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Background: Deep discussions play an important role in students' online learning. However, researchers have largely focused on engaging students in deep discussions in online asynchronous forums. Few studies have investigated how to promote deep discussion via mobile instant messaging (MIM). Objectives: In this study, we applied learning analytical tools (i.e., KBdeX and word clouds) to enhance students' shared epistemic agency and thereby support their deep discussions in MIM. Methods: Forty Chinese college students participated in this study and reflected on their MIM engagement by participating in the learning analytics (LA)‐augmented meta‐discourse sessions. The study used multiple data analysis methods, including content analysis, statistical analysis, epistemic network analysis and lag sequential analysis. Results: We found that LA engaged students in deep discussions and shared epistemic agency‐related discourse, such as creating shared understanding, creating knowledge objects, and projective and regulative processes. In particular, word clouds engaged students in more complete shared epistemic agency discourse trajectory which started from creating awareness of unknowns, then progressed to setting projective plans and sharing information, and ultimately, creating shared understanding. Moreover, our analysis indicated that epistemic agency discourse moves of creating shared understanding led students to a high level of deep discussion. Implications: This study contributes to research by extending the 'comparison paradigm', which focuses on comparing (a)synchronous forums with MIM, to a 'design paradigm', which mobilises design features from (a)synchronous forums to MIM and using learning analytical tools to engage students in deep online discussions by promoting their epistemic agency. Lay Description: What is known in this subject matter: Supporting students' online deep discussions is important.Prior studies have shown multiple technological and pedagogical strategies to support deep discussions in (a)synchronous discussion forums, but few studies in mobile instant messaging (MIM).MIM is a quasi‐synchronous tool. It is difficult to transform the traditional external scaffolds (i.e., scripts or teachers' facilitation) which promote deep discussions from (a)synchronous forums to MIM. What this paper adds to this: We constructed a design framework which used learning analytical tools‐augmented meta‐discourse to engage students in shared epistemic agency discourse moves to promote their deep discussion in MIM.We conducted multiple types of analyses (content analysis, statistical analysis, epistemic network analysis and lag sequential analysis) on students' WeChat message (a major MIM in China).Results indicated both learning analytical tools helped engage students in shared epistemic agency discourse moves, such as creating shared understanding, creating knowledge objects, projective and regulative, thus, to promote students' deep discussions. Word cloud especially engaged students in more complete shared epistemic agency discourse trajectory. Moreover, analysis results indicated that shared epistemic agency discourse moves of creating shared understanding would lead students to a high level of deep discussions. Implications of study findings for practitioners: Our study provides a feasible way to educational practitioners who want to promote students' deep discussions in their online learning course (both fully online and flipped classroom mode).Knowledge Building Discourse Explorer (KBDeX presents the original analysis results of students' written messages in discussion forums as socio‐cognitive networks.) could be implemented in the classroom. The objective is to encourage students to understand the importance of connecting with peers' ideas in a one‐session implementation period.If time allows, teachers could use word cloud and meta‐discourse to engage students in socio‐metacognitive conversations in class to promote their shared epistemic agency discourse in online discussions. [ABSTRACT FROM AUTHOR]

Background: Though pedagogical artificial agents are expected to play a crucial role in the years to come, earlier studies provide inconsistent results regarding their effect on learning. This might be because their potential for exhibiting subtle nonverbal behaviours we know from human teachers has been untapped. What is more, there is little evidence of the processes underlying the effect of nonverbal behaviours of teachers (either human or artificial) on learning, so as to better guide their practical application. Objectives: The aims of the current research were threefold: firstly, to examine the effect of an artificial agent's vocal expressiveness on non‐verbal immediacy (teachers' ability to increase psychological closeness through nonverbal communication). Secondly, to test whether an artificial agent showing strong vocal expressiveness will enhance affective and cognitive learning (perceived and actual), as compared to an artificial agent that shows weak vocal expressiveness. Thirdly, to examine whether the underlying mechanisms of motivation and attention explain the effect of immediacy (and thereby also of vocal expressiveness) on the two learning outcomes. Methods: The study used a between‐participants design, with the participants being randomly assigned to one of the two experimental conditions: artificial modelling with strong expressiveness and artificial modelling with weak vocal expressiveness. Results and conclusions: Results showed that an artificial agent with strong vocal expressiveness increased affective and perceived cognitive learning. Partial support was found for actual cognitive learning. What is more, our findings revealed that vocal expressiveness is related to affective and perceived cognitive learning because it promotes nonverbal immediacy. Finally, results provided evidence of motivation as a mediator of the path from immediacy to affective learning. Major takeaways: The current findings verify the important role of nonverbal immediacy found in traditional educational settings. However, showing that these results also apply to artificial teachers is essential, given that the educational landscape is changing and reshaping by artificial intelligence. Thus, taking into consideration the role of vocal expressiveness in the development of artificial teachers or voice assistants like Alexa, Siri, and Google assistant, as a way to enhance immediacy and affective experience of learners is of imperative value, since they will be found more and more in our societies. Lay Description: What is currently known about the subject matter: There is a positive effect of nonverbal cues of human teachers on students' learningThe artificial agent's voice is important for learning. What their paper adds to this: Provides strong evidence of the positive effect of vocal expressiveness of artificial agents on learning outcomes.Reveals that vocal expressiveness is related to learning outcomes because it enhances immediacy.Examines the proposed mechanisms of motivation and attention as mediators of the path from immediacy to affective and cognitive learning. Implications of study findings for practitioners: Taking into consideration vocal expressiveness of artificial agents during the design process is essential for increased learning.The combination of the vocal parameters of pitch and speech rate is effective for constructing vocal expressiveness.By showing the underlying mechanisms of artificial agents' nonverbal cues (i.e., vocal cues) on learning, it helps designers to make a better decisions during construction.The study's findings are also pertinent for human teachers in improving students' learning in traditional educational settings. [ABSTRACT FROM AUTHOR]