Open AccessFROM BIOLOGY TO EDUCATION: SCORING AND CLUSTERING MULTILINGUAL TEXT SEQUENCES AND OTHER SEQUENTIAL TASKS
Author(s) -
Sukkarieh Jana Z.,
Davier Matthias,
Yamamoto Kentaro
Publication year - 2012
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/j.2333-8504.2012.tb02307.x
Subject(s) - computer science , flexibility (engineering) , task (project management) , set (abstract data type) , cluster analysis , character (mathematics) , artificial intelligence , natural language processing , machine learning , statistics , mathematics , geometry , management , economics , programming language
This document describes a solution to a problem in the automatic content scoring of the multilingual character‐by‐character highlighting item type. This solution is language independent and represents a significant enhancement. This solution not only facilitates automatic scoring but plays an important role in clustering students' responses; consequently, it has a nontrivial impact on the refinement of the items and/or their scoring guidelines. Furthermore, though designed for a specific problem, the proposed solution is general enough for any educational task that can be transformed into a sequential one. To name a few: It can be used for a set of actions expected from a student in simulations or learning trajectories as projected by a teacher, inside an intelligent tutoring system, or even in a game—or it can simply be used for a set of student clicks, button selections, or keyboard hits expected to reach a correct answer. This solution provides flexibility for existing automatic‐scoring techniques and potentially could provide more flexibility if coupled with statistical data‐mining techniques.