Ranking & Metrics Conference Call for Papers Other Conferences in United States
LAK 2022 : International Conference on Learning Analytics And Knowledge

LAK 2022 : International Conference on Learning Analytics And Knowledge

Newport Beach, CA, United States

Submission Deadline: Monday 04 Oct 2021

Conference Dates: Mar 19, 2022 - Mar 23, 2022

Research
Impact Score 5.28

OFFICIAL WEBSITE

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Ranking & Metrics Impact Score is a novel metric devised to rank conferences based on the number of contributing top scientists in addition to the h-index estimated from the scientific papers published by top scientists. See more details on our methodology page.

Research Impact Score: 5.28
Contributing Top Scientist: 17
Papers published by Top Scientists 58
Research Ranking (Computer Science) 96

Conference Call for Papers

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

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.
Tracing 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 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.
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 organisations. 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 organisational structures that promote analytics innovation and impact in an institution.
Equity and fairness 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 have been previously disadvantaged. Discussions of how learning analytics may impact (positively or negatively) social change and transformative social justice.

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Austin, United States

Deadline: Monday 25 Oct 2021

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