Intelligent Tutoring Systems for Ill-Defined Domains - Philippe ...

Recommendations

Info

Defining “Ill-defined problems” Ill-defined problems typically: lack formal methods to verify solutions; lack widely accepted domain theories characterizing relevant concepts and relations; involve designing novel artifacts rather than verifying existing results; rely on open-textured concepts subject to debate; do not decompose into independent subproblems. ITS08 ITS4IDD Workshop © Kevin D. Ashley. 2008 3 types of ill-defined problems: Open-textured problems: – rely on open-textured concepts for their structure and definition. Open-specified problems: – have unstated specifications or goal criteria. Open-structured problems: – lack clear substructure allowing for definition of partial solutions or subproblems. Solution processes in ill-defined problems cycle through: Recharacterize problem to specify undefined components, identify constraints, or select among alternatives; Explore consequences of given characterization in the problem space; Justify characterization in terms of relevant concepts or principles and to deflect anticipated attacks. From Lynch, Ashley, Pinkwart, Aleven (submitted) “Concepts, Structures, and Goals: Redefining Ill-Definedness" ITS Development in Ill-Defined Domains Medical diagnosis Legal reasoning Intercultural relations Ethical reasoning Language learning: vocabulary, grammar Programming: Object-oriented design; logic programming; Database design Robot arm operation Causal reasoning in public policy Psychology Inquiry learning in sciences �� ITS08 ITS4IDD Workshop © Kevin D. Ashley. 2008 ITS 2006 � � � �� AIED 2007 �� Introduction vi ITS 2008 � � � � � � � 2
vii Ashley Methods for Assessment and Feedback in ITSs for Ill-Defined Domains Adaptive feedback on students’: – discussion posts based on simplified model of good posts, to tutee directly and indirectly via peer moderator (Walker, Ogan, Aleven, Jones) – selected actions in student-modified versions of pre-analyzed ethics narrative (Hodhod, Kudenko) Automatic generation of cloze (multiple choice) questions – with better-quality distractors (Pino, Heilman, Eskenazi) Compare student’s: – solution to template of solutions-plus-variations (Moritz, Blank) – problem-states-visited with mined patterns of partial problem spaces (Fournier- Viger, Nkambou, Mephu Nguifo) – diagrammatic reconstructions of arguments in terms of feedback-related features (Lynch, Pinkwart, Ashley, Aleven) Support self-assessment with expert decision map, visual representation of overall problem-solving process (Gauthier, Naismith, Lajoie, Wiseman) ITS08 ITS4IDD Workshop © Kevin D. Ashley. 2008 9:00 - 9:15 9:15 - 10:15 10:15 - 10:35 10:35 - 12:15 12:15 - 1:30 1:30 - 2:30 2:30 - 2:50 Schedule Introduction: Kevin D. Ashley Opening Session* 1.Two Approaches for Providing Adaptive Support for Discussion in an Ill-Defined Domain Erin Walker, Amy Ogan, Vincent Aleven, Chris Jones 2.Interactive Narrative and Intelligent Tutoring for Ethics Domain Rania Hodhod and Daniel Kudenko Coffee Break Prelunch Session* 1.A Selection Strategy to Improve Cloze Question Quality Juan Pino, Michael Heilman, and Maxine Eskenazi 2.Generating and Evaluating Object-Oriented Designs for Instructors and Novice Students Sally Moritz and Glenn Blank 3.General Discussion Lunch Post-lunch Session* 1.A Sequential Pattern Mining Algorithm for Extracting Partial Problem Spaces from Logged User Interactions Philippe Fournier-Viger, Roger Nkambou and Engelbert Mephu Nguifo 2.What Do Argument Diagrams Tell Us About Students' Aptitude Or Experience? A Statistical Analysis In An Ill-Defined Domain Collin Lynch, Niels Pinkwart, Kevin Ashley and Vincent Aleven Tea Break Closing Session* 1.Using Expert Decision Maps to Promote Reflection and Self-Assessment in Medical Case- 2:50 - 4:00 Based Instruction Geneviève Gauthier, Laura Naismith, Susanne P. Lajoie, and Jeffrey Wiseman 2.Closing Discussion *Paper presentations: 20 minutes each with 10 minutes for questions and discussion. ITS08 ITS4IDD Workshop © Kevin D. Ashley. 2008 3
Page 1 and 2: Intelligent Tutoring Systems for Il
Page 3 and 4: iii Aleven, Ashley, Lynch & Pinkwar
Page 5: v Ashley Intelligent Tutoring Syste
Page 9 and 10: Two Approaches for Providing Adapti
Page 11 and 12: 3 Walker, Ogan, Aleven & Jones 2 Ad
Page 13 and 14: 5 Walker, Ogan, Aleven & Jones 2.3
Page 15 and 16: 7 Walker, Ogan, Aleven & Jones 3 Pr
Page 17 and 18: 9 Walker, Ogan, Aleven & Jones plat
Page 19 and 20: 11 Walker, Ogan, Aleven & Jones ond
Page 21 and 22: Interactive Narrative and Intellige
Page 23 and 24: 15 Hodhod & Kudenko components, con
Page 25 and 26: 17 Hodhod & Kudenko actions and re-
Page 27 and 28: 19 Hodhod & Kudenko dollars. That S
Page 29 and 30: 21 Hodhod & Kudenko deeper understa
Page 31 and 32: 23 Pino, Heilman, & Eskenazi with a
Page 33 and 34: 25 Pino, Heilman, & Eskenazi 2 Cloz
Page 35 and 36: 27 Pino, Heilman, & Eskenazi Fig. 1
Page 37 and 38: 29 Pino, Heilman, & Eskenazi Table
Page 39 and 40: 31 Pino, Heilman, & Eskenazi Table
Page 41 and 42: 33 Pino, Heilman, & Eskenazi opinio
Page 43 and 44: Generating and Evaluating Object-Or
Page 45 and 46: 37 Moritz & Blank using them to der
Page 47 and 48: 39 Moritz & Blank searching for phr
Page 49 and 50: 41 Moritz & Blank as a related term
Page 51 and 52: 43 Moritz & Blank to match on data
Page 53 and 54: 45 Moritz & Blank 6. Liu, H., ieber
Page 55 and 56: 47 Fournier-Viger, Nkambou, & Mephu
Page 57 and 58:
49 Fournier-Viger, Nkambou, & Mephu
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
55 Sequential Pattern Mining/Partia
Page 65 and 66:
57 Lynch, Pinkwart, Ashley, & Aleve
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
69 Gauthier, Naismith, Lajoie, & Wi
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
show all

Intelligent Tutoring Systems for Ill-Defined Domains - Philippe ...

Create successful ePaper yourself

Delete template?

Save as template?