处方深度专注分数预测吸引学生参与度提高
Prescribing Deep Attentive Score Prediction Attracts Improved Student Engagement
摘要：
智能辅导系统（ITS）已经被开发为通过自适应地生成针对每个个体优化的学习路径来向学生提供个性化的学习体验。在ITS的广阔范围内，分数预测作为一个研究领域脱颖而出，使学生能够根据当前的位置构建个人现实的目标。通过ITS提供的预期分数，学生可以即时将自己的预期分数与自己的实际分数进行比较，这直接对应于ITS可以灌输的可靠性。换句话说，改进预测分数的精度与学生可能对ITS的信心水平密切相关，这显然会提高学生的参与度。然而，以前的研究仅仅集中在提高预测模型的性能，在很大程度上缺乏关注其实际应用所产生的好处。在本文中，我们通过提供经验证据证明，在现实世界中部署的分数预测模型的准确性会显著影响用户参与度。为此，我们将最先进的基于深度专注神经网络的分数预测模型应用于Santa，Santa是一个多平台英语ITS，在韩国拥有约78万用户，专门专注于TOEIC（国际交流英语考试）标准化考试。我们运行一个受控的A/B测试的ITS与两个模型，分别基于协同过滤和深度专注神经网络，以验证是否更准确的模型产生任何学生的参与。结果表明，注意力模式不仅能提高学生的士气（例如，更高的诊断测试完成率，回答问题的数量等），而且还鼓励积极参与（例如，更高的购买率，改善的总利润等）圣诞老人。
英文摘要：
Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, score prediction stands out as an area of study that enables students to construct individually realistic goals based on their current position. Via the expected score provided by the ITS, a student can instantaneously compare one's expected score to one's actual score, which directly corresponds to the reliability that the ITS can instill. In other words, refining the precision of predicted scores strictly correlates to the level of confidence that a student may have with an ITS, which will evidently ensue improved student engagement. However, previous studies have solely concentrated on improving the performance of a prediction model, largely lacking focus on the benefits generated by its practical application. In this paper, we demonstrate that the accuracy of the score prediction model deployed in a real-world setting significantly impacts user engagement by providing empirical evidence. To that end, we apply a state-of-the-art deep attentive neural network-based score prediction model to Santa, a multi-platform English ITS with approximately 780K users in South Korea that exclusively focuses on the TOEIC (Test of English for International Communications) standardized examinations. We run a controlled A/B test on the ITS with two models, respectively based on collaborative filtering and deep attentive neural networks, to verify whether the more accurate model engenders any student engagement. The results conclude that the attentive model not only induces high student morale (e.g. higher diagnostic test completion ratio, number of questions answered, etc.) but also encourages active engagement (e.g. higher purchase rate, improved total profit, etc.) on Santa.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
