Skip to main content Accessibility help
×
Home
Hostname: page-component-684899dbb8-ndjvl Total loading time: 0.233 Render date: 2022-05-24T08:16:54.969Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true }

Identifying off-topic student essays without topic-specific training data

Published online by Cambridge University Press:  22 May 2006

D. HIGGINS
Affiliation:
Educational Testing Service, Rosedale Road, Princeton, NJ 08541, USA e-mail: dhiggins@ets.org
J. BURSTEIN
Affiliation:
Educational Testing Service, Rosedale Road, Princeton, NJ 08541, USA e-mail: dhiggins@ets.org
Y. ATTALI
Affiliation:
Educational Testing Service, Rosedale Road, Princeton, NJ 08541, USA e-mail: dhiggins@ets.org

Abstract

Educational assessment applications, as well as other natural-language interfaces, need some mechanism for validating user responses. If the input provided to the system is infelicitous or uncooperative, the proper response may be to simply reject it, to route it to a bin for special processing, or to ask the user to modify the input. If problematic user input is instead handled as if it were the system's normal input, this may degrade users' confidence in the software, or suggest ways in which they might try to “game” the system. Our specific task in this domain is the identification of student essays which are “off-topic”, or not written to the test question topic. Identification of off-topic essays is of great importance for the commercial essay evaluation system CriterionSM. The previous methods used for this task required 200–300 human scored essays for training purposes. However, there are situations in which no essays are available for training, such as when users (teachers) wish to spontaneously write a new topic for their students. For these kinds of cases, we need a system that works reliably without training data. This paper describes an algorithm that detects when a student's essay is off-topic without requiring a set of topic-specific essays for training. This new system is comparable in performance to previous models which require topic-specific essays for training, and provides more detailed information about the way in which an essay diverges from the requested essay topic.

Type
Papers
Copyright
2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The authors would like to thank Chi Lu and Slava Andreyev for their help in carrying out the experiments described in this paper.
31
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Identifying off-topic student essays without topic-specific training data
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Identifying off-topic student essays without topic-specific training data
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Identifying off-topic student essays without topic-specific training data
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *