Abstract: The present invention uses an algorithm which evaluates learners' short free-text answers when the answer has as few as 10 words. The answer key uses only one correct answer, allowing instructors to ask learners to produce short open-ended text responses to questions. The algorithm automates the scoring of free-text answers, enabling instructors to embed such questions in online courses, and providing nearly immediate scoring and feedback on learners' responses. The algorithm is based on the semantic relatedness of the words in the learners' answer to the single correct answer. The semantic relatedness algorithm requires a dedicated domain specific index or collection of topic-focused documents (a corpus), which is created by an automated crawl mechanism that collects documents based upon descriptive domain keywords.

Abstract: The present invention uses an algorithm which evaluates learners' short free-text answers when the answer has as few as 10 words. The answer key uses only one correct answer, allowing instructors to ask learners to produce short open-ended text responses to questions. The algorithm automates the scoring of free-text answers, enabling instructors to embed such questions in online courses, and providing nearly immediate scoring and feedback on learners' responses. The algorithm is based on the semantic relatedness of the words in the learners' answer to the single correct answer. The semantic relatedness algorithm requires a dedicated domain specific index or collection of topic-focused documents (a corpus), which is created by an automated crawl mechanism that collects documents based upon descriptive domain keywords.

Abstract: An automated, computer-based reading tutoring system is accessed via a computer system and includes a plurality of instructional passages of different, predetermined levels of reading difficulty. A semantic space module of the reading tutoring system operates on a semantic space, which is produced by a machine-learning method, to automatically evaluate a student-submitted summary of a selected instructional passage for congruence with the selected instructional passage and to automatically determine which instructional passage the student should optimally read next. The reading tutoring system includes immediate feedback data provided to the student and including an indicator reflective of the student's reading comprehension and the identity of the instructional passage that the student should read next.

Abstract: An automated, computer-based reading tutoring system is accessed via a computer system and includes a plurality of instructional passages of different, predetermined levels of reading difficulty. A semantic space module of the reading tutoring system operates on a semantic space, which is produced by a machine-learning method, to automatically evaluate a student-submitted summary of a selected instructional passage for congruence with the selected instructional passage and to automatically determine which instructional passage the student should optimally read next. The reading tutoring system includes immediate feedback data provided to the student and including an indicator reflective of the student's reading comprehension and the identity of the instructional passage that the student should read next.