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Record W1601522989 · doi:10.19173/irrodl.v10i3.669

Teachers’ and Students’ Experiences of Simultaneous Teaching in an International Distance and On-Campus Master’s Programme in Engineering

2009· article· en· W1601522989 on OpenAlexvenueno aff
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Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationDisadvantagedOnline teachingMathematics educationDual (grammatical number)Dual modeData collectionTeaching methodPsychologyMode (computer interface)Dual enrollmentMedical educationPedagogyComputer scienceSociologyEngineeringPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Swedish universities frequently offer campus-based education as well as online courses, a system commonly referred to as dual mode. This paper analyses some challenging pedagogical aspects of a master’s programme in engineering developed and delivered simultaneously online and on campus. Course evaluations, questionnaires, and interviews with the programme teachers were the main instruments used in this study. Activity theory was used as a theoretical framework for data collection and analysis. The study evidences the nature of problems experienced by on-campus and distance students as well as conflicts of interest and expectations existing between these two student groups. Teaching simultaneously in two modes demands extra effort from the course teachers, who are aware of the problems related to pedagogical communication needed by both groups. Though teaching in the dual mode offers economic benefits for the department, the simultaneous mode of teaching is experienced as problematical by both groups of students, with distance students appearing to be more disadvantaged in the programme.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.131
GPT teacher head0.516
Teacher spread0.385 · 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".

Quick stats

Citations28
Published2009
Admission routes1
Has abstractyes

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