The importance of retention rate for higher education institutions has
encouraged data analysts to present various methods to predict at-risk
students. The present study, motivated by the same encouragement, proposes a
deep learning model trained with 121 features of diverse categories extracted
or engineered out of the records of 60,822 postsecondary students. The model
undertakes to identify students likely to graduate, the ones likely to transfer
to a different school, and the ones likely to drop out and leave their higher
education unfinished. This study undertakes to adjust its predictive methods
for different stages of curricular progress of students. The temporal aspects
introduced for this purpose are accounted for by incorporating layers of LSTM
in the model. Our experiments demonstrate that distinguishing between
to-be-graduate and at-risk students is reasonably achievable in the earliest
stages, and then it rapidly improves, but the resolution within the latter
category (dropout vs. transfer) depends on data accumulated over time. However,
the model remarkably foresees the fate of students who stay in the school for
three years. The model is also assigned to present the weightiest features in
the procedure of prediction, both on institutional and student levels. A large,
diverse sample size along with the investigation of more than one hundred
extracted or engineered features in our study provide new insights into
variables that affect students success, predict dropouts with reasonable
accuracy, and shed light on the less investigated issue of transfer between
colleges. More importantly, by providing individual-level predictions (as
opposed to school-level predictions) and addressing the outcomes of transfers,
this study improves the use of ML in the prediction of educational outcomes.

利用深度学习模型和 LSTM 层，研究了大规模收集的 121 种不同类别的特征对高等教育学生保留率的影响，早期对毕业和风险学生的区分能力良好，在后期对退学和转学的区分能力依赖于随时间累积的数据，提供个体层面的预测并解决转学结果问题，为教育结果预测提供了新的见解。

学生成功建模：最重要的因素

Students Success Modeling: Most Important Factors

This paper presents a comprehensive exploration of leveraging Large Language
Models (LLMs), specifically GPT-4, in the field of instructional design. With a
focus on scaling evidence-based instructional design expertise, our research
aims to bridge the gap between theoretical educational studies and practical
implementation. We discuss the benefits and limitations of AI-driven content
generation, emphasizing the necessity of human oversight in ensuring the
quality of educational materials. This work is elucidated through two detailed
case studies where we applied GPT-4 in creating complex higher-order
assessments and active learning components for different courses. From our
experiences, we provide best practices for effectively using LLMs in
instructional design tasks, such as utilizing templates, fine-tuning, handling
unexpected output, implementing LLM chains, citing references, evaluating
output, creating rubrics, grading, and generating distractors. We also share
our vision of a future recommendation system, where a customized GPT-4 extracts
instructional design principles from educational studies and creates
personalized, evidence-supported strategies for users' unique educational
contexts. Our research contributes to understanding and optimally harnessing
the potential of AI-driven language models in enhancing educational outcomes.

本论文探讨了在教学设计中利用大型语言模型（LLMs），尤其是 GPT-4 的全面应用。我们关注以缩小理论教育研究和实际实施之间的差距为目的，通过扩大基于证据的教学设计专业知识的规模。本文讨论了 AI 驱动内容生成的益处和局限性，强调人为监督保证教育材料质量的必要性。我们通过两个详细的案例研究阐释了这一工作，其中应用 GPT-4 创建了复杂的高阶评估和不同课程的主动学习组成部分。根据我们的经验，我们提供有效使用 LLM 的最佳实践，如利用模板，微调，处理意外输出，实施 LLM 链，引用参考文献，评估输出，创建量表，评分和生成干扰项。我们还分享了我们对未来的推荐系统的愿景，该推荐系统可以根据用户的独特教育背景，定制 GPT-4 从教育研究中提取教学设计原则，并创建个性化的、具备证据支持的策略。本研究有助于理解和最大限度地利用 AI 驱动的语言模型潜力，以增强教育成果。