Gennari vittorini-terence-ai*ia-2014
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How can we use Artificial Intelligence (AI) for improving reading comprehension of poor comprehenders? This presentation, delivered at AI*IA 2014 for Society in Pisa (Italy), explain how natural language processing and constraint-based reasoning technologies can help in automating the generation of serious games for improving the reading comprehension of poor comprehenders. The slides also analyse the results in terms of pedagogical effectiveness of the system for improving reading comprehension and quality of the automated generation process for delivering games.
![ONCE%UPON%A%TIME…
[…]%After%two%days%of%hot%sun%and%of%swimming%in%the%lake,%
today%it%was%the%big%match%for%Alessio,%the%last%game%of%the%
season%in%his%home%town,%l’Aquila.%There%was%only%a%minute%
of%the%game%left.%The%crowd%watched%in%silence%as%Alessio%
took%the%penalty%shot…%%And%the%crowd%of%l’Aquila%cheered%
and%roared!](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Gennari vittorini-terence-ai*ia-2014
- 1. Arti6icial*Intelligence for Improving*Children’s*Reading*Comprehension Rosella*Gennari http://www.inf.unibz.it/~gennari Pierpaolo*Vittorini* http://vittorini.univaq.it www.terenceproject.eu
- 2. ONCE%UPON%A%TIME… […]%After%two%days%of%hot%sun%and%of%swimming%in%the%lake,% today%it%was%the%big%match%for%Alessio,%the%last%game%of%the% season%in%his%home%town,%l’Aquila.%There%was%only%a%minute% of%the%game%left.%The%crowd%watched%in%silence%as%Alessio% took%the%penalty%shot…%%And%the%crowd%of%l’Aquila%cheered% and%roared!
- 3. ONCE%UPON%A%TIME… […]%After%two%days%of%hot%sun%and%of%swimming%in%the%lake,% today%it%was%the%big%match%for%Alessio,%the%last%game%of%the% season%in%his%home%town,%l’Aquila.%There%was%only%a%minute% of%the%game%left.%The%crowd%watched%in%silence%as%Alessio% took%the%penalty%shot…%%And%the%crowd%of%l’Aquila%cheered% and%roared! When%does%the%game%take%place?% Who%wins? What%does%Alessio%play?
- 4. ONCE%UPON%A%TIME… […]%After%two%days%of%hot*sun*and*of*swimming*in*the* lake,%today%it%was%the%big%match%for%Alessio,%the%last%game%of% the%season%in%his%home%town,%l’Aquila.%There%was%only%a% minute%of%the%game%left.%The%crowd%watched%in%silence%as% Alessio%took%the%penalty%shot…%%And%the%crowd%of%l’Aquila% cheered%and%roared! When%does%the%game%take%place?% Who%scores? What%does%Alessio%play?
- 5. ONCE%UPON%A%TIME… […]%After%two%days%of%hot%sun%and%of%swimming%in%the%lake,% today%it%was%the%big%match%for%Alessio,*the*last*game*of*the* season*in*his*home*town,*l’Aquila.%There%was%only%a% minute%of%the%game%left.%The*crowd*watched*in*silence*as* Alessio*took*the*penalty*shot…**And*the*crowd*of*l’Aquila* cheered*and*roared! When%does%the%game%take%place?% Who%scores? What%does%Alessio%play?
- 6. ONCE%UPON%A%TIME… […]%After%two%days%of%hot%sun%and%of%swimming%in%the%lake,% today%it%was%the%big%match%for%Alessio,%the*last*game*of*the* season*in*his*home*town,*l’Aquila.*There*was*only*a* minute*of*the*game*left.*The*crowd*watched*in*silence*as* Alessio*took*the*penalty*shot…**And*the*crowd*of*l’Aquila* cheered*and*roared! archivio.riparteilfuturo.it When%does%the%game%take%place?% Who%scores? What%does%Alessio%play?
- 7. THE%PROBLEM ‣More%than%10%%of%primary%school% children,%older%than%8,%are%diagnosed% with%deep%text%comprehension% problems,%e.g.,%they%fail%answering% when%does%the%game%take%place?%% ‣They%are%referred%to%as%poor* comprehenders ?
