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International Conference on Learning Analytics & Knowledge

International Conference on Learning Analytics & Knowledge (LAK)

Name: International Learning Analytics & Knowledge Conference
Start: 2023-03-13
End: 2023-03-17
Location: Arlington

Location: Arlington , United States

Submission deadline: 10/3/2022

Conference dates: 3/13/2023 - 3/17/2023

Research H-index

Official website

Ranking & Metrics

Discipline name	Position	Best Scientists	Publications	D-Index
Computer Science	106	50	145	22
Social Sciences and Humanities	2	16	68	18

Call for Papers

Other topics of interest include, but are not limited to, the following:

Implementing Change in Learning & Teaching:

Ethical issues around learning analytics: Analysis of issues and approaches to the lawful and ethical capture and use of educational data traces; tackling unintended bias and value judgements in the selection of data and algorithms; perspectives and methods that empower stakeholders.
Learning analytics adoption: Discussions and evaluations of strategies to promote and embed learning analytics initiatives in educational institutions and learning organizations. Studies that examine processes of organizational change and practices of professional development that support impactful learning analytics use.
Learning analytics strategies for scalability: Discussions and evaluations of strategies to scale capture and analysis of information in useful and ethical ways at the program, institution or national level; critical reflections on organizational structures that promote analytics innovation and impact in an institution.
Equity, fairness and transparency in learning analytics: Consideration of how certain practices of data collection, analysis and subsequent action impact particular populations and affect human well-being, specifically groups that experience long term disadvantage. Discussions of how learning analytics may impact (positively or negatively) social change and transformative social justice.
Understanding Learning & Teaching:

Data-informed learning theories: Proposals of new learning/teaching theories or revisions/reinterpretations of existing theories based on large-scale data analysis.
Insights into specific learning processes: Studies to understand particular aspects of a learning/teaching process through the use of data science techniques, including negative results.
Learning and teaching modeling: Creating mathematical, statistical or computational models of a learning/teaching process, including its actors and context.
Systematic reviews: Studies that provide a systematic and methodological synthesis of the available evidence in an area of learning analytics.
Evidencing Learning & Teaching:

Finding evidence of learning: Studies that identify and explain useful data for analysing, understanding and optimising learning and teaching.
Assessing student learning: Studies that assess learning progress through the computational analysis of learner actions or artefacts.
Analytical and methodological approaches: Studies that introduce novel analytical techniques, methods, and tools for modelling student learning.
Technological infrastructures for data storage and sharing: Proposals of technical and methodological procedures to store, share and preserve learning and teaching traces, taking appropriate ethical considerations into account.
Impacting Learning & Teaching:

Human-centered design processes: Research that documents practices of giving an active voice to learners, teachers, and other educational stakeholders in the design process of learning analytics initiatives and enabling technologies.
Providing decision support and feedback: Studies that evaluate the use and impact of feedback or decision-support systems based on learning analytics (dashboards, early-alert systems, automated messages, etc.).
Data-informed decision-making: Studies that examine how teachers, students or other educational stakeholders come to, work with and make changes using learning analytics information.
Personalised and adaptive learning: Studies that evaluate the effectiveness and impact of adaptive technologies based on learning analytics.
Practical evaluations of learning analytics efforts: Empirical evidence about the effectiveness of learning analytics implementations or educational initiatives guided by learning analytics.

Overview

This comprehensive ranking showcases the most significant scientific conferences in the field of Computer Science. Developed by Research.com, a distinguished authority in science research providing reliable data across all principal fields since 2014, this ranking is designed to assist academics, industry professionals, and research organizations in identifying leading venues for scientific exchange and publication.

The position of each conference in the ranking is determined by a unique bibliometric score formulated by Research.com. This score is calculated based on the estimated h-index and the number of prominent scientists who have presented at the conference over the preceding three years. The ranking includes Impact Score values collected as of 2024-11-27.

The ranking process was notably rigorous and involved a meticulous review of more than 2,742 conferences, selected only after a thorough assessment of over 148,739 scientific documents published within the last three years by 13,184 leading and highly respected researchers in the Computer Science domain. This extensive evaluation guarantees that the results reflect both the depth and the breadth of scholarly contribution and influence within the field.

To gain a deeper understanding of the methodology and criteria used in computing the ranking scores, please visit 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 International Learning Analytics & Knowledge Conference (based on the number of publications) are:

Dragan Gašević (4 papers) published 4 papers at the last edition,
Abelardo Pardo (3 papers) published 3 papers at the last edition,
Philip H. Winne (1 papers) published 1 paper at the last edition,
Shane Dawson (1 papers) published 1 paper at the last edition,
Katrien Verbert (1 papers) published 1 paper at the last 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 International Learning Analytics & Knowledge Conference (based on the number of publications) are:

University of South Australia (4 papers) published 4 papers at the last edition,
York College of Pennsylvania (1 papers) published 1 paper at the last edition,
University of Edinburgh (1 papers) published 1 paper at the last edition,
University College Dublin (1 papers) published 1 paper at the last edition,
Carnegie Mellon University (1 papers) published 1 paper at the last 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 2019 edition, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 100.00% were posted by at least one author from the top 10 institutions publishing at the conference. Another 0.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 0.00% 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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