Journal Article

Comparison of Dimension Reduction Methods for Automated Essay Grading

Erkki Sutinen, Tuomo Kakkonen, Niko Myller, Jari Timonen

2008

Overview

Automatic Essay Assessor (AEA) is a system that utilizes information retrieval techniques such as Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA) for automatic essay grading. The system uses learning materials and relatively few teacher-graded essays for calibrating the scoring mechanism before grading. We performed a series of experiments using LSA, PLSA and LDA for document comparisons in AEA. In addition to comparing the methods on a theoretical level, we compared the applicability of LSA, PLSA, and LDA to essay grading with empirical data. The results show that the use of learning materials as training data for the grading model outperforms the k-NN-based grading methods. In addition to this, we found that using LSA yielded slightly more accurate grading than PLSA and LDA. We also found that the division of the learning materials in the training data is crucial. It is better to divide learning materials into sentences than paragraphs.

• Publisher: International Forum of Educational Technology & Society
• Subject: Academic grading / Automation / Computer Uses in Education / Cosine function / Dimensionality / Dimensionality reduction / Dirichlet problem / Division / Educational aspects / Educational technology / Empirical analysis / Essay / Essays / Evaluation / Evaluation Methods / Full Length Articles / Grading / Grading and marking (Students) / Information retrieval / Instructional Materials / Latent semantic analysis / Learning / Linear discriminant analysis / Machine learning / Methods / Parametric models / Probabilistic modeling / Semantics / Semiotics / Sentences / Technology application / Training