Cohesion and Learning in a
Tutorial Spoken Dialog System
Art Ward
Diane Litman
1
Outline



Tutoring
Goals
4 issues in measuring cohesion



Why they’re interesting
How we test them
Results
2
Natural Language Dialog
Tutoring



Human tutors are better than classroom instruction
(Bloom 84)
Intelligent Tutoring Systems (ITSs) hope to replicate
this advantage
Is Dialog important to learning?



Dialog acts: question answering, explanatory reasoning, deep
student answers (Graesser et al. 95, Forbes-Riley et al. 05)
Difficult to automatically tag dialog input, so:
Automatically detectable dialog features


Average turn length, etc. (Litman et al. 04)
We look at Cohesion

Lexical Co-occurrence between turns
3
Goals and Results

Goals

Want to find if cohesion is correlated with learning in our
tutoring dialogs.


Want to find a computationally tractable measure of
cohesion


If it is, may inform ITS design
So can be used in a real-time tutor
Results

Do find strong correlations with learning



For low pre-testers
For interactive (tutor to student) measures of cohesion
Robust to multiple measures of lexical cohesion
4
4 Issues


Why/How identify cohesion in dialogs?
Do students of different skill levels respond to
cohesion in the same way?
(Is there an aptitude/treatment interaction?)


Is Interactivity Important?
What other processing steps help?
5
Issue 1: How identify cohesion
in dialogs?

Why might cohesion be important in tutoring?
 McNamara & Kintsch (96)

Students read high & low coherence text

High coherence text was low coherence version altered to:




Use consistent referring expressions
Identify anaphora
Supply background information
Interaction between pre-test score & response to textual
coherence


Low pre-testers learned more from more coherent text
High pre-testers learned LESS from more coherent text
6
Measuring Cohesion

Measurements from Computational Linguistics

Hearst(94) topic segmentation, text


Olney & Cai (05) topic segmentation, tutorial dialog


Thesaurus entries
Barzilay & Eldihad (97) Automatic Lexical Chains


Several measures, including Hearst’s
Morris & Hirst (91) Lexical Chains


Word-count similarity of spans of text
WordNet senses
We develop measures similar to Hearst’s

But novel in that:
Applied to dialog rather than text,
used to find correlations with learning

7
Issue 1: How identify cohesion
in dialogs?
Defining Cohesion

Halliday and Hassan (76)


Grammatical vs Lexical Cohesion
Lexical Cohesion


Reiteration
 Exact word repetition
 Synonym repetition
 Near Synonym repetition
 Super-ordinate class
 General referring noun
Cohesion measured by counting “cohesive ties”

Two words joined by a cohesive device (i.e. reiteration)
8
Issue 1: How identify cohesion
in dialogs?
Defining Cohesion

Halliday and Hassan (76)


Grammatical vs Lexical Cohesion
Lexical Cohesion


Reiteration
 Exact word repetition
 Synonym repetition
 Near Synonym repetition
 Super-ordinate class
 General referring noun
Cohesion measured by counting “cohesive ties”

Two words joined by a cohesive device (i.e. reiteration)
9
Issue 1: How identify cohesion
in dialogs?

How we measure Lexical Cohesion

We count cohesive ties between turns

Tokens (with stop words)


Tokens (stop words removed)


(token = “word”)
(Stops = high frequency, low information words)
Stems (stop words removed)
10
Stems




