Sentiment Analysis on Amazon Movie Reviews Dataset
Semelhante a Artificial Unintelligence:Why and How Automated Essay Scoring Doesn’t Work (most of the time) & the Perils and Promise of Automated Essay Evaluation
Semelhante a Artificial Unintelligence:Why and How Automated Essay Scoring Doesn’t Work (most of the time) & the Perils and Promise of Automated Essay Evaluation (20)
Artificial Unintelligence:Why and How Automated Essay Scoring Doesn’t Work (most of the time) & the Perils and Promise of Automated Essay Evaluation
1. Artificial Unintelligence:
Why and How Automated Essay
Scoring Doesn’t Work (most of the
time) & the Perils and Promise of
Automated Essay Evaluation
Les Perelman
Comparative Media Studies / Writing
MIT
2. Definition of Terms
Automated Essay Scoring
(AES)
• Computer produces
summative assessment for
evaluation
Automated Essay Evaluation
(AEE)
• Computer produces
formative assessment and
responses for learning
3. Overview
1. Brief recounting of mass-market writing
assessment in the United States
2. AES: how it works and its major flaws
3. The Turing Test: evaluate AES
4. AEE: a brief overview
5. AEE: evaluating current implementations
6. AEE: what we can reasonably hope to achieve
– Writelab
7. Demonstration: Playing with the BABEL
Generator
4. The First College Board Entrance
Examination in English − June 1901
• The two sides of the character of Achilles as
shown in The Iliad. Illustrate each and tell
whether we find anything like this contrast in
the character of Hector.
• At least two pages
• Four hours to write -- two in the morning & two
in the afternoon
5. SAT Essay June 2005
• Think carefully about the issue presented in the
following excerpt and the assignment below.
– Most of our schools are not facing up to their
responsibilities. We must begin to ask ourselves whether
educators should help students address the critical moral
choices and social issues of our time. Schools have
responsibilities beyond training people for jobs and getting
students into college.
• Adapted from Svi Shapiro
• Assignment:
– Should schools help students understand moral choices
and social issues?
• 25 minutes
6. The timed impromptu is an unnatural act
• The timed
impromptu does
not occur in the
real world
• No one writes on
demand without
reflecting about
a topic they may
never have
thought about
7. Why the change?
• Reliability
– Godshalk, F. et al.
The Measurement of
Writing Ability ETS
1966
– A. Myers et al. (1966)
Simplex structure in
the grading of essay
tests. Educational and
Psychological
Measurement, Vol
26(1), 1966, 41-54.
8. Where the reliability comes from:
Correlation between length and score is a
negative function of time allotted
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
SharedVariancebetween#Words&Score
25 min
1 hr.
72 hrs.
N=247
N =6498 N=2820
N=115
N= 106 N=106
N=660 N=1458
N=798
The greater the time; the smaller the correlation
12. Ellis B. Page – Project Essay Grade
• Trin -- Intrinsic variable of interest (e.g. word choice, diction;
sentence complexity)
• Prox – “some variable which it is hoped will approximate the
variable of true interest”
13. e-Rater construct
Quinlan, T., Higgins, D., & Wolff, S. (2009) Evaluating the Construct-
Coverage of the e-rater® Scoring Engine. ETS Research Report 09-01. p. 15
14. E-rater 2.0 Proxies
• Organization = # of Discourse Elements (i.e.
paragraphs)
• Development = Length of Discourse Elements
(i.e. # of sentences & # of words in paragraphs)
• Lexical Complexity = average word length +
frequency of infrequently used words + absence
of frequently repeated words
15. Machines Consistently Overvalue
Essay Length
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1 Argument
2A Argument
Holistic
2B Argument
Grammar
3 Literary
Analysis
4 Literary
Analysis
5 Reading
Summary
6 Reading
Summary
7 Narrative
Composite
8 Narrative
Compositive
AverageSharedVariance
Essay Sets
Hewlett ASAP Study (2012)
Average Shared Variance (r2)
between # of Words and Score for AES Machines & Human Readers
(7 of 9 vendors -- 2 vendors would not allow data to be released)
Average Vendors Average Human Readers
17. Percentage for Reader 1
# of
words
Other
Percentage for Reader 2
# of
words
Percentage AES Machine
Other
# of words
Other
Shared
Variance
# Words
Human
Machine
18. But what about voice recognition?
• Relatively very small set + Moore’s Law
19. What kind of writing AES can’t grade
• Long essays
– ETS’s e-rater has a 1,000 word limit
• Broad and open Writing Tasks
– Two AES machines could not approximate scores
on the essay portion of the Australian Scholastic
Aptitude Test that called for a fixed length (600
word) essay on a fairly open topic McCurry (2010)
33. What we can conclude
• The software does not do what it tells
students and teachers it is doing
• The metrics (proxies) used are irrelevant, at
best, and, probably, are largely antithetical to
good writing or communication.
