Architecting scalable data pipelines for learning analytics in higher education: A cloud-native approach
Bibliographic record
Abstract
This research presents a cloud-native architecture for architecting scalable, secure, and real-time data pipelines tailored to learning analytics in higher education. With a focus on modernising data workflows from legacy Student Information Systems (SIS) like PeopleSoft, the proposed pipeline leverages Oracle GoldenGate’s log-based Change Data Capture (CDC) capabilities and the Databricks Lakehouse platform to facilitate continuous data ingestion, transformation, and analytical readiness. A novel institutional deployment is demonstrated in which GoldenGate streams transactional data directly to Delta Lake without reliance on traditional staging zones, thereby reducing latency and complexity. Structured around Silver and Gold layers, the pipeline enables real-time data refinement and advanced transformation using PySpark and SQL, ensuring that institutional users have access to curated, analytics-ready data assets. The architecture integrates governance frameworks, observability tooling, and privacy-preserving features, supporting compliance with FERPA and GDPR. Anticipated outcomes include improved ingestion performance, elastic scalability via Databricks’ autoscaling Spark clusters, robust data lineage, and enhanced institutional agility in analytics-driven decision-making. This work contributes a replicable blueprint for higher education institutions seeking to modernise their data infrastructure for scalable learning analytics, real-time interventions, and regulatory resilience. Keywords: Learning, Pipeline, Analytics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".