Visualization based Intelligent Tutoring System (ITS) for Greedy Algorithms: GATutor

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Visualization based Intelligent Tutoring System
(ITS) for Greedy Algorithms GATutor

• By
• Meenakshi Verma
• Mukund Lahoti

Guided By Prof. S.R. Iyer
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INTRODUCTION

• We have presented a framework for effective
  teaching of greedy algorithms and implemented
  that framework as an Intelligent Tutoring System
  to prove its usage named as GATutor.

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NEED

• Earlier Algorithms have been taught in schools in
  a passive way.
• Researchers want teaching to become more
  interesting.
• To do so it is require to visualize algorithms
  and make students play with it3.
• It is necessary to guide them at each step so
  that they do not divert from right path.
• Greedy algorithms are the most common design
  techniques. Though they are simple but their
  learning objectives demand tough teaching.

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MOTIVATION

• Every student is always perturbed by the
  question7
• Yes the solution seems to work,
• it appears to be correct
• but how is it possible to invent such a
  solution?
• How could I invent or discover such things by
  myself?
• According to us with some pre-requisite
  knowledge, stimulating questions, providing hints
  can help a student to bring an algorithm right
  from scratch.

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Demo
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RELATED WORK-Different Algorithm Tutor Systems
that already exist
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GREED-EX SYSTEM4

• It is a algorithm tutor based on discovery
  learning approach
• This system mainly focuses on greedy algorithms.
• It teaches two greedy algorithms-Activity
  selection problem and Knapsack.
• They uses a didactic method to teach greedy
  algorithms. The didactic method asks the student
  to could characterize an optimal greedy algorithm
  for a given optimization problem.
• There has been no guided approach followed by
  them.
• It might be possible that sometimes student might
  get mislead if he does get right direction.
• They have also not given any questions on which
  student can be assessed. Main features of this
  system are students discover by himself, results
  table and history is provided to compare
  different functions.

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ALGO-TUTOR SYSTEM9

• Algo-Tutor contains Visual Algorithm Tracer and
  Program Pad embedded in it.
• It is a basic tutor which encourages learning
  programming through algorithmic design.
• It teaches students basic programs including
  while loop, for loop. It was tested over a group
  of students.
• The results were significant. It stated that
  teaching programming through algorithmic approach
  is beneficial rather than just making them learn
  for and while loops. It gives a option of drag
  and drop through which student can construct a
  new algorithm using some pre code generated by
  the student.
• It basically consist of three components as
• 1. building the algorithm
• 2. Executing it step wise
• 3. program pad

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ANIMAL SYSTEM8

• It uses animation to visualize algorithm with
  simultaneous code view provided.
• As the system is not interactive with the user,
  it make it somewhat bore.
• It supports Backtracking algorithms, Compression
  algorithms, Cryptography algorithms, Data
  structures, Graph algorithms, Graphics
  algorithms, Hashing algorithms, Mathematics
  which is a good collection.
• But providing only animations of algorithms
  cannot seek the attention of a student for a
  longer and it is also difficult to enhance their
  learning process and to check whether they have
  learned it or not

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JHAVE SYSTEM6

• It aims to animate algorithms which concludes
  that visualization of a algorithm increases its
  understanding rather than just reading the code
  of it.
• They are not interactive with student which makes
  it a little boring as student can get diverted
  while working on it.
• It supports many algorithms including graphs,
  Sorting, Hashing and miscellaneous.

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ALGOVIZ SYSTEM1

• It contains animation of Algorithm and a portal
  where a collection of links to algorithm
  visualizations exists.
• It Integrates many algorithm visualization
  systems.
• Only animation is included.
• They basically have not build their own system.

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Teaching Strategies of Other Systems

• Many algorithms tutor exist but some mainly focus
  on theoretical material(JHAVE) while other mainly
  focuses on visualization of the
  algorithm(ANIMAL).
• There does not exist any system which checks
  learning of the student at each phase.
• The GreedEx system focuses on Discovery learning
  and uses the concept of discovering optimal
  selection function by experimentation approach
  but other learning goals have not been taught
  such as to give the proof of wrong choice.
• Mainly systems focuses on animation of the greedy
  algorithms(algoviz).
• There exist no system which focuses on
  interactive learning of greedy algorithms and
  help in giving the proof of wrong selection
  functions. System also implement only understand
  and analyze level of Blooms Taxonomy.

