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Record W2885326550 · doi:10.24908/pceea.v0i0.10182

Online Evolution: Advantages and Challenges of Online Course Components

2018· article· en· W2885326550 on OpenAlexaffvenueabout
Jason Bazylak, Peter Weiß

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSet (abstract data type)Lecture hallMathematics educationMassive open online courseSociologyMedia studiesComputer sciencePsychologyPedagogyHistory

Abstract

fetched live from OpenAlex

Abstract –"For a Copernican revolution to take place it does not matter what means are used provided this goal is achieved: a shift in what counts as centre and what counts as periphery." – Bruno Latour [1] As Michael Wesch pointed out in his 2008 talk at the University of Manitoba, "A Portal to Media Literacy," the conventional lecture hall set-up reinforces an authoritarian view of education as the passive reception of scarce and valuable bits of information. [2] This is the opposite of the exploratory, questioning discovery we would like our students to has as their learning experience. The problem of the conventional lecture hall is, as well, exacerbated in large classes of 800 or more students. However, the evolution of media, from television through to online media of today, has created opportunities, challenges and obstacles that lecturers today continue to experiment with to create relevant, interactive classes - of whatever size.
 At the same time, the numbers of students who can be reached with Massive Open Online Courses (MOOCs) dwarfs even our largest lecture courses and there are definite advantages that can be gained through online learning - reaching people in remote areas, enabling working people to take courses, providing credible, university-level information to any curious person who wants to learn. And while so far MOOCs may not have been uniformly living up to their promise, we do have evidence that students are using them to augment their lecture experience.
 This teaching case will investigate how one large first year design/communication course has slowly incorporated online elements to shift the power dynamic from the instructor as centre of focus to the student and their learning experience as central. Begun simply, in the beginning, with audio capture of lectures, we have moved on to video lecture capture, live and recorded online help session, and supplementary videos. This last, importantly, are developed not only by the lecturers, but by interested Teaching Assistants (TA) and even students still in the course. We are, step-by-step, finding the components that will best allow students to be able to construct their own learning experience.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.380

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 communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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 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

Citations2
Published2018
Admission routes3
Has abstractyes

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