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Record W2346956590

Digital Examination in Higher Education--Experiences from Three Different Perspectives.

2015· article· en· W2346956590 on OpenAlexaff
Björn Berggren, Andreas Fili, Olle Nordberg

Bibliographic record

VenueThe International Journal of Education and Development using Information and Communication Technology (The University of the West Indies) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsHigher educationRevenueGovernment (linguistics)Grading (engineering)Mathematics educationSociologyPublic relationsPsychologyPolitical scienceBusinessEngineeringFinance
DOInot available

Abstract

fetched live from OpenAlex

Assessment through new technology has gained a firm foothold within the university system in the last decade. This paper summarizes the experiences that have been made during the introduction of digital examination over the past two years at the Royal Institute of Technology in Stockholm, Sweden. The experiences are divided between three different perspectives; the teachers, the students and the administrators. From the teachers perspective the experiences have been very positive – less time have been allocated to grading written exams, the grades are perceived as more just and the saved time can be spent on increasing the quality on other parts of the courses. From a student perspective the experiences have been very positive as well – most students are enjoying the fact that they get the results much quicker, that they can edit their answers on the exam easier and that the grades are more just. The experiences from the final perspective – that from the administrators’ point of view – are far more complex. Some parts of the administrative system encouraged the introduction of digital examination, whereas other parts tried to stop it, using different measures. The paper concludes with some advice on implementing changes in written exams, based on the experiences from the Swedish case.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.276
Teacher spread0.247 · 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.

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".

Quick stats

Citations17
Published2015
Admission routes1
Has abstractyes

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