Innovative Assessment Practices in Tertiary Education: Balancing Formative and Summative Methods for Diverse Student Populations
Bibliographic record
Abstract
Tertiary education has evolved to accommodate a more diverse student demographic, including under-represented groups such as individuals with disabilities and non-traditional backgrounds. This shift necessitates assessment practices that do not disadvantage students based on attributes unrelated to the criteria being evaluated. Effective assessment should support all students equally in demonstrating their capabilities. However, the adaptation of assessment models to specific local contexts remains under-explored, despite its potential to enhance both academic and institutional effectiveness. Assessment serves dual purposes in tertiary education: facilitating student learning and certifying achievement. The design and processes of assessments are critical, yet many academics find it challenging to create effective assessment tasks. The literature highlights a persistent gap between assessment methods and actual student learning, with traditional written exams often criticized for their passive nature. Formative and summative assessments are two main categories used to evaluate student performance. Formative assessments provide ongoing feedback, enabling students to improve continuously, while summative assessments judge student achievements at the end of a module. Effective assessment strategies often blend both types to enhance learning outcomes. “Assessment for learning" emphasizes authentic assessments that focus on the learning process rather than merely assigning grades. Learning-oriented assessment integrates tasks as learning opportunities, self- and peer-assessment, and constructive feedback. Authentic assessments, such as real-life tasks, promote deeper understanding and application of knowledge. In the field of Biokinetics, work-integrated learning (WIL) is essential, offering students hands-on experience and immediate feedback. This approach aligns with formative assessment principles, promoting active involvement and real-world application. However, integrating traditional assessment methods with experiential learning poses challenges. Academics must align assessments with real-world situations and adapt to predesigned criteria to ensure effective evaluation. This review critically examines various assessment methods in tertiary education, aligning them with practices in the field of Biokinetics. The findings underscore the importance of authentic, learning-oriented assessments in fostering student development and achieving educational objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".