The Balanced Scorecard: A Systemic Model for Evaluation and Assessment of Learning Outcomes?
Bibliographic record
Abstract
Objective – The goal of this paper is to explore using Kaplan and Norton’s balanced scorecard methodology as a systemic model for outcomes assessment. The expectations of academic accrediting agencies have shifted from measurement of inputs and outputs to that of the library’s impact on learning and demonstrating accountability. Recent literature has presented methods for performing specific aspects of outcomes assessment. However, the scorecard methodology may provide a systemic advantage beneficial to library administrators and managers. Methods – This paper provides a selective review of outcomes assessment in academic libraries and a description of the balanced scorecard methodology, focusing on its relevance to assessment and demonstration of accountability. Results – A theoretical scenario is outlined, including examples of a scorecard used for outcomes assessment. For each example, the benefits of using a systemic approach are examined. Conclusions – Using a systems-thinking approach to outcomes assessment may provide significant advantages to library administrators and managers. As the model includes traditional methods of outcomes assessment, the scorecard approach adds elements of process improvement, identification of the inputs and outputs that create outcomes, and a tool for communicating accountability for resources.
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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 | 0.057 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".