Year: 2017
Type: conference-paper
Abstract: The need for automated grading tools for essay writing and open-ended assignments has received increasing attention due to the unprecedented scale of Massive Online Courses (MOOCs) and the fact that more and more students are relying on computers to complete and submit their school work. In this paper, we propose an efficient memory networks-powered automated grading model. The idea of our model stems from the philosophy that with enough graded samples for each score in the rubric, such samples can be used to grade future work that is found to be similar. For each possible score in the rubric, a student response graded with the same score is collected. These selected responses represent the grading criteria specified in the rubric and are stored in the memory component. Our model learns to predict a score for an ungraded response by computing the relevance between the ungraded response and each selected response in memory. The evaluation was conducted on the Kaggle Automated Student Assessment Prize (ASAP) dataset. The results show that our model achieves state-of-the-art performance in 7 out of 8 essay sets.
Institution: Worcester Polytechnic Institute
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
Awards: Improving Formative Assessment Practices: Using Learning Trajectories to Develop Resources that Support Teacher Instructional Practice and Student Learning in CMP2, G5657672889, G5672457875, G5906920533, Empirical Research, Contextual Research: Taking advantage of Cognitive Science Principles: Adding to a Computer-Based Tutor an Automatic Reassessment and Relearning System, SI2-SSE: Adding Research Accounts to the ASSISTments' Platform: Helping Researchers Do Randomized Controlled Studies with Thousands of Students, Making Math Tutors More Engaging and Effective through Interaction Design Patterns and Educational Data Mining, Research: Predicting STEM Career Choice from Computational Indicators of Student Engagement within Middle School Mathematics Classes
OpenAlex ID: W2605898966