Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions
Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners r...
Ausführliche Beschreibung
Autor*in: |
Tegos, Stergios [verfasserIn] Demetriadis, Stavros [verfasserIn] Papadopoulos, Pantelis M. [verfasserIn] Weinberger, Armin [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: International journal of computer-supported collaborative learning - New York, NY : Springer, 2006, 11(2016), 4 vom: 18. Nov., Seite 417-440 |
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Übergeordnetes Werk: |
volume:11 ; year:2016 ; number:4 ; day:18 ; month:11 ; pages:417-440 |
Links: |
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DOI / URN: |
10.1007/s11412-016-9246-2 |
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Katalog-ID: |
SPR019045859 |
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520 | |a Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. | ||
650 | 4 | |a Conversational agent |7 (dpeaa)DE-He213 | |
650 | 4 | |a Academically productive talk |7 (dpeaa)DE-He213 | |
650 | 4 | |a Computer-supported collaborative learning |7 (dpeaa)DE-He213 | |
650 | 4 | |a Peer dialogue |7 (dpeaa)DE-He213 | |
700 | 1 | |a Demetriadis, Stavros |e verfasserin |4 aut | |
700 | 1 | |a Papadopoulos, Pantelis M. |e verfasserin |4 aut | |
700 | 1 | |a Weinberger, Armin |e verfasserin |4 aut | |
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10.1007/s11412-016-9246-2 doi (DE-627)SPR019045859 (SPR)s11412-016-9246-2-e DE-627 ger DE-627 rakwb eng 370 004 ASE 81.68 bkl Tegos, Stergios verfasserin aut Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. Conversational agent (dpeaa)DE-He213 Academically productive talk (dpeaa)DE-He213 Computer-supported collaborative learning (dpeaa)DE-He213 Peer dialogue (dpeaa)DE-He213 Demetriadis, Stavros verfasserin aut Papadopoulos, Pantelis M. verfasserin aut Weinberger, Armin verfasserin aut Enthalten in International journal of computer-supported collaborative learning New York, NY : Springer, 2006 11(2016), 4 vom: 18. Nov., Seite 417-440 (DE-627)512299277 (DE-600)2235884-5 1556-1615 nnns volume:11 year:2016 number:4 day:18 month:11 pages:417-440 https://dx.doi.org/10.1007/s11412-016-9246-2 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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 81.68 ASE AR 11 2016 4 18 11 417-440 |
spelling |
10.1007/s11412-016-9246-2 doi (DE-627)SPR019045859 (SPR)s11412-016-9246-2-e DE-627 ger DE-627 rakwb eng 370 004 ASE 81.68 bkl Tegos, Stergios verfasserin aut Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. Conversational agent (dpeaa)DE-He213 Academically productive talk (dpeaa)DE-He213 Computer-supported collaborative learning (dpeaa)DE-He213 Peer dialogue (dpeaa)DE-He213 Demetriadis, Stavros verfasserin aut Papadopoulos, Pantelis M. verfasserin aut Weinberger, Armin verfasserin aut Enthalten in International journal of computer-supported collaborative learning New York, NY : Springer, 2006 11(2016), 4 vom: 18. Nov., Seite 417-440 (DE-627)512299277 (DE-600)2235884-5 1556-1615 nnns volume:11 year:2016 number:4 day:18 month:11 pages:417-440 https://dx.doi.org/10.1007/s11412-016-9246-2 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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 81.68 ASE AR 11 2016 4 18 11 417-440 |
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10.1007/s11412-016-9246-2 doi (DE-627)SPR019045859 (SPR)s11412-016-9246-2-e DE-627 ger DE-627 rakwb eng 370 004 ASE 81.68 bkl Tegos, Stergios verfasserin aut Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. Conversational agent (dpeaa)DE-He213 Academically productive talk (dpeaa)DE-He213 Computer-supported collaborative learning (dpeaa)DE-He213 Peer dialogue (dpeaa)DE-He213 Demetriadis, Stavros verfasserin aut Papadopoulos, Pantelis M. verfasserin aut Weinberger, Armin verfasserin aut Enthalten in International journal of computer-supported collaborative learning New York, NY : Springer, 2006 11(2016), 4 vom: 18. Nov., Seite 417-440 (DE-627)512299277 (DE-600)2235884-5 1556-1615 nnns volume:11 year:2016 number:4 day:18 month:11 pages:417-440 https://dx.doi.org/10.1007/s11412-016-9246-2 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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 81.68 ASE AR 11 2016 4 18 11 417-440 |
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10.1007/s11412-016-9246-2 doi (DE-627)SPR019045859 (SPR)s11412-016-9246-2-e DE-627 ger DE-627 rakwb eng 370 004 ASE 81.68 bkl Tegos, Stergios verfasserin aut Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. Conversational agent (dpeaa)DE-He213 Academically productive talk (dpeaa)DE-He213 Computer-supported collaborative learning (dpeaa)DE-He213 Peer dialogue (dpeaa)DE-He213 Demetriadis, Stavros verfasserin aut Papadopoulos, Pantelis M. verfasserin aut Weinberger, Armin verfasserin aut Enthalten in International journal of computer-supported collaborative learning New York, NY : Springer, 2006 11(2016), 4 vom: 18. Nov., Seite 417-440 (DE-627)512299277 (DE-600)2235884-5 1556-1615 nnns volume:11 year:2016 number:4 day:18 month:11 pages:417-440 https://dx.doi.org/10.1007/s11412-016-9246-2 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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 81.68 ASE AR 11 2016 4 18 11 417-440 |