- 8. THE%PROBLEM ‣More%than%10%%of%primary%school% children,%older%than%8,%are%diagnosed% with%deep%text%comprehension% problems,%e.g.,%they%fail%answering% when%does%the%game%take%place?%% ‣They%are%referred%to%as%poor* comprehenders ‣Can%technology%help%them? ?
- 9. OUTLINE 1. Technology%enhanced%learning% 2. The%TERENCE%case%study% 3. Game%over%
- 11. TECHNOLOGY%ENHANCED%LEARNING%(TEL) ="technology2for2the2growth"of"a"learning"experience innaffiatoio AI**TEL
- 12. TECHNOLOGY%ENHANCED%LEARNING%(TEL) ="technology2for2the2growth"of"a"learning"experience innaffiatoio AI**TEL ARTIFICIAL INTELLIGENCE (AI)
- 13. TECHNOLOGY%ENHANCED%LEARNING%(TEL) ="technology2for2the2growth"of"a"learning"experience innaffiatoio AI**TEL How%can%we%% design%and%evaluate%% AI%products%that%support%% the%growth%of%their%users’% learning*experience?% ARTIFICIAL INTELLIGENCE (AI)
- 14. TEL%4%LEARNING%EXPERIENCE Montessori:2adequate"tasks2that2come2in2a2prepared"environment2 designed2on2top2of2the2learner2characteristics"can2effectively2support2 learning innaffiatoio
- 15. TEL%4%LEARNING%EXPERIENCE Montessori:2adequate"tasks2that2come2in2a2prepared"environment2 designed2on2top2of2the2learner2characteristics"can2effectively2support2 learning ousability%of%% learning%products innaffiatoio
- 16. TEL%4%LEARNING%EXPERIENCE Montessori:2adequate"tasks2that2come2in2a2prepared"environment2 designed2on2top2of2the2learner2characteristics"can2effectively2support2 learning ousability%of%% learning%products innaffiatoio opedagogical%effectiveness%% of%products%for%learning*growth
- 17. OUTLINE 1. Student%centred%learning%and%technology% enhanced%learning% 2. The%TERENCE%case%study% 3. Game%over
- 18. THE%TERENCE%ADAPTIVE%SOLUTION w"h"o"" a"r"e"" y"o"u"? www.terenceproject.eu
- 19. THE%TERENCE%ADAPTIVE%SOLUTION w"h"o"" a"r"e"" y"o"u"? a"d"e"q"u"a"t"e" b"o"o"k""o"f""" s"t"o"r"i"e"s www.terenceproject.eu
- 20. THE%TERENCE%ADAPTIVE%SOLUTION w"h"o"" a"r"e"" y"o"u"? a"d"e"q"u"a"t"e" b"o"o"k""o"f""" s"t"o"r"i"e"s a"d"e"q"u"a"t"e" s"m"a"r"t" g"a"m"e"s www.terenceproject.eu
- 21. THE%TERENCE%ADAPTIVE%SOLUTION w"h"o"" a"r"e"" y"o"u"? a"d"e"q"u"a"t"e" b"o"o"k""o"f""" s"t"o"r"i"e"s r"e"w"a"r"d a"d"e"q"u"a"t"e" s"m"a"r"t" g"a"m"e"s www.terenceproject.eu
- 22. THE%TERENCE%ADAPTIVE%SOLUTION a"d"e"q"u"a"t"e" b"o"o"k""o"f""" s"t"o"r"i"e"s s"i"g"n""i"n a"d"e"q"u"a"t"e" s"m"a"r"t" g"a"m"e"s r"e"w"a"r"d www.terenceproject.eu
- 23. HOW%DID%WE%GET%TO%THE% TERENCE%SOLUTION Requirements Prototypes Analytic + small ev. Int. prod. Large ev. Fin. prod. www.terenceproject.eu
- 24. HOW%DID%WE%GET%TO%THE% TERENCE%SOLUTION Requirements Prototypes Analytic + small ev. Int. prod. Large ev. Fin. prod. www.terenceproject.eu
- 25. HOW%DID%WE%GET%TO%THE% TERENCE%SOLUTION Requirements Prototypes Analytic + small ev. Int. prod. Large ev. Fin. prod. www.terenceproject.eu
- 26. HOW%DID%WE%GET%TO%THE% TERENCE%SOLUTION Requirements Prototypes Analytic + small ev. Int. prod. Large ev. Fin. prod. www.terenceproject.eu
- 27. HOW%DID%WE%GET%TO%THE% TERENCE%SOLUTION Requirements Prototypes Analytic + small ev. Int. prod. Large ev. Fin. prod. www.terenceproject.eu
- 28. Smart*game*design problem: 256 stories, each with ~12 games www.terenceproject.eu
