Learning prosocial skills through multiadaptive games: a case study
Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game co...
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Gespeichert in:
| Autor*in: |
Stefanidis, Kiriakos [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Beijing Normal University 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of computers in education - Berlin : Springer, 2014, 6(2019), 1 vom: März, Seite 167-190 |
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| Übergeordnetes Werk: |
volume:6 ; year:2019 ; number:1 ; month:03 ; pages:167-190 |
| Links: |
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| DOI / URN: |
10.1007/s40692-019-00134-8 |
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| Katalog-ID: |
SPR037458590 |
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| 520 | |a Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.” | ||
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| 700 | 1 | |a Daras, Petros |4 aut | |
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10.1007/s40692-019-00134-8 doi (DE-627)SPR037458590 (SPR)s40692-019-00134-8-e DE-627 ger DE-627 rakwb eng Stefanidis, Kiriakos verfasserin (orcid)0000-0002-9922-1916 aut Learning prosocial skills through multiadaptive games: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Beijing Normal University 2019 Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.” Game adaptation (dpeaa)DE-He213 Prosocial skills (dpeaa)DE-He213 Engagement (dpeaa)DE-He213 Serious games (dpeaa)DE-He213 Psaltis, Athanasios aut Apostolakis, Konstantinos C. aut Dimitropoulos, Kosmas aut Daras, Petros aut Enthalten in Journal of computers in education Berlin : Springer, 2014 6(2019), 1 vom: März, Seite 167-190 (DE-627)795566786 (DE-600)2783273-9 2197-9995 nnns volume:6 year:2019 number:1 month:03 pages:167-190 https://dx.doi.org/10.1007/s40692-019-00134-8 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2019 1 03 167-190 |
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10.1007/s40692-019-00134-8 doi (DE-627)SPR037458590 (SPR)s40692-019-00134-8-e DE-627 ger DE-627 rakwb eng Stefanidis, Kiriakos verfasserin (orcid)0000-0002-9922-1916 aut Learning prosocial skills through multiadaptive games: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Beijing Normal University 2019 Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.” Game adaptation (dpeaa)DE-He213 Prosocial skills (dpeaa)DE-He213 Engagement (dpeaa)DE-He213 Serious games (dpeaa)DE-He213 Psaltis, Athanasios aut Apostolakis, Konstantinos C. aut Dimitropoulos, Kosmas aut Daras, Petros aut Enthalten in Journal of computers in education Berlin : Springer, 2014 6(2019), 1 vom: März, Seite 167-190 (DE-627)795566786 (DE-600)2783273-9 2197-9995 nnns volume:6 year:2019 number:1 month:03 pages:167-190 https://dx.doi.org/10.1007/s40692-019-00134-8 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2019 1 03 167-190 |
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10.1007/s40692-019-00134-8 doi (DE-627)SPR037458590 (SPR)s40692-019-00134-8-e DE-627 ger DE-627 rakwb eng Stefanidis, Kiriakos verfasserin (orcid)0000-0002-9922-1916 aut Learning prosocial skills through multiadaptive games: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Beijing Normal University 2019 Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.” Game adaptation (dpeaa)DE-He213 Prosocial skills (dpeaa)DE-He213 Engagement (dpeaa)DE-He213 Serious games (dpeaa)DE-He213 Psaltis, Athanasios aut Apostolakis, Konstantinos C. aut Dimitropoulos, Kosmas aut Daras, Petros aut Enthalten in Journal of computers in education Berlin : Springer, 2014 6(2019), 1 vom: März, Seite 167-190 (DE-627)795566786 (DE-600)2783273-9 2197-9995 nnns volume:6 year:2019 number:1 month:03 pages:167-190 https://dx.doi.org/10.1007/s40692-019-00134-8 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2019 1 03 167-190 |
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10.1007/s40692-019-00134-8 doi (DE-627)SPR037458590 (SPR)s40692-019-00134-8-e DE-627 ger DE-627 rakwb eng Stefanidis, Kiriakos verfasserin (orcid)0000-0002-9922-1916 aut Learning prosocial skills through multiadaptive games: a case study 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Beijing Normal University 2019 Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.” Game adaptation (dpeaa)DE-He213 Prosocial skills (dpeaa)DE-He213 Engagement (dpeaa)DE-He213 Serious games (dpeaa)DE-He213 Psaltis, Athanasios aut Apostolakis, Konstantinos C. aut Dimitropoulos, Kosmas aut Daras, Petros aut Enthalten in Journal of computers in education Berlin : Springer, 2014 6(2019), 1 vom: März, Seite 167-190 (DE-627)795566786 (DE-600)2783273-9 2197-9995 nnns volume:6 year:2019 number:1 month:03 pages:167-190 https://dx.doi.org/10.1007/s40692-019-00134-8 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_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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2019 1 03 167-190 |
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Stefanidis, Kiriakos @@aut@@ Psaltis, Athanasios @@aut@@ Apostolakis, Konstantinos C. @@aut@@ Dimitropoulos, Kosmas @@aut@@ Daras, Petros @@aut@@ |
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Stefanidis, Kiriakos |
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Learning prosocial skills through multiadaptive games: a case study Game adaptation (dpeaa)DE-He213 Prosocial skills (dpeaa)DE-He213 Engagement (dpeaa)DE-He213 Serious games (dpeaa)DE-He213 |
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learning prosocial skills through multiadaptive games: a case study |
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Learning prosocial skills through multiadaptive games: a case study |
| abstract |
Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.” © Beijing Normal University 2019 |
| abstractGer |
Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.” © Beijing Normal University 2019 |
| abstract_unstemmed |
Abstract Digital games introduce an innovative means for teaching prosocial skills to students; however, the lack of proper personalization features in the games may result in the degradation of the learning process. This paper aims to study whether the performance of students in a prosocial game could be improved by an intelligent AI adaptation mechanism. To this end, a novel hybrid adaptation manager capable of assisting students playing prosocial games is presented. Our approach consists of a combination of two adaptation mechanisms that process personalization information both offline and in real-time. Both implementations are based on artificial intelligence techniques and adjust game content in order to increase the chances of players attaining the game’s specific learning objectives concerning prosocial skills. In particular, the online mechanism maintains a player engagement profile for game elements that are intended to represent the pedagogical practices of corrective feedback and positive reinforcement. On the other hand, offline adaptation matches players to game scenarios according to the players’ ability and the game scenarios’ ranking. The efficiency of the proposed adaptation manager as a tool for enhancing students’ performance in a prosocial game is demonstrated through a small-scale experiment, under real-time conditions in a school environment, using the prosocial game “Path of Trust.” © Beijing Normal University 2019 |
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Learning prosocial skills through multiadaptive games: a case study |
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https://dx.doi.org/10.1007/s40692-019-00134-8 |
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Psaltis, Athanasios Apostolakis, Konstantinos C. Dimitropoulos, Kosmas Daras, Petros |
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Psaltis, Athanasios Apostolakis, Konstantinos C. Dimitropoulos, Kosmas Daras, Petros |
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10.1007/s40692-019-00134-8 |
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2024-07-03T22:53:57.492Z |
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|
| score |
7.400154 |