Investigating the Relationship Between Dialogue States and Partner Satisfaction During Co-Creative Learning Tasks
Investigating the Relationship Between Dialogue States and Partner Satisfaction During Co-Creative Learning Tasks
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DOI：
10.1007/s40593-022-00302-5
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发表时间：
2022-08-01
影响因子：
4.9
通讯作者：
McKlin，Tom
中科院分区：
文献类型：
--
作者：
Griffith，Amanda E.;Katuka，Gloria Ashiya;McKlin，Tom
协作学习为学习者提供了许多好处，主要是由于他们之间展开的对话。然而，仍然有很多关于合作对话的结构，特别是在学习过程中的共同创造性对话所知甚少。本文报道了一项研究，与学习者从事合作创造性的任务，学习者写代码来创建一首歌，而从事文本对话，因为他们这样做。在收集了文本对话和界面中的动作之后，我们学习了一个隐马尔可夫模型（HMM）来揭示共同创造的状态。七状态模型揭示了主要由编码操作组成的四个状态，包括浏览课程文档、在代码编辑器中工作、成功编译代码以及接收编译错误。其余三种状态主要由对话组成，可以被描述为社会，美学和技术对话。接下来，我们分析了隐马尔可夫模型揭示的合作创造状态与学生合作伙伴满意度得分之间的关系。结果表明，在某些状态下的相对频率的行动和状态之间的一些过渡预测合作伙伴的满意度。例如，合作伙伴的满意度与编译错误状态和从课程浏览状态到代码编辑状态的相对转换频率呈负相关。合作伙伴的满意度也与从美学对话状态到技术对话状态和代码编辑状态的相对转换频率呈负相关。这条线的调查揭示了合作创造的过程是如何与合作伙伴的满意度，并持有潜在的通知脚手架协作学习。
Collaborative learning offers numerous benefits to learners, largely due to the dialogue that is unfolding between them. However, there is still much to learn about the structure of collaborative dialogue, and especially little is known about co-creative dialogues during learning. This paper reports on a study with learners engaged in co-creative tasks where the learners wrote code to create a song and while engaging in textual dialogue as they did so. After gathering the textual dialogue and the actions within the interface, we learned a hidden Markov model (HMM) to reveal co-creative states. The seven-state model revealed four states primarily composed of coding actions that included browsing the curriculum documents, working in the code editor, compiling the code successfully, and receiving a compile error. The remaining three states are primarily composed of dialogue that can be characterized as social, aesthetic, and technical dialogue. Next, we analyzed the relationships between the co-creative states revealed by the HMM and students’ partner satisfaction scores from a post-survey. The results reveal the relative frequency of actions in certain states and some transitions between states were predictive of partner satisfaction. For example, partner satisfaction was negatively associated with theCompilation Errorstate and with the relative frequency of transitions from theCurriculum Browsingstate to theCode Editingstate. Partner satisfaction was also negatively associated with the relative frequency of transitions from theAesthetic Dialoguestate to theTechnical Dialoguestate and theCode Editingstate. This line of investigation reveals how co-creative processes are associated with partner satisfaction, and holds the potential to inform scaffolding for collaborative learning.