With cross-disciplinary academic interests increasing and academic advising
resources over capacity, the importance of exploring data-assisted methods to
support student decision making has never been higher. We build on the findings
and methodologies of a quickly developing literature around prediction and
recommendation in higher education and develop a novel recurrent neural
network-based recommendation system for suggesting courses to help students
prepare for target courses of interest, personalized to their estimated prior
knowledge background and zone of proximal development. We validate the model
using tests of grade prediction and the ability to recover prerequisite
relationships articulated by the university. In the third validation, we run
the fully personalized recommendation for students the semester before taking a
historically difficult course and observe differential overlap with our
would-be suggestions. While not proof of causal effectiveness, these three
evaluation perspectives on the performance of the goal-based model build
confidence and bring us one step closer to deployment of this personalized
course preparation affordance in the wild.

通过建立循环神经网络推荐系统来为学生推荐课程，以帮助其准备所需的目标课程，这一体系结构是针对学生先前知识背景和发展区域进行个性化定制的，并且通过等级预测和挖掘大学先修关系等测试进行验证。

基于目标的课程推荐

Goal-based Course Recommendation

An enduring issue in higher education is student retention to successful
graduation. National statistics indicate that most higher education
institutions have four-year degree completion rates around 50 percent, or just
half of their student populations. While there are prediction models which
illuminate what factors assist with college student success, interventions that
support course selections on a semester-to-semester basis have yet to be deeply
understood. To further this goal, we develop a system to predict students'
grades in the courses they will enroll in during the next enrollment term by
learning patterns from historical transcript data coupled with additional
information about students, courses and the instructors teaching them.
We explore a variety of classic and state-of-the-art techniques which have
proven effective for recommendation tasks in the e-commerce domain. In our
experiments, Factorization Machines (FM), Random Forests (RF), and the
Personalized Multi-Linear Regression model achieve the lowest prediction error.
Application of a novel feature selection technique is key to the predictive
success and interpretability of the FM. By comparing feature importance across
populations and across models, we uncover strong connections between instructor
characteristics and student performance. We also discover key differences
between transfer and non-transfer students. Ultimately we find that a hybrid
FM-RF method can be used to accurately predict grades for both new and
returning students taking both new and existing courses. Application of these
techniques holds promise for student degree planning, instructor interventions,
and personalized advising, all of which could improve retention and academic
performance.

研究了高等教育中学生保留率的问题，通过学习历史记录数据和其他信息预测学生成绩，发现了教师特征和学生表现之间的强烈联系，探索了预测模型并发现了能够准确预测成绩的混合方法。