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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. MeenakshiVerma. Mukund Lahoti. Guided By: Prof. S.R. Iyer – PowerPoint PPT presentation

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


1
Visualization based Intelligent Tutoring System
(ITS) for Greedy Algorithms GATutor
  • By
  • Meenakshi Verma
  • Mukund Lahoti

Guided By Prof. S.R. Iyer
2
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.

3
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.

4
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.

5
Demo
6
RELATED WORK-Different Algorithm Tutor Systems
that already exist
7
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.

8
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9
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

10
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11
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12
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13
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

14
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15
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.

16
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17
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.

18
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20
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21
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.

22
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)

23
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).

24
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").

25
FRAMEWORK
26
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27
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

28
System Modules
29
DETAIL DESIGN
30
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

31
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.

32
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.

33
Screenshot stat1
34
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.

35
Screenshot Stat2
36
Screenshot Stat3
37
Technologies used
  • 1.JSP
  • 2.CSS3
  • 3.SVG
  • 4.JavaScript
  • 5.jQuery
  • 6.Mysql database

38
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.

39
JSP
40
CheckUser
41
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.

42
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.

43
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.

44
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45
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.

46
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47
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.

48
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49
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.

50
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.

51
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.

52
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.

53
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54
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.

55
REAL LIFE EXAMPLE
56
QUIZ
57
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.

58
Sample
  • Our sample is B.tech 2nd year students as design
    and analysis of algorithms is taught in 2nd year
    of B.tech.

59
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.

60
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.

61
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.

62
  • 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.

63
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.

64
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.

65
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.

66
Challenges
  • To design a system which is unique,
  • Effective
  • Interesting

67
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

68
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.

69
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.

70
Bibliography
  1. T.L. Naps. jhave Supporting algorithm
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  2. Polya. how to solve it a new aspect of
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  3. G. Roling and B. Freisleben. animal A system
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  4. Jungsoon Yoo Chrisila Pettey Suk Seo and Sung
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  5. J.A. Velazquez-Iturbide, A. Perez-Carrasco Proc.
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