The affordances and limitations of collaborative science simulations: The analysis from multiple evidences

Liu, Chen-Chung; Hsieh, I-Chen; Chen, Wen; Chang, Ming Wen; Chiang, Shih Hsun Fan; Tsai, Meng Jung; Chang, Chau‐Lyan; Hwang, Fu Kwun

doi:10.1016/j.compedu.2020.104029

Cited by 15 publications

(18 citation statements)

References 33 publications

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“…Therefore, we made an effort to clarify the effects of functional interdependence in the collaborative computer simulations on the collaborative learning process. Liu et al (2021) examined the collaborative process with individual-based and collaborative computer simulations. They found that collaborative simulations, compared to individual-based simulations, are helpful in terms of making the learning process cohesive and collaborative.…”

Section: Collaborative Computer Simulationsmentioning

confidence: 99%

“…Nevertheless, how the functional interdependency in the collaborative computer simulations affects the individual and collaborative learning process were not discussed. On the basis of previous studies (Buchs et al, 2004;Chang et al, 2017;Kwaku & Jan, 2011;Liu et al, 2021), this study attempted to clarify whether the functional interdependency would influence learning. We examined two collaborative learning models with different functional interdependencies.…”

Section: Collaborative Computer Simulationsmentioning

confidence: 99%

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The impact of functional interdependencies of computer simulations on collaborative learning: Evidence from multiple sources

Liu

Lin

Cheng

et al. 2021

Computer Assisted Learning

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Background: Collaborative computer simulations are available on some online platforms which support students at distributed locations to synchronously collaborate on the simulations to learn sciences. However, how students collaborate with each other in collaborative simulations is not clear. Objectives:The aim of this study was to investigate whether functional interdependency would influence learning.Methods: Two collaborative learning models with different functional interdependencies were examined (the synchronous model and the distributed accountability model). Multiple data sources obtained from 64 students were collected and analyzed. We discuss the collaborative learning with two levels of factors: the individuallevel factors (i.e., individual perceptions of teamwork quality, visual attention and learning performance before and after a simulation activity) and the pair-level factors (i.e., discourse data and joint attention). Given that the pair-level factors in the collaborative learning environment may affect the individual-level factors, we further discuss the interactions of the two levels of factors in different collaborative learning models with hierarchical linear modeling (HLM) analysis. Results and Conclusions:The analytical results of the individual-level factors found participants in the distributed accountability model demonstrated more frequent and longer attention to the learning activity. Furthermore, the results indicated that the two simulation learning activities would enhance the participants' learning performance except in Q2. For the pair-level factors, the results demonstrated that the participants in the distributed accountability model showed more active discourse segments but similar joint attention patterns in the learning activity. The results of the HLM analysis indicated that the students in the distributed accountability model had to collaborate with their partners through more constructive dialogue, and focused on the same areas of interest to enhance their learning performance, as the complexity of the distributed accountability design was implemented in a simulation learning activity.

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Section: Collaborative Computer Simulationsmentioning

confidence: 99%

Section: Collaborative Computer Simulationsmentioning

confidence: 99%

The impact of functional interdependencies of computer simulations on collaborative learning: Evidence from multiple sources

Liu

Lin

Cheng

et al. 2021

Computer Assisted Learning

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“…For example, when studying visual or perceptual joint attention and visual synchronisation in dyads (see, e.g. Liu et al, 2021;Olsen, Aleven, & Rummel, 2017;Schneider & Pea, 2013, 2014Schneider et al, 2016Schneider et al, , 2018, in which the object of attention is regarded as external, gaze can display alignment with the partners. In their dual eye-tracking study in a remote setting, Pea (2013, 2014) found that, when the participants could see the gaze of their partner, more visual joint attention (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Appropriately, to be successful, CPS not only necessitates the lower attentional levels that can be found by analysing visual joint attention (see, e.g. Liu et al, 2021;Schneider & Pea, 2013, 2014Schneider et al, 2016Schneider et al, , 2018 but also requires considering joint attention to 'internal', mental content (O'Madagain & Tomasello, 2019). This represents 'the ability to focus together in the conversation on the content of our mental states' (O'Madagain & Tomasello, 2019, p. 1).…”

Section: Introductionmentioning

confidence: 99%

“…To examine gaze patterns, in eye-tracking studies, the predominant focus has been on the overall looking times at predefined areas of interest (AOIs; spatial information as 'where' questions; see, e.g. de Leeuw, Segers, & Verhoeven, 2016;Hautala et al, 2019;Liu et al, 2021). Moreover, to study the 'when' question of eye gazing, cross-recurrence plots (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Joint attention behaviour in remote collaborative problem solving: exploring different attentional levels in dyadic interaction

Pöysä-Tarhonen

Awwal

Häkkinen

et al. 2021

RPTEL

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The current article describes an exploratory study that focussed on joint attention behaviour—the basis of interaction predicting productive collaboration—to better understand collaborative problem solving, particularly its social aspects during remote dyadic interaction. The study considered joint attention behaviour as a socio-linguistic phenomenon and relied on detailed qualitative interaction analysis on event-related measures of multiple observational data (i.e. log files, eye-tracking data). The aim was to illustrate and exemplify how the diverse attentional levels of joint attention behaviour (i.e. monitoring, common, mutual and shared attention) delineated by Siposova and Carpenter (Cognition 89:260–274, 2019) were achieved in remote collaborative problem solving in dyads, including the underlying basis of joint attention behaviour (i.e. individual attention experience). The results made visible the complex functioning of the social aspects of remote collaborative problem solving and provided preliminary insights into how the hierarchical and nested levels of ‘jointness’ and common knowledge were achieved in this context. The analysis reproduced all the theorised attentional levels as both isolated and parallel individualistic attention experiences whilst acknowledging the restrictions of the remote interaction environment and the specific task structures.

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How Tablet-Student Ratio and External Scripts Affect Knowledge Acquisition and Cognitive Load in Scientific Collaborative Inquiry Learning? A Three-Round Quasi-Experiment

Wang

Dong

Ma³

2023

J Sci Educ Technol

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The affordances and limitations of collaborative science simulations: The analysis from multiple evidences

Cited by 15 publications

References 33 publications

The impact of functional interdependencies of computer simulations on collaborative learning: Evidence from multiple sources

The impact of functional interdependencies of computer simulations on collaborative learning: Evidence from multiple sources

Joint attention behaviour in remote collaborative problem solving: exploring different attentional levels in dyadic interaction

How Tablet-Student Ratio and External Scripts Affect Knowledge Acquisition and Cognitive Load in Scientific Collaborative Inquiry Learning? A Three-Round Quasi-Experiment

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