Toward Embedding Robotics in Learning Environments With Support to Teachers: The IDEE Experience

摘要

Nowadays there is an increasing interest in using different technologies such as educational robotics in classrooms. However, in many cases, teachers have neither the necessary background to efficiently use these kits nor the information about how students are using robotics in class. To support teachers, learning environments with robotics tools should monitor the students' interaction data while they are interacting with the different resources provided. With the analysis of this data, teachers can obtain valuable information about students' learning progress. In previous work, we presented IDEE, an integrated learning environment that uses robotics to support physics laboratories in secondary education. Students' interactions with IDEE are stored and analyzed using the Additive Factor Model to show the teachers the most significant skills in the learning process and those students who have difficulties with these skills. Now, our goal is to enhance the information given to the teachers to allow them to focus on the specific needs of each student on every different skill involved in the activities and not only the significant skills. To this end, we use a Conjunctive Knowledge Tracing model based on a Hidden Markov Model. In this paper: i) we describe how the CKT model has been adapted to the pedagogical model of IDEE, ii) we show that this model can identify the skills that each student masters and thus, support teachers in identifying learning criticalities in students.