• Students can probably be trained to memorize
language and strategies to obtain high scores
(construct-irrelevant-strategies)
34. Some Evidence that Students Are
Using BABEL to Game AEE Products
4. What is meant by a “good faith” essay?
It is important to note that although PEG software is extremely reliable in terms of
producing scores that are comparable to those awarded by human judges, it can be
fooled. Computers, like humans, are not perfect.
PEG presumes “good faith” essays authored by “motivated” writers. A “good faith”
essay is one that reflects the writer’s best efforts to respond to the assignment and
the prompt without trickery or deceit. A “motivated” writer is one who genuinely
wants to do well and for whom the assignment has some consequence (a grade, a
factor in admissions or hiring, etc.).
Efforts to “spoof” the system by typing in gibberish, repetitive phrases, or off-topic,
illogical prose will produce illogical and essentially meaningless results.
35. Most of the Studies Are Conducted
and / or Controlled by the Vendors
36. Important Unanswered Questions
1. How easy will it be for students to “game” these
machines?
2. When essays are read by a human reader and a
machine and there is a discrepancy between scores,
after the adjudication procedure, what percentage
the machine’s scores are omitted or changed
compared to the scores of human reader?
3. When gamed essays are read by a reader and a
machine, will the human reader’s score always catch
the gamed score?
4. Can human readers also be “gamed”?
37. Negative Consequences
• What is tested is what is taught
• Emphasis on short writing
• Emphasis on impromptu on-demand writing
38. When can AES be useful?
• Grading short content-based writing
– Already useful applications
• Use in MOOC’s in conjunction with Peer
Review Applications such as Calibrated Peer
Review
39. Why are the testing companies so in
love with AES?
• ῥίζα γὰρ πάντων τῶν κακῶν ἐστιν ἡ
φιλαργυρία
• Radix omnium malorum est cupiditas
• The love of money is the root of all evil
• 1st Timothy 6:10
41. Three Proposals
• First, some sort of professional system of
disclosure for large sums of money, let’s say
more than $10,000, received from outside
professional organizations such as the College
Board.
– With textbooks, the disclosure is transparent.
42. Second, Grass roots development of
several different Honors English and
Writing curricula
• Teach skills students will need in college
• Developed jointly by high school and college
teachers
• Developed through organizations such as NCTE,
WPA, and NWP & College Admissions
• Accompanied by some sort of certification procedure
• Pacesetter Program as model
43. Create Tests of Our Own
• Design Criteria:
– Valid
– Fair
• Coaching has minimal effect
• Does not discriminate against bilingual or bidialectical
students
– Feasible
• Not College Board’s bad version of reverse engineering
– “The plane doesn’t fly, but we can make money on
it”
– Transparent design, development, and
administration
46. Test to the Teaching
• Different tests and testing communities for
different approaches
• Technology enabled
• Diverse and linked group of readers
– Opportunity to address problems of low-
performing minorities
– Show students that their essay will be read by a
diverse group of readers
47. So let us
• Urge our schools to stop using the SAT, ACT, &
even AP
• Begin a conversation with professional
organizations to involve
– K-12 teachers
– College admission officers
– College teachers
To envision new and different kinds of writing
tests
48. Act
• Act to divert some of the billions spent on testing to
improve teaching
• Act to reclaim testing from business and bring it back
to education
• Act to make testing a form of learning not only for
students but also for us
• And act by doing sound research on writing and
testing; because if we don’t do it we are leaving it to
people like the gang at Pearson.
49. Automated Essay Scoring (AES)
becomes Automated Essay Evaluation
(AEE)
• Teaching writing in the classroom
53. Grammar Checkers are Unreliable
Nick Carbone’s Comparison of Grammarly, MS Word,
and WriteCheck
Grammarly MS Word WriteCheck
(e-rater)
# Errors Flagged 52 30 23
# misdiagnosed,
false positives, or
poorly explained
11 8 14
% misdiagnosed,
false positives, or
poorly explained
21% 27% 61%
http://nccei12carbone.blogspot.com/2012/10/an-experiment-with-grammar-checkers.html
55. Dean Mark D. Shermis on Microsoft
Word and AEE
The feedback provided by the Web-based software is both quantitative and
qualitative. That is, in addition to an overall rating, students may receive
scores on individual attributes of writing, and the software may summarize or
highlight a variety of errors, ranging from simple grammar to style or content.
