Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children
Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool child...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Almousa, Ohoud [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) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: User modeling and user adapted interaction - [S.l.] : Proquest, 1991, 33(2022), 2 vom: 01. Sept., Seite 261-291 |
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| Übergeordnetes Werk: |
volume:33 ; year:2022 ; number:2 ; day:01 ; month:09 ; pages:261-291 |
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| DOI / URN: |
10.1007/s11257-022-09344-9 |
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| Katalog-ID: |
SPR050229222 |
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| 245 | 1 | 0 | |a Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children |
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| 520 | |a Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. | ||
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10.1007/s11257-022-09344-9 doi (DE-627)SPR050229222 (SPR)s11257-022-09344-9-e DE-627 ger DE-627 rakwb eng Almousa, Ohoud verfasserin aut Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. Child–robot interaction (dpeaa)DE-He213 Early literacy (dpeaa)DE-He213 Personalization (dpeaa)DE-He213 Robot tutor (dpeaa)DE-He213 Alghowinem, Sharifa (orcid)0000-0002-9391-0163 aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 33(2022), 2 vom: 01. Sept., Seite 261-291 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:33 year:2022 number:2 day:01 month:09 pages:261-291 https://dx.doi.org/10.1007/s11257-022-09344-9 kostenfrei 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_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_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_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 33 2022 2 01 09 261-291 |
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10.1007/s11257-022-09344-9 doi (DE-627)SPR050229222 (SPR)s11257-022-09344-9-e DE-627 ger DE-627 rakwb eng Almousa, Ohoud verfasserin aut Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. Child–robot interaction (dpeaa)DE-He213 Early literacy (dpeaa)DE-He213 Personalization (dpeaa)DE-He213 Robot tutor (dpeaa)DE-He213 Alghowinem, Sharifa (orcid)0000-0002-9391-0163 aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 33(2022), 2 vom: 01. Sept., Seite 261-291 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:33 year:2022 number:2 day:01 month:09 pages:261-291 https://dx.doi.org/10.1007/s11257-022-09344-9 kostenfrei 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_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_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_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 33 2022 2 01 09 261-291 |
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10.1007/s11257-022-09344-9 doi (DE-627)SPR050229222 (SPR)s11257-022-09344-9-e DE-627 ger DE-627 rakwb eng Almousa, Ohoud verfasserin aut Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. Child–robot interaction (dpeaa)DE-He213 Early literacy (dpeaa)DE-He213 Personalization (dpeaa)DE-He213 Robot tutor (dpeaa)DE-He213 Alghowinem, Sharifa (orcid)0000-0002-9391-0163 aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 33(2022), 2 vom: 01. Sept., Seite 261-291 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:33 year:2022 number:2 day:01 month:09 pages:261-291 https://dx.doi.org/10.1007/s11257-022-09344-9 kostenfrei 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_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_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_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 33 2022 2 01 09 261-291 |
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10.1007/s11257-022-09344-9 doi (DE-627)SPR050229222 (SPR)s11257-022-09344-9-e DE-627 ger DE-627 rakwb eng Almousa, Ohoud verfasserin aut Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. Child–robot interaction (dpeaa)DE-He213 Early literacy (dpeaa)DE-He213 Personalization (dpeaa)DE-He213 Robot tutor (dpeaa)DE-He213 Alghowinem, Sharifa (orcid)0000-0002-9391-0163 aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 33(2022), 2 vom: 01. Sept., Seite 261-291 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:33 year:2022 number:2 day:01 month:09 pages:261-291 https://dx.doi.org/10.1007/s11257-022-09344-9 kostenfrei 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_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_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_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 33 2022 2 01 09 261-291 |
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10.1007/s11257-022-09344-9 doi (DE-627)SPR050229222 (SPR)s11257-022-09344-9-e DE-627 ger DE-627 rakwb eng Almousa, Ohoud verfasserin aut Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. Child–robot interaction (dpeaa)DE-He213 Early literacy (dpeaa)DE-He213 Personalization (dpeaa)DE-He213 Robot tutor (dpeaa)DE-He213 Alghowinem, Sharifa (orcid)0000-0002-9391-0163 aut Enthalten in User modeling and user adapted interaction [S.l.] : Proquest, 1991 33(2022), 2 vom: 01. Sept., Seite 261-291 (DE-627)269758828 (DE-600)1475734-5 1573-1391 nnns volume:33 year:2022 number:2 day:01 month:09 pages:261-291 https://dx.doi.org/10.1007/s11257-022-09344-9 kostenfrei 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_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_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_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 33 2022 2 01 09 261-291 |
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Almousa, Ohoud |
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Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children Child–robot interaction (dpeaa)DE-He213 Early literacy (dpeaa)DE-He213 Personalization (dpeaa)DE-He213 Robot tutor (dpeaa)DE-He213 |
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conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children |
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Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children |
| abstract |
Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. © The Author(s) 2022 |
| abstractGer |
Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. © The Author(s) 2022 |
| abstract_unstemmed |
Abstract Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning. © The Author(s) 2022 |
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Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children |
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https://dx.doi.org/10.1007/s11257-022-09344-9 |
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Alghowinem, Sharifa |
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| doi_str |
10.1007/s11257-022-09344-9 |
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2024-07-03T14:12:39.273Z |
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