Technological barriers and incentives to learning analytics adoption in higher education: insights from users

Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public rese...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Klein, Carrie [verfasserIn] Lester, Jaime [verfasserIn] Rangwala, Huzefa [verfasserIn] Johri, Aditya [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Learning analytics Predictive analytics Technology adoption Technological barriers Technological incentives Higher education

Übergeordnetes Werk:	Enthalten in: Journal of computing in higher education - Boston, Mass. : Springer, 1989, 31(2019), 3 vom: 13. März, Seite 604-625
Übergeordnetes Werk:	volume:31 ; year:2019 ; number:3 ; day:13 ; month:03 ; pages:604-625

Links:	Volltext

DOI / URN:	10.1007/s12528-019-09210-5

Katalog-ID:	SPR026043106

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520			\|a Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice.
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allfields	10.1007/s12528-019-09210-5 doi (DE-627)SPR026043106 (SPR)s12528-019-09210-5-e DE-627 ger DE-627 rakwb eng 004 ASE Klein, Carrie verfasserin aut Technological barriers and incentives to learning analytics adoption in higher education: insights from users 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice. Learning analytics (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Technology adoption (dpeaa)DE-He213 Technological barriers (dpeaa)DE-He213 Technological incentives (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Lester, Jaime verfasserin aut Rangwala, Huzefa verfasserin aut Johri, Aditya verfasserin aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 31(2019), 3 vom: 13. März, Seite 604-625 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:31 year:2019 number:3 day:13 month:03 pages:604-625 https://dx.doi.org/10.1007/s12528-019-09210-5 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2019 3 13 03 604-625
spelling	10.1007/s12528-019-09210-5 doi (DE-627)SPR026043106 (SPR)s12528-019-09210-5-e DE-627 ger DE-627 rakwb eng 004 ASE Klein, Carrie verfasserin aut Technological barriers and incentives to learning analytics adoption in higher education: insights from users 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice. Learning analytics (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Technology adoption (dpeaa)DE-He213 Technological barriers (dpeaa)DE-He213 Technological incentives (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Lester, Jaime verfasserin aut Rangwala, Huzefa verfasserin aut Johri, Aditya verfasserin aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 31(2019), 3 vom: 13. März, Seite 604-625 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:31 year:2019 number:3 day:13 month:03 pages:604-625 https://dx.doi.org/10.1007/s12528-019-09210-5 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2019 3 13 03 604-625
allfields_unstemmed	10.1007/s12528-019-09210-5 doi (DE-627)SPR026043106 (SPR)s12528-019-09210-5-e DE-627 ger DE-627 rakwb eng 004 ASE Klein, Carrie verfasserin aut Technological barriers and incentives to learning analytics adoption in higher education: insights from users 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice. Learning analytics (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Technology adoption (dpeaa)DE-He213 Technological barriers (dpeaa)DE-He213 Technological incentives (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Lester, Jaime verfasserin aut Rangwala, Huzefa verfasserin aut Johri, Aditya verfasserin aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 31(2019), 3 vom: 13. März, Seite 604-625 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:31 year:2019 number:3 day:13 month:03 pages:604-625 https://dx.doi.org/10.1007/s12528-019-09210-5 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2019 3 13 03 604-625
allfieldsGer	10.1007/s12528-019-09210-5 doi (DE-627)SPR026043106 (SPR)s12528-019-09210-5-e DE-627 ger DE-627 rakwb eng 004 ASE Klein, Carrie verfasserin aut Technological barriers and incentives to learning analytics adoption in higher education: insights from users 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice. Learning analytics (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Technology adoption (dpeaa)DE-He213 Technological barriers (dpeaa)DE-He213 Technological incentives (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Lester, Jaime verfasserin aut Rangwala, Huzefa verfasserin aut Johri, Aditya verfasserin aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 31(2019), 3 vom: 13. März, Seite 604-625 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:31 year:2019 number:3 day:13 month:03 pages:604-625 https://dx.doi.org/10.1007/s12528-019-09210-5 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2019 3 13 03 604-625
