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Found 12 papers, 3 papers with code
Learning Aggregation Rules in Participatory Budgeting: A Data-Driven Approach
Participatory Budgeting (PB) offers a democratic process for communities to allocate public funds across various projects through voting.
Automatic Creativity Measurement in Scratch Programs Across Modalities
Promoting creativity is considered an important goal of education, but creativity is notoriously hard to measure. In this paper, we make the journey fromdefining a formal measure of creativity that is efficientlycomputable to applying the measure in a practical domain.
Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language
With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries.
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks
In contrast, our approach, called MultiplexNet, represents domain knowledge as a logical formula in disjunctive normal form (DNF) which is easy to encode and to elicit from human experts.
Revisiting Citizen Science Through the Lens of Hybrid Intelligence
This "HI lens" provides the CS community with an overview of several ways to utilize the combination of AI and human intelligence in their projects.
Personalization in Human-AI Teams: Improving the Compatibility-Accuracy Tradeoff
AI systems that model and interact with users can update their models over time to reflect new information and changes in the environment.
Applying Transparency in Artificial Intelligence based Personalization Systems
We encourage researchers to adopt the checklist in various environments and to work towards a consensus-based tool for measuring transparency in the personalization community.
The Phantom Steering Effect in Q&A Websites
Moreover, we conduct a qualitative survey of the users on Stack Overflow which provides further evidence that the insights from the model reflect the true behavior of the community.
EduBERT: Pretrained Deep Language Models for Learning Analytics
The use of large pretrained neural networks to create contextualized word embeddings has drastically improved performance on several natural language processing (NLP) tasks.
Interpretable Models for Understanding Immersive Simulations
We compared the performance of the models on these interpretability tests to their performance on statistical information criteria.
A difficulty ranking approach to personalization in E-learning
EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions.
Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content
We present a new computational approach to this problem called MAPLE (Multi-Armed Bandits based Personalization for Learning Environments) that combines difficulty ranking with multi-armed bandits.
