Remote Experimentation supported by Learning Analytics and Recommender Systems

This paper proposes a process based on learning analytics and recommender systems targeted at making suggestions to students about their remote laboratories activities and providing insights to all stakeholders taking part in the learning process. To apply the process, a log with requests and responses of remote experiments from the VISIR project were analyzed. A request is the setup of the experiment including the assembled circuits and the configurations of the measuring equipment. In turn, a response is a message provided by the measurement server indicating measures or an error when it is not possible to execute the experiment. Along the two phases of analysis, the log was analyzed and summarized in order to provide insights about students' experiments. In addition, there is a recommendation service responsible for analyzing the requests thus returning, in case of error, precise information about the assembly of circuits or configurations. The evaluation of the process is consistent in what regards its ability to afford recommendations to the students as they carry out the experiments. Moreover, the summarized information intends to offer teachers means to better understand and develop strategies to scaffold students' learning.