Stem
packag
packet
speed
veloc
horizont
displac
find
so
thu
Tokens
package, packages
Stem = non-inflected
packet, packets
speed, speeding, speeds
core of a word
veloc, velocities, velocity, velocitys
horizontal, horizontally
Porter Stemmer
displace, displaced, displacement, displacements, displacing
Allows us to find ties
find, finding
so
between various
thus
inflected forms of
the same word in adjacent turns.
“Turns” are tutor and student contributions to
Tutoring Dialogs collected by the ITSPOKE group.
11
Applying Cohesion measures
to our Corpora: example
Turn
Contribution
Student
Essay
No. The airplane and the packet have the same horizontal velocity. When the packet is dropped, the only
force acting on it is g, and the net force is zero. The packet accelerates vertically down, but does not
accelerate horizontally. The packet keeps moving at the same velocity while it is falling as it had when it was
on the airplane. There will be displacement because the packet still moves horizontally after it is dropped. The
packet will keep moving past the center of the swimming pool because of its horizontal velocity.
ITSPOKE
Uh huh. There is more still that your essay should cover. Maybe this will help you remember some of the
details need in the explanation. After the packet is released, the only force acting on it is gravitational force,
which acts in the vertical direction. What is the magnitude of the acceleration of the packet in the horizontal
direction?
Cohesive
Ties
Token w/stop
Token, no
stop
Stem, no stop
Matches
Count
packet, horizontal, the, it, is, of, only, force, acting, on, there, will, still, after
14
packet, horizontal, only, force, acting, there, will, still, after
9
packet, horizont, onli, forc, act, acceler, vertic, there, will, still, after
11
12
Applying Cohesion measures
to our Corpora: example
Turn
Contribution
Student
Essay
No. The airplane and the packet have the same horizontal velocity. When the packet is dropped, the only
force acting on it is g, and the net force is zero. The packet accelerates vertically down, but does not
accelerate horizontally. The packet keeps moving at the same velocity while it is falling as it had when it was
on the airplane. There will be displacement because the packet still moves horizontally after it is dropped.
The packet will keep moving past the center of the swimming pool because of its horizontal velocity.
ITSPOKE
Uh huh. There is more still that your essay should cover. Maybe this will help you remember some of the
details need in the explanation. After the packet is released, the only force acting on it is gravitational
force, which acts in the vertical direction. What is the magnitude of the acceleration of the packet in the
horizontal direction?
Cohesive
Ties
Token w/stop
Token, no
stop
Stem, no stop
Matches
Count
packet, horizontal, the, it, is, of, only, force, acting, on, there, will, still, after
14
packet, horizontal, only, force, acting, there, will, still, after
9
packet, horizont, onli, forc, act, acceler, vertic, there, will, still, after
11
13
Applying Cohesion measures
to our Corpora: example
Turn
Contribution
Student
Essay
No. The airplane and the packet have the same horizontal velocity. When the packet is dropped, the only
force acting on it is g, and the net force is zero. The packet accelerates vertically down, but does not
accelerate horizontally. The packet keeps moving at the same velocity while it is falling as it had when it was
on the airplane. There will be displacement because the packet still moves horizontally after it is dropped.
The packet will keep moving past the center of the swimming pool because of its horizontal velocity.
ITSPOKE
Uh huh. There is more still that your essay should cover. Maybe this will help you remember some of the
details need in the explanation. After the packet is released, the only force acting on it is gravitational
force, which acts in the vertical direction. What is the magnitude of the acceleration of the packet in the
horizontal direction?
Cohesive
Ties
Token w/stop
Matches
Count
packet, horizontal, the, it, is, of, only, force, acting, on, there, will, still, after
14
Token, no
stop
packet, horizontal, only, force, acting, there, will, still, after
9
Stem, no
stop
packet, horizont, onli, forc, act, acceler, vertic, there, will, still, after
11
14
Applying Cohesion measures
to our Corpora: example
Turn
Contribution
Student
Essay
No. The airplane and the packet have the same horizontal velocity. When the packet is dropped, the only
force acting on it is g, and the net force is zero. The packet accelerates vertically down, but does not
accelerate horizontally. The packet keeps moving at the same velocity while it is falling as it had when it was
on the airplane. There will be displacement because the packet still moves horizontally after it is dropped.
The packet will keep moving past the center of the swimming pool because of its horizontal velocity.
ITSPOKE
Uh huh. There is more still that your essay should cover. Maybe this will help you remember some of the
details need in the explanation. After the packet is released, the only force acting on it is gravitational
force, which acts in the vertical direction. What is the magnitude of the acceleration of the packet in the
horizontal direction?
Cohesive
Ties
Token w/stop
Matches
Count
packet, horizontal, the, it, is, of, only, force, acting, on, there, will, still, after
14
Token, no
stop
packet, horizontal, only, force, acting, there, will, still, after
9
Stem, no
stop
packet, horizont, onli, forc, act, acceler, vertic, there, will, still, after
11
15
Issue 2: Is there an
aptitude/treatment interaction?

