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Record W4200070093 · doi:10.5430/ijhe.v11n3p130

Understanding the Effect of the COVID-19 Pandemic on Management Accounting Students

2021· article· en· W4200070093 on OpenAlexvenueno aff
Sharon Zunckel, Mbali Portia Msomi, Stephanie Caroline Samuel, Ferina Marimuthu

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicGovernment (linguistics)Social distanceHigher educationCoronavirus disease 2019 (COVID-19)Perspective (graphical)Learning environmentPopulationPsychologyPublic relationsPedagogyMedical educationBusinessPolitical scienceSociologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

A switch to emergency remote teaching, learning, and assessment (TLA) has become necessary as a result of the social distancing brought about by the recent COVID-19 pandemic. Higher Education Institutions (HEIs) were forced to switch from face-to-face to online teaching and learning to ensure successful completion of the academic year as well as the safety of their staff and students from a global pandemic. This arrangement has created teaching problems in terms of familiarizing oneself with technology, losing face-to-face contact, and limiting access to essential facilities such as laboratories and libraries. The new normal is when remote learning is employed to fulfil TLA obligations. Therefore, students are expected to adjust from a traditional to a remote learning environment. This change in environment highlights the importance of exploring students’ perceptions as the recipients of this novel learning. Hence, the aim of this study was to explore the impact that the COVID-19 pandemic has had on student learning, underpinned by the activity theory. Quantitative research methods were applied to elicit students’ perceptions of remote learning through the use of an online questionnaire. The target population comprised undergraduate management accounting students. The paper provides interesting implications for government, policymakers, regulatory bodies, and other researchers because it offers a student perspective on the challenges experienced with remote learning.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.236
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.088
GPT teacher head0.456
Teacher spread0.368 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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