This study investigates game-based learning in the context of the educational
game "Jo Wilder and the Capitol Case," focusing on predicting student
performance using various machine learning models, including K-Nearest
Neighbors (KNN), Multi-Layer Perceptron (MLP), and Random Forest. The research
aims to identify the features most predictive of student performance and
correct question answering. By leveraging gameplay data, we establish complete
benchmarks for these models and explore the importance of applying proper data
aggregation methods. By compressing all numeric data to min/max/mean/sum and
categorical data to first, last, count, and nunique, we reduced the size of the
original training data from 4.6 GB to 48 MB of preprocessed training data,
maintaining high F1 scores and accuracy.
Our findings suggest that proper preprocessing techniques can be vital in
enhancing the performance of non-deep-learning-based models. The MLP model
outperformed the current state-of-the-art French Touch model, achieving an F-1
score of 0.83 and an accuracy of 0.74, suggesting its suitability for this
dataset. Future research should explore using larger datasets, other
preprocessing techniques, more advanced deep learning techniques, and
real-world applications to provide personalized learning recommendations to
students based on their predicted performance. This paper contributes to the
understanding of game-based learning and provides insights into optimizing
educational game experiences for improved student outcomes and skill
development.

研究了基于游戏的学习在教育游戏 “Jo Wilder and the Capitol Case” 中的应用，使用 K-Nearest Neighbors (KNN)、Multi-Layer Perceptron (MLP) 和 Random Forest 等机器学习模型预测学生表现，通过适当的数据预处理技术提高非深度学习模型的性能，提供优化教育游戏体验的见解，以促进学生成果和技能发展。

游戏化学习环境中学生表现建模

Modeling Student Performance in Game-Based Learning Environments

An essential element of K-12 AI literacy is educating learners about the
ethical and societal implications of AI systems. Previous work in AI ethics
literacy have developed curriculum and classroom activities that engage
learners in reflecting on the ethical implications of AI systems and developing
responsible AI. There is little work in using game-based learning methods in AI
literacy. Games are known to be compelling media to teach children about
complex STEM concepts. In this work, we developed a competitive card game for
middle and high school students called "AI Audit" where they play as AI
start-up founders building novel AI-powered technology. Players can challenge
other players with potential harms of their technology or defend their own
businesses by features that mitigate these harms. The game mechanics reward
systems that are ethically developed or that take steps to mitigate potential
harms. In this paper, we present the game design, teacher resources for
classroom deployment and early playtesting results. We discuss our reflections
about using games as teaching tools for AI literacy in K-12 classrooms.

本文报告了一种名为 “AI 审核” 的卡牌游戏，旨在教育 K-12 学习者关于 AI 系统的伦理和社会影响，其中使用了游戏化学习方法。该游戏旨在帮助学生了解 AI 系统的伦理和社会影响，培养负责任的 AI 创业者，该游戏已经在中学和高中学生的早期测试中得到了评估。

AI 审核：反思每日 AI 系统的卡牌游戏

AI Audit: A Card Game to Reflect on Everyday AI Systems

Intelligent systems designed for play-based interactions should be
contextually aware of the users and their surroundings. Spoken Dialogue Systems
(SDS) are critical for these interactive agents to carry out effective
goal-oriented communication with users in real-time. For the real-world (i.e.,
in-the-wild) deployment of such conversational agents, improving the Natural
Language Understanding (NLU) module of the goal-oriented SDS pipeline is
crucial, especially with limited task-specific datasets. This study explores
the potential benefits of a recently proposed transformer-based multi-task NLU
architecture, mainly to perform Intent Recognition on small-size
domain-specific educational game datasets. The evaluation datasets were
collected from children practicing basic math concepts via play-based
interactions in game-based learning settings. We investigate the NLU
performances on the initial proof-of-concept game datasets versus the
real-world deployment datasets and observe anticipated performance drops
in-the-wild. We have shown that compared to the more straightforward baseline
approaches, Dual Intent and Entity Transformer (DIET) architecture is robust
enough to handle real-world data to a large extent for the Intent Recognition
task on these domain-specific in-the-wild game datasets.

研究了一个新的多任务 NLU 结构在小型领域游戏数据集上进行意图识别的表现，发现相比基线方法，Dual Intent and Entity Transformer (DIET) 架构在野外数据上具有较强的鲁棒性。