Characteristics of learning tasks in accounting textbooks: an AI assisted analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AUVXHVCEI" target="_blank" >RIV/00216208:11320/22:UVXHVCEI - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1186/s40461-022-00138-2" target="_blank" >https://doi.org/10.1186/s40461-022-00138-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s40461-022-00138-2" target="_blank" >10.1186/s40461-022-00138-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Characteristics of learning tasks in accounting textbooks: an AI assisted analysis

Popis výsledku v původním jazyce

Tasks in accounting textbooks play a vital role when it comes to learning processes. However, hardly any empirical evidence on the quality of accounting tasks exists regarding accounting-relevant characteristics. This is why a new category system containing accounting-relevant aspects was developed to analyze a total of 3,361 tasks from 14 different German accounting textbooks. Descriptive analysis and correlation analysis were performed to assess task characteristics and identify relationships between categories. In addition, in light of the large number of tasks to be analyzed, AI assisted the content analysis, and its usefulness was evaluated. The results indicate that tasks are not sufficiently able to instill accounting competencies such as interpreting data, assessing the relevance of information, or identifying and solving underlying accounting problems. The findings further show that AI and human coding yield similar results in most categories, suggesting that AI assistance is useful for content analysis when evaluating a large number of tasks.

Název v anglickém jazyce

Characteristics of learning tasks in accounting textbooks: an AI assisted analysis

Popis výsledku anglicky

Tasks in accounting textbooks play a vital role when it comes to learning processes. However, hardly any empirical evidence on the quality of accounting tasks exists regarding accounting-relevant characteristics. This is why a new category system containing accounting-relevant aspects was developed to analyze a total of 3,361 tasks from 14 different German accounting textbooks. Descriptive analysis and correlation analysis were performed to assess task characteristics and identify relationships between categories. In addition, in light of the large number of tasks to be analyzed, AI assisted the content analysis, and its usefulness was evaluated. The results indicate that tasks are not sufficiently able to instill accounting competencies such as interpreting data, assessing the relevance of information, or identifying and solving underlying accounting problems. The findings further show that AI and human coding yield similar results in most categories, suggesting that AI assistance is useful for content analysis when evaluating a large number of tasks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Empirical Research in Vocational Education and Training [online]

ISSN

1877-6345

e-ISSN

1877-6345

Svazek periodika

14

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

33

Strana od-do

1-33

Kód UT WoS článku

000882374000001

EID výsledku v databázi Scopus

2-s2.0-85141659186