Workshop Topics

We are interested in novel contributions targeting DM and ML in education on the Web, focused but not limited to the following areas. We seek to receive papers that clearly state and contextualize how the proposed contribution is integrated in the real-world scenario and supports educational stakeholders during decision-making.

• Data Set Collection and Preparation:
  • New tools and systems for capturing educational data (e.g., eye-tracking, motion, physiological, etc.).
  • Proposals of procedures and tools to store, share and preserve learning and teaching traces.
  • Knowledge graphs and annotation schemas for data that can be leveraged for DM and ML in education.
  • Collecting and sharing data sets useful for applying DM and ML in online education contexts.
• Model, Tool, and System Design and Implementation:
  • Semantic content-based retrieval of educational materials to identify appropriate contents.
  • Tools for adaptive question-answering and dialogue or automatically generating test questions.
  • Personalized support tools and systems for communities of learners (e.g., recommendation).
  • Natural language processing applied on exam data in order to assign a grade to them.
  • Behavioral and physiological analysis of learners while interacting in online education platforms.
  • Student engagement assessment via machine-learning techniques (e.g., sentiment analysis).
  • Systems that detect and/or adapt the platform to sentiment or emotional states of learners.
  • Techniques to provide automated proctoring support during online examinations, e.g., via biometric recognition.
  • Tools able to predict the learner's success or failure along the educational path.
• Evaluation Protocol Design and Implementation:
  • Evaluation techniques, metrics, and protocols relying on computational analyses in online education contexts.
  • Interpretability and/or fairness of the models and the resulting impact on real-world adoption.
  • Error analysis aiming at understanding, measuring, and managing uncertainty in model design.
  • Strategies to evaluate effectiveness and impact of DM and ML systems on educational environments.
  • Exploration of cognition, affect, motivation, and attitudes of stakeholders, while deploying systems.
  • Learning-while-searching investigations conducted in the current educational contexts.
• Ethics and Privacy Investigation:
  • Analysis of issues and approaches to the lawful and ethical use of intelligent DM and ML systems.
  • Tackling unintended bias and value judgements in DM and ML intelligent systems.
  • Regulations and policies in data management ensuring privacy while designing intelligent DM and ML systems.
  • Broad discussion on potential and pitfalls of intelligent systems for educational contexts.
  • Studies on how teachers can be made part of the loop as moderators instead of being replaced.

Submission Details

The submissions must be in English and adhere to the CEUR-WS one-column template. The papers should be submitted as PDF files to Easychair at https://easychair.org/conferences/?conf=l2d2021. The review process will be single-blind. Please be aware that at least one author per paper must be registered and attend the workshop to present the work.

We will consider four different submission types:

• Full Papers (10-12 pages) should be clearly placed with respect to the state of the art and state the contribution of the proposal in the domain of application, even if presenting preliminary results. In particular, research papers should describe the methodology in detail, experiments should be repeatable, and a comparison with the existing approaches in the literature is encouraged.
• Reproducibility Papers (10-12 pages) should repeat prior experiments using the original source code and datasets to show (i) how, why, and when the methods work or not, (ii) or should repeat prior experiments, preferably using the original source code, in new contexts (e.g., different domains and datasets, different evaluation and metrics) to further generalize and validate or not previous work.
• Short Papers (5-9 pages) should describe significant novel work in progress. Compared to full papers, their contribution may be narrower in scope, be applied to a narrower set of application domains, or have weaker empirical support than that expected for a full paper. Submissions likely to generate discussions in new and emerging areas of data mining and machine learning in education are encouraged.
• Position Papers (4-5 pages) should introduce new point of views in the workshop topics or summarize the experience of a group in the field. Practice and experience reports should present in detail real-world scenarios in which data mining and/or machine learning are exploited in the educational context.

Submissions should not exceed the indicated number of pages, including any diagrams and references.

Each submission will be reviewed by three independent reviewers on the basis of relevance for the workshop, novelty/originality, significance, technical quality and correctness, quality and clarity of presentation, quality of references and reproducibility.

The accepted papers and the material generated during the meeting will be available on the workshop website. The workshop proceedings will be sent for inclusion in a CEUR-WS volume and consequently indexed on Google Scholar, DBLP, and Scopus. Authors of selected papers may be invited to submit an extended version in a journal special issue.

We expect authors, the programm committee, and the organizing committee to adhere to the ACM’s Conflict of Interest Policy and the ACM’s Code of Ethics and Professional Conduct.

Registration

The registration to the workshop is managed by the WSDM Main Conference through the Registration Portal.

You need to opt for a one-day registration fee and then select this workshop along the registration process. If you need any other information, please do not hesitate to contact us.

Contacts

For general enquiries on the workshop, please send an email to danilo.dessi@kit.edu and mirko.marras@epfl.ch.