Proceedings of the Sixth International Conference on Learning Analytics & Knowledge最新文献

{"title":"Design and evaluation of teacher assistance tools for exploratory learning environments","authors":"M. Mavrikis, S. Santos, A. Poulovassilis","doi":"10.1145/2883851.2883909","DOIUrl":"https://doi.org/10.1145/2883851.2883909","url":null,"abstract":"We present our approach to designing and evaluating tools that can assist teachers in classroom settings where students are using Exploratory Learning Environments (ELEs), using as our case study the MiGen system, which targets 1114 year old students' learning of algebra. We discuss the challenging role of teachers in exploratory learning settings and motivate the need for visualisation and notification tools that can assist teachers in focusing their attention across the whole class and inform their interventions. We present the design and evaluation approach followed during the development of MiGen's Teacher Assistance tools, drawing parallels with the recently proposed LATUX workflow but also discussing how we go beyond this to include a large number of teacher participants in our evaluation activities, so as to gain the benefit of different view points. We discuss the results of the evaluations, which show that participants appreciated the capabilities of the tools and were mostly able to use them quickly and accurately.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114464653","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}

{"title":"When to stop?: towards universal instructional policies","authors":"Tanja Käser, Severin Klingler, M. Gross","doi":"10.1145/2883851.2883961","DOIUrl":"https://doi.org/10.1145/2883851.2883961","url":null,"abstract":"The adaptivity of intelligent tutoring systems relies on the accuracy of the student model and the design of the instructional policy. Recently an instructional policy has been presented that is compatible with all common student models. In this work we present the next step towards a universal instructional policy. We introduce a new policy that is applicable to an even wider range of student models including DBNs modeling skill topologies and forgetting. We theoretically and empirically compare our policy to previous policies. Using synthetic and real world data sets we show that our policy can effectively handle wheel-spinning students as well as forgetting across a wide range of student models.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116554135","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}

{"title":"Using machine analysis to make elementary students' mathematical thinking visible","authors":"K. Koile, Andee Rubin, S. Chapman, M. Kliman, Lily Ko","doi":"10.1145/2883851.2883922","DOIUrl":"https://doi.org/10.1145/2883851.2883922","url":null,"abstract":"The INK-12: Teaching and Learning Using Interactive Ink Inscriptions in K-12 project has been developing and investigating the use of pen-based technology in elementary math classes. This paper reports on progress made on machine analysis of students' visual representations created using digital tools developed to support learning multiplication and division. The goal of the analysis is to make student thinking visible in order to (a) better understand how students learn multiplication and division, and (b) provide feedback to teachers, e.g., about strategies students use to solve problems. Student work from a five-week trial in a third grade class provides a corpus for development and evaluation of the machine analysis routines. Preliminary findings indicate that the routines can reproduce human analyses.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124641930","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}

{"title":"Smart environments and analytics on video-based learning","authors":"M. Giannakos, D. Sampson, L. Kidzinski, A. Pardo","doi":"10.1145/2883851.2883898","DOIUrl":"https://doi.org/10.1145/2883851.2883898","url":null,"abstract":"The International Workshop of Smart Environments and Analytics on Video-Based Learning (SE@VBL) aims to connect research efforts on Video-Based Learning with Smart Environments and Analytics to create synergies between these fields. The main objective is to build a research community around the intersection of these topical areas. In particular, SE@VBL aims to develop a critical discussion about the next generation of video-based learning environments and their analytics, the form of these analytics and the way they can be analyzed in order to help us to better understand and improve the value of educational videos to support teaching and learning. SE@VBL is based on the rationale that combining and analyzing learners' interactions with other available data obtained from learners, new avenues for research on video-based learning have emerged. This can have a significant impact in current educational trends such as Massive Open Online Courses (MOOCs) and Flipped Classroom.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124741804","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}

{"title":"Student differences in regulation strategies and their use of learning resources: implications for educational design","authors":"N. Bos, S. Brand‐Gruwel","doi":"10.1145/2883851.2883890","DOIUrl":"https://doi.org/10.1145/2883851.2883890","url":null,"abstract":"The majority of the learning analytics research focuses on the prediction of course performance and modeling student behaviors with a focus on identifying students who are at risk of failing the course. Learning analytics should have a stronger focus on improving the quality of learning for all students, not only identifying at risk students. In order to do so, we need to understand what successful patterns look like when reflected in data and subsequently adjust the course design to avoid unsuccessful patterns and facilitate successful patterns. However, when establishing these successful patterns, it is important to account for individual differences among students since previous research has shown that not all students engage with learning resources to the same extent. Regulation strategies seem to play an important role in explaining the different usage patterns students' display when using digital learning recourses. When learning analytics research incorporates contextualized data about student regulation strategies we are able to differentiate between students at a more granular level. The current study examined if regulation strategies could account for differences in the use of various learning resources. It examines how students regulated their learning process and subsequently used the different learning resources throughout the course and established how this use contributes to course performance. The results show that students with different regulation strategies use the learning resources to the same extent. However, the use of learning resources influences course performance differently for different groups of students. This paper recognizes the importance of contextualization of learning data resources with a broader set of indicators to understand the learning process. With our focus on differences between students, we strive for a shift within learning analytics from identifying at risk students towards a contribution of learning analytics in the educational design process and enhance the quality of learning; for all students.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127263801","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}

{"title":"Investigating boredom and engagement during writing using multiple sources of information: the essay, the writer, and keystrokes","authors":"L. Allen, Caitlin Mills, Matthew E. Jacovina, S. Crossley, S. D’Mello, D. McNamara","doi":"10.1145/2883851.2883939","DOIUrl":"https://doi.org/10.1145/2883851.2883939","url":null,"abstract":"Writing training systems have been developed to provide students with instruction and deliberate practice on their writing. Although generally successful in providing accurate scores, a common criticism of these systems is their lack of personalization and adaptive instruction. In particular, these systems tend to place the strongest emphasis on delivering accurate scores, and therefore, tend to overlook additional indices that may contribute to students' success, such as their affective states during writing practice. This study takes an initial step toward addressing this gap by building a predictive model of students' affect using information that can potentially be collected by computer systems. We used individual difference measures, text indices, and keystroke analyses to predict engagement and boredom in 132 writing sessions. The results suggest that these three categories of indices were successful in modeling students' affective states during writing. Taken together, indices related to students' academic abilities, text properties, and keystroke logs were able classify high and low engagement and boredom in writing sessions with accuracies between 76.5% and 77.3%. These results suggest that information readily available in writing training systems can inform affect detectors and ultimately improve student models within intelligent tutoring systems.","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123712360","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}

{"title":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","authors":"","doi":"10.1145/2883851","DOIUrl":"https://doi.org/10.1145/2883851","url":null,"abstract":"","PeriodicalId":343844,"journal":{"name":"Proceedings of the Sixth International Conference on Learning Analytics & Knowledge","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123470504","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}