauses more complex
• Order of clauses

– (age > 30) and (salary > 10000) – (salary > 10000) and (age > 30)

• Rephrasing of a clause

– not (age <= 30) – Age between 20 and 40 – Age > 20 and age < 40 – Not (Age <= 20 and age > 40)

• Efficiency and best practices issue

...

• Where clause given a set of rows
• Scaffolding and this, reduces focus from minor

syntax errors

Where clauses

• a) Pre-process them to remove connectives like

between, in, not in, etc

– Only AND, OR retained

• b) Atoms standardised

– Build a list of unique atoms – If match found, assign same number – Else different number – Negation and change of sign to be considered – age > 25, age > 30, age <= 25, 25 < age, etc

...

• c) truth table based by equivalence check

– (3 and 2) or (1 and not 2) – Small number of atoms – so manageable in size – Check if the TTs of the two expressions are same

• With these, most of the variations can be handled.

– Domain specific variations not included – Col 1 and 2 are related, and hence check on 1 can be

mapped to check on 2

Actions

• Depending on nature of mismatch, error tags

assigned

• Errors linked to remedial materials (for details
• n this topic, refer to ....)

Summary

• An early attempt at tutor development.
• Pilot use in our RDBMS course

– No systematic evaluation

• Student model in terms of concepts

• Solving simultaneous equations
• Use of multi-layer problem solving interface

Case study 3

Thank you.....

END