Why there might be:


McNamara & Kintsch
How we test it:

Mean pre-test split



All students
Above-mean pretest students (“high” pre-testers)
Below-mean pretest students (“low” pre-testers)
16
Issue 3: Is interactivity
Important?

Why it might be:

Chi et al. (01)


Tutor centered, Student centered, Interactive
Deep learning through self construction


Litman & Forbes-Riley (05)

Learning correlated with both:



Not tutor actions alone
student utterances that display reasoning
tutor questions that require reasoning
How we test it:



Interactive corpus – compare tutor to student turns
Tutor–only corpus
Student–only corpus
17
Issue 4: What other processing
steps help?

Tried several on training corpus:





Removing stop words
N-turn spans
Selecting “substantive” turns
TF-IDF normalization
Turn-normalized counts


Found final options on training corpus:



(Raw tie count / # of turns in dialog)
One turn spans, turn normalization, no TF-IDF, no
substantive turn selection
All reported results use these options
Tested options on new corpus
18
Where did the corpora come
from?

ITSPOKE is a speech-enabled version of
Why-2 Atlas (VanLehn et al. 02)


Qualitative physics
Tutoring Cycle



Student reads instructional materials
Takes a pre-test
Starts Interactive tutoring cycle






Problem
Essay
Tutor evaluates essay, engages in dialog
Revise essay
Repeat
Takes a post-test
19
Tutoring Corpora

Transcripts of tutoring sessions

Training corpus (fall 2003):




20 students, 5 problems each
95 dialogs (5 had no dialog)
13 low pre-testers, 7 high pre-testers
Testing corpus (spring 2005):



34 students, 5 problems each
163 dialogs (7 had no dialog)
18 low pre-testers, 16 high pre-testers
20
Results: Aptitude/Treatment
Tests





Test: partial correlation of
post-test & cohesion count,
controlling for pre-test
Cohesion correlated with
learning for low pre-test
students
Not for high pre-test
students
Little difference between
types of measurement
Less significant on testing
data, “token with stops”
level reduced to a trend
Train: 2003 Data
Students
R
P-Value
Test: 2005 Data
R
P-Value
Grouped by Token (with stop words)
All Students
0.380
0.098
0.207
0.239
Low Pretest
0.614
0.026
0.448
0.062
High Pretest
0.509
0.244
0.014
0.958
Grouped by Token (Stop words removed)
All Students
0.431
0.058
0.269
0.124
Low Pretest
0.676
0.011
0.481
0.043
High Pretest
0.606
0.149
0.132
0.627
Grouped by Stem (Stop words removed)
All Students
0.423
0.063
0.261
0.135
Low Pretest
0.685
0.010
0.474
0.047
High Pretest
0.633
0.127
0.121
0.655
21
Results: Aptitude/Treatment
Tests





Test: partial correlation of
post-test & cohesion count,
controlling for pre-test
Cohesion correlated with
learning for low pre-test
students
Not for high pre-test
students
Little difference between
types of measurement
Slightly less significant on
testing data
Train: 2003 Data
Students
R
P-Value
Test: 2005 Data
R
P-Value
Grouped by Token (with stop words)
All Students
0.380
0.098
0.207
0.239
Low Pretest
0.614
0.026
0.448
0.062
High Pretest
0.509
0.244
0.014
0.958
Grouped by Token (Stop words removed)
All Students
0.431
0.058
0.269
0.124
Low Pretest
0.676
0.011
0.481
0.043
High Pretest
0.606
0.149
0.132
0.627
Grouped by Stem (Stop words removed)
All Students
0.423
0.063
0.261
0.135
Low Pretest
0.685
0.010
0.474
0.047
High Pretest
0.633
0.127
0.121
0.655
22
Results: Aptitude/Treatment
Tests





