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An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
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Analyzing cross-college course enrollments via contextual graph mining.

Yongzhen Wang1, Xiaozhong Liu2, Yan Chen1

  • 1Transportation Management College, Dalian Maritime University, Dalian, Liaoning, China.

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Predicting student course enrollment is crucial for resource allocation. This study introduces a contextual graph and node2vec to analyze cross-college enrollments, improving prediction accuracy by capturing complex relationships.

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Area of Science:

  • Educational Data Mining
  • Graph Representation Learning

Background:

  • Accurate prediction of student course enrollment is vital for effective educational resource allocation.
  • Analyzing cross-college course enrollments presents unique challenges due to diverse student pathways and institutional structures.

Purpose of the Study:

  • To develop an innovative approach for characterizing students' cross-college course enrollments.
  • To leverage a novel contextual graph and representation learning for enhanced enrollment prediction.

Main Methods:

  • Constructing a contextual graph incorporating students, courses, colleges, and diplomas with their relations.
  • Applying the node2vec representation learning algorithm to extract sophisticated graph-based features.
  • Utilizing the random forest algorithm to analyze the impact of graph-based features on enrollment prediction.

Main Results:

  • The contextual graph approach significantly improves the analysis of cross-college course enrollments.
  • Three specific graph-based features demonstrated a stronger impact on prediction accuracy compared to others.
  • Student course preference was identified as the most influential factor in predicting future enrollments.

Conclusions:

  • The proposed contextual graph method effectively captures complex relationships in educational data.
  • This approach enhances the quantitative measurement of variable relationships, transforming nominal data into ratio data.
  • The findings underscore the importance of student preferences in course recommendation systems and resource planning.