allfieldsSound	10.1007/s12528-019-09210-5 doi (DE-627)SPR026043106 (SPR)s12528-019-09210-5-e DE-627 ger DE-627 rakwb eng 004 ASE Klein, Carrie verfasserin aut Technological barriers and incentives to learning analytics adoption in higher education: insights from users 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice. Learning analytics (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Technology adoption (dpeaa)DE-He213 Technological barriers (dpeaa)DE-He213 Technological incentives (dpeaa)DE-He213 Higher education (dpeaa)DE-He213 Lester, Jaime verfasserin aut Rangwala, Huzefa verfasserin aut Johri, Aditya verfasserin aut Enthalten in Journal of computing in higher education Boston, Mass. : Springer, 1989 31(2019), 3 vom: 13. März, Seite 604-625 (DE-627)588192287 (DE-600)2470187-7 1867-1233 nnns volume:31 year:2019 number:3 day:13 month:03 pages:604-625 https://dx.doi.org/10.1007/s12528-019-09210-5 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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 31 2019 3 13 03 604-625
language	English
source	Enthalten in Journal of computing in higher education 31(2019), 3 vom: 13. März, Seite 604-625 volume:31 year:2019 number:3 day:13 month:03 pages:604-625
sourceStr	Enthalten in Journal of computing in higher education 31(2019), 3 vom: 13. März, Seite 604-625 volume:31 year:2019 number:3 day:13 month:03 pages:604-625
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Learning analytics Predictive analytics Technology adoption Technological barriers Technological incentives Higher education
dewey-raw	004
isfreeaccess_bool	false
container_title	Journal of computing in higher education
authorswithroles_txt_mv	Klein, Carrie @@aut@@ Lester, Jaime @@aut@@ Rangwala, Huzefa @@aut@@ Johri, Aditya @@aut@@
publishDateDaySort_date	2019-03-13T00:00:00Z
hierarchy_top_id	588192287
dewey-sort	14
id	SPR026043106
language_de	englisch
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author	Klein, Carrie
spellingShingle	Klein, Carrie ddc 004 misc Learning analytics misc Predictive analytics misc Technology adoption misc Technological barriers misc Technological incentives misc Higher education Technological barriers and incentives to learning analytics adoption in higher education: insights from users
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topic_title	004 ASE Technological barriers and incentives to learning analytics adoption in higher education: insights from users Learning analytics (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Technology adoption (dpeaa)DE-He213 Technological barriers (dpeaa)DE-He213 Technological incentives (dpeaa)DE-He213 Higher education (dpeaa)DE-He213
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title	Technological barriers and incentives to learning analytics adoption in higher education: insights from users
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title_full	Technological barriers and incentives to learning analytics adoption in higher education: insights from users
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title_sort	technological barriers and incentives to learning analytics adoption in higher education: insights from users
title_auth	Technological barriers and incentives to learning analytics adoption in higher education: insights from users
abstract	Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice.
abstractGer	Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice.
abstract_unstemmed	Abstract Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives and barriers related to LA tool adoption and use. Findings indicate that lack of a trustworthy technological infrastructure, misalignment between LA tool capabilities and user needs, and the existence of ethical concerns about the data, visualizations, and algorithms that underlie LA tools created barriers to adoption. Improving tool integration, clarity, and accuracy, soliciting the technological needs and perspectives of LA tool users, and providing data context may encourage inclusion of these tools into teaching and advising practice.
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container_issue	3
title_short	Technological barriers and incentives to learning analytics adoption in higher education: insights from users
url	https://dx.doi.org/10.1007/s12528-019-09210-5
remote_bool	true
author2	Lester, Jaime Rangwala, Huzefa Johri, Aditya
author2Str	Lester, Jaime Rangwala, Huzefa Johri, Aditya
ppnlink	588192287
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s12528-019-09210-5
up_date	2024-07-03T18:32:13.615Z
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