Spoken Dialogue in Human and Computer Tutoring

Diane Litman

Learning Research and Development Center and

Computer Science DepartmentUniversity of Pittsburgh

Outline

Introduction and Background The ITSPOKE System and Corpora A Study of Spoken versus Typed Dialogue

Tutoring– Human tutoring condition– Computer tutoring condition

Current Directions and Summary

Adding Spoken Language to a Text-Based Dialogue Tutor (11/03-9/06)

Primary Research Question– How does speech-based dialogue interaction impact

the effectiveness of tutoring systems for student learning?

Hypotheses

Compared to typed dialogues, spoken interactions will yield better learning gains, and will be more efficient and natural

Different student behaviors will correlate with learning in spoken versus typed dialogues, and will be elicited by different tutor actions

Findings in human-human and human-computer dialogues will vary as a function of system performance

Motivation

Working hypothesis regarding learning gains– Human Dialogue > Computer Dialogue > Text

Most human tutoring involves face-to-face spoken interaction, while most computer dialogue tutors are text-based – Evens et al., 2001; Zinn et al., 2002; Vanlehn et

al., 2002; Aleven et al., 2001

Can the effectiveness of dialogue tutorial systems be further increased by using spoken interactions?

Spoken Tutorial Dialogue Systems

Recent tutoring systems have begun to add spoken language capabilities– Rickel and Johnson, 2000; Graesser et al. 2001;

Mostow and Aist, 2001; Aist et al., 2003; Fry et al., 2001; Schultz et al., 2003

However, little empirical analysis of the learning ramifications of using speech

Potential Benefits of Speech Self-explanation correlates with learning and occurs more in speech

– Hausmann and Chi, 2002

Speech contains prosodic information, providing new sources of information for dialogue adaptation – Forbes-Riley and Litman, 2004

Spoken computational environments may prime a more social interpretation that enhances learning– Moreno et al., 2001; Graesser et al., 2003

Potential for hands-free interaction – Smith, 1992; Aist et al., 2003

Spoken Computer Tutoring Excerpt

Tutor: What will the velocity of the object be a second after that (where the initial velocity is 9.8m/s the acceleration is 9.8m/s^2)?

Student: thirty nine point two

Tutor: Well... 19.6 m/s. So at every point in time during the fall of the man and his keys, how do their velocities compare with each other?

Student: same

Tutor: Yeah. Now we will compare the displacements of the man and his keys. Do you recall what displacement means?

Student: distance in a straight line

Outline

Introduction and Background The ITSPOKE System and Corpora A Study of Spoken versus Typed Dialogue

Tutoring– Human tutoring condition– Computer tutoring condition

Current Directions and Summary

ITSPOKE: Intelligent Tutoring SPOKEn Dialogue System

Back-end is text-based Why2-Atlas tutorial dialogue system (VanLehn et al., 2002)

Student speech digitized from microphone input; Sphinx2 speech recognizer

Tutor speech played via headphones/speakers; Cepstral text-to-speech synthesizer

Other additions: XML access to Why2-Atlas “internals”, speech recognition repairs, etc.

Architecture

Cepstral

www server

www browser

javaITSpoke

Text Manager

Spoken Dialogue Manager

essay

dialogue

student text

(xml)

tutor turn

(xml)

htmlxml

text

Speech Analysis (Sphinx)

dialogue

dialogue

repair goals

Essay Analysis (Carmel, Tacitus-

lite+)

Content Dialogue

Manager (Ape, Carmel)

Why2

tutorial goals

textessay

Speech Recognition: Sphinx2 (CMU)

56 dialogue-based, probabilistic language models Initial training data

– typed student utterances from Why2-Atlas corpora– human-human: 968 unique words– human-computer: 599 unique words

Later training data– spoken utterances obtained during development and pilot

testing of ITSPOKE– human-computer: 523 unique words

Total vocabulary– 1240 unique words

Language Models (LMs): Design Dialogue-dependent language models manually constructed by aggregating

prompts, e.g. example LM for prompts taking “yes/no” type answers prompt: Just as the car starts moving, the string is vertical, so it can't exert any horizontal

force on the dice. No other objects are touching the dice. So are there any horizontal forces on the dice as the car starts moving?

8.332“yes”4.171“yeah”4.171“none”83.3320“no”FrequencyCountUser response

prompt: When analyzing the motion of the two cars, one towing the other, can we treat them as a single compound body?

