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Found 17 papers, 11 papers with code
Course Recommender Systems Need to Consider the Job Market
In light of the job market's rapid changes and the current state of research in course recommender systems, we outline essential properties for course recommender systems to address these demands effectively, including explainable, sequential, unsupervised, and aligned with the job market and user's goals.
Towards Modeling Learner Performance with Large Language Models
Recent work exploring the capabilities of pre-trained large language models (LLMs) has demonstrated their ability to act as general pattern machines by completing complex token sequences representing a wide array of tasks, including time-series prediction and robot control.
InterpretCC: Conditional Computation for Inherently Interpretable Neural Networks
Real-world interpretability for neural networks is a tradeoff between three concerns: 1) it requires humans to trust the explanation approximation (e. g. post-hoc approaches), 2) it compromises the understandability of the explanation (e. g. automatically identified feature masks), and 3) it compromises the model performance (e. g. decision trees).
Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
This survey article has grown out of the GAIED (pronounced "guide") workshop organized by the authors at the NeurIPS 2023 conference.
Finding Paths for Explainable MOOC Recommendation: A Learner Perspective
In this work, we propose an explainable recommendation system for MOOCs that uses graph reasoning.
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance
Our results demonstrate that there is no significant difference in gender bias between the resulting peer reviews of groups with and without LLM suggestions.
MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks
Predicting multiple real-world tasks in a single model often requires a particularly diverse feature space.
Let Me Teach You: Pedagogical Foundations of Feedback for Language Models
In a different world, research in pedagogy has long established several effective feedback models.
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Understanding Revision Behavior in Adaptive Writing Support Systems for Education
Revision behavior in adaptive writing support systems is an important and relatively new area of research that can improve the design and effectiveness of these tools, and promote students' self-regulated learning (SRL).
Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design
Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency.
Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning
This weakness is one of the main factors undermining users' trust, since model predictions could for instance lead an instructor to not intervene on a student in need.
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
Time series is the most prevalent form of input data for educational prediction tasks.
Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling
We conduct a Word Embedding Association Test (WEAT) analysis on (1) our collected corpus in connection with the clustered labels, (2) the most common pre-trained German language models (T5, BERT, and GPT-2) and GloVe embeddings, and (3) the language models after fine-tuning on our collected data-set.
Generalisable Methods for Early Prediction in Interactive Simulations for Education
In this paper, we investigate the quality and generalisability of models for an early prediction of conceptual understanding based on clickstream data of students across interactive simulations.
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs
Neural networks are ubiquitous in applied machine learning for education.
Meta Transfer Learning for Early Success Prediction in MOOCs
Despite the increasing popularity of massive open online courses (MOOCs), many suffer from high dropout and low success rates.
