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Found 7 papers, 5 papers with code
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices.
SyllabusQA: A Course Logistics Question Answering Dataset
Automated teaching assistants and chatbots have significant potential to reduce the workload of human instructors, especially for logistics-related question answering, which is important to students yet repetitive for instructors.
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning
Second, we propose a framework for feedback generation that optimizes both correctness and alignment using reinforcement learning (RL).
Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable form of assessment.
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
Recent developments in large pre-trained language models have enabled unprecedented performance on a variety of downstream tasks.
Tree-Based Representation and Generation of Natural and Mathematical Language
In this paper, we propose a series of modifications to existing language models to jointly represent and generate text and math: representing mathematical expressions as sequences of node tokens in their operator tree format, using math symbol and tree position embeddings to preserve the semantic and structural properties of mathematical expressions, and using a constrained decoding method to generate mathematically valid expressions.
Process-BERT: A Framework for Representation Learning on Educational Process Data
One can use process data for many downstream tasks such as learning outcome prediction and automatically delivering personalized intervention.
