S. Kleanthous, G. Michael, G. Samaras, V. Dimitrova
Learning through video watching has been popular through the education community and is considered as a common choice especially for self-directed informal learning. However, the learner in this situation acts only as a passive consumer and does not receives any feedback for improving his/her performance, an element important in any educational context. Studies in music psychology reveal that gender, perceptual, and cognitive, differences, along with the level of music education of the individual, should be considered when support is generated to a person who is watching a music video for educational purposes. In this sense, individual differences should be exploited when designing an adaptive learning support aiming to suit the individual in music learning. In this line, this paper presents an exploratory study into interaction data of music experts and amateurs when they were actively watching a music video. Linguistic analysis is also employed for taking an insight into the written comments provided by participants at several timepoints in the music videos. Results reveal significant differences between genders in their interaction behavior but also in their perception processing of the music videos, reflected in their comments. Suggestions are provided based on the results on how these can be utilized for the design of personalized support in informal music education.
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Open-source mobile notification datasets are a rarity in the research community. Due to the sensitive nature of mobile notifications it is difficult to find a dataset which captures their features in such a way that their inherently personal information is kept private. For this reason, the majority of research in the domain of Notification Management requires ad-hoc software to be developed for capturing the data necessary to test hypotheses, train algorithms and evaluate proposed systems. As an alternative, this paper discusses the process, advantages and limitations with harnessing a large-scale mobile usage dataset for deriving a synthetic mobile notification dataset used in testing and improving an intelligent Notification Management System (NMS).
{"title":"Synthesis & Evaluation of a Mobile Notification Dataset","authors":"Kieran Fraser, Bilal Yousuf, Owen Conlan","doi":"10.1145/3099023.3099046","DOIUrl":"https://doi.org/10.1145/3099023.3099046","url":null,"abstract":"Open-source mobile notification datasets are a rarity in the research community. Due to the sensitive nature of mobile notifications it is difficult to find a dataset which captures their features in such a way that their inherently personal information is kept private. For this reason, the majority of research in the domain of Notification Management requires ad-hoc software to be developed for capturing the data necessary to test hypotheses, train algorithms and evaluate proposed systems. As an alternative, this paper discusses the process, advantages and limitations with harnessing a large-scale mobile usage dataset for deriving a synthetic mobile notification dataset used in testing and improving an intelligent Notification Management System (NMS).","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"59 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115212335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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