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Record W2467266107 · doi:10.19173/irrodl.v17i4.2330

Instructors’ Perceptions of Instructor Presence in Online Learning Environments

2016· article· en· W2467266107 on OpenAlexvenueno aff
Jennifer Richardson, Erin D. Besser, Adrie A. Koehler, Jieun Lim, Marquetta Strait

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionPsychologyOnline learningConstruct (python library)Mathematics educationClass (philosophy)Public universityEducational technologyDistance educationInstructional designPedagogyComputer scienceMultimedia

Abstract

fetched live from OpenAlex

<p class="2">As online learning continues to grow significantly, various efforts have been explored and implemented in order to improve the instructional experiences of students. Specifically, research indicates that how an instructor establishes his or her presence in an online environment can have important implications for the students’ overall learning experience. While instructor presence appears to be an important aspect of online learning, more research is needed to fully understand this construct. The purpose of this study was to consider online instructors’ perceptions related to presence, beliefs about actions, and the perceived impact of instructional presence. Using an explanatory multiple-case study approach, this research considered the perspectives of 13 instructors teaching in an online master’s program at a large Midwestern public university. Results indicate instructors viewed instructor presence as an important component in online courses but their reasons varied. Furthermore, the instructors discussed a number of communication strategies they used, the importance of using such strategies to connect to students, and the potential impact of these strategies on student participation and learning. Additional themes from the interview data are discussed, and implications for online teaching and learning are suggested.</p>

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.448
Teacher spread0.382 · 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

Citations138
Published2016
Admission routes1
Has abstractyes

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