---
res:
bibo_abstract:
- "\r\n Motivation and Objectives.
Computational Thinking\r\n (CT) has become a
central theme in K–12 Computer Science education. Over the past twenty years,
multiple conceptualizations of CT have emerged, many forming the basis for assessment
instruments. One such conceptualization was developed for the large-scale\r\n
\ International Computer and Information
Literacy Study\r\n (ICILS), which assessed CT
across 24 countries using representative sampling. The size and sampling quality
of the ICILS data set allow for robust statistical analyses which in turn will
be of interest to researchers and policy-makers alike. This study situates the
ICILS 2023 conceptualization of CT within other established frameworks and conducts
a secondary analysis of the ICILS 2023 CT data on non-cognitive antecedents and
processes.\r\n \r\n \r\n Methods\r\n . Structured deductive
content analyses compare the ICILS 2023 items with those from the\r\n Bebras Challenge on Informatics and Computational Thinking\r\n
\ [13] (\r\n Bebras\r\n
\ ) and the\r\n Computational
Thinking Test\r\n [55]) (\r\n CTt\r\n
\ ), mapped across three CT frameworks—ICILS [28], Shute et al.
[65] and Weintrop et al. [71]—and aligned with Bloom's revised taxonomy [2]. Linear
regression analyses on the data of the 20 educational contexts that provided not
only CT performance data but also a complete coverage of student data relative
to the predictors of CT performance studied in prior work examine the predictive
effect of non-cognitive factors on CT performance.\r\n \r\n
\ \r\n Results\r\n
\ . The qualitative analyses showed that the ICILS 2023 CT items
can be mapped to existing frameworks. Conversely, items from both\r\n Bebras\r\n
\ and\r\n CTt\r\n can
be mapped to the ICILS framework. The distinct, partially overlapping profiles
of the instruments across the frameworks as well as Bloom's taxonomy indicate
that they are complementary in assessing CT, confirming and expanding prior comparisons
of\r\n Bebras\r\n and\r\n
\ CTt\r\n . The regression
analyses indicate no single dominant predictor of CT performance. The association
of socio-economic status, gender, or the home language was consistent with prior
findings, predictors related to learning processes, however, vary across educational
contexts.\r\n \r\n \r\n Discussion\r\n . Our results demonstrate
that ICILS 2023 items can be mapped onto multiple established CT frameworks, supporting
their broader validity and utility for comparative research. The findings of the
regression analysis underscore the complex interplay of non-cognitive factors
affecting CT and illustrate the significance of contextual interpretation within
educational systems.\r\n @eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Jan
foaf_name: Vahrenhold, Jan
foaf_surname: Vahrenhold
- foaf_Person:
foaf_givenName: Jan
foaf_name: Niemann, Jan
foaf_surname: Niemann
foaf_workInfoHomepage: http://www.librecat.org/personId=32467
- foaf_Person:
foaf_givenName: Kerstin
foaf_name: Drossel, Kerstin
foaf_surname: Drossel
foaf_workInfoHomepage: http://www.librecat.org/personId=48921
bibo_doi: 10.1145/3813115
dct_date: 2026^xs_gYear
dct_isPartOf:
- http://id.crossref.org/issn/1946-6226
dct_language: eng
dct_publisher: Association for Computing Machinery (ACM)@
dct_title: 'Computational Thinking in ICILS 2023: Analyzing the Construct and Its
Antecedent- and Process-Level Predictors@'
...