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Speech and Language Processing for Educational Applications
Professor Diane Litman
Computer Science Department &
Intelligent Systems Program &
Learning Research and Development Center
2
A few words about me… Currently
– Professor in CS and ISP (director)– Senior Scientist at LRDC– ITSPOKE research group
2 PhD students, your name here?, 3 CS undergrads, 1 postdoc, 1 programmer
– AI Research (speech and NLP, tutoring and education, applied learning, affective computing)
Previously– Member Technical Staff, AT&T Labs Research, NJ– Assistant Professor, CS at Columbia University, NY
More generally...
NLP and the Learning Sciences
More generally...
NLP and the Learning Sciences
Learning Language(reading, writing,
speaking)
Tutors
Scoring
More generally...
NLP and the Learning Sciences
Learning Language(reading, writing,
speaking)
Using Language (to teach everything else)
Tutors
Scoring
ConversationalTutors / Peers
CSCL
More generally...
NLP and the Learning Sciences
Learning Language(reading, writing,
speaking)
Using Language (to teach everything else)
Tutors
Scoring
Readability
Processing Language
ConversationalTutors / Peers
(Michael LipschultzJoanna DrummondHeather Friedberg)
CSCL
DiscourseCoding
LectureRetrieval
Questioning& Answering
NLP for Peer Review (Wenting Xiong)
•An Affect-Adaptive Spoken Dialogue System that Responds Based on User Model and Multiple Affective States
– Detect and adapt to student disengagement– Vary tutor responses based on user model (expertise, gender) to increase
learning and satisfaction
•Improving Learning from Peer Review with NLP and ITS Techniques - Detect important feedback features (i.e. is a solution given, is the review
helpful)- Enhance reviewer, author, and instructor interfaces
•Improving a Natural-Language Tutoring System That Engages Students in Deep Reasoning Dialogues About Physics
- Use of tutor specialization/abstraction- Research “in-vivo” (in a high school!)
Current Research Grants
Prior Dissertations Supervised Machine Learning for Dialogue
– Hua Ai, User Simulation for Spoken Dialog System Development
– Min Chi, Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning to Induce Pedagogical Tutorial Tactics
Discourse Theory for User Interfaces– Mihai Rotaru, Applications of Discourse Structure for Spoken Dialogue
Systems
Cognitive Science for Intelligent Tutoring– Arthur Ward, Reflection and Learning Robustness in a Natural Language
Conceptual Physics Tutoring System
Today: Spoken Tutorial Dialogue Motivation The ITSPOKE Tutorial Dialogue System & Corpora Detecting and Adapting to Student Uncertainty
– Uncertainty Detection – System Adaptation– Impact on Student Meta(Cognition)
» Wizarded and fully-automated experiments
Summing Up
What is Tutoring?
• “A one-on-one dialogue between a teacher and a student for the purpose of helping the student
learn something.”
[Evens and Michael 2006]
• Human Tutoring Excerpt [Thanks to Natalie Person and Lindsay Sears,
Rhodes College]
Intelligent Tutoring Systems
Students who receive one-on-one instruction perform as well as the top two percent of students who receive traditional classroom instruction [Bloom 1984]
Unfortunately, providing every student with a personal human tutor is infeasible– Develop computer tutors instead
Tutorial Dialogue Systems Why is one-on-one tutoring so effective?
“...there is something about discourse and natural language (as opposed to sophisticated pedagogical strategies) that explains the effectiveness of unaccomplished human [tutors].”
[Graesser, Person et al. 2001]
Currently only humans use full-fledged natural language dialogue
Spoken Tutorial Dialogue Systems Most human tutoring involves face-to-face
spoken interaction, while most computer dialogue tutors are text-based
Can the effectiveness of dialogue tutorial systems be further increased by using spoken interactions?
