5. The method of claim 4 , wherein the extracting knowledge points includes: receiving the segmented term sequences having positional information indicating a position in the electronic learning materials of the segmented term sequence; after the receiving the segmented term sequences, unifying abbreviations of the segmented term sequences; after the unifying the abbreviations, constructing generalized suffix trees of the segmented term sequences; after the constructing the generalized suffix trees, discovering repeated phrase instances of the segmented term sequences using the generalized suffix trees, wherein the phrase instances are limited by a particular maximum length; after the discovering the repeated phrase instances, adjusting frequency of the discovered repeated phrases instances based on positions of phrase instances in the learning materials; after the adjusting the frequency, measuring a cohesion and a separation of the segmented term sequences; after the measuring the cohesion and the separation, removing stop phrases from the segmented term sequences; after the removing the stop phrases, generating candidate knowledge points from the segmented term sequences; after the generating the candidate knowledge points, calculating weights of the candidate knowledge points based on the adjusted frequency; after the calculating the weights, analyzing appearance positions of the candidate knowledge points; after the analyzing the appearance positions, constructing a hierarchy of knowledge points based on the appearance positions; and after the constructing the hierarchy, presenting coverage overview of the learning materials.
5. The method of claim 4 , wherein the extracting knowledge points includes: receiving the segmented term sequences having positional information indicating a position in the electronic learning materials of the segmented term sequence; after the receiving the segmented term sequences, unifying abbreviations of the segmented term sequences; after the unifying the abbreviations, constructing generalized suffix trees of the segmented term sequences; after the constructing the generalized suffix trees, discovering repeated phrase instances of the segmented term sequences using the generalized suffix trees, wherein the phrase instances are limited by a particular maximum length; after the discovering the repeated phrase instances, adjusting frequency of the discovered repeated phrases instances based on positions of phrase instances in the learning materials; after the adjusting the frequency, measuring a cohesion and a separation of the segmented term sequences; after the measuring the cohesion and the separation, removing stop phrases from the segmented term sequences; after the removing the stop phrases, generating candidate knowledge points from the segmented term sequences; after the generating the candidate knowledge points, calculating weights of the candidate knowledge points based on the adjusted frequency; after the calculating the weights, analyzing appearance positions of the candidate knowledge points; after the analyzing the appearance positions, constructing a hierarchy of knowledge points based on the appearance positions; and after the constructing the hierarchy, presenting coverage overview of the learning materials. 6. The method of claim 5 , wherein: the cohesion is measured according to a mutual information cohesion metric; the separation is measured according to an accessor variety separation metric; the abbreviations are unified according to a principal component analysis or a singular value decomposition; and the weights are calculated according to a number of appearances and an authority of a particular learning material in which the segmented term sequences appear.