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Our Teaching Strategy-1

• A rule based framework for teaching greedy
  algorithms.
• Our system follow recall, understand, apply,
  analyze and evaluate level of Bloom's taxonomy4
  .
• 1. Basic understanding of what are greedy
  algorithms(Understanding level)
• 2. Understanding of specific greedy
  algorithms(Understanding level)3. Analyzing which
  selection function is optimal(Analyze level)
• 4. Proving its optimality by showing counter
  examples for non-optimal functions(evaluate
  level)
• 5. Solving different such problems(Apply level)

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Our Teaching Strategy-2

• We are using guided discovery learning approach.
• There have been many variations of definition of
  discovery learning2. Discovery learning
  occurs whenever the learner is not provided with
  the target information or conceptual
  understanding and must understand it
  independently and with only the provided
  materials.
• Within discovery-learning methods, there is an
  opportunity to provide the learners with
  intensive or, conversely, minimal guidance, and
  both types can take many forms (e.g., manuals,
  simulations, feedback, ex-ample problems).Sweller
  reported that a better alternative to Discovery
  Learning was Guided Instruction.(Kirschner,
  Sweller, Clark, 2006).

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Our Teaching Strategy-3

• Guided Instruction produced more immediate recall
  of facts than unguided approaches along with
  longer term transfer and problem-solving skills
  as per Sweller.
• Support for the regulation the learning process
  in discovery learning includes various measures
• 1. Model progression, such as step-by-step model
  expansion (e. g. expanding the complexity of the
  model).
• 2. Planning support (e. g. using guiding
  questions, quests or even assignments).
• 3. Monitoring support (e. g. show what has
  already be done in the simulation)
• 4. Structuring the discovery process (e. g.
  providing students with a sequenced structure
  such as "set-up, do, reflect").

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FRAMEWORK
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System Modules
Evolving a theoretical framework on which the
system will be designed -meenakshi
Implementation of Kruskals Algorithm, Knapsack
Problem-mukund
Implementation of Prims, Activity Selection
Algorithm and Dijkstras-meenakshi

• Login module and Statistical Analysis
  Module-mukund

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System Modules
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DETAIL DESIGN
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Why Greedy Algorithms

• Text Book Material not suffice
• 2. Passive way of teaching
• 3. Student just rode them
• 4. Difficult for them to construct a new
  algorithm by themselves

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LOGIN AND STATISTICAL ANALYSIS

• New users are supposed to sign up before using
  the system.
• A Sign up jsp page is created to take user input.
• A servlet takes this input and stores in a mysql
  database through jdbc.
• No part of the user input is directly used as an
  Sql command
• Registered users login into the system.
• A CheckUser servlet verifies user credentials in
  the database and redirects accordingly.
• Checking into the database is with limited
  privileges for further security.
• Instructor or admin can also log in to view
  activities of the users..
• This is further explained in detail.

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Statistics

• Instructor can check the overall progress of
  class and also detailed progress of each student.
• Overall progress of the class shows statistics
  like how many students have registered for this
  system, how many have attempted it, how many have
  completed doing the work.
• Instructor can also view email-ids of all the
  users.
• Instructor can choose for the detailed progress
  of any student.

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Screenshot stat1
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Statistics

• Here instructor can see the no. of parts of the
  tutor completed by the user.
• Instructor also sees the flow of student through
  the system.
• This tells him the misconceptions in the mind of
  student.

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Screenshot Stat2
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Screenshot Stat3
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Technologies used

• 1.JSP
• 2.CSS3
• 3.SVG
• 4.JavaScript
• 5.jQuery
• 6.Mysql database

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Technologies Used

• 1.JSP
• All the web pages are java servlet pages.
• All pages contain an inline header to send the
  details to mysql database.
• The details that get entered include the user who
  is browsing the page and the date at which it is
  browsed.
• For logging in and registering new user a form
  field is used which gives the user input to
  CheckUser and SaveUser servlet.
• They connect the database through jdbc drivers.