Test: partial correlation of
post-test & cohesion count,
controlling for pre-test
Cohesion correlated with
learning for low pre-test
students
Not for high pre-test
students
Little difference between
types of measurement
Less significant on testing
data, “token with stops”
level reduced to a trend
Train: 2003 Data
Students
R
P-Value
Test: 2005 Data
R
P-Value
Grouped by Token (with stop words)
All Students
0.380
0.098
0.207
0.239
Low Pretest
0.614
0.026
0.448
0.062
High Pretest
0.509
0.244
0.014
0.958
Grouped by Token (Stop words removed)
All Students
0.431
0.058
0.269
0.124
Low Pretest
0.676
0.011
0.481
0.043
High Pretest
0.606
0.149
0.132
0.627
Grouped by Stem (Stop words removed)
All Students
0.423
0.063
0.261
0.135
Low Pretest
0.685
0.010
0.474
0.047
High Pretest
0.633
0.127
0.121
0.655
23
Results: Aptitude/Treatment
Tests





Test: partial correlation of
post-test & cohesion count,
controlling for pre-test
Cohesion correlated with
learning for low pre-test
students
Not for high pre-test
students
Little difference between
types of measurement
Less significant on testing
data, “token with stops”
level reduced to a trend
Train: 2003 Data
Students
R
P-Value
Test: 2005 Data
R
P-Value
Grouped by Token (with stop words)
All Students
0.380
0.098
0.207
0.239
Low Pretest
0.614
0.026
0.448
0.062
High Pretest
0.509
0.244
0.014
0.958
Grouped by Token (Stop words removed)
All Students
0.431
0.058
0.269
0.124
Low Pretest
0.676
0.011
0.481
0.043
High Pretest
0.606
0.149
0.132
0.627
Grouped by Stem (Stop words removed)
All Students
0.423
0.063
0.261
0.135
Low Pretest
0.685
0.010
0.474
0.047
High Pretest
0.633
0.127
0.121
0.655
24
Results: Aptitude/Treatment
Tests





Test: partial correlation of
post-test & cohesion count,
controlling for pre-test
Cohesion correlated with
learning for low pre-test
students
Not for high pre-test
students
Little difference between
types of measurement
Less significant on testing
data, “token with stops”
level reduced to a trend
Train: 2003 Data
Students
R
P-Value
Test: 2005 Data
R
P-Value
Grouped by Token (with stop words)
All Students
0.380
0.098
0.207
0.239
Low Pretest
0.614
0.026
0.448
0.062
High Pretest
0.509
0.244
0.014
0.958
Grouped by Token (Stop words removed)
All Students
0.431
0.058
0.269
0.124
Low Pretest
0.676
0.011
0.481
0.043
High Pretest
0.606
0.149
0.132
0.627
Grouped by Stem (Stop words removed)
All Students
0.423
0.063
0.261
0.135
Low Pretest
0.685
0.010
0.474
0.047
High Pretest
0.633
0.127
0.121
0.655
25
Results: Aptitude/Treatment
Tests





Test: partial correlation of
post-test & cohesion count,
controlling for pre-test
Cohesion correlated with
learning for low pre-test
students
Not for high pre-test
students
Little difference between
types of measurement
Less significant on testing
data, “token with stops”
level reduced to a trend
Train: 2003 Data
Students
R
P-Value
Test: 2005 Data
R
P-Value
Grouped by Token (with stop words)
All Students
0.380
0.098
0.207
0.239
Low Pretest
0.614
0.026
0.448
0.062
High Pretest
0.509
0.244
0.014
0.958
Grouped by Token (Stop words removed)
All Students
0.431
0.058
0.269
0.124
Low Pretest
0.676
0.011
0.481
0.043
High Pretest
0.606
0.149
0.132
0.627
Grouped by Stem (Stop words removed)
All Students
0.423
0.063
0.261
0.135
Low Pretest
0.685
0.010
0.474
0.047
High Pretest
0.633
0.127
0.121
0.655
26
Results: Aptitude/Treatment
Tests





