Data driven coachting in self regulation

Supervisor: alieke van dijk

In cooperation with maurice melenberg - taltn tree

The Self-Learning-System (SLS) of Talent Tree puts learners behind the wheel of their own learning process. The functionalities of the SLS are designed to support the most important self-regulation skills: goal setting, planning, monitoring, strategy use, and reflecting. Via the SLS, learners develop routines in their learning that allow them to keep track of their own learning progress, instead of relying solely on their teacher for this.

During the self-regulation process, learners' interests, ambitions, limitations, and skills are gathered. These data (e.g., logbook, portfolio) provide insight into learners' skills and personal development. This information also provides insights for teachers on how to coach their learners in an efficient way, as it provides tools for more targeted support and higher levels of intrinsic motivation amongst learners.

Some measures and their features are:

ASSIGNMENT

This assignment intends to shed more light on the possibilities of operationalizing learners' behaviors to make them visible. What is the desired behavior we want learners to show, and how can we help them show this behavior? What steps can be identified in the learners' process in the earlier years of secondary education? In other words: How can we measure and monitor learners' development by looking at the behavioral indicators (in relation to self-regulation) using the SLS of Talent Tree?

You will be challenged to work on (one of) the following products:

REFERENCES

https://www.talenttree.education/