Research Topic

Motivational, Affective and Metacognitive Aspects in Intelligent Learning Environments

About this Research Topic

Learning involves changes in the learner's knowledge, and cognitive factors do not solely determine it. Previous experiences, beliefs, metacognitive skills, and motivational and affective factors play a similarly important role in learning. Emotions, for example, can either promote or hinder learning, as they influence cognitive, metacognitive, and motivational processes directly related to learning, such as attention and interest.

Although the essential role that emotions and affective and motivational aspects play in learning has been highlighted for some decades, many questions are still open about how they interact with cognitive processes related to learning. The growing interest in the role of affective and motivational aspects in cognitive processes and learning has intensified research in the area, generating more elaborate theoretical models on the interaction between affect and learning. Moreover, these models have been enriched by the research results in Artificial Intelligence applied to Education. It means that research in computational learning environments has helped identify how intelligent learning environments can support learning considering affective and motivational aspects and learn more about the interaction between emotions and learning.

As knowledge about this interrelationship between affect, motivation, and learning deepens, the adaptation of learning environments becomes more specific. This adaptation usually occurs through the regulation of emotions and other affective and motivational states. Although most works on this theme are about regulating students' affective states to make learning more effective, the system can also show the student to recognize their own feeling states and regulate them, which is also known as teaching socio-emotional skills or meta-affect. Teaching learners about how their emotions, and other affective and motivational states, arise (recognition of emotions) and how to regulate them will allow them to transfer this meta-knowledge to other situations in their life, instead of only having the regulation in a specific moment.

In this Research Topic, we invite papers that address topics related to how intelligent learning environments detect students' affect and motivation and how these systems regulate students' affect and motivation and teach students to regulate and identify their affective states.

Some of the topics include but are not limited to:
● Affective states dynamic in intelligent learning environments
● Cultural aspects of the dynamics of the affective states and motivation in intelligent learning environments
● Automatic detection of affective and motivational states
● Regulation of emotions and motivation in intelligent learning environments
● Intelligent environments that teach socio-emotional skills
● Meta-affect in intelligent learning environments


Keywords: affective learning, motivational learning, artificial intelligence, ai, Intelligent Learning Environments


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Learning involves changes in the learner's knowledge, and cognitive factors do not solely determine it. Previous experiences, beliefs, metacognitive skills, and motivational and affective factors play a similarly important role in learning. Emotions, for example, can either promote or hinder learning, as they influence cognitive, metacognitive, and motivational processes directly related to learning, such as attention and interest.

Although the essential role that emotions and affective and motivational aspects play in learning has been highlighted for some decades, many questions are still open about how they interact with cognitive processes related to learning. The growing interest in the role of affective and motivational aspects in cognitive processes and learning has intensified research in the area, generating more elaborate theoretical models on the interaction between affect and learning. Moreover, these models have been enriched by the research results in Artificial Intelligence applied to Education. It means that research in computational learning environments has helped identify how intelligent learning environments can support learning considering affective and motivational aspects and learn more about the interaction between emotions and learning.

As knowledge about this interrelationship between affect, motivation, and learning deepens, the adaptation of learning environments becomes more specific. This adaptation usually occurs through the regulation of emotions and other affective and motivational states. Although most works on this theme are about regulating students' affective states to make learning more effective, the system can also show the student to recognize their own feeling states and regulate them, which is also known as teaching socio-emotional skills or meta-affect. Teaching learners about how their emotions, and other affective and motivational states, arise (recognition of emotions) and how to regulate them will allow them to transfer this meta-knowledge to other situations in their life, instead of only having the regulation in a specific moment.

In this Research Topic, we invite papers that address topics related to how intelligent learning environments detect students' affect and motivation and how these systems regulate students' affect and motivation and teach students to regulate and identify their affective states.

Some of the topics include but are not limited to:
● Affective states dynamic in intelligent learning environments
● Cultural aspects of the dynamics of the affective states and motivation in intelligent learning environments
● Automatic detection of affective and motivational states
● Regulation of emotions and motivation in intelligent learning environments
● Intelligent environments that teach socio-emotional skills
● Meta-affect in intelligent learning environments


Keywords: affective learning, motivational learning, artificial intelligence, ai, Intelligent Learning Environments


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 September 2021 Abstract
31 December 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 September 2021 Abstract
31 December 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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