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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]

This paper presents a novel approach and a method of learning analytics to study student agency in higher education. Agency is a concept that holistically depicts important constituents of intentional, purposeful, and meaningful learning. Within workplace learning research, agency is seen at the core of expertise. However, in the higher education field, agency is an empirically less studied phenomenon with also lacking coherent conceptual base. Furthermore, tools for students and teachers need to be developed to support learners in their agency construction. We study student agency as a multidimensional phenomenon centring on student-experienced resources of their agency. We call the analytics process developed here student agency analytics, referring to the application of learning analytics methods for data on student agency collected using a validated instrument. The data are analysed with unsupervised and supervised methods. The whole analytics process will be automated using microservice architecture. We provide empirical characterisations of student-perceived agency resources by applying the analytics process in two university courses. Finally, we discuss the possibilities of using agency analytics in supporting students to recognise their resources for agentic learning and consider contributions of agency analytics to improve academic advising and teachers' pedagogical knowledge. [ABSTRACT FROM AUTHOR]