TY - GEN
T1 - Predicting human scores of essay quality using computational indices of linguistic and textual features
AU - Crossley, Scott A.
AU - Roscoe, Rod
AU - McNamara, Danielle S.
PY - 2011/6/23
Y1 - 2011/6/23
N2 - This study assesses the potential for computational indices to predict human ratings of essay quality. The results demonstrate that linguistic indices related to type counts, given/new information, personal pronouns, word frequency, conclusion n-grams, and verb forms predict 43% of the variance in human scores of essay quality.
AB - This study assesses the potential for computational indices to predict human ratings of essay quality. The results demonstrate that linguistic indices related to type counts, given/new information, personal pronouns, word frequency, conclusion n-grams, and verb forms predict 43% of the variance in human scores of essay quality.
UR - http://www.scopus.com/inward/record.url?scp=79959298144&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959298144&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21869-9_62
DO - 10.1007/978-3-642-21869-9_62
M3 - Conference contribution
AN - SCOPUS:79959298144
SN - 9783642218682
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 438
EP - 440
BT - Artificial Intelligence in Education - 15th International Conference, AIED 2011
T2 - 15th International Conference on Artificial Intelligence in Education, AIED 2011
Y2 - 28 June 2011 through 1 July 2011
ER -