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Background: There is a strong association between interactions and cognitive engagement, which is crucial for constructing new cognition and knowledge.Although interactions and cognitive engagement have attracted extensive attention in online learning environments, few studies have revealed the evolution of cognitive engagement with interaction levels.Objectives: The study aims to automatically identify learners' interactions and cognitive engagement and then analyse the evolution of learners' cognitive engagement with interaction levels and during different stages of online learning. Methods:The participants of the study were learners who participated in an online open course. Their text data from discussion forums on five learning themes were collected. Data were analysed using text mining and ENA.Results: Learners' cognitive engagement in online learning was related to interaction levels. As learners' online interaction levels changed from surface to deep, cognitive engagement levels changed from low to high. With the continuous occurrence of deep interactions, cognitive feedback became more complex. At the social-emotional interaction level, although learners' cognitive engagement levels began to change from low to high, complex cognitive feedback was still insufficient. In addition, the analysis of the evolution of cognitive engagement during different stages of online learning showed that learners' patterns of cognitive engagement changed significantly as the learning process continued, from initially dynamic and complex to a stable development pattern. Implications:The results of the study are of theoretical significance and practical guidance for further understanding the relationship between online interaction levels and cognitive engagement as well as the process of online collaborative knowledge exploration, construction, and even connectivity.

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In an open and flexible context of Massive Open Online Courses (MOOCs), learners who take final assessments exhibit the motivation for performance goals. The learning trajectories of this group usually provide more clues for course design and teaching improvement in that this group tend to interact more fully with course learning activities and resources for better learning outcomes. This study focused on such learners to investigate their learning engagement, time organization, content visit sequences, and activity participation patterns by applying statistical analysis, lag sequence analysis, and other data mining methods. This study examined the data of 535 learners taking the assessment in a MOOC to detect the differences in learning engagement and the above learning patterns amongst three groups of learners with different achievement levels, labeled failed, satisfactory and excellent. We found differences in both learning engagement and learning patterns among the three groups. The results indicated that for the learners to be successful, they require a certain degree of task completion as a basic guarantee for passing the course, effective session workload organization, reasonable learning content arrangement, and more cognitive engagement (rather than investing more time and energy). Based on the outcomes, implications for personalized instructional design and intervention to promote academic achievement in MOOCs are discussed.

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