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Found 9 papers, 3 papers with code
Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring?
Deep Learning (DL) techniques have been increasingly adopted for Automatic Text Scoring in education.
Bigger Data or Fairer Data? Augmenting BERT via Active Sampling for Educational Text Classification
Pretrained Language Models (PLMs), though popular, have been diagnosed to encode bias against protected groups in the representations they learn, which may harm the prediction fairness of downstream models.
Towards Detecting AI-Generated Text within Human-AI Collaborative Hybrid Texts
Our empirical findings highlight (1) detecting AI-generated sentences in hybrid texts is overall a challenging task because (1. 1) human writers' selecting and even editing AI-generated sentences based on personal preferences adds difficulty in identifying the authorship of segments; (1. 2) the frequent change of authorship between neighboring sentences within the hybrid text creates difficulties for segment detectors in identifying authorship-consistent segments; (1. 3) the short length of text segments within hybrid texts provides limited stylistic cues for reliable authorship determination; (2) before embarking on the detection process, it is beneficial to assess the average length of segments within the hybrid text.
Unveiling the Tapestry of Automated Essay Scoring: A Comprehensive Investigation of Accuracy, Fairness, and Generalizability
Automatic Essay Scoring (AES) is a well-established educational pursuit that employs machine learning to evaluate student-authored essays.
Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education
Then we proposed a two-step approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other.
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution.
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification
Then, the study investigates how the AL methods can select informative samples to support DA classifiers in the AL sampling process.
Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review
Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content.
Feature Engineering for Second Language Acquisition Modeling
Knowledge tracing serves as a keystone in delivering personalized education.