Test: partial correlation of
post-test & cohesion count,
controlling for pre-test
Cohesion correlated with
learning for low pre-test
students
Not for high pre-test
students
Little difference between
types of measurement
Less significant on testing
data, “token with stops”
level reduced to a trend
Train: 2003 Data
Students
R
P-Value
Test: 2005 Data
R
P-Value
Grouped by Token (with stop words)
All Students
0.380
0.098
0.207
0.239
Low Pretest
0.614
0.026
0.448
0.062
High Pretest
0.509
0.244
0.014
0.958
Grouped by Token (Stop words removed)
All Students
0.431
0.058
0.269
0.124
Low Pretest
0.676
0.011
0.481
0.043
High Pretest
0.606
0.149
0.132
0.627
Grouped by Stem (Stop words removed)
All Students
0.423
0.063
0.261
0.135
Low Pretest
0.685
0.010
0.474
0.047
High Pretest
0.633
0.127
0.121
0.655
27
Results: Aptitude/Treatment
(2003 data)


No significant difference
between amounts of
Token (with Stop words)
(turn normalized) cohesion Token (Stops removed)
Stem (Stops removed)
in high and low pre-test
groups.
Difference in correlation between high and low
pre-testers not due to different amounts of
cohesion.
Mean Cohesion
High Pre Low Pre
9.978
9.449
5.375
5.209
5.713
5.611
P-Val
0.581
0.768
0.867
28
Results: Interactivity (2003)

Cohesion between
tutor utterances is not
correlated with
learning
Tutor Turns Only
Train: 2003 Data
Test: 2005 Data
Students
R
P-Value
R
P-Value
Grouped by Token (with stop words)
All Students
0.060
0.800
0.190
0.270
Low Pretest
0.121
0.695
0.260
0.290
High Pretest
0.531
0.220
0.100
0.700
Grouped by Token (Stop words removed)
All Students
0.004
0.987
0.160
0.380
Low Pretest
0.114
0.710
0.230
0.350
High Pretest
0.351
0.440
0.070
0.780
Grouped by Stem (with stop words)
All Students
0.010
0.967
0.160
0.350
Low Pretest
0.107
0.727
0.240
0.340
High Pretest
0.465
0.293
0.080
0.760
29
Results: Interactivity (2003)

No evidence that
cohesion between
student productions is
correlated with
learning (but student
utterances are very
short with computer
tutor)
Student Turns Only
Train: 2003 Data
Test: 2005 Data
Students
R
P-Value
R
P-Value
Grouped by Token (with stop words)
All Students
0.242
0.304
0.070
0.695
Low Pretest
0.325
0.279
0.121
0.634
High Pretest
0.451
0.310
0.076
0.779
Grouped by Token (Stop words removed)
All Students
0.111
0.640
0.191
0.280
Low Pretest
0.010
0.974
0.248
0.322
High Pretest
0.493
0.261
0.214
0.426
Grouped by Stem (with stop words)
All Students
0.113
0.637
0.187
0.291
Low Pretest
0.009
0.976
0.253
0.311
High Pretest
0.501
0.252
0.199
0.460
30
Discussion



Both high and low pre-testers successfully
learned from these dialogs
Our measure of lexical cohesion seems to
reflect only what the low pre-testers do to
learn, not correlated with what high pretesters do.
McNamara & Kintsch also found a positive
correlation for low pre-testers, but a negative
correlation for high pre-testers.
31
Discussion

Our measures are slightly different:

