9th ACM Conference on Learning at Scale (L@S)

Name: ACM Conference on Learning @ Scale
Start: 2022-06-01
End: 2022-06-03
Location: New York City

Location: New York City , United States

Submission deadline: 2/7/2022

Conference dates: 6/1/2022 - 6/3/2022

Research H-index

Official website

Ranking & Metrics

Discipline name	Position	Best Scientists	Publications	D-Index
Computer Science	228	53	119	14
Social Sciences and Humanities	11	12	28	6

Call for Papers

We solicit empirical and theoretical papers on a diverse range of topics relevant to successful learning at scale. For Learning@Scale 2022, we specifically solicit work in six areas of interest to grow our community whilst being inclusive to other work: (1) Instruction @ scale, (2) Studies and interventions @ scale, (3) Intelligence @ Scale, (4) Informal learning @ scale, (5) Systems and tools @ scale, and (6) Review and Synthesis papers. Accounts of robust methodologies from the learning sciences theory, practice, and/or engineering perspectives are encouraged. Regardless of approach, strong contributions address relevance in terms of theory and practice.

Each area is represented by a community champion who can answer questions about the fit of potential submissions and who helps ensure a high-quality reviewing process in the area. The L@S 2022 areas of interest are:

Instruction @ Scale (Champion: Marco Kalz) — Studies that explore what aspects of instruction could be scaled effectively, as well as which of them are the most effective for learning. Some of the research questions to explore are: What kinds of instructional designs help educators to scale learning online and in hybrid settings? How can learning make use of scaled environments while remaining embedded in a learning community?

Studies And Interventions @ Scale (Champion: René Kizilcec) — Studies that take a qualitative or mixed-methods approach to understand learners’ and teachers’ experiences and contextual factors in scaled or scalable learning environments to inform theory and/or design. Some of the research questions to explore are: What kind of learning support is efficient, effective and enjoyable in hybrid learning environments at scale?

Intelligence @ Scale (Champion: Kenneth Koedinger) — Putting Artificial Intelligence models and techniques at the service of education at scale. Some of the research questions to explore are: How can AI and hybrid models help to scale learning practices? How can AI technologies be used to adapt and personalize learning at scale?

Informal Learning @ Scale (Champion: Justin Reich) – Studies that explore how people take advantage of online environments to pursue their interests informally. Some of the research questions to explore are: What features of online environments motivate and sustain informal learning at scale? Who has access to informal learning experiences at scale, who does not, and why?

Systems and Tools @ Scale (Champion: Pedro Muñoz-Merino) — Studies that build and evaluate novel systems or tools for supporting learning scenarios at scale. Some research questions to explore are: What type of architectures do we need? What type of processes do we need to follow to scale tools institutionally, and what actors do we involve in these processes?

Review and Synthesis papers (Champion: Yannis Dimitriadis) – To support collaboration between learning scientists, computer scientists and contributors from other relevant fields, we invite papers that evaluate, synthesize, and contextualize existing bodies of knowledge and research that may be targeted at one or more communities. Such papers may have high value to the community but might not otherwise be accepted only on the basis of original research contributions. Suitable papers include survey papers that provide useful perspectives on major research areas, papers that support or challenge long-held beliefs with compelling evidence, or papers that provide an extensive and realistic evaluation of competing approaches to solving specific problems.

Overview

This ranking presents a comprehensive list of scientific conferences in the field of Computer Science, meticulously curated to guide researchers, academics, and professionals towards the most impactful venues in the discipline. The ranking has been compiled by Research.com—one of the leading and most trusted websites for science research across all major fields, including Computer Science—renowned for providing reliable data on scientific contributions since 2014.

Conferences are ranked according to a unique bibliometric score developed by Research.com. This metric is calculated using a combination of the estimated h-index and the number of leading scientists who have presented at each conference over the past three years, resulting in a nuanced and robust assessment of conference impact. The Impact Score values featured in this ranking were gathered as of 2024-11-27, ensuring that the data reflects the most recent advancements and trends in the field.

The evaluation process involved a thorough examination of more than 2,742 conferences. These were selected following a detailed and rigorous assessment of over 148,739 scientific documents published in the last three years by 13,184 leading and well-respected scientists specializing in Computer Science. This comprehensive methodology underlines the depth of research and the complexity of analysis conducted by domain experts in the creation of this ranking.

For a detailed explanation of the methodology employed in computing the ranking scores, please refer to our Methodology Page.

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing at Learning at Scale (based on the number of publications) are:

David A. Joyner (25 papers) published 4 papers at the last edition, 5 less than at the previous edition,
Neil T. Heffernan (16 papers) published 2 papers at the last edition the same number as at the previous edition,
René F. Kizilcec (15 papers) published 5 papers at the last edition, 2 more than at the previous edition,
Joseph Jay Williams (11 papers) published 4 papers at the last edition, 2 more than at the previous edition,
Christoph Meinel (11 papers) published 3 papers at the last edition, 1 more than at the previous edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing at Learning at Scale (based on the number of publications) are:

Massachusetts Institute of Technology (45 papers) published 1 paper at the last edition, 4 less than at the previous edition,
Georgia Institute of Technology (39 papers) published 7 papers at the last edition, 11 less than at the previous edition,
Stanford University (39 papers) published 3 papers at the last edition, 2 less than at the previous edition,
Carnegie Mellon University (36 papers) published 4 papers at the last edition the same number as at the previous edition,
University of Illinois at Urbana–Champaign (27 papers) published 3 papers at the last edition, 1 less than at the previous edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 7.02% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 47.17% were posted by at least one author from the top 10 institutions publishing at the conference. Another 15.09% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 11.32% of all publications and 26.42% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

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