Abstract:

The concept of learner profiling has become highly prominent in personalized learning, aiming to comprehend and address the distinct requirements of each learner. This abstract sets the stage for an in-depth article discussing the concept of learner profiling and its implications for personalized learning.

Learner profiling includes collecting and analyzing data on learners` backgrounds, preferences, prior knowledge, learning styles, interests, and goals.

The upcoming article explores a range of techniques employed in building learner profiles, encompassing self-reported information, performance records, and interactions within the learning environment. It thoroughly examines the challenges tied to the accurate capture and updating of learner profiles, taking into account the reliability of self-reported data and the scalability of profiling techniques.

Moreover, the article delves into the advantages and consequences of learner profiling within the context of personalized learning. It emphasizes how learner profiles empower creating precise interventions, choose appropriate instructional approaches, and deliver personalized feedback that aligns with learners' preferences and requirements. Through the utilization of learner profiles, personalized learning environments foster engagement, deepen comprehension, and elevate learning outcomes.

To sum up, the article emphasizes the transformative potential of learner profiling in shaping the future of personalized learning. It underscores the importance of continuous research and innovative approaches to learner profiling methodologies, while maintaining a delicate equilibrium between respecting learners' privacy and harnessing the power of learner profiles to enrich personalized learning experiences.

This article offers a comprehensive examination of learner profiling for personalized learning, encompassing the construction methods of learner profiles, associated challenges, and the far-reaching implications and potential for advancing the field of personalized education.

Keywords:

Learner Profiling, Personalized learning.