Ranking Difficulty of Knowledge Units Based on Learning Dependency

Using knowledge maps to compute and rank the difficulty level of knowledge units can guide learners plan their learning activities more effectively. This paper focuses on how to use the topology of a knowledge map to compute and rank the difficulty of knowledge units. We present the hierarchical structure and properties of knowledge maps, propose three hypotheses based on the correlation between the difficulty of knowledge units and learning dependency, and then provide a method for ranking knowledge units with objective and subjective difficulty. The experiment on the “plane geometry” knowledge map shows that our methods can precisely calculate the difficulty of knowledge units.