2011 10 07 (uam) emadrid aortigosa uam estilos aprendizaje sistemas adaptativos enseñanza


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2011 10 07
estilos aprendizaje sistemas adaptativos enseñanza

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  • Concept learning is the induction of a concept (category) from observations Deductive arguments are attempts to show that a conclusion necessarily follows from a set of premises or hypotheses
  • 18 estudiantes We found a strong correlation between maximum vertical speed and sequential/global dimension score. In addition, it was possible to predict this dimension of the students’ learning style with high accuracy (94.4%, correlation coefficient r = - 0.8). This suggests that mouse movement patterns can be a powerful source of information for user model acquisition.
  • Se capturan las coordenadas del cursor cada 40 secs
  • So, if the teacher (course designer) wants to implement some form of cooperative learning, on key issue is, of course, divide the people in groups.
  • I may be the case where groups built following some rational criteria would produce better results. E-learning: usually people don ’t know one to the other, and coordination use to be more difficult than in a face2face environment
  • If we were able to built groups following some “intelligent” criterion, which would be such a criterion? With “heterogeneous” groups I mean groups with diversity, with people with different ...
  • On the most fascinating books I have read ever ... In this books you can read statements like: Even if one of the best, it ’s not the only work to appraise the benefits of diversity
  • So the goal is diversity. I mean: building groups with people as diverse as possible. What you take into account (from the student profile) to measure diversity can change. Even if our focus is on L.S. the techniques can apply to any measurable dimension of the student personality
  • El problema es explicarlo sin fórmulas ni gráficos
  • We can think on genetic algorithms
  • is behind the name of the fifth track from U2's 1993 album, Zooropa,
  • Good results are more likely to be obtained if we could try different configurations
  • So, with the goal of trying different setups, is when TOGETHER comes to scene Basically, the tool executes the algorithm 100 times with different order of students, and store the best solution. Now, WAIT A MOMENT! What about “it’s no possible to decide which is the best solution” story I was telling?
  • Now, how does TOGETHER visualize this information? Remember that visualization is an aid for the final decision, and because of that showing only a two most significant dimensions seems a sensible decision. Each dot represents a student, and the position of the dot represents the respetive L.S. dimension values.
  • Hablar del significado de los colores
  • Then we have two numbers (describing somehow the group), that we can use in a new, different visualization. The numbers on each dimension are sum of distances, and of course they are always positive numbers
  • When we put together the information about all the groups, we have this chart. Colors are used to show how much groups share the same point in the “distances’ space”
  • In this chart, Up and right is good, down and left is bad
  • OK de los profesores Hablar del cambio de actitud
  • 2011 10 07 (uam) emadrid aortigosa uam estilos aprendizaje sistemas adaptativos enseñanza

    1. 1. Learning Style Adaptation in Adaptive Educational Systems October 7th, 2011
    2. 2. People <ul><li>Alvaro Ortigosa </li></ul><ul><li>Danilo Spada </li></ul><ul><li>José Ignacio Quiroga </li></ul><ul><li>José Martín </li></ul><ul><li>Pedro Paredes </li></ul><ul><li>Pilar Rodríguez </li></ul><ul><li>Rosa M. Carro </li></ul>
    3. 3. Learning Styles <ul><li>Definition </li></ul><ul><li>Acquisition </li></ul><ul><li>Use </li></ul><ul><ul><li>Individual </li></ul></ul><ul><ul><li>Collaboration </li></ul></ul>
    4. 4. 1: LS definition <ul><li>Learning style: </li></ul><ul><li>Characteristic strengths and preferences in the ways [people] take in and process information (Richard Felder, “Matters of Style”) </li></ul><ul><li>Each individual learn differently </li></ul><ul><li>Two approaches: </li></ul><ul><ul><li>To overlook the L.S. giving priority to teaching style (teacher- or content-centered teaching) </li></ul></ul><ul><ul><li>To identify L.S. and to adapt taking them into consideration (student-centered teaching) </li></ul></ul><ul><ul><ul><ul><li>Fostering her way of learning </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Helping her to develop new ways of learning </li></ul></ul></ul></ul>
