Authentic Assessment in Higher Education: Applying a Habermasian Framework
Bibliographic record
Abstract
The pursuit of authentic assessment has challenged educators to redesign and reformulate assessment and evaluation to better meet the needs of digital-era learners. Its primary goal has been to bring more accurate representations of “real world” situations and characteristics to education through assessment. However, these moves toward authentic assessment have too often limited notions of authenticity to an external real world, which itself is often limited to the world of work. This restricted view of authenticity in assessment risks neglecting key aspects of students’ ontological and epistemological subjectivity, and their ever-changing, evolving and authentic notions of self. Authentic assessment requires a holistic approach that underscores the student as an individual within a society. This means we must strike a balance between social expectations and individual autonomy because authentic assessment that aims to replicate the world of work risks neglecting student agency, self-determination and the desire to achieve subjective authenticity. This paper’s purpose is to critically interrogate authentic assessment and analyse theoretical frameworks upon which higher education can build and implement balanced and holistic approaches to authenticity in assessment. Resting on Jurgen Habermas’ Knowledge Constitutive Interests, the authors argue for a more balanced approach to authentic assessment that incorporates human drives for objectified knowledge, communicative rationality, and emancipatory learning. After establishing the relevance of Habermas’ theoretical framework to authentic assessment, this paper examines the value of self, peer and negotiated assessment and the potential of digital tools to aid these processes.
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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 | 0.036 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.063 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".