User Response Count Frequency

“no” 2 8.70

“yes” 21 91.30

Language Models: Evaluation

Test Data: ITSPOKE 2003-2004 evaluation– 20 students, 100 physics problems (dialogues), 2445

turns, 398 unique words– 39 of 56 language models

• 17 models were either specific to 5 unused physics problems, or to specific goals that were never accessed

Word Error Rate = 31.2% “Concept Error” Rate = 7.6%

Speech Synthesis: Cepstral

Commercial outgrowth of Festival text-to-speech synthesizer (Edinburgh, CMU)

Required additional processing of Why2-Atlas prompts (e.g., f=m*a)

Spoken Tutoring Corpora Human-Human Corpus

– 14 students, 128 physics problems (dialogues)– Student and tutor turns were manually transcribed and

segmented for content and boundaries Computer-Human Corpus

– 20 students, 100 physics problems (dialogues) – Noisy student turn boundaries and transcriptions

extractable from ITSPOKE logs– Content of student turns also manually transcribed

Corpus Transcription & Annotation

ITSPOKE Corpora Comparison Human-Human Human-Computer

…1.3 minutes into session…

Student: Can I ask you questions?

Tutor: Yes

Student: Is that ok?

Tutor: Mm-hm

Student: Um from what we learned from the last problem they're gonna have the same- the keys and you have the same acceleration right?

Tutor: Yes

Student: So that means they'll both hit the bottom at the same time. But I don't understand where- I don't understand if you're in the elevator-

Tutor: You see

Student: Where are you going to-?

Tutor: The uh let me uh the key uh- the person holds the key in front of-

Student: Their face yeah-

Tutor: Uh his or her face and then lets it uh uh it is let go so the the the question relates to the relative position of the key and the face uh-

Student: So the key and the face-

Tutor: Uh how will they- as they go- as they both fall what will be their relative position? That is the question.

Student: (sigh)

…3.5 minutes into session…

Tutor: I didn't understand. What force acts on all objects within close proximity to earth?

Student: uh the force of gravity

Tutor: Fine. Besides the Earth's gravitational force, are there any other forces acting on the person, keys, and elevator after the release?

Student: no

Tutor: Yep. Thus, keep in mind that after the release, the only force on the person, keys, and elevator is the force of gravity. If gravity is the only force acting on an object, will it be moving or staying still?

Student: it will be moving

Tutor: Fine. Not only are the person, keys, and elevator moving, they have only gravitational forces on them. When an object is falling and has only gravitational force on it, it is said to be in what?

Student: freefall

Tutor: Good. Thus, after the release the only force on the keys and man is the downward force of earth's gravity, so they are in freefall. Before the release, what is the relationship between the man's velocity and the keys' velocity?“

Student: they're equal

Tutor: Correct. We know that after the release, the keys, the person, and the elevator are in freefall. Is freefall acceleration near earth a constant? Or does it vary depending upon which object is in freefall?“

Student: it's a constant

Outline

Introduction and Background The ITSPOKE System and Corpora A Study of Spoken versus Typed Dialogue

Tutoring– Human tutoring condition– Computer tutoring condition

Current Directions and Summary

Spoken Versus Typed Human and Computer Dialogue Tutoring

Diane Litman, Carolyn Penstein Rosé, Kate Forbes-Riley, Kurt VanLehn, Dumisizwe Bhembe, and Scott Silliman

Proceedings of the Seventh International Conference on

Intelligent Tutoring Systems (to appear)

Empirical Results

Overview Given the effort to develop a spoken tutorial

system, it is important to investigate whether and how the use of speech can improve student learning– by determining the aspects of dialogue that correlate

with learning, we can construct our systems to better elicit such behaviors

Two experiments: spoken versus typed dialogues– Human tutoring – an upper bound on speech and

natural language processing– Computer tutoring – current state of the art

Common Aspects of Both Experiments

Students take a physics pretest Students read background material Students use web interface to work through up

to 10 problems with either a computer or a human tutor

Students take a posttest– 40 multiple choice questions, isomorphic to pretest

Human Tutoring: Experiment 1 Same human tutor, subject pool, physics problems, web

interface, and experimental procedure across two conditions Typed dialogue condition (20 students, 171 dialogues)

– Student and tutor in separate rooms

– Strict turn-taking enforced

– Student and tutor type via chat interface

Spoken dialogue condition (14 students, 128 dialogues)– Student and tutor in same room, separated by a partition

– Interruptions and overlapping speech permitted

– Student and tutor speak through head-mounted microphones

– Dialogue history box remains empty

Typed Excerpt Spoken Excerpt (Human Tutoring Corpora)

Problem: Suppose that you released 3 identical balls of clay in a vacuum at exactly the same instant. They would all hit the ground at the same instant. Now you stick two of the balls together, forming one ball that is twice as heavy as the remaining, untouched clay ball. Both balls are released in a vacuum at exactly the same instant. Which ball hits the ground first?

