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LAS 2024 : 10th ACM Learning @ Scale Conference

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Link: https://learningatscale.hosting.acm.org/las2024/
 
When Jul 15, 2024 - Jul 19, 2024
Where Atlanta, Georgia
Abstract Registration Due Feb 5, 2024
Submission Deadline Feb 12, 2024
Categories    machine learning   artificial intelligence   data mining   generative ai
 

Call For Papers

Rapid advances in AI have created new opportunities but also challenges for the Learning@Scale community. The advances in generative AI show potential to enhance pedagogical practices and the efficacy of learning at scale. This has led to an unprecedented level of interest in employing generative AI for scaling tutoring and feedback. The prevalence of such tools calls for new practices and understanding on how AI-based methods should be designed and developed to enhance the experiences and outcomes of teachers and learners.

At this year’s Learning@Scale conference, we focus on scaling learning in the age of AI. We enthusiastically welcome submissions that discuss learning-at-scale research with the aim of improving the experiences and outcomes of learners, teachers, and educators. This year we are particularly interested in, but not limited to, contributions that explore the technological, social, cultural aspects of the responsible use of AI in scaling learning. We specifically encourage works that address the use of generative AI in both classrooms and informal learning settings. This encompasses contributions related to systems and designs to facilitate learning at scale, qualitative and quantitative empirical studies seeking to understand stakeholders’ experiences with AI in scaling learning, empirical studies and interventions that address equity, trust, algorithmic transparency and explainability when using AI in education, methodologies for evaluating and quantifying the impact of AI-based interventions, and synthesis papers discussing the potentials and risks involved in utilizing AI to scale learning.


Submission Guidelines
The ACM Learning@Scale conference solicits original research paper submissions on methodologies, case studies, qualitative and quantitative analyses, tools, or technologies that can contribute to learning at scale, broadly construed. Four kinds of contributions will be accepted: Research Papers, Work-in-Progress, Demonstrations, and Workshops. Accepted works must be presented at the conference and will be included in the proceedings (more details about these contributions can be found below).

Paper submissions, reviewing and notification to authors will be handled using the conference page at EasyChair (https://easychair.org/conferences/?conf=ls24). Submissions must be in PDF format, anonymized for double-blind review (see below), follow the ACM 2-column proceedings template (Word or Latex), written in English, contain original work, and not be under review for any other venue while under review for this conference. The page limits for the different submissions exclude the pages used for references. For research papers, the abstract must be submitted before the final contribution. The length of the abstract should not exceed 350 words.

Anonymization Policy for Double Blind Review
Submissions will be reviewed on the basis of originality, research quality, potential impact, and value to the development of future learning at scale. In order to increase high quality papers and independent merit, the evaluation process will be double-blind. All submissions, with the exception of workshop proposals (see below), should be anonymous. Thus, papers submitted for review MUST NOT contain the authors’ names, affiliations, or any information that may disclose the authors’ identity (this includes names or logos of labs/centers, tools, grant numbers, and acknowledgments). Identifying information is to be restored in the camera-ready version upon acceptance. Please replace author names and affiliations with Xs on submitted papers. In particular, in the version submitted for review please avoid explicit self-references, such as “in [1] we show” — consider instead “in [1] it is shown” or “in [1] Smith et al. show …” (citing yourself in the third person just like how you would cite other researchers). You should definitely cite your own relevant previous work, so that a reviewer can access it and see the new contributions. However, the text should not explicitly state that the cited work belongs to the authors (i.e., do not use “Author (year)” to anonymize). You are permitted to post your work in public archives (e.g., arXiv, EdArXiv). Reviewers will be asked not to check archives during the review process.

Statement on Open Science
Authors are encouraged to conduct their scientific inquiry using emerging best practices in open science. Authors are encouraged to pre-register their study design, hypotheses, and analysis plans, and publish these using platforms such as OSF.io (https://osf.io/) or AsPredicted.org (https://aspredicted.org/). Whenever possible, feasible, and ethical, authors are encouraged to make their data, materials, and scripts openly available for inspection, replication, and follow-up analysis. The best way to share these materials is to use an established platform like OSF.io.

Full Research Papers
Up to 10 pages (not including references). We recommend keeping the length of the papers proportional to the size and the scope of the research contribution.

Abstract due – February 5, 2024 (11:59 PM AoE)
Papers due – February 12, 2024 (11:59 PM AoE)
Learning@Scale 2024 solicits empirical and theoretical papers on, but not limited to, the following topics (in no particular order):

