The real-time and granularized learning information and recommendations available from adaptive learning technology can provide learners with feedback that is personalized. However, at an individual level, learners often experience technological and pedagogical conflicts. Learners have more freedom to accept, ignore or reject the feedback while also having the challenges of building learning strategies and utilizing learning information that requires self-regulated learning skills. Given the conflicts, both understanding how learners learn and providing support for learners to be more self-regulated in the learning environment are imperative. This investigation explores how learners processed their learning in an adaptive technology-integrated learning analytics dashboard (ALAD). It employed mixed-methods using a lens of self-regulated learning (SRL). Three groups were identified based on clustering analysis of the learners' usage of warm-up (WU) tests. Sequence analysis revealed the time trends of each group's interactions with course content. Reflexive thematic analysis brought insights on how learners built their learning strategies (eg, ways of using WU tests and submodule assessments) and how they monitored and controlled their learning. It showed their dynamic interactions with core adaptive learning analytics dashboard elements. Challenges such as difficulties in rehearsing and monitoring through segmented course content arose from the new structural changes. We suggest the need of future improvement to individual learning support through the learning analytics dashboard to be more diverse and dynamic (real-time) over the course of learning while reducing potential undesirable consequences. Practitioner notes What is already known about this topic One purpose of learning analytics and adaptive learning is to help learners identify learning goals and take action to achieve their goals. Learning analytics intervention showed support in learners' reflection phase SRL. However, it is not clear how to better support actionable and strategic changes to learning. Learning improvement through learning analytics interventions varies depending on how learners utilize feedback and monitor their learning progress, interacting with their digital learning environments. What this paper adds Learners established certain learning strategies with core adaptive learning analytics dashboard elements (eg, use of assessments, monitoring strategies). Learners' perceptions about learning support from the ALAD were built by interacting with the learners' task and cognitive conditions. Based on the perceptions, individuals' various SRL strategies and the need for diverse support were found. Monitoring and rehearsals can be challenging when course content is broken down for individuals' support. Implications for practice and/or policy Courses with adaptive learning analytics dashboards need to be designed more carefully, considering possible undesirable consequences, and to improve SRL support. Learners need more diverse learning support at an individual level based on how they interact with the learning environment.
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