X. Ochoa, Federico Domínguez, B. Guamán, Ricardo Maya, Gabriel Falcones, Jaime Castells
{"title":"The RAP system: automatic feedback of oral presentation skills using multimodal analysis and low-cost sensors","authors":"X. Ochoa, Federico Domínguez, B. Guamán, Ricardo Maya, Gabriel Falcones, Jaime Castells","doi":"10.1145/3170358.3170406","DOIUrl":null,"url":null,"abstract":"Developing communication skills in higher education students could be a challenge to professors due to the time needed to provide formative feedback. This work presents RAP, a scalable system to provide automatic feedback to entry-level students to develop basic oral presentation skills. The system improves the state-of-the-art by analyzing posture, gaze, volume, filled pauses and the slides of the presenters through data captured by very low-cost sensors. The system also provides an off-line feedback report with multimodal recordings of their performance. An initial evaluation of the system indicates that the system's feedback highly agrees with human feedback and that students considered that feedback useful to develop their oral presentation skills.","PeriodicalId":437369,"journal":{"name":"Proceedings of the 8th International Conference on Learning Analytics and Knowledge","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"53","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th International Conference on Learning Analytics and Knowledge","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3170358.3170406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 53
Abstract
Developing communication skills in higher education students could be a challenge to professors due to the time needed to provide formative feedback. This work presents RAP, a scalable system to provide automatic feedback to entry-level students to develop basic oral presentation skills. The system improves the state-of-the-art by analyzing posture, gaze, volume, filled pauses and the slides of the presenters through data captured by very low-cost sensors. The system also provides an off-line feedback report with multimodal recordings of their performance. An initial evaluation of the system indicates that the system's feedback highly agrees with human feedback and that students considered that feedback useful to develop their oral presentation skills.