    5. 5. LS: Models <ul><li>The Myers-Briggs Type Indicator (MBTI) </li></ul><ul><ul><li>Extraverts-introverts </li></ul></ul><ul><ul><li>Sensors-intuitors </li></ul></ul><ul><ul><li>Thinkers-feelers </li></ul></ul><ul><ul><li>Judgers-perceivers </li></ul></ul><ul><li>Kolb ’s Learning Style Model </li></ul><ul><ul><li>Type I (divergers) </li></ul></ul><ul><ul><li>Type II (assimilators) </li></ul></ul><ul><ul><li>Type III (convergers) </li></ul></ul><ul><ul><li>Type IV (accomodators) </li></ul></ul><ul><li>Herrmann Brain Dominance Instrument (HBDI) </li></ul><ul><ul><li>Quadrant A ( left brain, cerebral) </li></ul></ul><ul><ul><li>Quadrant B (left brain, limbic) </li></ul></ul><ul><ul><li>Quadrant C (right brain, limbic) </li></ul></ul><ul><ul><li>Quadrant D (right brain, cerebral) </li></ul></ul>
    6. 6. LS: Felder model <ul><li>Four dimensions (Five initially): </li></ul><ul><ul><li>Sensing/Intuitive </li></ul></ul><ul><ul><li>Visual/Verbal </li></ul></ul><ul><ul><li>Active/Reflexive </li></ul></ul><ul><ul><li>Sequential/Global </li></ul></ul><ul><ul><li>Inductive/Deductive (removed by pedagogical reasons) </li></ul></ul>LEARNING AND TEACHING STYLES IN ENGINEERING EDUCATION [ Engr. Education, 78(7), 674–681 (1988)]
    7. 7. LS: Felder model: sensing/intuitive <ul><li>Sensors like facts, data, and experimentation; i ntuitors prefer principles and theories. </li></ul><ul><li>Sensors are patient with detail but do not like complications; intuitors are bored by detail and welcome complications. </li></ul><ul><li>Sensors are good at memorizing facts; intuitors are good at grasping new concepts. </li></ul><ul><li>Sensors are careful but may be slow; intuitors are quick but may be careless. </li></ul>
    8. 8. LS: Felder model: visual /verbal <ul><li>Visual learners remember best what they see: pictures, diagrams, flow charts, time lines, films, demonstrations. If something is simply said to them they will probably forget it. </li></ul><ul><li>Verbal (auditory) learners remember much of what they read (hear) and more of what they read (hear) and then write (say). They get a lot out of discussion, prefer verbal explanation to visual demonstration, and learn effectively by explaining things to others. </li></ul>
    9. 9. LS: Felder model: active /reflective <ul><li>An “active learner” is someone who feels more comfortable with, or is better at, active experimentation than reflective observation, and conversely for a reflective learner. </li></ul><ul><li>Active learners do not learn much in situations that require them to be passive, and reflective learners do not learn much in situations that provide no opportunity to think about the information being presented. </li></ul><ul><li>Active learners work well in groups; reflective learners work better by themselves or with at most one other person. </li></ul>
    10. 10. LS: Felder model: sequential /global <ul><li>Sequential learners follow linear reasoning processes when solving problems; global learners make intuitive leaps and may be unable to explain how they came up with solutions. </li></ul><ul><li>Sequential learners can work with material when they understand it partially or superficially, while global learners may have great difficulty doing so. </li></ul><ul><li>Sequential learners learn best when material is presented in a steady progression of complexity and difficulty; global learners sometimes do better by jumping directly to more complex and difficult material. </li></ul>
    11. 11. 2: UM adquisition Asking the user (or teacher or …) Observing user behavior Student User Model
    12. 12. UM adquisition regarding <ul><li>Deduction </li></ul><ul><ul><li>Test / questionnaires (ILS) </li></ul></ul><ul><li>Induction </li></ul><ul><ul><li>User behavior when interacting with the application or related applications </li></ul></ul><ul><ul><ul><li>Mouse movements </li></ul></ul></ul><ul><ul><ul><li>Behavior in social network applications </li></ul></ul></ul><ul><ul><ul><li>Mouse movements in s.n. applications </li></ul></ul></ul><ul><ul><li>Adaptive questionnaires </li></ul></ul>
