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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Empirical Research in Vocational Education and Training [online]

  • ISSN

    1877-6345

  • e-ISSN

    1877-6345

  • Volume of the periodical

    14

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    33

  • Pages from-to

    1-33

  • UT code for WoS article

    000882374000001

  • EID of the result in the Scopus database

    2-s2.0-85141659186