Year: 2015
Type: article
Abstract: This article presents new algorithms for inferring users' activities in a class of flexible and open‐ended educational software called exploratory learning environments (ELEs). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students' progress and to assess their performance. This article presents techniques for recognizing students' activities in ELEs and visualizing these activities to students. It describes a new plan‐recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries. It was able to outperform the state‐of‐the‐art plan‐recognition algorithms when compared to a gold standard that was obtained by a domain expert. We also show that visualizing students' plans improves their performance on new problems when compared to an alternative visualization that consists of a step‐by‐step list of actions.
Source: AI Magazine
Institution: Ben-Gurion University of the Negev
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
Estimated APC paid: $2,400
OpenAlex ID: W2096561702