Fig 1.
Framework of the proposed method.
(A) The cross-college course enrollment scenario. The characters in navy color represent students in the Informatics College, whereas the characters in orange, green and blue s represent students in the Business College. Of the three art-major students, the orange studied both the Data Structure and C Programming, the green only enrolled in the C Programming, and the blue took none. (B) The contextual graph example. The two courses with thick edges represent the Data Structure and C Programming respectively, and the students with thick edges represent the ones who have studied both the two courses. (C) The information extracted from the contextual graph. As the green has taken the C Programming, he/she will have more and shorter connectivity paths to the Data Structure than the blue, which can be described as a higher probability.
Fig 2.
Illustration of the variable type transformation.
The circles represent the nominal variables, and the blue dotted lines represent the ratio variables.
Table 1.
Definitions of the logs-based features.
Table 2.
Nodes and edges in the contextual graph.
Fig 3.
Illustration of the skip-gram network.
Fig 4.
Illustration of the random walk procedure in node2vec.
This random walk just traverses from v to u and now is evaluating its next move out of the node u. Edge label indicates the corresponding search biases αpq.
Table 3.
Binary operators for learning the edge vector representations.
Table 4.
Definitions of the graph-based features.
Fig 5.
Illustration of the graph-based features.
(A) The course enrollment status. (B) The measured relations.
Table 5.
Statistics for the cross-college course enrollment records.
Table 6.
Oob error estimates for the five analysis models.
Table 7.
Significance ranking of each parameter.
Fig 6.
Multiple comparison of decreases on the oob error estimates for the three contrast models.
Fig 7.
Mean FIMs for the three contrast models.
(A) Average. (B) Hadamard. (C) Weight-1.
Fig 8.
Box plots of FIMs for all the features.
Fig 9.
Pie chart of FIMs on the feature categories.
Table 8.
Friedman rankings for all the features.
Fig 10.
Value distributions of features in the course preference.
(A) stuCrs. (B) stuCrsOutSch-stuCrsInSch.