Instruction at scale: studies that examine how teachers and educators scale their instructions, what aspects of instruction could be scaled effectively, and which of these instructional strategies are the most effective for learning.
Interventions at scale: studies that examine the effects of interventions on student learning and performance when implemented at scale. We welcome studies that use both qualitative and quantitative methods.
The use of generative AI to scale learning: studies that investigate stakeholders’ experiences with generative AI, students’ and teachers’ interactions with generative AI, the potentials and limitations of using generative AI in education.
Systems and tools to support learning at scale: research that designs and develops systems and tools to support learning at scale. For example, this involves scaling learning through web-based systems, MOOCs, visualization, intelligent tutoring systems, gamification, immersive techniques (AR/VR/MR), mobile technologies, tangible interfaces, and various other technologies.
The evaluation of existing learning at scale systems and online learning environments using but not limited to the above mentioned technologies.
Methods and algorithms that model learner behavior: research that contributes methods, algorithms, pipelines that process large student data to enhance learning at scale.
Scaling learning in informal contexts: studies that explore how people take advantage of online environments to pursue their interests informally.
Review and synthesis of existing literature related to learning at scale.
Empirical studies and interventions that address equity, trust, algorithmic transparency and explainability, fairness and bias when using AI in education.
Research that addresses accessibility in learning at scale contexts.
Design and deployment of learning at scale systems for learners from underrepresented groups.
Work-in-Progress
Up to 4 pages (not including references)

Submission due – April 15, 2024 (11:59 PM AoE)

A Work-in-Progress (WiP) concisely summarizes recent findings or other types of innovative or thought-provoking work that has not yet reached a level of completion for a full paper. Areas of interest are the same as for full papers. At the conference, all accepted WiP submissions will be presented in poster form. Selected WiPs may be invited for oral presentation during the conference. Rejected full papers can be resubmitted as WiP and will be evaluated accordingly.

Demonstrations
Up to 2 pages (not including references)

Submission due – April 15, 2024 (11:59PM AoE)

Demonstrations show aspects of learning at scale in an interactive, hands-on form. A live demonstration is a great opportunity to communicate ideas and concepts in a powerful way that a regular presentation cannot provide. We invite demonstrations of learning and analytical environments and other systems that have direct relevance to learning at scale. We especially encourage authors of accepted papers to showcase their technologies using this format.

A demonstration submission should address two components:

The merit and nature of the demonstrated technology. If the proposed demonstration is associated with a Full Paper or a WiP submission, please point to the title of the submission instead of repeating the information here.
Details of how the demo will be executed in practice, and how visitors will interact with it during the conference.
Workshops and Tutorials
Up to 4 pages (including references)

Submission due – February 26, 2024 (11:59PM, AoE)

Workshops serve as a gathering place for attendees with shared interests to build community, and are expected to dedicate a substantial time for interaction between participants. A workshop can be half-day or full-day, depending on the goals of the organizers. Workshops can address any Learning @ Scale topic.

Tutorials aim to offer the opportunity to acquire new skills and knowledge valuable to the members of the community. Tutorials should aim to promote participation and interaction between participants as well. Organizers need to prepare a guided introduction to a topic including hands-on activities for the participants. Proposals should be clear about what the need is for a particular knowledge, target audience and their prior knowledge, and the intended learning outcomes.

The organizers of accepted workshops and tutorials are expected to set up a website including at least a call for participation and a description of the planned activities. The content of the call for papers and the planned activities will be provided at submission time by means of a form (see below). The description of the accepted workshop and tutorial proposals will be published in the conference proceedings. To that aim, the submission requires that organizers upload a file with this description.

Submission format: Workshop and Tutorials organizers should submit their proposals by means of a workshop/tutorial submission form. This form asks organizers to describe: Type of event (workshop / tutorial), Title, Organizers, Duration of the event (half-day or full day), Intended audience, Call for Participation, Planned activities, Requirements for participation. In addition to this form, workshop organizers should submit a document with the description of the workshop / tutorial using the conference page at EasyChair (see below).

Workshop/tutorial description for the proceedings: The document with the workshop and tutorial proposals must not exceed 4 pages (not including references) and use the ACM Proceedings Format, available in LaTeX, Word, or Overleaf. Unlike other submission types, workshop and tutorial submissions are not anonymous and should therefore include all author names, affiliations and contact information. The suggested structure for workshop/tutorial description is the following: Title, Organizers’ names and affiliations, Theme and goals, Theoretical background and relevance to Learning at Scale, Expected outcomes and contributions, and References.

Open Access to Proceedings
The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of your conference. The official publication date affects the deadline for any patent filings related to published work. (For those rare conferences whose proceedings are published in the ACM Digital Library after the conference is over, the official publication date remains the first day of the conference).

Important Dates

Full Research Papers
Please, note that this year there will NOT be any extensions of the deadlines for all types of submissions! All deadlines are final.

Abstract Submissions: February 5, 2024 (11:59PM AoE)
Full Paper Submission: February 12, 2024 (11:59PM AoE)
Author Notification: April 8, 2024 (11:59PM AoE)
Camera-ready Version: April 22, 2024 (11:59PM AoE)
Work-in-Progress and Demonstrations
Paper Submission: April 15, 2024 (11:59PM AoE)
Author Notification: May 6, 2024 (11:59PM AoE)
Camera-ready Version: May 13, 2024 (11:59PM AoE)
Workshops and Tutorials
Workshop Proposal Submission: February 26, 2024 (11:59PM AoE)
Organizer Notification: March 18, 2024 (11:59PM AoE)
Camera.-ready Version: April 22, 2024 (11:59PM AoE)

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