A scoping review of computational thinking assessments in higher education
Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes exi...
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| Autor*in: |
Lu, Chang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of computing in higher education - Boston, Mass. : Springer, 1989, 34(2022), 2 vom: 23. Jan., Seite 416-461 |
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| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:2 ; day:23 ; month:01 ; pages:416-461 |
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| DOI / URN: |
10.1007/s12528-021-09305-y |
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SPR047510471 |
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| 520 | |a Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. | ||
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10.1007/s12528-021-09305-y doi (DE-627)SPR047510471 (SPR)s12528-021-09305-y-e DE-627 ger DE-627 rakwb eng Lu, Chang verfasserin aut A scoping review of computational thinking assessments in higher education 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. Computational thinking (dpeaa)DE-He213 Programming and programming languages (dpeaa)DE-He213 Learning and assessment (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Macdonald, Rob aut Odell, Bryce aut Kokhan, Vasyl aut Demmans Epp, Carrie aut Cutumisu, Maria (orcid)0000-0003-2475-9647 aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 34(2022), 2 vom: 23. Jan., Seite 416-461 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:34 year:2022 number:2 day:23 month:01 pages:416-461 https://dx.doi.org/10.1007/s12528-021-09305-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 2 23 01 416-461 |
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10.1007/s12528-021-09305-y doi (DE-627)SPR047510471 (SPR)s12528-021-09305-y-e DE-627 ger DE-627 rakwb eng Lu, Chang verfasserin aut A scoping review of computational thinking assessments in higher education 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. Computational thinking (dpeaa)DE-He213 Programming and programming languages (dpeaa)DE-He213 Learning and assessment (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Macdonald, Rob aut Odell, Bryce aut Kokhan, Vasyl aut Demmans Epp, Carrie aut Cutumisu, Maria (orcid)0000-0003-2475-9647 aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 34(2022), 2 vom: 23. Jan., Seite 416-461 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:34 year:2022 number:2 day:23 month:01 pages:416-461 https://dx.doi.org/10.1007/s12528-021-09305-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 2 23 01 416-461 |
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10.1007/s12528-021-09305-y doi (DE-627)SPR047510471 (SPR)s12528-021-09305-y-e DE-627 ger DE-627 rakwb eng Lu, Chang verfasserin aut A scoping review of computational thinking assessments in higher education 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. Computational thinking (dpeaa)DE-He213 Programming and programming languages (dpeaa)DE-He213 Learning and assessment (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Macdonald, Rob aut Odell, Bryce aut Kokhan, Vasyl aut Demmans Epp, Carrie aut Cutumisu, Maria (orcid)0000-0003-2475-9647 aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 34(2022), 2 vom: 23. Jan., Seite 416-461 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:34 year:2022 number:2 day:23 month:01 pages:416-461 https://dx.doi.org/10.1007/s12528-021-09305-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 2 23 01 416-461 |
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10.1007/s12528-021-09305-y doi (DE-627)SPR047510471 (SPR)s12528-021-09305-y-e DE-627 ger DE-627 rakwb eng Lu, Chang verfasserin aut A scoping review of computational thinking assessments in higher education 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. Computational thinking (dpeaa)DE-He213 Programming and programming languages (dpeaa)DE-He213 Learning and assessment (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Macdonald, Rob aut Odell, Bryce aut Kokhan, Vasyl aut Demmans Epp, Carrie aut Cutumisu, Maria (orcid)0000-0003-2475-9647 aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 34(2022), 2 vom: 23. Jan., Seite 416-461 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:34 year:2022 number:2 day:23 month:01 pages:416-461 https://dx.doi.org/10.1007/s12528-021-09305-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 2 23 01 416-461 |
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10.1007/s12528-021-09305-y doi (DE-627)SPR047510471 (SPR)s12528-021-09305-y-e DE-627 ger DE-627 rakwb eng Lu, Chang verfasserin aut A scoping review of computational thinking assessments in higher education 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. Computational thinking (dpeaa)DE-He213 Programming and programming languages (dpeaa)DE-He213 Learning and assessment (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Macdonald, Rob aut Odell, Bryce aut Kokhan, Vasyl aut Demmans Epp, Carrie aut Cutumisu, Maria (orcid)0000-0003-2475-9647 aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 34(2022), 2 vom: 23. Jan., Seite 416-461 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:34 year:2022 number:2 day:23 month:01 pages:416-461 https://dx.doi.org/10.1007/s12528-021-09305-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 2 23 01 416-461 |
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scoping review of computational thinking assessments in higher education |
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A scoping review of computational thinking assessments in higher education |
| abstract |
Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The field of computational thinking (CT) is developing rapidly, reflecting its importance in the global economy. However, most empirical studies have targeted CT in K-12, thus, little attention has been paid to CT in higher education. The present scoping review identifies and summarizes existing empirical studies on CT assessments in post-secondary education, aiming to reveal the current trends of empirical research in this domain and key features of recent CT assessment instruments. It examines 33 peer-reviewed journal articles published between 2013 and 2019 from six databases. Results show that most assessment tools are designed for computing science and engineering undergraduates or pre-service and in-service teachers in these subjects. Most tools involve in-class interventions to promote CT skills. Several assessment formats were adopted in the selected studies, including selected-response questions, constructed-response questions, Likert scales, interviews, programming artefacts, observations, and interviews. Finally, most assessment instruments attempt to measure skills from a combination of dimensions including CT Concepts, Practices, and Perspectives from a hybrid-competency framework. More specifically, the skills assessed in most studies are algorithmic thinking, problem solving, data, logic and logical thinking, and abstraction. Findings may help instructors to select CT assessments for higher education and researchers to focus on less explored research areas. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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A scoping review of computational thinking assessments in higher education |
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Macdonald, Rob Odell, Bryce Kokhan, Vasyl Demmans Epp, Carrie Cutumisu, Maria |
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10.1007/s12528-021-09305-y |
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2024-07-04T03:22:04.970Z |
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| score |
7.4010277 |