- 29. Smart*game*design how can we automatise the development of smart games via AI (and, hopefully, be efficient)? www.terenceproject.eu
- 30. SemiRautomated*generation story GUI Layer ALS Layer Educator Expert Expert GUI Learner GUI Learner Reasoning Module Reasoner Adaptive Engine NPL illustrations Visualisation Annotation
- 31. Module Visualisation Module www.terenceproject.eu
- 32. SemiRautomated*generation story annotations GUI Layer ALS Layer Educator Expert Expert GUI Learner GUI Learner Reasoning Module Reasoner Adaptive Engine NPL illustrations Visualisation Annotation
- 33. Module Visualisation Module www.terenceproject.eu
- 34. SemiRautomated*generation story annotations enriched annotations GUI Layer ALS Layer Educator Expert Expert GUI Learner GUI Learner Reasoning Module Reasoner Adaptive Engine NPL illustrations Visualisation Annotation
- 35. Module Visualisation Module www.terenceproject.eu
- 36. SemiRautomated*generation story annotations enriched annotations GUI Layer ALS Layer Educator Expert Expert GUI Learner GUI Learner Reasoning Module Reasoner Adaptive Engine NPL illustrations Visualisation Annotation
- 37. Module Visualisation Module www.terenceproject.eu
- 38. SemiRautomated*generation story annotations enriched annotations GUI Layer ALS Layer Educator Expert Expert GUI Learner GUI Learner Reasoning Module Reasoner Adaptive Engine NPL illustrations Visualisation Annotation
- 39. Module Visualisation Module www.terenceproject.eu
- 40. SemiRautomated*generation story enriched annotations text Educator Expert Expert GUI Learner GUI text text text annotations GUI Layer ALS Layer Learner Reasoning Module Reasoner Adaptive Engine NPL illustrations Visualisation Annotation
- 41. Module Visualisation Module www.terenceproject.eu
- 42. SemiRautomated*generation image text image image image text tex t text story annotations enriched annotations www.terenceproject.eu
- 43. SemiRautomated*generation image text image image image text text text story annotations enriched annotations www.terenceproject.eu
- 44. SemiRautomated*generation template visual image text image image image text text text story annotations enriched annotations GUI Layer Educator Expert Learner Expert GUI Learner GUI Visualisation Module Visualisation www.terenceproject.eu
- 45. games SemiRautomated*generation template visual image text image image image text text text story annotations enriched annotations GUI Layer Educator Expert Learner Expert GUI Learner GUI Visualisation Module Visualisation www.terenceproject.eu
- 46. SemiRautomated*generation story text games text + visual AUTOM. ➤MANUAL ➤ AUTOM. www.terenceproject.eu
- 47. HOW%DID%WE%GET%TO%THE% TERENCE%SOLUTION Requirements Prototypes Analytic + small ev. Int. prod. Large ev. Fin. prod. www.terenceproject.eu
- 48. EVALUATION%IN%TERENCE APPROACH WITH%WHOM EXAMPLE%METHODS WHEN analytical HMI%experts%or% domain%experts heuristic%evaluation formative,% expert%evaluation summative cognitive%walk`through small`scale learners observations formative think%aloud large`scale learners aield%studies summative www.terenceproject.eu
- 49. EVALUATION%IN%TERENCE APPROACH WITH%WHOM EXAMPLE%METHODS WHEN analytical HMI%experts%or% domain%experts heuristic%evaluation formative,% expert%evaluation summative cognitive%walk`through small`scale learners observations formative think%aloud large`scale learners aield%studies summative From D7.4
