Biweekly Student Collaboration Network Dataset for Early Prediction of Success

This dataset consists of biweekly nominations from students in a collaborative learning environment, collected over a specified period. Students were asked to nominate up to five peers with whom they collaborated most during each two-week period. The dataset captures these peer nominations across multiple intervals, allowing for the construction of dynamic collaboration networks. This structure facilitates the calculation of network centrality metrics, which can be analyzed to identify predictive indicators of academic success. The dataset includes:

• Student Identifiers: Anonymized IDs and fakenames for each student.
• Nomination Records: Each record includes the nominating student, the nominated peers (up to five), and the time interval of the nomination.
• Temporal Data: Each nomination event is timestamped, allowing for the exploration of evolving collaboration patterns.

The dataset is designed for educational data mining, particularly in analyzing peer network structures within collaborative learning contexts. It serves as a resource for exploring predictive indicators of academic performance based on social interactions and network positions. Researchers can apply various network centrality measures to understand how early nomination patterns correlate with student outcomes, offering potential insights for interventions aimed at enhancing collaborative learning.