Some of the software packages also provide a discourse analysis of the work
Shermis, M. (May11, 2012). How automated grading can make good writers. Los Angeles Times
56. Category: Usage
• Missing or Extra Article
– Rather than rely on commercials or expert
opinions about a film, individuals often make their
viewing choices based on blogs and the1 collected
reviews of peers on various sites on the Internet.
• 1 You may need to remove this article.
57. Category: Usage
• Type: Confused Words
– Because the consumption of entertainment is so
ephemeral, one could posit that advertising might
affect1 a consumer's decision more than when by
buying a durable good, such as a toaster or a
blender.
• 1 You have used affect in this sentence. You may need to
use effect instead.
58. Category: Usage
• Type: Preposition Error
– Rather than rely on commercials or expert
opinions about1 a film, individuals often make
their viewing choices based on blogs and the
collected reviews of peers on various sites on the
Internet.
• 1 You may be using the wrong preposition.
59. Category: Organization & Development
• Type: Thesis Statement
– The question of how important advertising is to the sale of any product
is an important one. This question is extremely important in the media
industries.1 Because the consumption of entertainment is so
ephemeral, one could posit that advertising might affect a consumer's
decision more than when by buying a durable good, such as a toaster
or a blender.2 The advertising department of the Silver Screen Movie
Production Company has recommended spending more on advertising
and less on movie production. The advertising director's arguments
are not only self-serving, but also logically flawed and, at the least,
inconclusive, resting on several very dubious assumptions.
• 1 Is this part of the essay your thesis? The purpose of a thesis is to organize,
predict, control, and define your essay. Look in the Writer's Handbook for ways
to improve your thesis.
• 2 Is this sentence really a part of your thesis? Remember that a thesis controls
the whole content of your essay. You need to strengthen this thesis so that you
clearly state the main point you will be making. Look in the Writer's Handbook
for tips on doing this.
60. Category: Organization &
Development
• Type: Supporting Ideas
– First, the motives behind this particular argument
need to be questioned. In essence, the advertising
director is arguing that resources should be taken
away from producing films and given to his
department.1 Although people often make
reasonable requests in their own self-interest, that
this policy would greatly enhance the director's
fiefdom is a consequence that should elicit some
skepticism.1
• 1 Criterion has identified only two sentences to support
your topic sentence. Try to include one more sentence in
this paragraph.
61. The Missing Piece in Research on
Classroom Use
• Controlled
experiments to avoid
placebo effect
• Comparison with the
default writing tool
of the 21st century,
MS Word.
62. Both Pearson and Measurement Inc. Concede
that Grammar Checkers are Imperfect
http://doe.sd.gov/oats/documents/WToLrnFAQ.pdf
Q: Why does the grammar check not catch all of a student’s
errors?
A: The technology that supports grammar check features in
programs such as Microsoft Word often return false
positives. Since WriteToLearn is a educational product, the
creators of this program have decided, in an attempt to not
provide students with false positives, to err on the side of
caution. Consequently, there are times when the grammar
check will not catch all of a student’s errors.
Teachers can address these missed grammar errors by using the
post‐it note feature within the program to flag additional errors
students might have missed.
63. PEG Writer
8. Why does PEG seem to ignore some grammar “trouble spots” identified
by Microsoft Word (or other programs)?
PEG’s grammar checker can detect and provide feedback for a wide variety
of syntactic, semantic and punctuation errors. These errors include, but are
not limited to, run-on sentences, sentence fragments and comma splices;
homophone errors and other errors of word choice; and missing or
misused commas, apostrophes, quotation marks and end punctuation. In
addition, the grammar checker can locate and offer feedback on style
choices inappropriate for formal writing.
Unlike commercial grammar checkers, however, PEG only reports those
errors for which there is a high degree of confidence that the “error” is
indeed an error. Commercial grammar checkers generally implement a
lower threshold and as a result, may report more errors. The downside is
they also report higher number of “false positives” (errors that aren’t
errors). Because PEG factors these error conditions into scoring decisions,
we are careful not to let “false positives” prejudice an otherwise well
constructed essay.