Original Essay: Both balls will hit the ground at the same time. The balls are in free fall (only gravitational forces). The ratio of the masses and weight are equal.

--------------------------2 min into dialogue--------------------------------Tutor: You have correctly said the balls are in free fall. What do

you conclude from this fact? Student: There is no air resistance and the balls’ accelerations are

constant in regards to one another Tutor: Right, so the conclusion is that they both have the same

acceleration, the free fall acceleration. Now you have to show that time taken by them to reach the ground will be the same. How do you do that?

Student: F (net forces) / mass = f/m because F = m*a therefore ratio

should equal one another Tutor: But that does not tell you anything about the time taken.

What physical quantities will determine the time taken. Will the initial velocity of the balls matter?

Student: yes, assuming starting at rest? sorry, kind of at a loss at the moment

Problem: If a car is able to accelerate at 2 m/s2, what acceleration can it attain if it is towing another car of equal mass?

Original Essay: If the car is towing another car of equal mass, the maximum acceleration would be the same because the car would be towed behind and the friction caused would only be by the front of the first car.

-----------------------------6.5 min into dialogue--------------------------Tutor: Now this law that force is equal to mass times acceleration,

what's this law called? This is uh since this it is a very important basic uh fact uh it is it is a law of physics. Um you have you have read it in the background material. Can you recall it?

Student: Um no it was one of Newton's laws but I don't- remember which one. (laugh)

Tutor: Right, right- That- is Newton's second law of motion. Student: he I- Ok, because I remember one, two, and three, but I

didn't know if there was a different name

Tutor: Yeah that's right you know Newton was a genius-

Student: (laugh)

Tutor: and uh he looked at a large number of experiments and experimental data that was available and from that he could come to this general law and it is known as Newton's second law of motion. Um many many other scientists before him had seen all this data which was collected by scientists but had not concluded this now it looks very simple but to come to the conclusion from a mass of data was something which required the genius of Newton.

Student: mm hm

Typed versus Spoken Tutoring: Overview of Analyses

Tutoring and Dialogue Evaluation Measures – learning gains – efficiency

Correlation of Dialogue Characteristics and Learning– do dialogue aspects differ across conditions?– which dialogue aspects correlate with learning in each

condition?

Learning and Training Time

Dependent

Measure

Human

Spoken (14)

Human

Typed (20)

Pretest Mean (std dev.) .42 (.10) .46 (.09)

Posttest Mean (std dev.) .72 (.11) .67 (.13)

Adj. Posttest Mean (std dev.)

.74 (.11) .66 (.11)

Dialogue Time

(std dev.)

166.58 (45.06)

430.05 (159.65)

Discussion

There was a robust main effect for test phase (p=0.000), indicating that students in both conditions learned during tutoring

The adjusted posttest scores show a strong trend of being reliably different (p=0.053), suggesting that students learned more in the spoken condition

Students in the spoken condition completed their tutoring in less than half the time than in the typed condition (p=0.000)

Dialogue Characteristics Examined

Motivated by previous work suggesting that learning correlates with increased student language production and interactivity (Core et al., 2003; Rose et al., pilot studies of typed corpora; Katz et al., 2003)– Average length of turns (in words)– Total number of words and turns– Initial values and rate of change– Ratios of student and tutor words and turns– Interruption behavior (in speech)

Human Tutoring Dialogue Characteristics (means)

Dependent MeasureSpoken (14)

Typed (20)

p

Tot. Stud. Words 2322.431569.30

.03

Tot. Stud. Turns 424.86 109.30

.00

Ave. Stud. Words/Turn 5.21 14.45

.00

Slope: Stud. Words/Turn -.01 -.05

.04

Intercept: Stud. Words/Turn

6.51 16.39

.00

Tot. Tut. Words 8648.293366.30

.00

Tot. Tut. Turns 393.21 122.90

.00

Ave. Tut. Words/Turn 23.04 28.23

.01

Stud-Tut Tot. Words Ratio

.27 .45

.00

Stud-Tut Words/Turn Ratio

.25 .51

.00

Discussion

For every measure examined, the means across conditions are significantly different– Students and the tutor take more turns in speech, and

use more total words– Spoken turns are on average shorter– The ratio of student to tutor language production is

higher in text

Learning Correlations after Controlling for Pretest

Dependent MeasureHuman Spoken (14)