    13. 13. ILS questionnaire <ul><li>For each of the four dimensions </li></ul><ul><ul><li>11 questions, 2 possible answers (1 or -1) </li></ul></ul><ul><ul><li>12 different possible values </li></ul></ul><ul><li>It provides a lot of opportunities for adaptation </li></ul>
    14. 14. But… <ul><li>(At least in Engineering fields) Students are not motivated to fulfill questionnaires </li></ul><ul><ul><li>44Q x LS + 60Q x Personality + 15 ’ test x IQ </li></ul></ul><ul><ul><li>Surveys about teacher performance, workload, “ Bologna system ” , etc. etc. </li></ul></ul><ul><ul><li>“ Is it part of the evaluation? ” </li></ul></ul><ul><li>Students tend to answer more careless as they go through the questions </li></ul><ul><li> As the number of questions grows, answers become less reliable </li></ul>
    15. 15. Inducing from mouse movements Rosa M. Carro, Department of Computer Science, Universidad Autónoma de Madrid Mouse movements Learning styles (ILS) Offline processing sequential global seq global m aximum vertical speed (pixels/ms) m aximum vertical speed (pixels/ms) r = -0.8 accuracy = 94.4%
    16. 16. Inducing from behavior in S.N. <ul><li>Facebook application </li></ul><ul><ul><li>Users answered two questionnaires: </li></ul></ul><ul><ul><ul><li>Personality and L.S. </li></ul></ul></ul><ul><ul><li>The application took data from user profiles </li></ul></ul><ul><li>Personality test: > 74000 users </li></ul><ul><ul><li>“ Only ” 20988 users with all data required for the analysis </li></ul></ul><ul><li>L.S. test: 680 users </li></ul><ul><ul><li>Only 378 users with all data </li></ul></ul>
    17. 19. L.S. from Facebook behavior: results Learning style test (n=378) Tree (friends) Tree Fisher (friends) Fisher Active/Reflexive 46,44% 57,00% 60,98% 57,97% Visual/Verbal 52,00% 52,66% 52,41% 37,68% Sensing/Intuitive 40,80% 43,48% 43,09% 41,55% Sequential/Global 60,80% 57,91% 66,67% 62,80%
    18. 20. L.S. from Facebook behavior: results If users has posted more than 10 links, more than 6 friends has posted in ther wall during the last year, she has more than 85 friends, is member of at most 13 groups and has more than 34 posts in her wall, then she has a preference for the verbal style
    19. 21. Mouse movements in S.N. <ul><li>Facebook application </li></ul><ul><ul><li>Read a text </li></ul></ul><ul><ul><li>Test: choose an answer using radio buttons </li></ul></ul><ul><ul><li>Answer T/F questions (check boxes) </li></ul></ul><ul><ul><li>Sort chronologically a list of events </li></ul></ul><ul><ul><li>Browse through a menu </li></ul></ul>
    20. 22. Mouse movements in S.N. (ii) <ul><li>Data analyzed: </li></ul><ul><ul><li>Time to complete the activity </li></ul></ul><ul><ul><li>Distance (total, horizontal, vertical) </li></ul></ul><ul><ul><li>Speed (average, maxi) </li></ul></ul><ul><ul><li>Acceleration (average, max, min) </li></ul></ul><ul><li>Users with mouse separated from users with touchpad </li></ul>
    21. 23. Mouse movements in S.N. (iii) Dimension Mouse (n=20) Touchpad(n=13) Active/Reflexive 60,00% 46,15% Visual/Verbal 50,00% 61,54% Sensing/Intuitive 55,00% 53,85% Sequential/Global 35,00% 46,15% Dimension Tool Activity Relevant variables Active/Reflexive Mouse Test, text Mean horizontal acceleration Max vertical acceleration Visual/Verbal Touchpad Sorting Time used Sensing/Intuitive Mouse T/F T/F Min vertical acceleration Max vertical acceleration Sensing/Intuitive Touchpad Browsing Max vertical acceleration
    22. 24. Downsizing the ILS <ul><li>In our experience with teachers, most of the times they just require categorization </li></ul>-11 -9 -7 -5 -3 -1 1 3 5 7 9 11 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 Sequential Neutral Global
    23. 25. Do we need all the questions? <ul><li>If only three categories are needed, would it be possible to ask fewer questions? </li></ul><ul><li>If possible, which questions (among the 11 for a given dimension) would provide more (enough) information about the student learning style? </li></ul>1) I understand something better after I a) try it out b) think it through 2) I would rather be considered a) realistic b) innovative
    24. 26. The goal <ul><li>To ask each student as few questions as possible </li></ul><ul><li>We don ’ t even need to ask the same questions! </li></ul>
    25. 27. The goal (ii) <ul><li>Not a new questionnaire, but an adaptive version of the ILS </li></ul>In groups Alone Something I have done Something I have thought a lot about …
