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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]

In the increasing pervasiveness of today's digital society, mobile devices are changing the face of education. Students can interact with mobile devices in context‐aware environment. This paper presents a mobile application based on expert system (Plant‐E) for students to acquire knowledge about plant classification by answering decision‐making questions. In order to study effectiveness of Plant‐E and cognitive load of students who experience different kinds of interaction in learning process, another mobile application (Plant‐G) only providing information pages of plants was developed. A quasi‐experiment was conducted with three classes of 137 seventh graders. The three classes, Class A with 46 students using Plant‐E in campus, Class B with 44 students using Plant‐G in campus, and Class C with 47 students using Plant‐G in a traditional classroom, constitute three groups with different interaction complexity. The research conducted pretest, posttest, and delayed test to evaluate learning performance of students in three classes and used a questionnaire to investigate their perceptions and attitudes towards proposed system. Results show that interaction complexity has an impact on students' learning performance and mental effort in mobile learning; the higher the interaction complexity is, the higher mental effort and the better learning performance in mobile learning will be. Lay Description: What is already known about this topic: Mobile devices can be used to meet the urgency of learning need and can provide guidance and clues for learners in context‐aware environment.During mobile learning in context‐aware environment, students need to interact with mobile devices and environment.Mental effort reflects cognitive load related to interaction complexity, and it can affect learning performance.Self‐efficacy affects the degree of mental effort by affecting the firmness of performance goal.Cognitive load and learning performance are the main concerns that need to be considered when evaluating the effectiveness of mobile learning. What this paper adds: Interaction complexity has an impact on students' learning performance and mental effort in mobile learning.In terms of the three levels of interaction complexity in this study, the higher the interaction complexity is, the better the results that students will get in mobile learning.The higher the interaction complexity is, the higher the mental effort that students invested into in mobile learning would be.The medium self‐efficacy group invested the most mental effort and got the best grades, rather than the high or low group.Rule‐based mobile learning expert system is feasible and effective for students to learn by interacting with learning objects in context‐aware environment. Implications for practice and/or policy: Efforts should be made to make mobile learning systems more interactive; for example, learners can learn more effectively by interacting with mobile system, which can improve the frequency of observing learning objects.Rule‐based mobile learning expert system can give learners more opportunities to know how experts do when solving problems and can provide a potential method for students to learn in context‐aware environment. [ABSTRACT FROM AUTHOR]