Mechanism to capture learner’s interaction in VR-based learning environment: design and application

Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR...
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Gespeichert in:

Autor*in:	Pathan, Rumana [verfasserIn] Rajendran, Ramkumar [verfasserIn] Murthy, Sahana [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Virtual reality Learner’s behavior Log-file Sequential pattern mining

Übergeordnetes Werk:	Enthalten in: Smart Learning Environments - Berlin : SpringerOpen, 2014, 7(2020), 1 vom: 26. Nov.
Übergeordnetes Werk:	volume:7 ; year:2020 ; number:1 ; day:26 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s40561-020-00143-6

Katalog-ID:	SPR041630610

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allfields	10.1186/s40561-020-00143-6 doi (DE-627)SPR041630610 (SPR)s40561-020-00143-6-e DE-627 ger DE-627 rakwb eng 370 ASE Pathan, Rumana verfasserin aut Mechanism to capture learner’s interaction in VR-based learning environment: design and application 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners. Virtual reality (dpeaa)DE-He213 Learner’s behavior (dpeaa)DE-He213 Log-file (dpeaa)DE-He213 Sequential pattern mining (dpeaa)DE-He213 Rajendran, Ramkumar verfasserin aut Murthy, Sahana verfasserin aut Enthalten in Smart Learning Environments Berlin : SpringerOpen, 2014 7(2020), 1 vom: 26. Nov. (DE-627)805638547 (DE-600)2800615-X 2196-7091 nnns volume:7 year:2020 number:1 day:26 month:11 https://dx.doi.org/10.1186/s40561-020-00143-6 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1 26 11
spelling	10.1186/s40561-020-00143-6 doi (DE-627)SPR041630610 (SPR)s40561-020-00143-6-e DE-627 ger DE-627 rakwb eng 370 ASE Pathan, Rumana verfasserin aut Mechanism to capture learner’s interaction in VR-based learning environment: design and application 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners. Virtual reality (dpeaa)DE-He213 Learner’s behavior (dpeaa)DE-He213 Log-file (dpeaa)DE-He213 Sequential pattern mining (dpeaa)DE-He213 Rajendran, Ramkumar verfasserin aut Murthy, Sahana verfasserin aut Enthalten in Smart Learning Environments Berlin : SpringerOpen, 2014 7(2020), 1 vom: 26. Nov. (DE-627)805638547 (DE-600)2800615-X 2196-7091 nnns volume:7 year:2020 number:1 day:26 month:11 https://dx.doi.org/10.1186/s40561-020-00143-6 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1 26 11
allfields_unstemmed	10.1186/s40561-020-00143-6 doi (DE-627)SPR041630610 (SPR)s40561-020-00143-6-e DE-627 ger DE-627 rakwb eng 370 ASE Pathan, Rumana verfasserin aut Mechanism to capture learner’s interaction in VR-based learning environment: design and application 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners. Virtual reality (dpeaa)DE-He213 Learner’s behavior (dpeaa)DE-He213 Log-file (dpeaa)DE-He213 Sequential pattern mining (dpeaa)DE-He213 Rajendran, Ramkumar verfasserin aut Murthy, Sahana verfasserin aut Enthalten in Smart Learning Environments Berlin : SpringerOpen, 2014 7(2020), 1 vom: 26. Nov. (DE-627)805638547 (DE-600)2800615-X 2196-7091 nnns volume:7 year:2020 number:1 day:26 month:11 https://dx.doi.org/10.1186/s40561-020-00143-6 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1 26 11
allfieldsGer	10.1186/s40561-020-00143-6 doi (DE-627)SPR041630610 (SPR)s40561-020-00143-6-e DE-627 ger DE-627 rakwb eng 370 ASE Pathan, Rumana verfasserin aut Mechanism to capture learner’s interaction in VR-based learning environment: design and application 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners. Virtual reality (dpeaa)DE-He213 Learner’s behavior (dpeaa)DE-He213 Log-file (dpeaa)DE-He213 Sequential pattern mining (dpeaa)DE-He213 Rajendran, Ramkumar verfasserin aut Murthy, Sahana verfasserin aut Enthalten in Smart Learning Environments Berlin : SpringerOpen, 2014 7(2020), 1 vom: 26. Nov. (DE-627)805638547 (DE-600)2800615-X 2196-7091 nnns volume:7 year:2020 number:1 day:26 month:11 https://dx.doi.org/10.1186/s40561-020-00143-6 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1 26 11
allfieldsSound	10.1186/s40561-020-00143-6 doi (DE-627)SPR041630610 (SPR)s40561-020-00143-6-e DE-627 ger DE-627 rakwb eng 370 ASE Pathan, Rumana verfasserin aut Mechanism to capture learner’s interaction in VR-based learning environment: design and application 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners. Virtual reality (dpeaa)DE-He213 Learner’s behavior (dpeaa)DE-He213 Log-file (dpeaa)DE-He213 Sequential pattern mining (dpeaa)DE-He213 Rajendran, Ramkumar verfasserin aut Murthy, Sahana verfasserin aut Enthalten in Smart Learning Environments Berlin : SpringerOpen, 2014 7(2020), 1 vom: 26. Nov. (DE-627)805638547 (DE-600)2800615-X 2196-7091 nnns volume:7 year:2020 number:1 day:26 month:11 https://dx.doi.org/10.1186/s40561-020-00143-6 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2020 1 26 11
language	English
source	Enthalten in Smart Learning Environments 7(2020), 1 vom: 26. Nov. volume:7 year:2020 number:1 day:26 month:11
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topic_facet	Virtual reality Learner’s behavior Log-file Sequential pattern mining
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container_title	Smart Learning Environments
authorswithroles_txt_mv	Pathan, Rumana @@aut@@ Rajendran, Ramkumar @@aut@@ Murthy, Sahana @@aut@@
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title	Mechanism to capture learner’s interaction in VR-based learning environment: design and application
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title_sort	mechanism to capture learner’s interaction in vr-based learning environment: design and application
title_auth	Mechanism to capture learner’s interaction in VR-based learning environment: design and application
abstract	Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners.
abstractGer	Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners.
abstract_unstemmed	Abstract Virtual Reality (VR) is a multi-sensory technology that stimulates learning and has the potential for pedagogical applications. While researchers in VR have demonstrated several applications to support understanding and learning in STEM education, the research regarding which features of VR leverage learning is in its infancy. The existing studies exploring how learners interact with VR are based on human observations or learners’ perceptions. This paper describes a novel mechanism to capture learner’s interaction behavior, in the context of a mobile-based static VR to learn the human circulatory system. The data capturing mechanism is based on screen recordings of VR interaction, which is further annotated manually to form a time-sequenced action series. In a preliminary test conducted with three learners, the interaction data was analyzed based on the time spent in each action in the VR environment, frequently co-occurring actions, and sequence of actions. The test results are described and the implications of using such a mechanism to capture learners’ interaction behavior is discussed. We conclude that capturing data in this manner gives a rich and detailed profile of learners and enables use of various analytics methods to provide personalized and adaptive support to learners.
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