L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale

Country

United States

Universities and research institutions in United States
Media Ranking in United States

Subject Area and Category

Computer Science

Engineering
- Biomedical Engineering

Medicine
- Radiology, Nuclear Medicine and Imaging

Social Sciences
- Education

Publisher

H-Index

Publication type

Conferences and Proceedings

ISSN

Coverage

Information

Homepage

Scope

It is our great pleasure to present the Proceedings of the Fourth Annual ACM Conference on Learning at Scale, L@S 2017, held on April 20-21 at the Massachusetts Institute of Technology, Cambridge, MA, USA. Learning at Scale investigates large-scale, technology-mediated learning environments. The conference was created by the Association for Computing Machinery (ACM), inspired by the emergence of Massive Open Online Courses (MOOCs) and the accompanying shift in thinking about education. While the conference was originally inspired by the emergence of MOOCs, large-scale technology mediated learning environments are very diverse and the conference series is a venue for discussion of the highest quality research on how learning and teaching can be transformed by that diversity of environments. Intelligent tutoring systems, open learning courseware, learning games, citizen science communities, collaborative programming communities, community tutorial systems, and the countless informal communities of learners are all examples of learning at scale. These systems either depend upon large numbers of learners, or they are enriched through use of data generated by the previous use of many learners. They share a common purpose--to increase human potential--and a common infrastructure of data and computation to enable learning at scale. Investigations of learning at scale naturally bring together two different research communities. Learning scientists are drawn to these innovative environments to study established and emerging forms of knowledge production, transfer, modeling, and co-creation. Computer scientists are drawn to the field as powerful site for the development and application of advanced computational techniques. At it's very best, the Learning at Scale community supports the interdisciplinary investigation of these important sites of learning and human development.

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SJR

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Year	SJR
2018	0.268
2019	0.533
2020	0.448

Total Documents

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

Year	Documents
2018	0
2019	0
2020	0

Citations per document

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Cites per document	Year	Value
Cites / Doc. (4 years)	2018	2.642
Cites / Doc. (4 years)	2019	2.463
Cites / Doc. (4 years)	2020	2.478
Cites / Doc. (3 years)	2018	2.642
Cites / Doc. (3 years)	2019	2.463
Cites / Doc. (3 years)	2020	2.478
Cites / Doc. (2 years)	2018	2.642
Cites / Doc. (2 years)	2019	2.463
Cites / Doc. (2 years)	2020	0.000

Total Cites

Self-Cites

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years.
Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Cites	Year	Value
Self Cites	2018	0
Self Cites	2019	0
Self Cites	2020	0
Total Cites	2018	177
Total Cites	2019	165
Total Cites	2020	166

External Cites per Doc

Cites per Doc

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

Cites	Year	Value
External Cites per document	2018	2.642
External Cites per document	2019	2.463
External Cites per document	2020	2.478
Cites per document	2018	2.642
Cites per document	2019	2.463
Cites per document	2020	2.478

% International Collaboration

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Year	International Collaboration
2018	0
2019	0
2020	0

Citable documents

Non-citable documents

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Documents	Year	Value
Non-citable documents	2018	2
Non-citable documents	2019	2
Non-citable documents	2020	2
Citable documents	2018	65
Citable documents	2019	65
Citable documents	2020	65

Cited documents

Uncited documents

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

Documents	Year	Value
Uncited documents	2018	20
Uncited documents	2019	20
Uncited documents	2020	25
Cited documents	2018	47
Cited documents	2019	47
Cited documents	2020	42

% Female Authors

Evolution of the percentage of female authors.

Year	Female Percent
2018	0.00
2019	0.00
2020	0.00

Documents cited by public policy (Overton)

Evolution of the number of documents cited by public policy documents according to Overton database.

Documents	Year	Value
Overton	2018	0
Overton	2019	0
Overton	2020	0

Documents related to SDGs (UN)

Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

Documents	Year	Value
SDG	2018	0
SDG	2019	0
SDG	2020	0

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