Human Typed (20)

R p R pAve. Stud. Words/Turn -.209 .49 .515 .03Intercept: Stud. Words/Turn -.441 .13 .593 .01Ave. Tut. Words/Turn -.086 .78 .536 .02

Additional Analyses: Spoken Human Tutoring

Dependent Measure Mean ControlledR p

Tot. Stud. Questions 35.29 -.500 .08

Ave. Stud. Questions/Dial 3.86 -.477 .10

Std. Tut. Questions/Dial 13.55 -.489 .09

Std. Stud-Tut Word Ratio/Dial 0.14 -.584 .04

Std. Stud-Tut Words/Turn/Dial 0.22 -.640 .02

Removing Student “Groundings”: Spoken Human Tutoring

Dependent Measure Mean Controlled

R p

Tot. Student Words 2133.57 -.298 .32

Tot. Student Turns 251.36 -.281 .35

Ave. Student Words/Turn 8.33 -.243 .42

Std. Student Turns/Dial. 15.79 -.478 .10

Discussion Measures correlating with learning in the typed condition do

not correlate in the spoken condition– Typed results suggest that students who give longer answers, or

who are inherently verbose, learn more

– Initial analyses removing “groundings” do not change spoken results

Deeper analyses needed (requires manual coding)– do longer student turns reveal more reasoning and explanation?

– do longer tutor turns mean better explanations or feedback?

– results need to be further examined for student question types, substantive contributions versus groundings, tutor moves, etc.

Computer Tutoring: Experiment 2 Same physics problems, web interface, and experimental

procedure as Experiment 1; however– only 5 problems (dialogues) per student– pretest taken after background reading– strict turn taking enforced in both conditions

Typed dialogue condition (23 students, 115 dialogues)– Why2-Atlas computer tutor– student and tutor interact via text-based chat interface

Spoken dialogue condition (20 students, 100 dialogues)– ITSPOKE computer tutor– student and tutor interact through microphone and headset– utterances displayed when student or ITSPOKE finished speaking– speech recognition output rather than actual student utterances

Spoken Computer Tutoring Excerpt

ITSPOKE: What else do you need to know to find the box's acceleration?

Student: the direction ASR: add directionsITSPOKE : If you see a body accelerate, what caused that

acceleration?Student: force ITSPOKE : Good job. Say there is only one force acting on

the box. How is this force, the box's mass, and its acceleration related?

Student: velocity ITSPOKE : Could you please repeat that? ASR: REJECTStudent: velocity

Learning and Training Time

Dependent

Measure

Computer

Spoken (20)

(ITSPOKE)

Computer

Typed (23)

(Why2-Atlas)

Pretest Mean (std dev.) .48 (.17) .49 (.20)

Posttest Mean (std dev.) .69 (.18) .70 (.16)

Adj. Posttest Mean (std dev.)

.69 (.13) .69 (.13)

Dialog Time (std dev.) 97.85 (32.8)

68.93 (29.0)

Discussion

There was a robust main effect for test phase (p=0.000), indicating that students in both conditions learned during tutoring

The adjusted posttest scores were not reliably different (p=0.950), suggesting that students learned the same in both conditions

Students in the typed condition completed their tutoring in less time than in the spoken condition (p=0.004)

New Computer Tutoring Dialogue Characteristics

Why2-Atlas and ITSPOKE conditions– Total Subdialogues per Knowledge Construction

Dialogue (KCD) Only ITSPOKE (speech recognition) condition

– Word Error Rate– Concept Accuracy– Timeouts– Rejections

Computer Tutoring Dialogue Characteristics (means)Dependent Measure Spoken Typed p

