We present a graph-based approach to discover and extend semantic relationships found in a mathematics curriculum to more general network structures that can illuminate relationships within the instructional material. Using words representative of a secondary level mathematics curriculum we identified in separate work, we constructed two similarity networks of word problems in a mathematics textbook, and used analogous random walks over the two networks to discover patterns. The two graph walks provide similar global views of problem similarity within and across chapters, but are affected differently by number of math words in a problem and math word frequency.
|Original language||English (US)|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - Jan 1 2015|
|Event||8th International Conference on Educational Data Mining, EDM 2015 - Madrid, Spain|
Duration: Jun 26 2015 → Jun 29 2015
All Science Journal Classification (ASJC) codes
- Computer Science(all)