- 50. LARGE`SCALE%EVALUATION%DESIGN Common*design*of*the*intervention*with*TERENCE:% ‣ how:*pretest/posttest%design,%with%experimental%and%control%groups% ‣ hypothesis:%TERENCE%improves%reading%comprehension%measured% with%standardised%text%comprehension%tests Experimental Control From D7.4
- 51. LARGE`SCALE%EVALUATION%DESIGN Common*design*of*the*intervention*with*TERENCE:% ‣ how:*pretest/posttest%design,%with%experimental%and%control%groups% ‣ hypothesis:%TERENCE%improves%reading%comprehension%measured% with%standardised%text%comprehension%tests Control*vs*experimental% ‣ analysis:%repeated%measure%ANOVA%% ‣ results:*the%hypothesis%is%conairmed%(F=25.65,%p0.0001)% Experimental Control From D7.4
- 52. EXPERIMENTAL%GROUP Pre`post%performances%for%text%comprehension% (dependent%variable)%were%as%follows:% ‣ Pescina:% ` pre:%14%poor%comprehenders%(20.59%)%% ` post:%6%poor%comprehenders%(8.82%)% ‣ Avezzano:% ` pre:%15%poor%comprehenders%(5.95%)%% ` post:%2%poor%comprehenders%(0.79%) Pescina Avezzano Pre 5,95% 20,59% From D7.4
- 53. EXPERIMENTAL%GROUP Pre`post%performances%for%text%comprehension% (dependent%variable)%were%as%follows:% ‣ Pescina:% ` pre:%14%poor%comprehenders%(20.59%)%% ` post:%6%poor%comprehenders%(8.82%)% ‣ Avezzano:% ` pre:%15%poor%comprehenders%(5.95%)%% ` post:%2%poor%comprehenders%(0.79%) Pescina Avezzano Post 0,79% 8,82% From D7.4
- 54. EXPERIMENTAL%GROUP Pre`post%performances%for%text%comprehension% (dependent%variable)%were%as%follows:% ‣ Pescina:% ` pre:%14%poor%comprehenders%(20.59%)%% ` post:%6%poor%comprehenders%(8.82%)% ‣ Avezzano:% ` pre:%15%poor%comprehenders%(5.95%)%% ` post:%2%poor%comprehenders%(0.79%) Pescina Avezzano Post 0,79% 8,82% ‣Wilcoxon%signed`rank%test%supports%that% differences%are%statistically*signi6icant* ` Pescina:%z=`4.904,%p0.0001% ` Avezzano:%z=`2.266,%p=0.0234 From D7.4
- 55. EXAMPLE%METHODS%IN%TERENCE APPROACH WITH%WHOM EXAMPLE%METHODS HOW analytical HMI%experts%or% domain%experts heuristic%evaluation formative,% expert%evaluation summative cognitive%walk`through small`scale learners observations formative think%aloud large`scale learners aield%studies summative From D4.2 and D4.3 technical annex
- 56. From D4.2 and D4.3 technical annex overall%generation story text games text$+$visual revision%of%Natural% Language%Processing% (NLP)%generation%of% texts%for%labelling%%%%%%%%% text text text text Evaluation*tasks revision%of%Automated% Reasoning%selection%of% central%events%and% solutions
- 57. From D4.2 and D4.3 technical annex overall%generation story text games text$+$visual revision%of%Natural% Language%Processing% (NLP)%generation%of% texts%for%labelling%%%%%%%%% text text text text Evaluation*tasks revision%of%Automated% Reasoning%selection%of% central%events%and% solutions
- 58. Selection*of*central*events Automated*reasoning*selection*of*central*events*for*games:%% Results:%only%in%15%out%of%250%cases%(6%),%it%was%necessary%to% select% a% different% central% event% than% the% automatically% generated%one From D4.2 and D4.3 technical annex