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JSP
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CheckUser
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Technologies used

• 2.CSS3
• For styling of the webpage css is used.
• A base template was taken from free sources then
  it was tweaked for our purpose.

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SVG

• Scalable Vector Graphics are used for making
  puzzle drawings.
• They are lightweight and do not require any
  settings on the part of browser for them to run.
• They are based of pathElements which can be
  accessed by JavaScript objects.
• Their attributes can easily be changed for our
  need on the client side itself.

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JavaScript

• JavaScript is used for client side programming.
• For this JavaScript must be enabled in browsers.
• JavaScript according to user input like
  mousehover or mouseclick changes the attributes
  of svg elements.
• It is also used for making timer or time limit
  within which the puzzle is to be solved.
• Overall client side interactivity is managed by
  JavaScript.

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jQuery

• jQuery is used for making the quiz module.
• Styled progress bars were tweaked for our purpose
  available from the open sources.
• jQuery was used as it serves many purposes like
• Student cannot jump to any random question by
  just observing the url pattern and then by
  manually typing that pattern.
• The calculation of the marks on the server side
  without user control and without the use of
  database.

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MySql

• MySql is used to serve as a database on the
  server side.
• The connection to it is through a secured user
  with limited privileges. So it cannot be
  manipulated through the client side.
• Two tables are maintained one for the registered
  members and other for keeping the log.
• No part of the user input is made a direct sql
  command to prevent any sql injections.

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Color Scheme

• To maintain the consistency and to be able to
  relate to the user's sub-conscious mind a color
  coding scheme is followed throughout.

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Click-ability feature

• All the svg images are made clickable wherever
  possible, to come to an answer user has to click
  on appropriate parts of the image.
• Thus user is not just sitting and watching some
  "movie" but is actually doing the things.

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Finding Satisfying Condition

• Explanation of Satisfying Condition along with
  choices provided which are close to what a user
  can think.
• User has to select one of them, and is visualized
  according to his/her respective choice.

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Finding Optimal Selection Criteria

• We want student to find the optimal selection
  criteria by himself.
• This will not make learning boring as student
  himself will have to discover the optimal
  function apart from the fact that he will be
  guided if he goes to the wrong path.
• To make the design process explicit, we list some
  selection functions initially and ask learner to
  discover the optimal one.

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Visualization of Contradicting Examples

• 1. A controller has been provided which will
  visulaize the wrong functions based on the user
  input.
• 2. It helps in finding the proof of wrong
  functions and will help in removing common
  misconceptions from user's mind.

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REAL LIFE EXAMPLE
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QUIZ
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LEARNING AND ATTRACTIVENESS EVALUATION

• Implementation
• We took 20 students, from B.tech 2nd year from
  IIT Bombay who were not knowing about this
  algorithm before.We asked them to ?ll a survey
  form to evaluate our system on di?erent
  parameters.

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Sample

• Our sample is B.tech 2nd year students as design
  and analysis of algorithms is taught in 2nd year
  of B.tech.

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Data Collection

• The instruments of our data collection were
• 1. Survey
• Test Score
• Attempts
• Open ended Feedback
• 4 point likert scale
• 2. we interacted with student to know their
  general view about the system.

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Data Analysis

• The instruments of our data collection were
• 1. Survey
• Test Score - is used to identify whether student
  has learned the algorithm or not.
• Attempts - helpful to infer that in how many
  attempts was student able to learn
• and perform.
• Open ended Feedback - we qualitativly analysed
  the text in consultation with
• education technology researchers to verify the
  quotes that we obtained from the
• qualitative analyses.
• 4 point likert scale - We have used it to deduce
  inferences.
• 2. we interacted with student to know their
  general view about the system.

