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Record W4320020714 · doi:10.14434/jotlt.v11i1.34594

Teaching Experiences of E-Authentic Assessment: Lessons Learned in Higher Education

2022· article· en· W4320020714 on OpenAlexaff
Audrey Raynault, Géraldine Heilporn, Alice Mascarenhas, Constance Denis

Bibliographic record

VenueJournal of Teaching and Learning with Technology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsUniversité de SherbrookeUniversité Laval
Fundersnot available
KeywordsAuthentic assessmentGrading (engineering)LiteracyPsychologyArgumentation theoryCreativityPedagogyEngineering ethicsMathematics educationEngineeringCurriculum

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0100.009
Open science0.0020.013
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.041
GPT teacher head0.429
Teacher spread0.388 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations6
Published2022
Admission routes1
Has abstractyes

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