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检索条件"任意字段=1st International Workshop on Enabling Data-Driven Decisions from Learning on the Web, L2D 2021"

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l2d 2021 - Proceedings of the 1st international workshop on enabling data-driven decisions from learning on the web, co-located with the 14th ACM international Conference on web Search and data Mining, WSdM 2021

L2D 2021 - Proceedings of the 1st International Workshop on ...

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1st international workshop on enabling data-driven decisions from learning on the web, l2d 2021

The proceedings contain 6 papers. The topics discussed include: incorporating wide context information for deep knowledge tracing using attentional bi-interaction;collaboration patterns in students’ teams working on business cases;modeling student behavior in synchronous online learning during the COVId-19 pandemic;learning testing model using test generators and mobile applications;test students’ attention in class using a mobile application;and designing intelligent systems for online education: open challenges and future directions.

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Modelling student behavior in synchronous online learning during the covid-19 pandemic 1

Modelling student behavior in synchronous online learning du...

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1st international workshop on enabling data-driven decisions from learning on the web, l2d 2021

作者： Fenu, Gianni Galici, Roberta Department of Mathematics and Computer Science University of Cagliari V. Ospedale 72 Cagliari09124 Italy

The outbreak of COVId-19 has significantly impacted on education, training, and mobility opportunities provided to learners, teachers, and educators. In response to this situation, synchronous online learning has been massively adopted in universities and, therefore, the analysis of student behavior in this context is becoming essential. However, the literature has mainly tackled student behavior modelling in asynchronous online learning (e.g., interactions with pre-recorded videos), making how students learn in synchronous online learning so far under-explored. Grounding on the experience on online learning at the University of Cagliari, this paper proposes a preliminary study on student participation in synchronous lessons, covering more than 80 courses, and identifies patterns and strategies of engagement in classes over the semester. Then, by means of clustering techniques applied to data from more than 25,000 students, we model fine-grained behavioral strategies and discuss how they are related with the student's experience across courses and whether planning elements (e.g., the hour of the day a lesson is delivered) influence their level of participation. We expect that this study will support the educational stakeholders with preliminary data-driven informed decisions and pose the basis for data-driven personalization for students and teachers involved in online synchronous learning. © 2021 CEUR-WS. All rights reserved.

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learning testing model using test generators and mobile applications 1

Learning testing model using test generators and mobile appl...

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1st international workshop on enabling data-driven decisions from learning on the web, l2d 2021

作者： Popescu, doru Anastasiu Gosoiu, Cosmin Iulian Nijloveanu, daniel University of Pitesti Department of Mathematics - Computer Science Pitesti Arges Romania University of Pitesti Department of Mathematics - Computer Science Pitesti Arges Romania University of Agronomical Sciences and Veterinary Medicine Bucharest Faculty of Management and Rural Development Slatina Branch Romania

The knowledge testing method can be done in many ways, being dependent on the content of the course, the characteristics of the student group, the existing technical means for the course, knowledge related to IT. However, testing based on the use of databases with questions and answers selected from several possible choices is a fast, efficient and preferred way for teachers and students. This paper aims to present a test model that uses existing generators and a mobile application created specifically to transmit the test and use the email service which is used for a long time by teachers and students. The mobile application provides the interface for presenting the test and retrieves from the student the answers he sends to the teacher for assessment. The model in this paper has two important stages: Generating the test and sending it to the student and retrieving the result through the email service. © 2021 CEUR-WS. All rights reserved.

关键词： students

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designing intelligent systems for online education: Open challenges and future directions 1

Designing intelligent systems for online education: Open cha...

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1st international workshop on enabling data-driven decisions from learning on the web, l2d 2021

作者： dessì, danilo Käser, Tanja Marras, Mirko Popescu, Elvira Sack, Harald Epfl Switzerland Fiz Karlsruhe - Leibniz Institute for Information Infrastructure Germany Karlsruhe Institute of Technology Institute Aifb Germany University of Craiova Romania

The design and delivering of platforms for online education is fostering increasingly intense research. Scaling up education online brings new emerging needs related with hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely, as examples. However, with the impressive progress of the data mining and machine learning fields, combined with the large amounts of learning-related data and high-performance computing, it has been possible to gain a deeper understanding of the nature of learning and teaching online. Methods at the analytical and algorithmic levels are constantly being developed and hybrid approaches are receiving an increasing attention. Recent methods are analyzing not only the online traces left by students a posteriori, but also the extent to which this data can be turned into actionable insights and models, to support the above needs in a computationally efficient, adaptive and timely way. In this paper, we present relevant open challenges lying at the intersection between the machine learning and educational communities, that need to be addressed to further develop the field of intelligent systems for online education. Several areas of research in this field are identified, such as data availability and sharing, time-wise and multi-modal data modelling, generalizability, fairness, explainability, interpretability, privacy, and ethics behind models delivered for supporting education. Practical challenges and recommendations for possible research directions are provided for each of them, paving the way for future advances in this field. © 2021 CEUR-WS. All rights reserved.

关键词： E-learning

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