Student Learning Outcomes for Online and In-Person Instruction Analysis

Encompassing processed and anonymized student-level data used to explore subgroup heterogeneity in learning outcomes between online and in-person instruction using causal forest methods. It includes the data, R scripts for replication, a variable dictionary, and a README file. The data is fully anonymized with no personally identifiable information.

Use Cases

• Estimate heterogeneous treatment effects for online versus face-to-face learning outcomes across student subgroups using causal forest models.
• Replicate statistical analyses and figures from the associated manuscript using the provided R scripts and processed student-level dataset.
• Analyze subgroup heterogeneity in educational interventions as referenced in the study's focus on precision education.

Strengths

• Includes R scripts required to replicate all statistical analyses and figures from the published study.
• Contains a variable dictionary describing coding and transformations for the processed data.
• Data is fully anonymized with no personally identifiable information, complying with data protection regulations.

Limitations

• Raw institutional student records are not available, limiting analysis to the provided processed variables.
• The dataset size, row count, and specific column features are not detailed in the input.
• Geographic and temporal coverage of the student population is unspecified.

Provenance

Source

Humanities & Social Sciences Communications Dataverse

Collection Method

Processed and anonymized from institutional student records for a research study.

Time Range

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Freshness

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Geography

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