Learning Analytics and Recommender Systems toward Remote Experimentation

• Autor(a) principal: Gonçalves, Alexandre L.
• Data de Publicação: 2018
• Outros Autores: Carlos, Lucas, Alves, Gustavo R., Silva, Juarez B. da, Alves, João B.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/12059
• topic: Learning Analytics; Remote labs; Recommender systems

This paper presents a process based on learning analytics and recom- mender systems to provide suggestions to students about remote laboratories ac- tivities in order to scaffold their performance. For this purpose, the records of remote experiments from the VISIR project were analyzed taking into account one of its installations. Each record is composed of requests containing the as- sembled circuits and the configurations of the measuring equipment, as well as the response provided by the measurement server that evaluates whether a par- ticular request can be performed or not. With the log analysis, it was possible to obtain information in order to determine some initial statistics and provide clues about the student’s behavior during the experiments. Using the concept of rec- ommendation, a service is proposed through request analysis and returns to the students more precise information about possible mistakes in the assembly of circuits or configurations. The process as a whole proves consistent in what re- gards its ability to provide suggestions to the students as they conduct the exper- iments. Furthermore, with the log, relevant information can be offered to teach- ers, thus assisting them in developing strategies to positively impact student’s learning.