1. Automated Essay Evaluation Martin Angert Rachel Drossman

2. The Problem… It takes humans a long time to evaluate written text Lack of teacher time to assess student writing samples “As I have proven on the board, all of your essays are terrible and I’m tired of reading them. What should I do?”

3. The Solution… Automated essay evaluation! Criterion Evaluation Service – Critique: evaluates and provides feedback for grammatical, usage, and mechanical errors – E-Rater 2.0: gives essays a holistic score Vantage Learning – Intellimetric “Thank God for Artificial Intelligence!”

4. What is Automated Essay Evaluation? Teachers assign essays to students Student submit essays online Students get feedback Teachers get summary reports of students’ performance “I love you e-Rater 2.0!”

5. Nuts and Bolts Automated essay evaluation relies on four main areas of Artificial Intelligence – Machine Learning – Natural Language Processing – Pattern Matching – Heuristics Integration BOLT

6. Machine Learning Teacher supplies training data – Corpus of edited and graded essays Uses statistical methods to evaluate essays Ex: word sense disambiguation – Looks at 2 words to the left and right of word to determine context “I love Machine Learning!”

7. Nuts and Bolts Automated essay evaluation relies on four main areas of Artificial Intelligence – Machine Learning – Natural Language Processing – Pattern Matching – Heuristics Integration BOLT

8. Natural Language Processing Parse trees used to analyze sentence structure Compares linguistic style of student essays to training data to evaluate grammar, mechanics, and usage “Bertha, do your hands hurt from processing natural languages all day?”

9. Nuts and Bolts Automated essay evaluation relies on four main areas of Artificial Intelligence – Machine Learning – Natural Language Processing – Pattern Matching – Heuristics Integration BOLT

10. Pattern Matching System contains examples of good vocabulary, sentence structure, etc. Tries to match patterns in student essays and awards corresponding scores ? =

11. Nuts and Bolts Automated essay evaluation relies on four main areas of Artificial Intelligence – Machine Learning – Natural Language Processing – Pattern Matching – Heuristics Integration BOLT

12. Heuristics Integration Searches students’ essays for phrases that occur more or less often than expected based on corpus frequencies Example: repetitious words – If a single word accounts for more than 5% of the word count in the essay, that word is repetitive

13. Criterion Diagnostic Analysis Tools GrammarUsageMechanicsStyleOrg/Dev Fragments Run-ons Garbled Sentences S-V agreement Ill-formed verb Pronoun error Missing Possessive Wrong word Wrong article Missing article Nonstandard verb or word form Confused words Wrong word form Faulty comparisons Spelling Capitalization of proper nouns Initial capitalization in a sentence Missing apostrophe for contractions Missing end punctuation Comma error Repetition Inappropriate words Sentences containing passive voice Long Sentences Essay statistics - # of words - # of sentences - Average # of words in sent. Transitional words and phrases Introductory material Thesis statement Topic sentences Supporting Ideas Conclusion Other

14. E-Rater Score Generation 12 features used when scoring an essay – 11 features reflect essential characteristics in essay writing and are aligned with human scoring criteria – 12 th feature: word count Weighted less heavily so that longer essays do not automatically earn higher scores Trained on a sample of 200-250 scored essays with scores between 1 and 6

15. Implementation For GMAT grading using automated essay evaluation… – Both a human and e-Rater grade the essay on a six-point scale – If scores agree, essay is assigned that score – If scores differ by 1 point, essay is assigned score of human grader – If scores differ by more than 1 point, automated score is discarded and another human grader evaluates the essay “How am I supposed to get into Harvard when e-Rater gave me a 0.1 on my essay?”

16. Benefits Immediate feedback to students Enables teachers to spend more time with students and less time grading Provides students with more practice writing “Thanks a lot e- Rater, now instead of playing soccer I get to stay inside and practice my writing!”

17. Limitations Not always as accurate as teacher feedback – Would rather miss an error than tell a student that a well-formed construction is ill-formed Machine cannot understand unique writing styles, humor, irony, etc. Input sentence: “This presentation deserves an A” E-Rater Output: “Well- constructed sentence. I concur!”

18. Limitations - Example Which sentence do you think is better? 1. It is with the greatest esteem and confidence that I write to support Joey as a candidate for a faculty position. I have known Joey in a variety of capacities for more than five years, and I find him to be one of the most eloquent… 2. It is with chimpanzee greatest esteem and confidence that I write to support Joey as a candidate for a faculty position. I have known Joey in a variety of capacities for more than five years, and I find him to be one of chimpanzee most eloquent…

19. Conclusion Currently has over 500,000 users in 445 institutions Strongest use in the K-12 market System’s understanding of word meaning (rather than just grammar) is improving Companies survey current users to receive feedback for future releases “I pity da fool who doesn’t use e-Rater 2.0!”