Research Methods Tutoring in the classroom Peter Wiemer-Hastings David Allbritton Elizabeth Arnott...

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

Transcript of Research Methods Tutoring in the classroom Peter Wiemer-Hastings David Allbritton Elizabeth Arnott...

Page 1: Research Methods Tutoring in the classroom Peter Wiemer-Hastings David Allbritton Elizabeth Arnott Oussama BenKhadra Jesse Efron.

Research Methods Tutoring in the classroom

Peter Wiemer-Hastings

David Allbritton

Elizabeth Arnott

Oussama BenKhadra

Jesse Efron

Page 2: Research Methods Tutoring in the classroom Peter Wiemer-Hastings David Allbritton Elizabeth Arnott Oussama BenKhadra Jesse Efron.

Overview• Background of AI in Education and

Intelligent Tutoring Systems• On human learning• AutoTutor• RMT

• Architecture• Techniques• Deployment• Evaluation

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Overview of AIEd

• The goal: Interact with student to increase learning

• The ideal system:• Models domain knowledge• Models student knowledge• Adapts to student• Adapts its approaches to optimize

pedagogy

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Some influential systems

• Algebra tutor

• Project Listen

• Scenario-based interaction (e.g. Wolf)

• Inquiry-based learning

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What affects learning?

• Motivation

• Being “constructive”

• Doing example problems

• On the other hand: the Hawthorne effect

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Bloom's hierarchy (1956)

• Knowledge: recall facts

• Comprehension: understanding info

• Application: ability to use info

• Analysis: see patterns, organization

• Synthesis: use old ideas to create new

• Evaluation: compare, discriminate, assess

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Human tutors• Much better than classroom practice: up

to 2 standard deviations• Rely on doing problems with their

students• Don’t actually have deep understanding of

their students• Use basic dialog moves: question, pump,

prompt, hint, summarize• Have a script of topics to cover

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AutoTutor

• Started in 1997

• Models (non-expert) human tutors

• Talking head “incarnates” the tutor

• Uses dialog moves above

• Evaluates student answers by comparing to expected answers

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Latent Semantic Analysis

• Take a large corpus of texts

• Make a matrix of words in documents (paragraphs)

• Weight by inverse frequency

• Do singular value decomposition to “re-orient” the dimensions, reduce the noise

• Use vectors to represent “meaning” of words and texts.

• Use cosine to compare meanings.

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RMT

• Follows the AutoTutor approach

• Re-implemented in Lisp

• Interacts via the internet

• Includes spell-checking and synonym substitution

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Architecture

• Dialog manager handles logins, providing tutor utterances, collecting student responses.

• Responses evaluated with NLU components (LSA, aspell)

• Dialog Transition Network determines tutor’s next response

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DATN

• Graphical representation of tutor’s behavior

• Directly controls the tutor’s choice of response.

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Curriculum Script

• 5 topics: Ethics, types of studies, variables, reliability, validity

• Over 4000 lines• Each item contains:

• Question• (Picture)• Target good answers• Associated prompts, hints, questions• Bad answers• Summary

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RM Curriculum• Ethics of psychological experiments

• privacy, informed consent, ethics code, costs versus benefits

• Types of studies• experiments and correlational (observational, archival, case

studies, survey research)

• Variables• independent and dependent, operationalization, levels,

causality, confounding, categorical and quantitative variables, scales

• Reliability• systematic and random error, test-retest, internal

consistency, factors that affect reliability

• Validity• types of validity (internal, construct, external, statistical),

threats to validity.

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Types of student tasks

• Conceptual: facts, definitions, issues

• Analytic: Given a scenario, evaluate weaknesses, or fill in missing slots

• Synthetic: Given a task, create an approach (frame and slots)

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Example conceptual dialog

• Tutor: What does the principle of justice imply about how research should be done?

• Student: No group should have to take more risk than another group.

• Tutor: Good. What else?