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10.1007/s11412-016-9246-2 doi (DE-627)SPR019045859 (SPR)s11412-016-9246-2-e DE-627 ger DE-627 rakwb eng 370 004 ASE 81.68 bkl Tegos, Stergios verfasserin aut Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. Conversational agent (dpeaa)DE-He213 Academically productive talk (dpeaa)DE-He213 Computer-supported collaborative learning (dpeaa)DE-He213 Peer dialogue (dpeaa)DE-He213 Demetriadis, Stavros verfasserin aut Papadopoulos, Pantelis M. verfasserin aut Weinberger, Armin verfasserin aut Enthalten in International journal of computer-supported collaborative learning New York, NY : Springer, 2006 11(2016), 4 vom: 18. Nov., Seite 417-440 (DE-627)512299277 (DE-600)2235884-5 1556-1615 nnns volume:11 year:2016 number:4 day:18 month:11 pages:417-440 https://dx.doi.org/10.1007/s11412-016-9246-2 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_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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 81.68 ASE AR 11 2016 4 18 11 417-440 |
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Tegos, Stergios @@aut@@ Demetriadis, Stavros @@aut@@ Papadopoulos, Pantelis M. @@aut@@ Weinberger, Armin @@aut@@ |
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370 004 ASE 81.68 bkl Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions Conversational agent (dpeaa)DE-He213 Academically productive talk (dpeaa)DE-He213 Computer-supported collaborative learning (dpeaa)DE-He213 Peer dialogue (dpeaa)DE-He213 |
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Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions |
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conversational agents for academically productive talk: a comparison of directed and undirected agent interventions |
title_auth |
Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions |
abstract |
Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. |
abstractGer |
Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. |
abstract_unstemmed |
Abstract Conversational agents that draw on the framework of academically productive talk (APT) have been lately shown to be effective in helping learners sustain productive forms of peer dialogue in diverse learning settings. Yet, literature suggests that more research is required on how learners respond to and benefit from such flexible agents in order to fine-tune the design of automated APT intervention modes and, thus, enhance agent pedagogical efficacy. Building on this line of research, this work explores the impact of a configurable APT agent that prompts peers to build on prior knowledge and logically connect their contributions to important domain concepts discussed in class. A total of 96 computer science students engaged in a dialogue-based activity in the context of a Human-Computer Interaction (HCI) university course. During the activity, students worked online in dyads to accomplish a learning task. The study compares three conditions: students who collaborated without any agent interference (control), students who received undirected agent interventions that addressed both peers in the dyad (U treatment), and students who received directed agent interventions addressing a particular learner instead of the dyad (D treatment). The results suggest that although both agent intervention methods can improve students’ learning outcomes and dyad in-task performance, the directed one is more effective than the undirected one in enhancing individual domain knowledge acquisition and explicit reasoning. Furthermore, findings show that the positive effect of the agent on dyad performance is mediated by the frequency of students’ contributions displaying explicit reasoning, while most students perceive agent involvement favorably. |
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title_short |
Conversational agents for academically productive talk: a comparison of directed and undirected agent interventions |
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https://dx.doi.org/10.1007/s11412-016-9246-2 |
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author2 |
Demetriadis, Stavros Papadopoulos, Pantelis M. Weinberger, Armin |
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Demetriadis, Stavros Papadopoulos, Pantelis M. Weinberger, Armin |
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doi_str |
10.1007/s11412-016-9246-2 |
up_date |
2024-07-03T23:53:33.203Z |
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|
score |
7.399534 |