Potential Benefits of Spoken Dialogue: I
Dialogue provides a learning environment that promotes student activity (e.g., self-explanation)– Tutor: The right side pumps blood to the lungs, and the left side pumps blood to
the other parts of the body. Could you explain how that works?
– Student (self-explains): So the septum is a divider so that the blood doesn't get mixed up. So the right side is to the lungs, and the left side is to the body. So the septum is like a wall...
Self-explanation occurs more in speech [Hausmann and Chi 2002], and correlates with learning [Chi et al. 1994]
Potential Benefits of Spoken Dialogue: II
Speech contains prosodic information, providing new sources of information about the student for teacher adaptation [Fox 1993; Tsukahara and Ward 2001; Pon-Barry et al. 2005]
A correct but uncertain student turn– ITSPOKE: How does his velocity compare to that of
his keys?– STUDENT: his velocity is constant
Potential Benefits of Spoken Dialogue: III Spoken conversational environments may foster
social relationships that may enhance learning– AutoTutor [Graesser et al. 2003]
Potential Benefits of Spoken Dialogue: IV
• Some applications inherently involve spoken dialogue– Conversational Skills [Seneff, Johnson]– Reading Tutors [Mostow, Cole]
• Others require hands-free interaction– e.g., NASA training
Outline
Motivation The ITSPOKE System and Corpora Detecting and Adapting to Student Uncertainty
– Uncertainty Detection – System Adaptation– Experimental Evaluation
Summing Up
• Back-end is Why2-Atlas system [VanLehn, Jordan, Rose et al. 2002]• Sphinx2 speech recognition and Cepstral text-to-speech
• Back-end is Why2-Atlas system [VanLehn, Jordan, Rose et al. 2002]• Sphinx2 speech recognition and Cepstral text-to-speech
• Back-end is Why2-Atlas system [VanLehn, Jordan, Rose et al. 2002]• Sphinx2 speech recognition and Cepstral text-to-speech
Three Types of Tutoring Corpora Human Tutoring
– 14 students / 128 dialogues (physics problems)
Computer Tutoring– 72 students / 360 dialogues
Wizard Tutoring– 81 students / 405 dialogues– human performs speech recognition, semantic analysis– computer performs dialogue management
Experimental Procedure
College students without physics– Read a small background document– Took a multiple-choice Pretest – Worked 5 problems (dialogues) with ITSPOKE – Took an isomorphic Posttest
Goal was to optimize Learning Gain– e.g., Posttest – Pretest
Outline
Motivation The ITSPOKE System and Corpora Detecting and Adapting to Student Uncertainty
– Uncertainty Detection – System Adaptation– Experimental Evaluation
Summing Up
Monitoring Student State (motivation)
Tutor26: Let me ask my question in a more general way. What happens to the velocity of a body when there is no force acting on it?
Student27: dammit (ASR: it is)Tutor28 : Could you please repeat that?Student29 : same (ASR: i same)Tutor30 : Right. And what happens to the HORIZONTAL velocity of a body when
there is no HORIZONTAL force acting on it?Student31: zero (ASR: the zero)Tutor32 : Well... OK, let's consider how forces in the vertical direction affect the
motion of a body in a horizontal direction in a different scenario <…omitted…>Student33: oh gravity you already said this (ASR: beats gravity you're exert less)Tutor34: Fine. Are there any other forces acting on the apple as it falls?Student35: no why are you doing this again (ASR: no y and to it yes)Tutor36: Correct. The only force acting on the apple as it falls to the earth is the
earth's gravitational force. In which direction does gravitational force act?Student37: downward you computer (ASR: downward you computer)
Why Uncertainty? Most frequent student state in our dialogue corpora
[Litman and Forbes-Riley 2004]
Focus of other learning sciences, speech and language processing, and psycholinguistic studies [Craig et al. 2004; Liscombe et al. 2005; Pon-Barry et al. 2006; Dijkstra et al. 2006]
.73 Kappa [Forbes-Riley et al. 2008]
Corpus-Based Detection Methodology
Learn detection models from training corpora– Use spoken language processing to automatically extract
features from user turns– Use extracted features (e.g., prosodic, lexical) to predict
uncertainty annotations Evaluate learned models on testing corpora
– Significant reduction of error compared to baselines [Litman and Forbes-Riley 2006; Litman et al. 2007]
Outline
Motivation The ITSPOKE System and Corpora Detecting and Adapting to Student Uncertainty
– Uncertainty Detection – System Adaptation– Experimental Evaluation
Summing Up
System Adaptation: How to Respond?