66. Focus on style
• MS Word is flawed but it may be hard to build
something better that won’t confuse students
• What can be emphasized is style:
– Clarity
– Cohesion
– Emphasis
– Concision
– Elegance
• e. g. Parallel structures
67. What we need to do to build effective
AEE tools
• Start with General Principles
– Then use statistical modeling
– Follow the model of the development of voice
recognition apps
• Transparency
• Independent Research
68. The right way: by asking questions not
giving answers
Letting the student own the process
69. Products should be transparent in
displaying their limitations – again,
showing warts and all
70. The Real Danger of AES and bad AEE:
Widening the Educational Divide
• Private well-endowed institutions
do not use AES
• Flawed AEE will be used in large
classes to “give students more
opportunities to write” poorly
• But what flawed AEE teaches not
only dumbs down the ability to
communicate – it has the
potential to almost totally
eliminate it
71. But AEE also provides a real opportunity to
provide a cheap accessible tool to teach
and improve writing in multiple contexts
• In classrooms
• Writing at home
• In the workplace
72. Demo of BABEL Generator
• http://babel-generator.herokuapp.com/
• https://www.dxrgroup.com/cgi-
bin/scoreitnow/password.pl
73. Break Up into Six Groups for
MyAccess Experiment
• Hypothesis: MyAccess will give high scores to computer-generated
gibberish
• Login to MyAccess
– http://www.vantagelearning.com/login/myaccess-home-edition/
Username Password
studentone one
studentwo two
studentthree three
studentfour four
studentfive five
studentsix six
• Open new window and open BABEL Generator
http://babel-generator.herokuapp.com/
74. Instructions
1. Click ASSIGNMENTS
2. Select Ages 15-18
3. Select one of the following topics (suggested keywords for
BABEL generator are in parentheses) & click START ESSAY
or START REVISION
a. Nature v. nurture (nature, nurture)
b. A sense of wonder (wonder)
c. Invasion of privacy (privacy)
d. Rating movies, music, & video games (violence, obscenity,
teenagers)
e. What makes a good coach (coach, encouragement)
4. Open another window, open BABEL Generator, generate
essay, & then copy and paste it into the MY ACCESS
window
5. Click SUBMIT ESSAY
75. References
• Attali, Yigal & Burstein, J. (2006). Automated essay scoring with e-rater V.2. Journal of Technology,
Learning, and Assessment. 4:3. pp. 1-29.
• Bejar, I. I., Flor, M., Futagi, Y., Ramineni, C. (2014). On the vulnerability of automated scoring to
construct-irrelevant response strategies (CIRS): An illustration. Assessing Writing 22 pp. 43-59.
• Condon, W. (2013). Large-scale assessment, locally-developed measures, and automated scoring of
essays: Fishing for red herrings? Assessing Writing 18, pp. 100-108.
• Dikli, S. & Bleyle, S. (October 2014). Automated Essay Scoring feedback for second language writers:
How does it compare to instructor feedback?, Assessing Writing, 22, pp. 1-17.
http://www.sciencedirect.com/science/article/pii/S1075293514000221.
• Elliot, N. & Klobucar, A. “Automated Essay Evaluation and the teaching of writing.” Handbook of
Automated Essay Evaluation: Current Applications and New Directions. Ed. Mark D. Shermis, Jill
Burstein, and Sharon Apel. London: Routledge. June 2013.
• Freitag Ericsson, P. & Haswell, R. Ed. (2006) Machine Scoring of Student Essays: Truth or
Consequences. Logan, UT: Utah State UP
• Godshalk, F. I, Swineford, F., Coffman, W. E. (1966). The Measurement of Writing Ability. New York,
NY: College Entrance Examination Board.
• Haudek, K. C. et al. (2012). What are they thinking? Automated analysis of student writing about
acid-based chemistry in introductory biology. CBE—Life Sciences Education 11, pp. 149-155.
• Haudek, K. C. et al. (2011). Harnessing technology to improve formative assessment of student
conceptions in STEM: Forging a national network. CBE—Life Sciences Education 10, pp. 283-293.
• Herrington, A. & Moran, C. (2012). When writing to a machine in not writing at all. Writing
Assessment in the 21st Century: Essays in Honor of Edward M. White. Ed. Norbert Elliot and Les
Perelman. New York, NY: Hampton Press, pp.219-232
76. c
• Higgins, D. & Heilman, M. Managing what we can measure: Quantifying the susceptibility of
Automated Scoring Systems to gaming behavior. Educational Measurement 33:3. pp. 36-46.