McNamara & Kintsch: Manipulated coherence in text





Reader does not contribute to coherence
Coherence is the extent to which semantic relations are spelled out in
the text, rather than inferred by the reader.
Low pre-testers probably learned because high coherence text allowed
them to make inferences they couldn’t from the low cohesion text.
 Low pre-testers & low coherence: didn’t know the terms
High coherence may allow a greater number of successful inferences
for their low pre-testers
Our work: Dialog




Student does contribute to cohesion
Higher cohesion means using more of same terms
Speculation: High cohesion may indicate the number of successful
inferences our low pre-testers already made.
High pre-testers already know the terms, so new inferences are not
involved in using them.
32
Summary

We have taken automatically
computable measures of cohesion from
computational linguistics


Applied them to tutorial dialog
Found correlations with student learning
33
Conclusions

Simple, automatically computable measures
of lexical cohesion correlate with learning



But only for students with low pre-test scores,
even though low and high pre-testers showed
similar amounts of cohesion.
Correlation is robust to differences in type of
measurement
It’s the cohesion between student and tutor
that’s important
34
Future Work

Short term:

Cohesion may also be related with learning in high pretesters, but we’re measuring the wrong kind of cohesion

Work underway to try “sense” level measures



New issues:




Word sense disambiguation (one sense per discourse?)
Or measuring it in the wrong places


Halliday & Hassan’s “synonym” levels of reiteration
“Acceleration” & “speeding up”
Try finding cohesion at impasses (VanLehn 03)
Try finding change in cohesion over time (Pickering & Garrod
04)
Is it the dialog, or the essay?
Long term:

