Mathematics Content Understanding for Cyberlearning via Formula Evolution Map
This addresses the problem of daunting math-content comprehension for scholars and students in cyberlearning, though it appears incremental as it builds on existing educational technology with novel scaffolding methods.
The paper tackles the challenge of understanding mathematical content in STEM literature by proposing a Formula Evolution Map (FEM) and an online learning environment called PDF Reader with Math-Assistant (PRMA), which auto-characterizes student math-information needs and recommends educational resources, with evaluation showing it effectively assists master and PhD students.
Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula. In this paper, we propose a novel problem, ``mathematics content understanding'', for cyberlearning and cyberreading. To address this problem, we create a Formula Evolution Map (FEM) offline and implement a novel online learning/reading environment, PDF Reader with Math-Assistant (PRMA), which incorporates innovative math-scaffolding methods. The proposed algorithm/system can auto-characterize student emerging math-information need while reading a paper and enable students to readily explore the formula evolution trajectory in FEM. Based on a math-information need, PRMA utilizes innovative joint embedding, formula evolution mining, and heterogeneous graph mining algorithms to recommend high quality Open Educational Resources (OERs), e.g., video, Wikipedia page, or slides, to help students better understand the math-content in the paper. Evaluation and exit surveys show that the PRMA system and the proposed formula understanding algorithm can effectively assist master and PhD students better understand the complex math-content in the class readings.