• Klobucar, Andrew, Paul Deane, Norbert Elliot, Chaitanya Ramineni, Perry Deess, & Alex
Rudniy. (2012).“Automated Essay Scoring and the Search for Valid Writing Assessment.”
International Advances in Writing Research: Cultures, Places, Measures. Ed. Charles.
Bazerman, Chris Dean, Jessica Early, Karen Lunsford, Suzie Null, Paul Rogers, and Amanda
Stansell. Fort Collins, Colorado: WAC . pp. 103-119
http://wac.colostate.edu/books/wrab2011/chapter6.pdf
• McCurry, D. (2010). Can machine scoring deal with broad and open writing tests as well as
human readers? Assessing Writing. Vol. 15 pp. 118–129
• Morgan, J., Shermis, M. D., Van Deventer, L., & Vander Ark, T. (2013). Automated Student
Assessment Prize: Phase 1 & Phase 2: A case study to promote focused innovation in student
writing assessment. Seattle, WA: Getting Smarthttp://gettingsmart.com/cms/wp-
content/uploads/2013/02/ASAP-Case-Study-FINAL.pdf
• Nehm, R. H., Ha, M., Mayfield, E. (2011). Transforming biology with machine learning:
automated scoring of written evolutionary explanations. Journal of Science Education and
Technology. 21:1, pp. 183-196
• Page, E. B.(1966). The imminence of grading essays by computer. Phi Delta Kappan 76:7 pp.
561-565
• Perelman, L. (July, 2014). When ‘the state of the art’ is counting words. Assessing Writing
Vol. 21 pp. 104–111 http://dx.doi.org/10.1016/j.asw.2014.05.001
77. • Perelman, L. (August 2013). Critique of Mark D. Shermis & Ben Hammer,
“Contrasting State-of-the-Art Automated Scoring of Essays: Analysis.” Journal of
Writing Assessment 6:1
http://journalofwritingassessment.org/article.php?article=69
• Perelman, L. (2012). “Mass-Market Writing Assessments as Bullshit.” Writing
Assessment in the 21st Century: Essays in Honor of Edward M. White. Ed. Norbert
Elliot and Les Perelman. New York, NY: Hampton Press, 2012, pp. 425-438.
• Perelman, L. (2012). Length, Score, Time, & Construct Validity in Holistically Graded
Writing Assessments: The Case against Automated Essay Scoring (AES). In New
Directions in International Writing Research, Ed. C. Bazerman, C. Dean, K. Lunsford,
S. Null, P. Rogers, A. Stansell, and T. Zawacki. Anderson, SC: Parlor Press, pp. 121-
132.http://wac.colostate.edu/books/wrab2011/chapter7.pdf
• Powers, D. et al. (2002). Stumping e-rater: Challenging the validity of automated
essay scoring. Computers in Human Behavior. 18:2 pp. 103-134.
• Quinlan, T., Higgins, D., & Wolff, S. (2009) Evaluating the Construct-Coverage of the
e-rater® Scoring Engine. ETS Research Report 09-01.
• Sandene, B. et al. (2005) Online Assessment in Mathematics and Writing: Reports
From the NAEP Technology-Based Assessment Project, Research and Development
Series. National Center for Education Statistics Report 2005-457.
http://files.eric.ed.gov/fulltext/ED485780.pdf
78. • Shermis, M. D. (In press). The challenges of emulating human behavior in
writing assessment.
http://www.sciencedirect.com/science/article/pii/S1075293514000373#
• Shermis, M. D. (2014). State-of-the-art automated essay scoring:
Competition, results, and future directions from a United States
demonstration. Assessing Writing, 20, 53-76
• Shermis, M. D., & Hamner, B. (2013). Contrasting state-of-the-art
automated scoring of essays. In M. D. Shermis & J. Burstein (Eds.),
Handbook of automated essay evaluation: Current applications and new
directions . New York, NY: Routledge. pp. 313-346
• Shermis, M. (May11, 2012). How automated grading can make good
writers. Los Angeles Times
http://articles.latimes.com/2012/may/11/news/la-ol-automated-scoring-
blowback-20120510
• Stevenson, M. Phakiti, A. (2014). The effects of computer-generated
feedback on the quality of writing. Assessing Writing. 19, pp.51-65.