Test by manipulating cohesion in ITSPOKE
35
Thanks


Diane Litman
ITSPOKE group
36
Questions?
37
03speech93_sess00_prob55 Low cohesion Tie-count = .419
tutor
Enter your response to the question at the top right, in the essay window. When you are finished hit the submit
'when',
" If the airplane drops the packet when it is directly above the swimming pool, it will not hit the pool. The packet
'at', 'ar', and the plane are moving at the same horizontal velocity, but then the packet is dropped from the plane. The
essay
'hit',
packet's horizontal velocity slowly decreases and the vertical velocity increases. The packet passes the pool
'ar',
You have the correct answer except that you need to go into more depth in your essay. I'll try to help you
tutor
'packet', remember some details to include in the explanation. After the packet is released, what are the forces acting on
student
gravity air resistance
Almost. Let's consider the kinds of forces that air can exert on an object traveling through it. There are two. a
frictional force and a buoyancy force. Sometimes they are so small compared to the gravitational and other forces
present that they can be ignored, and sometimes they are so large that they cannot be ignored. For each one,
tutor
'air',
let's discuss when they can be ignored. Let's consider first the frictional force. If you stick your hand out the
student
strong wind
You feel the wind blowing your hand backwards, hard. That force is the frictional force of the air that we are
talking about. The air exerts a frictional force on your hand. Now if the car moves at about the same speed as you
tutor
'wind',
would walk, what would you feel on your hand when you stick it out the window?
student
nothing
Right. So when the relative speed of the object and the air is large, the air exerts a force on the object. When it is
small compared to the other forces present, it can be neglected. Thus the speed of the object through the air is
tutor
one factor. There are others. Suppose you have a large umbrella open above you, and the air is still. You pull
student
no
Well... If you really jerk down hard on the umbrella, it will resist so much that it might turn inside out. You can
really feel the force then! But even if you pull down gently, you can feel some force resisting your pull. The
resistance you feel is caused by the air resisting the passage of the open umbrella through it. Although the
tutor
umbrella is not moving fast, it presents a large surface area to the air along the direction of motion. This makes
student
'larg',
large
Correct. The larger the surface area presented to the air, the larger the resistive force. So when an object moving
tutor
through the air presents a small surface area and it is not moving very fast, how big is the force exerted by the air
student
'small',
small
tutor
Right. Can we ignore the force that the air exerts on a baseball that is dropped from the roof of a one-story
student
yes
tutor
Fine. Can we ignore the force that the air exerts on a large truck barreling down the freeway?
student
no
Correct. You seem to understand the frictional force that air can exert on objects. Let's discuss the other force.
tutor
When a boat floats in water, what is the name of the force that keeps it from sinking? ~~ When a boat floats in
38
student
tutor
student
tutor
student
tutor
student
tutor
student
tutor
student
'forc',
'forc',
'dens',
'dens',
tutor
student
tutor
student
tutor
student
tutor
student
tutor
student
'ignor',
tutor
essay
tutor
'forc',
'vertic',
'earth',
i don't know
The buoyant force of the water on the boat keeps it from sinking. Air can exert a buoyancy force, too. For
instance, when a helium-filled balloon rises, the force acting on it is what?
field force
The buoyant force of the air on the balloon lifts it up. An object, such as a piece of stone, sinks in water but a
piece of cork floats. However, even stones float in liquid mercury, because mercury is denser than a piece of
stone. So whether something sinks or floats depends on the density of the object (taken as a whole including any
voids etc in it) RELATIVE to the medium it is in. How dense is air as compared to the objects you come across?
less dense
Correct. Because air is not at all dense, what kinds of objects can float in air?
clouds
Could you please repeat that?
clouds
I'm sorry, I'm having trouble understanding you. Please try again.
balloons
Well... Only objects that are less dense than air will float. That is, the buoyant force acting upward is larger than
the gravitational force acting downward. Since many objects that move through air (e.g., balls, rocks, pumpkins,
cars, etc.) are much denser than air, would you consider the buoyancy force exerted on them by the air to be
rate
Could you please repeat that?
ignore it
Good. To summarize, we can ignore the forces that air exerts on an object if the object is dense, it is moving
slowly relative to the air, and it presents a small surface area in the direction of motion. Air resistance is negligible.
Earth's gravity is the only force on the packet after its release. Air resistance is negligible. In which direction does
downward
Could you please repeat that?
downward
I'm sorry, I'm having trouble understanding you. Please try again.
down
The earth's gravitational force acts vertically downward. I think this is an important point to include. Try to fix it. If
you're finished, press the submit button.
" If the airplane drops the packet when it is directly above the swimming pool, it will not hit the pool. The packet
and the plane are moving at the same horizontal velocity, but then the packet is dropped from the plane. We can
ignore the forces that air exterts on the packet because it is dense, and presents a small surface area in the
direction of motion. The packet's horizontal velocity slowly decreases and the vertical velocity increases. The
packet passes the pool because it still has a horizontal velocity when it leaves the plane. It moves further than the
39