    26. 28. The idea <ul><li>Using a database of actual answers from real students </li></ul><ul><li>To use machine learning techniques in order </li></ul><ul><li>To find most relevant questions for each dimension </li></ul><ul><ul><li>Depending on previous answers </li></ul></ul>
    27. 29. Using classification techniques Model Training examples (instances) Learning algorithm New instances Classified Instances
    28. 30. How does a classifier work? <ul><li>Each instance is represented by a set of attribute values. </li></ul><ul><li>Training examples are (usually) already classified. </li></ul><ul><li>Classifier model (usually) uses a subset of attributes (conditions, linear combinations, etc.) </li></ul><ul><li>Each student represented by her answers to the 11 questions </li></ul><ul><li>The class is the category she belongs </li></ul><ul><li>Which attributes (questions) does the learnt model use? </li></ul>-11 -9 -7 -5 -3 -1 1 3 5 7 9 11 Sequential Neutral Global
    29. 31. Classification trees <ul><li>In classification trees, each node tests a single attribute (question). </li></ul><ul><li>Classification trees explicitly shows the learnt model. </li></ul><ul><ul><li>It points to the relevant questions. </li></ul></ul><ul><li>Different branches on a classification tree can test different attributes. </li></ul><ul><li>Tree construction aimed to get shorter paths </li></ul><ul><ul><li>C4.5 algorithm chooses next attribute (question) based on the information gain . </li></ul></ul>
    30. 32. Data collection <ul><li>Three different samples: </li></ul><ul><ul><li>42 secondary school level students. </li></ul></ul><ul><ul><li>88 post-secondary level students. </li></ul></ul><ul><ul><li>200 university level students </li></ul></ul><ul><ul><li>Between 15 and 30 years old </li></ul></ul><ul><ul><li>101 women and 229 men </li></ul></ul>
    31. 33. Data collection (ii) Active/reflective Sensing/intuitive Visual/verbal Sequential/global
    32. 34. Results
    33. 35. Results (ii) <ul><li>Other results seem to indicate: </li></ul><ul><ul><li>a) The relevance of a question does not vary significantly with the age of the student. </li></ul></ul><ul><ul><li>b) The trees seem to converge to a common tree, independently from the origin of the sample, or at least to a common subset of questions. </li></ul></ul>
    34. 36. Analyzing results <ul><li>Some questions of the ILS provide more information than others. </li></ul><ul><li>We were able to build dynamic (shorter) questionnaires with high precision. </li></ul><ul><ul><li>On the average, 4-5 questions needed for each dimension. </li></ul></ul><ul><li>The size of the sample (>300) enough for providing good information about 11 questions. </li></ul><ul><li>Ad-hoc trees would be better only if the sample is large enough. </li></ul><ul><li>Gender does not seem to affect the outcome </li></ul>
    35. 37. Some limitations <ul><li>More categories will require more questions and larger training sets </li></ul><ul><li>The approach is not useful when the exact value for each dimension is needed </li></ul><ul><ul><li>For example, automatic grouping </li></ul></ul>
    36. 38. 3: Using L.S. for adaptation <ul><li>Adapting to individual activities </li></ul><ul><li>Adapting to collaborative activites </li></ul>
    37. 39. Adapting to LS: an example ILS VALUE ON SEQUENTIAL/GLOBAL: Extreme and mild Sequential Well balanced Extreme and moderate Global
    38. 40. Adapting to LS: an example ILS VALUE ON SEQUENTIAL/GLOBAL: Extreme and mild Sequential Well balanced Extreme and moderate Global
    39. 41. Improving collaborative work <ul><li>Benefits of cooperative learning are well known: </li></ul><ul><ul><li>Better understanding, more skills are developed, ... </li></ul></ul><ul><li>One issue to be decided is group formation </li></ul><ul><ul><li>just another little task for the teacher </li></ul></ul>
    40. 42. Group formation <ul><li>Usual way in face to face education: “Split up in groups and do ...” </li></ul>
    41. 43. Group formation <ul><li>Usual way in face to face education: “Split up in groups and do ...” </li></ul><ul><li>“ Rational” groups wouldn’t be better? </li></ul><ul><li>What about e-learning systems? </li></ul><ul><li>There is a need for supporting group formation </li></ul>