Theory-based– [VanLehn et al. 2003; Craig et al. 2004]
Corpus-based– [Forbes-Riley and Litman 2005, 2007, 2008, 2010]
Theory-Based Adaptation:Uncertainty as Learning Opportunity
Uncertainty represents one type of learning impasse, and is also associated with cognitive disequilibrium– An impasse motivates a student to take an active role in
constructing a better understanding of the principle. [VanLehn et al. 2003]
– A state of failed expectations causing deliberation aimed at restoring equilibrium. [Craig et al. 2004]
Hypothesis: The system should adapt to uncertainty in the same way it responds to other impasses (e.g., incorrectness)
Corpus-Based Adaptation: How Do Human Tutors Respond?
An empirical method for designing dialogue systems adaptive to student state– extraction of “dialogue bigrams” from annotated
human tutoring corpora
– χ2 analysis to identify dependent bigrams
– generalizable to any domain with corpora labeled for user state and system response
Example Human Tutoring Excerpt
S: So the- when you throw it up the acceleration will stay the same? [Uncertain]
T: Acceleration uh will always be the same because there is- that is being caused by force of gravity which is not
changing. [Restatement, Expansion]
S: mm-k. [Neutral]
T: Acceleration is– it is in- what is the direction uh of this acceleration- acceleration due to gravity?
[Short Answer Question]
S: It’s- the direction- it’s downward. [Certain]
T: Yes, it’s vertically down. [Positive Feedback, Restatement]
Findings Statistically significant dependencies exist
between students’ state of certainty and the responses of an expert human tutor– After uncertain, tutor Bottoms Out and avoids
expansions – After certain, tutor Restates– After any emotion, tutor increases Feedback
Dependencies suggest adaptive strategies for implementation in our computer tutor [Forbes-Riley and Litman 2010]
Outline
Motivation The ITSPOKE System and Corpora Detecting and Adapting to Student Uncertainty
– Uncertainty Detection – System Adaptation– Experimental Evaluation
Summing Up
Adaptation to Student Uncertainty in ITSPOKE
Most systems respond only to (in)correctness
Recall that literature suggests uncertain as well as incorrect student answers signal learning impasses
Experimentally manipulate tutor responses to student uncertainty, over and above correctness, and investigate impact on learning– Platform: Adaptive version(s) of ITSPOKE
Normal (non-adaptive) ITSPOKE
System Initiative Dialogue Format: – Tutor Question – Student Answer – Tutor Response
Tutor Response Types:
– to Corrects (C): positive feedback (e.g. “Fine”)
– to Incorrects (I): negative feedback (e.g. “Well…”) and
» Bottom Out: correct answer with reasoning (easier)
» Subdialogue: questions walk through reasoning (harder)
Our Prior Work: Rank correctness (C, I) + uncertainty (U, nonU) states in terms of impasse severity
State: I+nonU I+U C+U C+nonU
Severity: most less least none
Adaptive ITSPOKE(s)
Our Prior Work: Rank correctness (C, I) + uncertainty (U, nonU) states in terms of impasse severity
State: I+nonU I+U C+U C+nonU
Severity: most less least none
Adaptation Hypothesis:
– ITSPOKE already resolves I impasses (I+nonU, I+U), but it ignores one type of U impasse (C+U)
– Performance improvement if ITSPOKE provides additional content to resolve all impasses
Adaptive ITSPOKE(s)
Simple Adaptation
– Same response for all 3 impasses
– Feedback on only (in)correctness
Complex Adaptation
– Different responses for the 3 impasses
» Based on human responses [Forbes-Riley and Litman 2010]
– Feedback on both uncertainty and (in)correctness
» Based on empathetic computer tutor literature [Wang et al., 2005; Hall et al., 2004; Burleson et al., 2004]
Two Uncertainty Adaptations
Simple Adaptation Example: C+UTUTOR1: By the same reasoning that we used for the car, what’s the
overall net force on the truck equal to?
