United States

This talk is a comprehensible and general introduction to Educational Data Mining and Learning Analytics and how they have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This talk provides the current state of the art, the key concepts, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods and techniques, the main educational objectives, and the future trends in this research area.More info in: https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1355

CSEDU 2022

Educational Data Mining and Learning Analytics

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

keynote

CSEDU, the International Conference on Computer Supported Education, is a yearly meeting place for presenting and discussing new educational tools and environments, best practices and case studies on innovative technology-based learning strategies, and institutional policies on computer supported education including open and distance education. CSEDU provides an overview of current technologies as well as upcoming trends, and promote discussion about the pedagogical potential of new educational technologies in the academic and corporate world. 

CSEDU, the International Conference on Computer Supported Education, is a yearly meeting place for presenting and discussing new educational tools and environments, best practices and case studies on innovative technology-based learning strategies, and institutional policies on computer supported education including open and distance education. 

Individuals participate in learning activities within a broader social context that spans interactions with peers, mentors, teachers, and parents. However, many intelligent technologies that support learning focus on a learner as an individual actor rather than a member of a community. In addition, these technologies can be highly prescriptive in the ways that they expect learners to interact with content, limiting learner agency and ignoring the varied ways that students might naturally engage within a learning space. In this talk, I will present a vision for a new type of intelligent technology that supports social learning in ways that are sensitive to the learner's individual preferences and broader context. Using specific technology-supported collaborative learning projects from my lab as examples, I will explore the unique ability of technology to provide personalized social experiences that allow for learner agency.

Context-Sensitive Technology Supports for Social Learning

In this talk, I will present our work on learning analytics dashboards that aim to support students, teachers and study advisors. These dashboards visualise traces of learningactivities, in order to promote awareness, reflection and sense-making. In recent years, we also increasingly employ machine learning models to make predictions and personalise contentdelivery in such dashboards, and we focus particularly on how to explain such models to end-users. In this talk, I will present lessons learned from the user-centred designand evaluation of these dashboards and explanations to support blended learning, group work, and communication between students and study advisors, as well as future research challenges.

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