    42. 44. What kind of groups? <ul><li>Some previous work showed that heterogeneous groups perform better than homogeneous </li></ul><ul><ul><li>Learning styles </li></ul></ul><ul><ul><li>Abilities, skills </li></ul></ul><ul><ul><li>Background </li></ul></ul><ul><ul><li>Etc. </li></ul></ul>
    43. 45. The Wisdom of Crowds <ul><li>“ ... the simple fact of making a group diverse makes it better at problem solving. ” </li></ul><ul><li>“ ... groups that are too much alike find it harder to keep learning, because each member is bringing less and less new information to the table. ” </li></ul>James Surowiecki
    44. 46. Heterogeneus groups <ul><li>In previous experiments, we have found that students with different Learning Styles: </li></ul><ul><ul><li>Learn better (post tests) </li></ul></ul><ul><ul><li>Produce better results (products of the collaborative task) </li></ul></ul>The impact of learning styles on student grouping for collaborative learning: a case study. Alfonseca el al. UMUAI 16(3–4), 377–401
    45. 47. The goal <ul><li>Building groups as diverse as possible </li></ul><ul><li>Different student ’s features can be considered </li></ul><ul><ul><li>L.S. is the focus of our research </li></ul></ul>Diversity
    46. 48. What we need <ul><li>Criteria for comparing (distance of) students </li></ul><ul><li>Criteria for comparing groups </li></ul><ul><li>Criteria for comparing sets of groups </li></ul>
    47. 49. The problem <ul><li>Finding an optimal solution </li></ul><ul><ul><li>Can be very slow with a brute force approach </li></ul></ul><ul><ul><li>Sophisticated algorithms (genetic algorithms) can make the trick </li></ul></ul><ul><li>Even then, there is no clear candidate for optimal solution </li></ul><ul><ul><li>In 3D area of triangles is good candidate </li></ul></ul>
    48. 50. Supervised method <ul><li>The Faraway-so-close algorithm, able to build well-balanced heterogeneous groups in “short time” </li></ul><ul><ul><li>The goodness of the solution depends on some parameters </li></ul></ul><ul><li>TOGETHER visualization tool, which supports the application of the algorithm with different parameters and comparing the results visually </li></ul>
    49. 51. Analyzing Faraway-so-close <ul><li>It does not look for an optimal solution, but it uses heuristics to approximate a (fairly) good one </li></ul><ul><li>Heavy dependency on: </li></ul><ul><ul><li>Initial order of the students </li></ul></ul><ul><ul><li>Pair and group thresholds </li></ul></ul><ul><li>Good solutions are more likely to be obtained if different configurations are tried </li></ul><ul><ul><li>Teacher can use her criteria for choosing “ best ” solution </li></ul></ul>
    50. 52. TOGETHER <ul><li>Iterative application of Faraway-so-close with different initial sorting </li></ul><ul><ul><li>Typically > 100 runs </li></ul></ul><ul><ul><li>“ Best” set of groups is kept </li></ul></ul><ul><li>User (teacher?) responsible for: </li></ul><ul><ul><li>Choosing the criterion to “ optimize ” </li></ul></ul><ul><ul><li>Choosing preferred solution => VISUALIZATION </li></ul></ul>
    51. 53. Visualizing students Visualization of the 2 dimensions that showed to be the most relevant in previous research values for dimension 1 values for dimension 2
    52. 54. Visualizing students <ul><li>S(-3,7) </li></ul><ul><ul><li>Dim 2 (S(-3,7))=-7 </li></ul></ul><ul><li>S(5,-3) </li></ul>
    53. 55. Visualizing 166 students
    54. 56. Visualizing groups 1 28 2 24
    55. 57. Visualizing groups 1 group 2 groups 3 groups
    56. 58. Visualizing groups worse better 1 group 2 groups 3 groups
    57. 59. Constrasting solutions
    58. 60. Constrasting groups
    59. 61. Results <ul><li>Tested with 165 students (high school and professional formation). </li></ul><ul><ul><ul><li>Some groups formed by the tool and some decided by the own students. </li></ul></ul></ul><ul><li>Groups built by TOGETHER had a mean of 7.86 “ points ” , vs. 6.61 of the control group. </li></ul>
    60. 62. The future <ul><li>Are findings (regarding L.S.) still valid when considering on-line collaboration? </li></ul><ul><li>Can we improve L.S. (personality) detection through related applications? </li></ul><ul><ul><li>Text analysis in Facebook </li></ul></ul><ul><li>Can L.S. support other type of adaptations? </li></ul>
    61. 63. Thanks! Questions? 7th International Workshop on Authoring of Adaptive and Adaptable Hypermedia [email_address]