That all looks good. We're done with this problem. For comparison purposes, I'm going to show you one ideal
03speech88_sess00_prob58 High Cohesion Tie-count = 2.40
Enter your response to the question at the top right, in the essay window. When you are finished hit the submit
tutor
button and we'll discuss it.
" The man's keys will be above his face. This will occur because the elevator will move at a faster rate than he
essay
'at'
will. Since the man is falling at the same rate as the elevator, he is also moving faster than his keys. "
You have answered the problem correctly except that what you have in your essay is not a sufficient explanation.
tutor
'kei', 'man'
I'll give you some hints about what is needed in the explanation. Before the release, what is the relationship
student
'kei', 'veloc', 'man'
the man's velocity is faster than the keys velocity
Considering that the man is holding the keys before the release, what is the relationship between the man's
tutor
'kei', 'veloc', 'man' velocity and the keys velocity?
student
'kei', 'veloc', 'man'
the man's velocity is larger the the keys velocity
They must be the same. So, back to the original question. Before the release, what is the relationship between
tutor
'kei', 'veloc', 'man' the man's velocity and the keys velocity?
student
'thei', 'same'
they are the same
Good. What we've just covered is an important point to include in your essay. You should fix your essay now.
tutor
Press the submit button when you're done.
" Before the man releases the keys, the man's velocity is the same as the keys' velocity. After the man releases
the keys, they will be above his face. This will occur because the elevator will move at a faster rate than he will.
essay
Since the man is falling at the same rate as the elevator, he is also moving faster than his keys. "
Yeah. There are additional details to include in your essay. I'll remind you of what is needed in the explanation.
tutor
What force acts on all objects within close proximity to earth?
student
'forc'
the force of gravity
tutor
'forc'
Yep. Besides the Earth's gravitational force, are there any other forces acting on the person, keys, and elevator
student
'forc'
the normal force
When the elevator is in freefall, there are no other forces acting on it, the man, or the keys. There is no air
resistance on the man and the keys because the air in the elevator is moving right along with the man and the
keys. There is no air resistance on the elevator because it is in freefall, which is defined to mean that air
resistance is negligible. Thus, keep in mind that after the release, the only force on the person, keys, and elevator
tutor
'forc'
is the force of gravity. I think this is an important point to include. Please fix your essay now. If you're finished,
40
essay
tutor
student
tutor
student
tutor
student
tutor
essay
tutor
'move', 'becaus',
'kei', 'releas', 'act',
'there', 'forc', 'ar',
'no', 'other', 'graviti',
'after', 'man', 'elev'
'kei', 'releas', 'there',
'will', 'ar', 'after',
'upon', 'elev'
'depend', 'freefal',
'object', 'which',
'upon'
'freefal'
" Before the man releases the keys, the man's velocity is the same as the keys' velocity. After the man releases
the keys, the force of gravity acts upon the keys. Since there is no other force acting on the keys they will be
above his face after they are released. This will occur because the elevator will move at a faster rate than he will.
Since the man is falling at the same rate as the elevator, he is also moving faster than his keys. "
Yes. There is one detail we need to discuss. Maybe this will help you remember some of the details need in the
explanation. 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?
it depends upon which object is in freefall
Well... It is constant. Freefall acceleration is another name for acceleration due to what?
gravity
Good. What is the earth's gravitational acceleration?
nine point eight meters per second squared
Yep. Thus, freefall acceleration is the same for all objects, and the keys, the person, and the elevator are all
accelerating downwards with the same acceleration. Try to add something about this to your essay. Try to fix it.
" The elevator, man, and his keys are all in free-fall acceleration. Free-fall acceleration means that only the force
of gravity is acting on an object. The force of gravity on Earth is 9.8 meters per second squared. The force of
gravity is the only force acting on the elevator, keys, and the man, they are all falling at a rate of 9.8 meters per
'all', 'freefal',
second squared. Before the man releases the keys, the man's velocity is the same as the keys' velocity. After the
'object', 'kei',
man releases the keys, the force of gravity acts seperatly upon the keys. The man will have a larger mass than
'acceler', 'ar', 'same', the keys, so he will fall at a faster velocity than the keys. Since there is no force other than the force of gravity
'elev'
acting on the keys they will be above his face after they are released. This will occur because the elevator will
Good. You need some additional details in your essay. We'll count the problem as done. Although this would
'as', 'have', 'so'
need to be rewritten to fit in your essay, so you can compare, here's what I would have said:
41
Cohesion vs Coherence

Cohesive Devices

Things that “tie” different parts of a discourse
together:


But still may not make sense:


Anaphora, repetition, etc…
John hid Bill’s car keys. He likes spinach. (Jurafsky &
Martin 00)
Coherence relations

Semantic relations between utterances.

Result, Explanation, elaboration, etc. (Hobbs 79)
42
Britton & Gulgoz 91
Original text:
Air war in the North, 1965
By the fall of 1964, Americans in both Saigon and
Washington had begun to focus on Hanoi as the
source of the continuing problem in the south.
 Modified text:
Air war in North Vietnam, 1965
By the beginning of 1965, Americans in both Saigon
and Washington had begun to focus on Hanoi, capital
of North Vietnam, as the source of the continuing
problems in the south.

43