Tot. Stud. Words 296.85 238.17.12

Tot. Stud. Turns 116.75 87.96.02

Ave. Stud. Words/Turn 2.42 2.77.29

Slope: Stud. Words/Turn -.02 -.00.02

Intercept: Stud. Words/Turn

3.21 2.88.40

Tot. Tut. Words 6314.90 4972.61.03

Tot. Tut. Turns 148.20 110.22.01

Ave. Tut. Words/Turn 42.11 44.33.06

Stud-Tut Tot. Words Ratio .05 .05.57

Stud-Tut Words/Turn Ratio

.06 .06.64

Tot. Subdialogues/KCD 3.29 1.98.01

Learning Correlations after Controlling for Pretest

Dependent MeasureSpoken(ITSPOKE)

Typed (Why2-Atlas)

R p R pTot. Stud. Words .394 .10 .050 .82Tot. Subdialogues/KCD - .018 .94 - .457 .03

Additional Analyses: Spoken Computer Tutoring

Dependent Measure

MeanControlledR p

Tot. Dial. Time (min)

97.85 .580

.01

Ave. Dial. Time (min)

17.07 .580

.01

Std. Dial. Time (min)

9.99 .541

.02

Std. Tot. Stud. Words

42.39 .457

.05

Word Error Rate 32.45-.201

.41

Concept Accuracy 0.92 .113

.65

Tot. Timeouts 5.50 .296

.22

Tot. Rejects 8.15-.244

.31

Learning Correlations for 7 ITSPOKE Students with Pretest < .4

Dependent Measure Mean ControlledR p

Slope: Student

Words/Turn -.03 -.877 .02

Intercept: Student

Words/Turn 3.06 .900 .02

Discussion

Means across conditions are no longer significantly different for many measures– total words produced by students – average length of student turns and initial verbosity– ratios of student to tutor language production

Different measures again correlate with learning– Speech: student language production and time – Text: less subdialogues/KCD – Degradation due to speech does not correlate

Outline

Introduction and Background The ITSPOKE System and Corpora A Study of Spoken versus Typed Dialogue

Tutoring– Human tutoring condition– Computer tutoring condition

Current Directions and Summary

Current and Future Directions

Data Analysis– Deeper coding for question types and other dialogue phenomena

ITSPOKE version 2– Pre-recorded prompts and domain-specific TTS– Shorter tutor prompts and/or changed display procedure– Barge-in, Always Available Vocabulary– Monitoring and adaptation capabilities

Data Collection– Additional human tutors and computer voices– Other dialogue evaluation metrics

Summary Goal: generate an empirically-based understanding of the

implications of adding speech to text-based dialogue tutors Accomplishments

– Completion of ITSPOKE (version1)

– Transcription, “annotation”, and preliminary analysis of two spoken tutoring corpora (human tutoring, computer tutoring)

– Initial empirical comparisons of typed and spoken tutorial dialogues (performance evaluation, correlation of dialogue characteristics with learning)

Results will impact the design of future systems incorporating speech, by highlight the performance gains that can be expected, and the requirements for their achievement

References

Diane J. Litman, Carolyn P. Rose, Kate Forbes-Riley, Kurt VanLehn, Dumisizwe Bhembe, and Scott Silliman. Spoken Versus Typed Human and Computer Dialogue Tutoring. To appear, Proceedings of the Seventh International Conference on Intelligent Tutoring Systems (ITS), Maceio, Brazil, August-September 2004.

Diane J. Litman and Scott Silliman. ITSPOKE: An Intelligent Tutoring Spoken Dialogue System. In Proceedings of the Human Language Technology Conference: 4th Meeting of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL) (Companion Proceedings), Boston, MA, May 2004.

Related papers available at http://www.cs.pitt.edu/~litman/itspoke.html

Acknowledgments

Kurt VanLehn and the Why2 Team

The ITSPOKE Group

– Kate Forbes-Riley, LRDC, Research Associate – Scott Silliman, LRDC, Programmer

– Art Ward, Intelligent Systems, PhD Student– Alison Huettner, LRDC, Research Associate

Thank You!

Questions?

Zero-Order Learning Correlations

Dependent MeasureHuman Spoken (14)

Human

Typed (20)R p R p

Tot. Stud. Words -.473 .09 .065 .78Ave. Stud. Words/Turn -.167 .57 .491 .03Slope: Stud. Words/Turn -.275 .34 -.375 .10Intercept: Stud. Words/Turn -.176 .55 .625 .00Tot. Tut. Words -.482 .08 .027 .91Ave. Tut. Words/Turn -.139 .64 .496 .03

Human-Human Corpus Transcription and Annotation

Human-Computer ExcerptTutor26: 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)

Why2 Conceptual Physics Tutoring