• Student: The benefits of the research should be available to everyone

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Analytical example• Tutor: I'm going to tell you about a research project

someone conducted that has some ethical problems, and then you will tell me what the researcher should have done differently. OK? Here is how the project was done. Joe, a graduate student, did a study of high school students' attitudes about racism. He created a survey about racism and gave it out to 100 high school students. Before he gave out the survey, he got permission from his dissertation advisor and from the principal of the high school to give out an anonymous survey to the students. He also made sure each student read and signed a consent form that was attached to the survey. What should Joe have done differently to make the project comply with the requirements for ethical practices in research?

• Student: Huh?

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Synthetic example• Now let's practice designing a study. I'll give you

a hypothesis to test, and tell you what resources you have available. Then you design a study to test the hypothesis. Let's say that you want to find out whether reading a humorous greeting card causes people to be more likely to buy humorous magazines - that is your hypothesis. You have an agreement with a local magazine shop that allows you to use some of their customer purchase records for your research. …How will you set up the study?

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Demo

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Cool stuff on the way

• Shallow thinking traps

• Dialog Chains

• Research supervisor scam

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Leading students astray

• Failure to “get it right” strengthens learning

• Shallow thinking traps (STTs) in content material provide opportunity for failure

• Dialog chains (DCs) should get students back on track• A DC has a trigger and a network of

student responses and tutor questions

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Different tutoring style

• Traditional:• Tutor (usually) asks the questions and the

student (hopefully) answers.

• Pseudo-real world:• The tutor is disguised as a research

supervisor• Asks questions to assess student

knowledge (conceptual, analytic)• Assigns research tasks (analytic, synthetic)• Evaluates student’s work performance

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To the classroom

• Use in association with two core courses in Psychology next term

• Students use the system after they’ve covered the topic in the course

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In-course evaluation

• Pilot testing on various aspects

• 2x2 design: • Tutor or static information• Traditional tutor or research supervisor

• Pretests and posttests

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Pilot testing

• Questions:• 1. How easy/difficult was it for you to use the tutor?

• VERY EASY 1——2——3——4——5——6 VERY DIFFICULT

• 2. Was the tutor’s speech easy/difficult to understand?

• 1. How easy/difficult was the material presented by the tutor?

• N. Did you feel like you may learn/may not learn something during a tutoring session?

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Early results

• In general, subjects disliked the computerized speech -

• -Every subject in the "Miyako" mentioned her voice as something they did not like, and something that could be improved.

• "It was a female tutor with a male voice. Made it annoying."

• "Try to use a less computerized voice."• "The animation was fine, but the woman's

voice was annoying.“

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More results

• In general, subjects liked the animation - the only problem seemed to be that Miyako appears on top of the response box. Ss have to keep moving her back to the center of the screen. Merlin seemed to be the favorite.

• "I liked his facial expressions."• "He was funny looking. Relieved stress in a

way."• "Liked his outfit."

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Even more

• In general, subjects liked the feedback given and the hints and prompts and felt that the level of the material was appropriate

• "It was easy because he asked a question and in case you got it wrong, he will help you to get to the right answer.

• "The feedback was helpful."• "It didn't just give you the right answer if you

got it wrong."

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Some suggestions

• -Keep questions on the screen in the dialog box in case the student misses what the tutor asks.

• -Improve voices on the animated characters.• -Move Miyako so that she appears in the center of

the screen.• -Provide instructions at the beginning; a longer intro.• -More explanation of right answer.• -Sometimes the tutor asks the question before giving

the details of the experiment.

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Things they liked:

• -The feedback

• -The hints and prompts

• -Multiple chances to get the right answer

• -Various ways of asking questions

• -Lead me to the right answer

• -Merlin animation

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Issues

• For the research supervisor, does text-only make more sense?

• If not, what head do we give it?

• Is the supervisor scenario realistic enough to motivate students?

• Will it work?