- 59. Selection*of*central*events Automated*reasoning*selection*of*central*events*for*games:%% Results:%only%in%15%out%of%250%cases%(6%),%it%was%necessary%to% select% a% different% central% event% than% the% automatically% generated%one Implications2 for2 AR:% none%picked%up From D4.2 and D4.3 technical annex
- 60. Selection*of*solutions*and*distractors Automated*reasoning*selection*of*plausible*solutions:%% Results:%out%of%140%changes%of%selection%of%solutions,%the%majority%was% for%wrong%solutions From D4.2 and D4.3 technical annex
- 61. Selection*of*solutions*and*distractors Automated*reasoning*selection*of*plausible*solutions:%% Results:%out%of%140%changes%of%selection%of%solutions,%the%majority%was% for%wrong%solutions Implications2for2WP4:*new%heuristics%for%wrong*plausible*solutions% in%the%last%part%of%Y3, From D4.2 and D4.3 technical annex
- 62. Selection*of*solutions*and*distractors Automated*reasoning*selection*of*plausible*solutions:%% Results:%out%of%140%changes%of%selection%of%solutions,%the%majority%was% for%wrong%solutions Implications2for2WP4:*new%heuristics%for%wrong*plausible*solutions% in%the%last%part%of%Y3, ` generate% a% wrong% solution% from% correct% one% by% changing% participants,% e.g.,% correct_sentence%id=2 The%man%ran%and%fell%on%the%ground.%%%%% /correct_sentence% wrong_sentence%id=2wh1 Peter%ran%and%fell%on%the%ground.% /wrong_sentence% From D4.2 and D4.3 technical annex
- 63. Overall*generation Overall*generation:%development% times:%% Results2for2revision2time:22 ` 12’6”%per%game%instance:% ↑%12’8”%for%time%games% ↓%10’6”%for%who%games% Results2for2creation2time:22 ` avg.%23”%per%game%instance story text games text$+$visual From D4.2 and D4.3 technical annex
- 64. Overall*generation Overall*generation:%development% times:%% Results2for2revision2time:22 ` 12’6”%per%game%instance:% ↑%12’8”%for%time%games% ↓%10’6”%for%who%games% Results2for2creation2time:22 ` avg.%23”%per%game%instance story text games text$+$visual Implications2for2the2generation2process:%the%AI`based%semi`automated% development%process%seems%to%be%promising%for%optimising%development% times From D4.2 and D4.3 technical annex
- 65. OUTLINE 1. Student%centred%learning%and%technology% enhanced%learning% 2. The%TERENCE%case%study% 3. Game%over%
- 67. MAIN%REFERENCES 1. R.%Gennari,%S.%Tonelli,%and%P.%Vittorini.%An*AIRbased*Process*for* Generating*Games*from*Flat*Stories.%In%Proc.%of%the%33rd%SGAI% Conference%(AI`2013),%Cambridge,%UK,%December%2013.% 2. F.%de%la%Prieta,%T.%Di%Mascio,%R.%Gennari,%I.%Marenzi,%P.%Vittorini.%User* Centred*and*Evidence*Based*Design:*an*Experience*Report.%In%IJTEL% Journal,%Inderscience%Enterprise%Ltd.,%forthcoming%in%2014.% 3. D.%Di%Giacomo,%R.%Cecilia,%V.%Cofini,%T.%Di%Mascio,%R.%Gennari,%P.%Vittorini.% Silent*Reading*Supported*by*Adaptive*Learning*Technologies:* Influence*In*the*Children*Outcomes.%In%Computers%in%Human%Behavior% Journal,%Elsevier,%forthcoming%in%2015.% 4. TERENCE%deliverables%for%technical*annex*to*D4.3.1,*D4.3.2,*D4.3.3%and% D7.4:%http://www.terenceproject.eu/web/guest/public`deliverables%%
- 68. Merry Seasonal Festivities with TERENCE books for remembering its best stories THE TERENCE BOOK 2 THE TERENCE BOOK 1 Dedicated to:TERENCE team, reviewers, teachers, children, and all those that contributed to the TERENCE stories. Our sincere “grazie”.