arbiter
JournalsAccount

Accounting Students’ perceptions of learning Python: A technology Acceptance model study using natural language processing

Xin Guo

Journal of Accounting Education2026https://doi.org/10.1016/j.jaccedu.2026.101004article
AJG 2ABDC B
Weight
0.50

Abstract

This paper aims to examine accounting students’ perceptions of learning Python through the lens of the Technology Acceptance Model (TAM). Reflective survey data were collected from 25 accounting students enrolled in a Python module at a UK university. A structured scaffolding approach was adopted to support students without prior coding experience, progressing from conceptual introduction to guided practice and independent tasks. Natural language processing techniques were used to analyse the data, including topic modelling and sentiment analysis. The findings show that students perceived Python as useful for automating tasks, handling data, and supporting employability, while reporting moderate ease and varied technical challenges. Positive attitudes persisted despite challenges. The paper contributes to accounting education by showing how TAM can explain accounting students’ experiences of coding. From a teaching excellence perspective, the paper shows that a structured scaffolding approach could support teaching by building confidence among non-technical learners.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1016/j.jaccedu.2026.101004

Or copy a formatted citation

@article{xin2026,
  title        = {{Accounting Students’ perceptions of learning Python: A technology Acceptance model study using natural language processing}},
  author       = {Xin Guo},
  journal      = {Journal of Accounting Education},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jaccedu.2026.101004},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Accounting Students’ perceptions of learning Python: A technology Acceptance model study using natural language processing

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.