QA on AutoTutor 2004 paper CPI 494, March 31, 2009 Kurt VanLehn.

Slides:

Advertisements

Similar presentations

Effective Instructional Conversations + TuTalk Instruction Carolyn Penstein Rosé Language Technologies Institute/ Human-Computer Interaction Institute.

Advertisements

ED 300: Human Growth and Learning Welcome Opening Prayer.

Did I Address ALL Parts of the Assignment?. For Example: HCA#4  Analyzing use of verbal communication  Message communicated  How verbal communication.

Lecturing (with PowerPoint) Donna Ellis, TRACE & Colin Campbell, IST.

Designer’s dilemma Director says a first version “seems a little dry—can you spice it up a bit?” Why might spicing it up with extra graphics or background.

Rhee Dong Gun. Chapter The speaking process The differences between spoken and written language Speaking skills Speaking in the classroom Feedback.

The interaction plateau CPI 494, April 9, 2009 Kurt VanLehn 1.

REFINE YOUR NONVERBAL DELIVERY

QA on Anderson et al Intro to CTAT CPI 494 & 598 Jan 27, 2009 Kurt VanLehn.

Optimizing General Compiler Optimization M. Haneda, P.M.W. Knijnenburg, and H.A.G. Wijshoff.

Viewing AutoTutor as a step- based tutoring system CPI 494, April 2, 2009 Kurt VanLehn.

Chan & Chou’s system Chan, T.-W., & Chou, C.-Y. (1997). Exploring the design of computer supports for reciprocal tutoring. International Journal of Artificial.

Strategies for Research Methods Tutoring using Latent Semantic Analysis David AllbrittonPeter Wiemer-Hastings Elizabeth ArnottJesse Efron DePaul University.

© Tefko Saracevic1 Search strategy & tactics Governed by effectiveness&feedback.

Sims for soft skills CPI 494, April 23, 2009 Kurt VanLehn.

Click to edit the title text format Methodology for Authoring Dialogues Pamela Jordan University of Pittsburgh Learning Research and Development Center.

An introduction to intelligent interactive instructional systems

QA on “The behavior of tutoring systems” CPI 494 Feb 3, 2009 Kurt VanLehn.

Sung Young’s comments Start up was rough. Asked Mr. Davis SY was not able to enter whole content Didn’ thave to learn whole content; could look up insead;

HRM-755 PERFORMANCE MANAGEMENT

28/9/14Dialogue 1 TP: Characters reveal themselves through what they say Bell work: Why do you think writers include dialogue (speech) in their stories?

Simulation-based tutoring systems CPI 494/598, April 16 Kurt VanLehn.

Preparing a Presentation

Making Effective Presentations Dr. Ruth Yontz Department of Finance University of Illinois.

31 st October, 2012 CSE-435 Tashwin Kaur Khurana.

‘Learning without reviewing is like filling a bath with water without the plug in.’ Assessment for Learning (AFL)

Training Methods Presentation method Hands on method

Instructional design principles and models Mart Laanpere, M.Sc. in educational technology Tallinn University of Educational and Social Sciences

Teaching Tactics and Simulating Human Tutor Dialog Moves in Auto Tutor Rupali Mukherjee and Marvin Schiller 8. Juli 2003 Intelligent Tutoring Systems,

Tell your partners about your speaking activity.

1 Pedagogical implications of mobile technologies Diana Laurillard WLE Symposium on M-Learning 9 February 2007.

Research Methods Tutoring in the classroom Peter Wiemer-Hastings David Allbritton Elizabeth Arnott Oussama BenKhadra Jesse Efron.

DIRECT INTERACTIVE INSTRUCTION

The Power of Nonverbals in Competitive Speech

Speech Analysing Component in Automatic Tutoring Systems Presentation by Doris Diedrich and Benjamin Kempe.

Rational/Theoretical Cognitive Task Analysis Ken Koedinger Key reading: Zhu, X., & Simon, H. A. (1987). Learning mathematics from examples and by doing.

Learning Styles Adapted from: How best do.

Simulations: The teacher’s perspective Ruth Thomas, Colin Milligan, SCROLLA, JeLSIM Partnership.

ELPSS RLO Scripting Templates VERSION 3 (Jan 09).

Cognitive Science Overview Cognitive Apprenticeship Theory.

Incorporating Self Regulated Learning Techniques into Learning by Teaching Environments Biswas, G., Leelawong, K. and Belynne, K. The twenty sixth Annual.

1 USC Information Sciences Institute Yolanda GilFebruary 2001 Knowledge Acquisition as Tutorial Dialogue: Some Ideas Yolanda Gil.

LESSON PLANNING What? Why? And How?. Goals of this session Participants will be able to identify and explain: 1.What is a lesson plan and how to develop.

