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Record W1525385927

Student Perceptions of Learning Technologies in Introductory Accounting Courses

2015· article· en· W1525385927 on OpenAlexaff
Lynn Carty, Ron Baker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVariety (cybernetics)PerceptionPsychologyEmerging technologiesMathematics educationComputer science
DOInot available

Abstract

fetched live from OpenAlex

The past two decades have seen a dramatic increase in the development and use of various classroom technologies purported to enhance student learning. Accompanying this has been a large volume of studies aimed at assessing the effectiveness of these technologies from a variety of disciplines. This study contributes to this discourse and the accounting education literature in particular by examining student perceptions of the effectiveness of multiple technologies used in an introductory management accounting course. Perceptions of the effectiveness of a traditional textbook were also collected. Students were then asked to compare their experience in this course to that of the prerequisite introductory financial accounting course where no learning technologies were used. This study shows that students perceive practice problems and problem-based lectures to be the most effective learning activities whether they are employed using technology or not. Two learning technologies to be particularly effective – online practice problems/quizzes and video lectures- but all learning technologies tended to support superficial rather than deep learning approaches.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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