Exploring Learning Analytics on YouTube: a tool to support students’ interactions analysis

Proceedings of the 22nd International Conference on Computer Systems and Technologies Pub Date : 2021-06-18 DOI:10.1145/3472410.3472442

Daniele Schicchi, Benedetto Marino, D. Taibi

{"title":"Exploring Learning Analytics on YouTube: a tool to support students’ interactions analysis","authors":"Daniele Schicchi, Benedetto Marino, D. Taibi","doi":"10.1145/3472410.3472442","DOIUrl":null,"url":null,"abstract":"YouTube is a free online video-sharing platform that is often used by students for their learning activities. The interactions of the students when using the platform to shape new concepts, are worth to be investigated to better understand and to optimize the learning opportunities that take place in this platform. In this paper, we investigate which types of data are relevant to analyse the interactions of students with content on YouTube, and we introduce a new tool that emulates students’ interactions with the platform in order to provide data to be used in supporting Learning Analytics approaches. Our preliminary study inspects the tool effectiveness in data collection and analyses the effects of the YouTube recommendation system in students’ activities. We also identify methodologies based on statistical indexes and social network analysis that can be adopted to analyse students’ experiences. Results show how the YouTube recommendation system plays a critical role in affecting the student learning trajectory.","PeriodicalId":115575,"journal":{"name":"Proceedings of the 22nd International Conference on Computer Systems and Technologies","volume":"205 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd International Conference on Computer Systems and Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3472410.3472442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 2

Abstract

YouTube is a free online video-sharing platform that is often used by students for their learning activities. The interactions of the students when using the platform to shape new concepts, are worth to be investigated to better understand and to optimize the learning opportunities that take place in this platform. In this paper, we investigate which types of data are relevant to analyse the interactions of students with content on YouTube, and we introduce a new tool that emulates students’ interactions with the platform in order to provide data to be used in supporting Learning Analytics approaches. Our preliminary study inspects the tool effectiveness in data collection and analyses the effects of the YouTube recommendation system in students’ activities. We also identify methodologies based on statistical indexes and social network analysis that can be adopted to analyse students’ experiences. Results show how the YouTube recommendation system plays a critical role in affecting the student learning trajectory.

查看原文本刊更多论文

在YouTube上探索学习分析:一个支持学生互动分析的工具

YouTube是一个免费的在线视频分享平台，经常被学生用于他们的学习活动。学生在使用平台形成新概念时的互动值得调查，以更好地理解和优化在这个平台上发生的学习机会。在本文中，我们研究了哪些类型的数据与分析学生与YouTube上内容的互动相关，并且我们引入了一个新的工具，模拟学生与平台的互动，以便提供数据用于支持学习分析方法。我们的初步研究检验了工具在数据收集方面的有效性，并分析了YouTube推荐系统在学生活动中的效果。我们也确定了基于统计指标和社会网络分析的方法，可以用来分析学生的经历。结果表明，YouTube推荐系统在影响学生学习轨迹方面发挥了关键作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings of the 22nd International Conference on Computer Systems and Technologies

自引率

0.00%

发文量