Developing Learning by Teaching Environments that support Self-Regulated Learning Gautam Biswas, Krittaya Leelawong, Kadira Belynne, Karun Viswanath, Daniel.

Module 3.2.  Learn the differences between kinds of textbooks  Learn ways to help students focus their reading and manage multiple or very large reading.

Advanced English, Language Arts, and Reading Mrs. Lubojacky.

AutoTutor Benjamin Kempe Tutoring Research Group, University of Memphis

T he 7 H igh R eliability L iteracy T eaching P rocedures Getting Knowledge Ready {G.K.R} Vocabulary Reading aloud Paraphrasing Saying questions the text.

Artificial Intelligence, Expert Systems, and Neural Networks Group 10 Cameron Kinard Leaundre Zeno Heath Carley Megan Wiedmaier.

When completing application exercises, ask yourself:  Did I Address ALL Parts of the Assignment?  Did I Go Beyond Common Knowledge and Basic Explanation?

Assessment embedded in step- based tutors (SBTs) CPI 494 Feb 12, 2009 Kurt VanLehn ASU.

QuizMASter – A Multi-Agent Game-Style Learning Activity Mark Dutchuk Vancouver Island University, Canada Khalid Aziz Muhammadi Government of Alberta, Canada.

EDU 3201 Lg Def Lr/GM/UPM/ Supporting Students with Learning Disabilities: An Introduction to Process- Oriented Instruction.

The Level 2 Exam What do each of the underlined words mean? Apply knowledge of and make judgements about drama processes and performance in a new.

Giving Classroom Instructions & Writing Instruction Manuals

Classroom Interaction Prepared by :Remah Hassan Submitted to :Dr. Suzan Arafat.

Evaluation, Testing and Assessment June 9, Curriculum Evaluation Necessary to determine – How the program works – How successfully it works – Whether.

Applying the Personalization Principle ( Chapter 9) Ken Koedinger 1.

WHOLE BRAIN TEACHING Overview. EXPLANATION Whole brain teaching is a new "radical" idea to some, however it is nothing more than tried and true teaching.

CEIT 225 Instructional Design Prof. Dr. Kürşat Çağıltay

Questionnaire-Part 2. Translating a questionnaire Quality of the obtained data increases if the questionnaire is presented in the respondents’ own mother.

THE SCIENTIFIC METHOD Murtaugh 1A Living Environment.

How to teach writing Why teach writing?

Dialogue-Learning Correlations in Spoken Dialogue Tutoring

Constraint-based tutoring

Learning companions 1 CPI 494, Kurt VanLehn March 26, 2009.

Teachers as Authors of Computer Based Tutoring Systems Dr

The Behavior of Tutoring Systems

Drill & Practice Programs

Presentation transcript:

QA on AutoTutor 2004 paper CPI 494, March 31, 2009 Kurt VanLehn

What did AutoTutor look like? Animated agent + dialogue box; voice; Input from student = typed response; may be a couple of words Technology for voice = MS voice (Alan Black; LTI – Festivox) – There was version that understood speech, sortof Gestures = Facial, hand waves inward, – Was version that pointed Can switch faces; no big diff on gains & motivation – Absent agent may not make a different Voice first then text

What was AutoTutor’s “script”? Give overall question; first student turn = usually not complete or correct; Tutor picks an aspect of the correct, complete answer and tries to get student to say it. Tutor pump/prompts; T hints; T assert Repeat for each missing aspect – Follow the student vs. own ordering – conceptual prereqs Also answes the student’s questions

What is AutoTutor trying to teach? Drive stueent with hints to articulate all the aspects of the ideal answer Its main questions are deep Constructivist practice; learn by (self-) questioning – Transfer studies? Types of knowledge – Facts? Concepts? Procedures? Principles? – Facts are too shallow to benefit from AT vs. drill – Concepts OK – Princpiles, especially how to apply them – Could be procedure, but not perhaps the best;

How is AutoTutor different from human tutoring on the same content? AutoTutor is based on shallower understanding of the student. Based on pattern match (LSA) between its ideal answer aspects and the student’s response. Human’s do not use super sophisticated tutoring Humans often do same remediation cycle By-stander turing test

Is AutoTutor’s dialogue natural? By stander test – May be is Hand out dialogues later – Logic is screwy

In terms of learning gains, was AutoTutor effective? All conditions similar on shallow questions AutoTutor beat no-tutor and textbook on deep qustions and cloze questions Interaction plateaus Motivation unknow resutls…

What kinds of task domains would AutoTutor be good for?

How did AutoTutor analyze the student’s responses? LSA Bag of words (unordered list with duplicates allowed)

In terms of step-based tutoring, what are AutoTutor’s steps?

In terms of step-based tutoring, what feedback and hints exist for steps?

Does AutoTutor have an outer loop over tasks?

What model of the student does AutoTutor have?