The importance of supporting self-regulated learning (SRL) at school has been acknowledged for several decades, and SRL models have been developed by different researchers (e.g., Zimmerman, Pintrich, Winne, Efklides). The majority of the models differentiate between preparatory, performance, and appraisal phases and describe – albeit with varying emphasis – three areas for regulation – cognition, motivation, emotion/affect. While all the researchers distinguish cognitive and metacognitive levels and processes, so far, less attention has been paid to metamotivational and metaemotional processes. However, as motivational and emotional processes strongly affect learning, regulating these processes is a vital part of SRL. Similarly to metacognition, metamotivation and metaemotion are defined as the knowledge of and processes involved in regulating one’s motivational and emotional states in the service of achieving goals. Hence, metacognition, metamotivation and metaemotion include both meta-level knowledge and skills. In essence, the main SRL activities planning, monitoring, regulating, and evaluating are applied on a conscious, metacognitive level and affect cognitive-motivational-emotional processes (top-down regulation).

We propose an SRL model that we plan to use in developing digital learning tasks and interventions. The model is strongly based on the Metacognitive and Affective model of Self-Regulated Learning (MASRL) proposed by Efklides, which explicitly describes motivational and affective processes, and metacognitive experiences that trigger meta-level processes. Additionally, we differentiate between the preparatory, performance, and appraisal phases. The model includes the TASK, the PERSON and TASK X PERSON levels. The learning process (also in a digital environment) is initiated by a learning task that is objectively defined and gives direction to the whole process. However, cognitive processing is based on the cognitive representation of the task, which is affected by Person-level characteristics, and therefore differs for each learner. Conscious and goal-driven top-down regulation is triggered by non-conscious bottom-up regulation, which makes use of subjective experiences related to cognitive-motivational-emotional states or performance during completing the task. The whole process is dynamic and includes feedback loops.

Digital learning environments enable integrating specific indicators into tasks in order to detect processes of cognition, motivation and affect. In this way, information can be gathered to enable data-based personalized decisions about the need for support in different phases and aspects of SRL, thus paving way for planning dynamic need-based interventions.

Keywords: Self-regulated learning, MASRL model of SRL, cognition, motivation, emotion, digital learning.