Teaching Experiences of E-Authentic Assessment: Lessons Learned in Higher Education
Bibliographic record
Abstract
The realities of the 21st century have led professors and lecturers to renew their learning assessment practices so that they are more adapted to and contextualized in the current professional world. Despite advances in teaching and learning, assessment methods may still deviate from practice in authentic contexts. Although some instructors are already familiar with more authentic assessments, most are accustomed to using exams as standard practices to test students’ achievement of course objectives and essays to prepare students for research or written argumentation. Despite their benefits, such typical assessments often lack authenticity and do not develop the full potential of students’ 21st-century learning or literacy skills such as communication, creativity, or working with technologies. Over the past decade, we have been witnessing the beginnings of a broader reflection on teaching, learning, and evaluating with technologies, including more authentic assessments. This reflective essay will present how technologies make it possible to diversify assessment methods, resulting in enhanced authenticity and development of 21st-century learning and literacy skills. Authentic assessment methods with technologies will be illustrated, e.g., recorded video presentations, explanatory interviews with descriptive assessment grids, PechaKucha presentations, blog posts, and social media and e-portfolios, with examples from several disciplines. Authors will also explain how proposing a number of methods to students for the same assessment may help answer their various needs and preferences without increasing instructors’ grading load. Furthermore, authors will discuss how diversifying assessment methods with technologies often results in a transformation of assessment modalities. Beyond assessments as an evaluation of knowledge and/or skills at a fixed schedule, authentic assessments with technologies may become continuous or iterative processes with multiple feedbacks from instructors, thereby combining synchronous interactions and/or discussions with asynchronous reflections to improve students’ involvement and active learning.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".