STUDENT1: The force of the car hitting it?? [C+U]
TUTOR2: Fine. [FEEDBACK] We can derive the net force on the truck by summing the individual forces on it, just like we
did for the car. First, what horizontal force is exerted on the truck during the collision? [SUBDIALOGUE]
Same TUTOR2 subdialogue if student was I+U or I+nonU
TUTOR2: That’s exactly right, but you seem unsure, so let’s sum up. [FEEDBACK] The net force on the truck is equal to the impact force on it. We can prove this just like we did for the car. First, we know that gravity and the normal force on the truck must cancel each other, otherwise the truck would not be at rest vertically. Second, we know that the impact force is the only horizontal force exerted on the truck. [LONG BOTTOM OUT]
Complex Adaptation Example: C+U
TUTOR1: By the same reasoning that we used for the car, what’s the overall net force on the truck equal to?
STUDENT1: Zero?? [I+U]
TUTOR2: That’s not correct, but don’t worry. You seem to be aware of your mistake. Let’s resolve it. [FEEDBACK] The net force on the truck is equal to the impact force on it. Let’s walk through this answer step by step. [SHORT BOTTOM OUT] We can derive the net force on the truck by summing the individual forces on it, just like we did for the car. First, what horizontal force is exerted on the truck during the collision? [SUBDIALOGUE]
Complex Adaptation Example: I+U
TUTOR1: By the same reasoning that we used for the car, what’s the overall net force on the truck equal to?
STUDENT1: The force of gravity. [I+nonU]
TUTOR2: I’m sorry, but I see an error in your answer. Let’s fix it. [FEEDBACK] We can derive the net force on the truck by summing the individual forces on it, just like we did for the car. First, what horizontal force is exerted on the truck during the collision? [SUBDIALOGUE]
Complex Adaptation Example: I+nonU
Experiment 1: ITSPOKE-WOZ Wizard of Oz version of ITSPOKE
– Human recognizes speech, annotates correctness and uncertainty – Provides upper-bound language performance
4 Conditions– Simple Adaptation: used same response for all impasses– Complex Adaptation: used different responses for each impasse– Normal Control: used original system (no adaptation) – Random Control: gave Simple Adaptation to random 20% of
correct answers (to control for additional tutoring)
Prediction: Complex Adaptation > Simple Adaptation > Random Control > Normal Control (for increasing learning)
Procedure: reading, pretest, 5 problems, survey, posttest
Results I: Learning
Metric Condition N Mean Diff p
Learning Gain(Posttest – Pretest)
Normal Control 21 .183 < Simple Adaptation .03
Random Control 20 .269 -
Simple Adaptation 20 .307 -
Complex Adaptation 20 .213 -
F(3, 77) = 3.275, p = 0.02
Results I: Learning
Metric Condition N Mean Diff p
Learning Gain(Posttest – Pretest)
Normal Control 21 .183 < Simple Adaptation .03
Random Control 20 .269 -
Simple Adaptation 20 .307 -
Complex Adaptation 20 .213 -
Simple Adaptation yields more student learning than Normal Control (original ITSPOKE)
[Forbes-Riley and Litman 2010]
F(3, 77) = 3.275, p = 0.02
Results I: Learning
Metric Condition N Mean Diff p
Learning Gain(Posttest – Pretest)
Normal Control 21 .183 < Simple Adaptation .03
Random Control 20 .269 -
Simple Adaptation 20 .307 -
Complex Adaptation 20 .213 -
Simple Adaptation yields more student learning than Normal Control (original ITSPOKE)
[Forbes-Riley and Litman 2010]
Similar results for learning efficiency [Forbes-Riley and Litman 2009]
F(3, 77) = 3.275, p = 0.02
Discussion
Predictions versus results:
- Complex Adaptation > Simple Adaptation > Random Control > Normal Control
Why didn’t Complex Adaptation outperform Simple Adaptation?
