MétaCan
Menu
Back to cohort
Record W4402026107 · doi:10.18357/otessaj.2024.4.1.64

Designing an Online Collaborative Exam:

2024· article· en· W4402026107 on OpenAlexaffvenue
Mariel Miller, Safoura Askari

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsProcess (computing)Equity (law)Key (lock)Reflection (computer programming)Collaborative learningComputer scienceCoronavirus disease 2019 (COVID-19)Engineering ethicsPsychologyKnowledge managementEngineeringPolitical science

Abstract

fetched live from OpenAlex

The ability to collaborate online is an essential skill that allows learners to bring together diverse perspectives to deepen understanding regardless of physical location. As online learning and hybrid workplaces have become more prevalent since the COVID-19 global pandemic, the critical nature of this skill has become particularly relevant for post-secondary graduates. While exams are traditionally viewed as solitary endeavors, collaborative exams offer invaluable opportunities for learners to develop these skills. However, this form of collaborative assessment can be challenging for both learners and educators, and few studies offer guidance for the effective design of collaborative exams. As such, in this paper, we report on the design and implementation of a synchronous collaborative midterm exam in a large first-year undergraduate course. Specifically, we describe how we drew on a theoretical framework of self- and socially shared regulation of learning to design a three-phase exam fostering learners’ engagement in key processes of planning, strategic enactment, and reflection on collaboration processes and products. Finally, we discuss key considerations that arose during the design and implementation of the exam, including ensuring an emphasis on process and authenticity, ethical use of video, and equity of access.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.444
Teacher spread0.367 · 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 teacher head, not a consensus.

Study designObservational
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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueThe Open/Technology in Education Society and Scholarship Association JournalSame topicInnovative Teaching and Learning MethodsFrench-language works237,207