Promoting active learning in introductory financial accounting through the flipped classroom design
Bibliographic record
Abstract
Purpose – The purpose of this paper is to describe a classroom design for introductory financial accounting that promotes active learning through a flipped classroom approach. A course learning management system, white-board voice-over video applications, an online homework manager and online tutorials pre-packaged with the course textbook were all adopted to facilitate the flipped classroom. The in-class sessions were refocussed around active learning strategies, including case analysis, concept mapping, solving comprehensive problems, mini lectures with bookends, and small group discussions. Design/methodology/approach – A quasi-experimental design, combined with student surveys, are utilized. A Wilcoxon rank-sum test is used to assess the significance of any difference in student performance between a lecture-based course (control group, n=92) and the flipped classroom course (experimental group, n=97). Student performance is measured based on final exams and overall course grades. Findings – The results suggest that the flipped classroom improved student grade point averages, final exam performance, and pass rates. Both the stronger and weaker students benefited from the technologies and active learning strategies adopted in the flipped classroom. Originality/value – This is the first known study to investigate the efficacy of promoting active learning in introductory financial accounting through a flipped classroom design. This study is valuable for accounting educators, and educators in other similarly technical disciplines, who seek to combat the high failure rates that typically plague complex, technical introductory courses.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".