– Complex Adaptation’s human-based content responses were based on frequency, not effectiveness
– Better data mining methods (e.g. reinforcement learning) needed
Additional Evaluations - Metacognition
Do metacognitive performance measures differ across experimental conditions?– Monitoring Accuracy [Nietfield et al. 2006]
Monitoring Accuracy
Correct Incorrect
NonUncertain CnonU InonU
Uncertain CU IU
• The wizard's annotations for each student are first represented in an array, where each cell represents a mutually exclusive option
• motivated by Feeling of (Another’s) Knowing [Smith and Clark 1993; Brennan and Williams 1995] which is closely related to uncertainty [Dijkstra et al. 2006]
• The array is then used to compute monitoring accuracy
Monitoring Accuracy
Correct Incorrect
NonUncertain CnonU InonU
Uncertain CU IU
)()(
)()(
CUInonUIUCnonU
CUInonUIUCnonUefficientHarmann Co
• Ranges from -1 (no monitoring accuracy) to 1 (perfect monitoring accuracy)
Additional Results I
Metacognitive
Measure
ComplexAdaptation
(20)
Simple Adaptation
(20)
Random Control
(20)
Normal Control
(21)Monitoring Accuracy .58 .62 .62 .52
Simple (and random) increased monitoring accuracy, compared to normal (p < .06 in paired contrasts)
[Litman and Forbes-Riley 2009]
Additional Results II
Metacognitive Measure (n=81) R p
Average Impasse Severity - .56 .00
Monitoring Accuracy .42 .00
Monitoring Accuracy (where higher is better) is positively correlated with learning
[Litman and Forbes-Riley 2009]
Experiment 2: ITSPOKE-AUTO
Sphinx2 speech recognizer– Word Error Rate of 25%
TuTalk semantic analyzer – Correctness Accuracy of 84.7%
Weka uncertainty model– Logistic regression (includes lexical, prosodic, dialogue features)
– Uncertainty Accuracy of 76.8%
Preliminary Results: ITSPOKE-AUTO
Metacognitive Measure WOZ AUTO
R p R p
Monitoring Accuracy .42 .00 .35 .00
Monitoring Accuracy remains correlated with learning under noisy conditions
More modest Local and Global learning differences across experimental conditions
s
Current and Future Work Reduce noise in fully automated system
Incorporation of student disengagement and
user modeling
Crowd sourcing (for acquiring training data)
Remediate metacognition, not just domain content
Outline
Motivation The ITSPOKE System and Corpora Detecting and Adapting to Student Uncertainty
– Uncertainty Detection – System Adaptation– Experimental Evaluation
Summing Up
Summing Up
Spoken dialogue contributes to the success of human tutors By modifying presently available technology, successful
tutorial dialogue systems can also be built Adapting to uncertainty can further improve performance
Similar opportunities and challenges in many educational applications
59
Resources Recommended classes
– Introduction to Natural Language Processing– Foundations of Artificial Intelligence– Machine Learning– Knowledge Representation– Seminar classes
Other resources– ITSPOKE Group Meetings– NLP @ Pitt– Intelligent Systems Program (ISP) Forum– Pittsburgh Science of Learning Center (PSLC)
Thank You!
Questions?
Further Information– http://www.cs.pitt.edu/~litman/itspoke.html