Learning to learn STEM courses
We curate a new dataset from MIT EECS (Course 6), Physics (Course 8), Economics (Course 14), Mathematics (Course 18), Harvard Statistics, and Columbia Computer Science course questions, transform them into programming tasks using OpenAI Codex, and solve them by executing programs. We curate, transform, and solve ten courses: (i) MIT EECS 6.003 Signal Processing, (ii) MIT EECS 6.036 Introduction to Machine Learning, (iii) MIT EECS 6.042 Mathematics for Computer Science, (iv) MIT Physics 8.282 Introduction to Astronomy, (v) MIT Economics 14.01 Principles of Microeconomics, (vi) MIT Mathematics 18.05 Introduction to Probability and Statistics, (vii) MIT Mathematics 18.06 Linear Algebra, (viii) MIT Mathematics 18.781 Theory of Numbers, (ix) Harvard Statistics STATS110 Probability, and (x) Columbia University COMS3251 Computational Linear Algebra. Our approach works surprisingly well since question solutions and programs share an underlying tree representation. We are able to use Codex to correctly solve all questions by specifying both question and programming contexts such as which mathematical rules to use or which programming packages to load. In addition to generating code which solves problems the resulting code generates plots which are useful for understanding the solutions. We interactively transform the original course questions until they are solved correctly and measure the similarity between the original and transformed questions. Finally, we automatically generate novel questions for each course, providing a way to rapidly synthesize new course content. Our approach is the first scalable solution towards automatically learning to learn all university STEM courses by machine.
Code (0)
등록된 구현이 없습니다.
Tasks
AstronomySimilar Papers 제목 키워드 기반
Design and Implementation of Curriculum System Based on Knowledge Graph
With the fact that the knowledge in each field in university is keeping increasing, the number of university courses is becoming larger, and the content and curriculum system is becoming much more complicated than it use…
Identifying Hubs in Undergraduate Course Networks Based on Scaled Co-Enrollments: Extended Version
Understanding course enrollment patterns is valuable to predict upcoming demands for future courses, and to provide student with realistic courses to pursue given their current backgrounds. This study uses undergraduate …
Academic Performance Estimation with Attention-based Graph Convolutional Networks
Student's academic performance prediction empowers educational technologies including academic trajectory and degree planning, course recommender systems, early warning and advising systems. Given a student's past data (…
Decision MakingPredictionRecommendation SystemsLeveraging Large Language Model and Story-Based Gamification in Intelligent Tutoring System to Scaffold Introductory Programming Courses: A Design-Based Research Study
Programming skills are rapidly becoming essential for many educational paths and career opportunities. Yet, for many international students, the traditional approach to teaching introductory programming courses can be a …
Language ModelingLanguage ModellingLarge Language ModelHelping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization
The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear as tools that help …
Collaborative FilteringRecommendation Systems