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Results

• Following are the inferences we drawn from the
  qualitative analysis
• Intelligent Tutoring System has been effective
  in learning as students were able to understand
  the algorithm as they answered and can be deduce
  from the test results.
• ITS was interesting as students and it to be
  fun working with it.
• It build up confidence in students to apply same
  algorithm in other example also.
• Student would like to study other topics also in
  these kind of ITS as it helps in fast learning,
  teaches more in less time, easy to learn and
  interesting.
• It increases the understanding of how to
  approach a problem.
• It was beneficial as it is good to learn new
  things from basics, better than class passive
  ways.
• Re-attempts embedded in the test has been proved
  useful as almost all have improved their score
  using that.

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• Interface
• Interface is user friendly.
• Content
• Content was crisp and attracting user's
  attention as well.
• Using it, understanding was developed in gradual
  manner giving answers to all the questions in
  mind.
• The improvements suggested to us are
• The arrows were taking time to click as they
  were thin.
• More theory should be embedded.
• In Quiz, option to jump to other questions
  should be provided.

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Usability Evaluation

• Implementation
• We have used System Usability Scale(SUS) to
  measure the usability of the system.We took 20
  Btech 2nd year students for testing our system.

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SAMPLE

• We took 20 students, from B.tech 2nd year from
  IIT Bombay who were not knowing about this
  algorithm before.We asked them to ?ll a survey
  form to evaluate our system on di?erent
  parameters.

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DATA COLLECTION

• We asked the students to ?ll System Usability
  Scale(SUS) question are based on 4 point likert
  scale that is Strongly agree, Agree, Disagree and
  Strongly Disagree. Following were the
  questions11
• 1. I think that I would like to use this system
  frequently.
• 2. I found the system unnecessarily complex.
• 3. I thought the system was easy to use.
• 4. I think that I would need the support of a
  technical person to be able to use this system.
• 5. I found the various functions in this system
  were well integrated.
• 6. I thought there was too much inconsistency in
  this system.
• 7. I would imagine that most people would learn
  to use this system very quickly.
• 8. I found the system very cumbersome to use.
• 9. I felt very con?dent using the system.
• 10. I needed to learn a lot of things before I
  could get going with this system.

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Challenges

• To design a system which is unique,
• Effective
• Interesting

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Results

• SUS score was 86.8
• From the graphs we can ?gure out students would
  like to use the system for other topics also and
  would prefer to use this system for studying
  algorithms. System was overall easy to use and
  user friendly which can be depicted from
  questions 2 and 3.The system can be used
  independently and it was homogeneous too. It
  increased students understanding of a the topic

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Future work and Conclusion

• Our system to teach Greedy Algorithms to students
  through visualization is ready. It contains most
  of the popular greedy algorithms.
• We have used the approach of guided discovery
  learning along with a interactive interface which
  makes this system unique in the area of Computer
  Science.
• Through Pilot experiments conducted in ?rst phase
  of this project, we were able to detect the minor
  faults which existed in the system and it helped
  us to make the system more e?ective.
• We tested the system on a bunch of students to
  know its e?ectiveness and we were proved right.
  The system is e?ective overall in teaching greedy
  algorithms in an active way. In near future, we
  would like this system to contain almost all
  popular algorithms of computer science with this
  general framework.
• It would be really helpful for students to study
  from this system as they will gain con?dence
  while working on this system as it will make them
  feel that they themselves have explored the
  algorithm.

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Bibliography

1. AlgoViz.org. the algorithm visualization
   portal, http// algoviz.org/, 2012.
2. Louis Al?eri Patricia J. Brooks and Naomi J.
   Aldrich Harriet R. Tenenbaum Kingston University
   City University of New York. does
   discovery-based instruction enhance learning?.
3. M. Ashraf Iqbal and Sara Tahir. does
   discovery-based instructions should we teach
   algorithms?,iranian journal of electrical and
   computer engineering, vol . 2, no. 2, summer-
   fall 2003.
4. IEEE Computer Society J. Angel Velazquez-Iturbide,
   Member. greedex A visualization tool for
   experimentation and discovery learning of greedy
   algorithms.
5. I Escuela Tcnica Superior de Ingeniera Informtica
   Universidad Rey Juan Carlos C/ Tulipn s/n 28933
   Mstoles Madrid Spain J. ngel Velzquez-Iturbide
   Departamento de Lenguajes y Sistemas Informticos.
   the design and coding of greedy algorithms
   revisited.

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