Experiments with ITSPOKE: An Intelligent Tutoring Spoken Dialogue System

Experiments with ITSPOKE: An Intelligent Tutoring Spoken Dialogue System Dr. Diane Litman Associate Professor, Computer Science Department and Research Scientist, Learning Research & Development Center University of Pittsburgh www.cs.pitt.edu/~litman

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 • 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?

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

Feasibility of Speech Very Large Voc.; Dialog; Limited Tasks & Environments Large Vocabulary; Syntax, Semantics Medium Vocabulary Large Vocabulary Small Vocabulary Isolated Words; Connected Digits; Continuous Speech Connected Words; Continuous Speech Continuous Speech; Speech Understanding Conversational Speech; Spoken dialog; Multiple modalities Isolated Words 1962 1967 1972 1977 1982 1987 1992 1997 2004 Continuing Challenges - accuracy - efficiency (speed, memory) - robustness - unlimited tasks

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

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

Speech Recognition: Sphinx2 (CMU) • Probabilistic language models for different dialogue states • Initial training data • typed student utterances from Why2-Atlas corpora • Later training data • spoken utterances obtained during development and pilot testing of ITSPOKE • Total vocabulary • 1240 unique words • “Semantic Accuracy” Rate = 92.4%

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), 5948 student turns, 5505 tutor turns • Student and tutor turns were manually transcribed for content and segmented for boundaries

Corpus Transcription & Annotation

Spoken Tutoring Corpora (cont.) • Computer-Human Corpus • 20 students, 100 physics problems (dialogues) , 2445 student turns, 2967 tutor turns • Noisy student turn boundaries and transcriptions extractable from ITSPOKE logs • Content of student turns also manually transcribed

ITSPOKE Corpora ComparisonHuman-Human Human-Computer …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 …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)

Empirical Results 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 (2004)

Research Questions • Given that natural language tutoring systems are becoming more common, is it worth the extra effort to develop spokenrather than text-based systems? • Given the current limitations of speech and natural processing technologies, how do computer tutors compare to the upper bound performance of human tutors?

Common Experimental Aspects • 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 dialoguecondition (20 students, 171 dialogues/physics problems) • Strict turn-taking enforced • Spoken dialoguecondition (14 students, 128 dialogues/physics problems) • Interruptions and overlapping speech permitted • Dialogue history box remains empty

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 Excerpt SpokenExcerpt (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

Typed versus Spoken Tutoring: Overview of Analyses • Tutoring and Dialogue Evaluation Measures • learning gains • efficiency • Correlation of Dialogue Characteristics and Learning • do dialogue means differ across conditions? • which dialogue aspects correlate with learning in each condition?

Learning and Training Time Key: statistical trend statistically significant

Discussion • Students in both conditions learned during tutoring (p=0.000) • The adjusted posttest scores suggest that students learned more in the spoken condition (p=0.053) • Students in the spoken condition completed their tutoring in less than half the time (p=0.000)

Dialogue Characteristics Examined • Motivated by previous learning correlations with student language production and interactivity (Core et al., 2003; Rose et al.; 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)

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

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 • Deeper analyses needed (requires manual coding) • e.g., do longer student turns reveal more explanation? • results need to be further examined for student question types, substantive contributions, etc.

Computer Tutoring: Experiment 2 • Same 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 • Spoken dialoguecondition (20 students, 100 dialogues) • ITSPOKE • (noisy) 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 directionASR: add directions ITSPOKE : 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: REJECT Student: velocity

Results: Learning and Training Time • Students in both conditions learned during tutoring (p=0.000) • Students learned the same in both conditions (p=0.950) • Students in the typed condition completed their tutoring in less time than in the spoken condition (p=0.004)

Results: Dialogue Characteristics and Learning • 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 • Text: less subdialogues/KCD • Degradation due to speech does not correlate!

Recap • Human Tutoring: spoken dialogue yielded significant performance improvements • Greater learning gains • Reduced dialogue time • Many differences in superficial dialogue characteristics • Computer Tutoring: spoken dialogue made little difference • No change in learning • Increased dialogue time • Fewer dialogue differences

Current and Future Directions • Data Analysis • Deeper coding for question types and other dialogue phenomena • Analysis beyond the turn level • ITSPOKE version 2 • Pre-recorded prompts and domain-specific TTS • Shorter tutor prompts • Barge-in • Data Collection • Additional human tutors and computer voices • Other dialogue evaluation metrics

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)

Summary • Goal: an empirically-based understanding of the implications of adding speech to dialogue tutors • Accomplishments • ITSPOKE • Collection and analysis of two spoken tutoring corpora • Comparisons of typed and spoken tutorial dialogues • Results will impact the design of future systems incorporating speech, by highlighting the performance gains that can be expected, and the requirements for their achievement

Acknowledgments • Kurt VanLehn and the Why2 Team • The ITSPOKE Group • Kate Forbes-Riley • Alison Huettner • Beatriz Maeireizo • Amruta Purandare • Mihai Rotaru • Scott Silliman • Art Ward • NSF and ONR

Thank You! Questions?

www server Cepstral Architecture ITSpoke Why2 html java xml Text Manager student text (xml) essay www browser Essay Analysis (Carmel, Tacitus-lite+) essay text Speech Analysis (Sphinx) dialogue tutorial goals repair goals dialogue Content Dialogue Manager (Ape, Carmel) Spoken Dialogue Manager tutor turn (xml) text dialogue

Current Projects (www.cs.pitt.edu/~litman/itspoke.html) • Monitoring Student Emotions in Tutorial Spoken Dialogue • Adding Spoken Language to a Text-Based Dialogue Tutor(this talk) • Tutoring Scientific Explanations via Natural Language Dialogue

Performance Evaluations Word Error Rate NAB Resource Management ATIS Year

Learning and Training Time

New Computer Tutoring Dialogue Characteristics • Both conditions • Total Subdialogues per Knowledge Construction Dialogue (KCD) • Only ITSPOKE condition • Speech Recognition Errors

Computer Tutoring Dialogue Characteristics (means)

Experiments with ITSPOKE: An Intelligent Tutoring Spoken Dialogue System