Conference theme and topics

We welcome submissions from both research and practice, covering different theoretical, methodological, empirical and technical contributions to the learning analytics field. Specifically, this year, we invite contributors to think about how learning analytics is contributing to our understanding of learning and learning processes. Learning research occurs in many distinct academic fields, including psychology, learning sciences, education, neuroscience, and computer science. Since its inception, LA has reflected a tight coupling between research and practice. What has been the impact of the methods, the approaches, the studies, and related outputs of the LA field?

For our 11th Annual conference, we encourage authors to address some of the following questions:

1. How is LA contributing to our understanding of learning?
2. What does impact mean in the context of online, blended, and in-classroom learning analytics?
3. How have learning-related discoveries and research by the LA field influenced learning practices?
4. What are the practical and scholarly implications of the presented work for the future?
5. What are the challenges of the presented work we need to address to improve its impact in the future?

We also explicitly encourage research that validates, replicates and examines the generalisability of previously published findings, as well as the aspects of practical adoption of the existing learning analytics methods and approaches.

Some of the topics of interest include, (but are not limited) are:

Capturing 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 capturing and 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.

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.
• 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.

Impacting Learning & Teaching:

• Providing decision support and feedback: Studies that evaluate the impact of feedback or decision-support systems based on learning analytics (dashboards, early-alert systems, automated messages, etc.).
• Practical evaluations of learning analytics efforts:  Empirical evidence about the effectiveness of learning analytics implementations or educational initiatives guided by learning analytics.
• Personalised and adaptive learning: Studies that evaluate the effectiveness and impact of adaptive technologies based on 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 for value-sensitive, participatory design that empowers stakeholders.
• Learning analytics adoption: Discussions and evaluations of strategies to promote and embed learning analytics initiatives in educational institutions and learning organisations.
• Learning analytics strategies for scalability: Discussions and evaluations of strategies to scale the capture and analysis of information at the program, institution or national level; critical reflections on organisational structures that promote analytics innovation and impact in an institution.

Conference tracks

The conference has three different tracks with distinct types of submissions. Please see the submission guidelines page for more information about each track.

1. Research track

The focus of the research track is on advancing scholarly knowledge in the field of learning analytics through rigorous reports of learning analytics research studies. The primary audience includes academics, doctoral students, postdoctoral researchers and other types of educational research staff working in different capacities on learning analytics research projects.

Submission types for the research track are:

• Full research papers (10 pages, ACM proceedings template) include a clearly explained substantial conceptual, technical or empirical contribution. The scope of the paper must be placed appropriately with respect to the current state of the field, and the contribution should be clearly described. This includes the conceptual or theoretical aspects at the foundation of the contribution, an explanation of the technical setting (tools used, how are they integrated into the contribution), analysis, and results.
• Short research papers (6 pages, ACM proceedings template) can address on-going work, which may include a briefly described theoretical underpinning, an initial proposal or rationale for a technical solution, and preliminary results, with consideration of stakeholder engagement issues.

2. Practitioner and Corporate Learning Analytics track

The Practitioner and Corporate Learning Analytics (PaC-LA) track is complementary to the research track and brings real-world experiences of adoption of learning analytics systems in education. PaC-LA participants include; 1) policy makers, project managers, department leads, instructional technologists, analysts, learning designers and other non-research staff; 2) developers, designers, analysts, and other representatives from commercial and industry entities, non-profit organizations, and government bodies. We consider this track an important vehicle to share experiences and learnings surrounding learning analytics implementations and related tools, programs, product development and researched-based product evaluations.

Submissions for the PaC-LA track have a special format which emphasizes practical aspects of project implementations. All accepted submissions to the PaC-LA track will be published in the LAK21 Companion Proceedings and archived on the SoLAR website.

Submission types for the PaC-LA track are:

• PaC-LA Presentation Reports (2 page abstract, SoLAR companion proceedings template) are a way in which learning analytics implementations and/or related tools, products, product development and researched-based product evaluations in use by practitioners can be shared with the entire community. The reports include accounts and findings that stem from practical experience in implementing learning analytics projects. PaC-LA reports are presented alongside research track submissions as part of the main conference. Some of the goals of PaC-LA presentations are to 1) contribute to the conversation between researchers and practitioners around adoption and implementation of learning analytics, 2) provide insights from practice around factors affording or constraining learning analytics adoption and implementation, and 3) present effective learning analytics adoption strategies and approaches.
• These presentations give PaC-LA participants a channel for sharing:
  • The background of why the a) project was implemented and/or b) product was developed
  • Data and research that drove the development of the project or product
  • Details about how the project or product has been implemented in a real-world environment
  • Findings from the project or product implementation including significance**
• The submission form should include:
  • PaC-LA participant information (name, organization)
  • LAK Topic that the PaC-LA report is aligned with
  • Title (75 characters)
  • Abstract (50 words)
  • Background about why/how the project or product was developed (250 words)
  • Description of implementation (250 words)
  • Findings from project evaluation or product usage (250 words)

The intent of the stream is to contribute to our collective understanding of the practices prominent in learning analytics adoption, what appears to be having impact, and why. Specifically, our interest is to explore the growing role of learning analytics in corporate learning, including the skills development needs of employees, alternative credentialing models, reliance on non-traditional education providers, and the impact of using data to guide corporate learning programs. As such, we encourage you, in your findings, to reflect on the stated purpose of your initiative and discuss learnings and outcomes from the initiative in light of these stated goals. We also encourage submissions where an initiative did not achieve what was expected, as we believe that such papers can also provide valuable knowledge to the community. While a detailed research paper is not required for submission, the more complete the abstract, including usage and impact, the higher the probability of being selected for inclusion. Further, while the stream is intended for non-researchers, we expect papers to still adhere to high standards of scholarly writing.

** significance should include a reflection on the importance of the reported initiatives in your paper  to the broader LAK community.

3. Posters and Demos

• Posters (3 pages, SoLAR companion proceedings template) represent i) a concise report of recent findings or other types of innovative work not ready to be submitted as a full or short research paper or ii) a description of a practical learning analytics project implementation which may not be ready to be presented as a practitioner report. Poster presentations are part of the LAK Poster & Demo session, and authors are given a physical board to present and discuss their projects with delegates. Alternatively, a poster submission may be work that you prefer to present interactively.
• Interactive demos (200 words abstract in SoLAR companion proceedings template + 5 min video) provide opportunities to communicate interactive learning analytics tools. Interactive demonstrations are part of the LAK Poster & Demo session, and presenters are given table space and demonstrate their latest learning analytics projects, tools, and systems. Use demos to communicate innovative user interface designs, visualisations, or other novel functionality that tackles a real user problem. Tools may be at an early concept demonstrator stage or relatively mature, all the way through to products. While LAK encourages participation from commercial analytics partners, interactive demos should be built around actual field experience, results, and feedback. Submissions for conceptual products or for products that have not been used by instructors and/or students are unlikely to be accepted.

4. Pre-conference event track

The focus of pre-conference events is on providing space for new and emerging ideas in learning analytics and their development. Events can have either research or practical focus and can be structured in the way which best serves their particular purpose.

The types of submissions for the pre-conference event track are:

• Workshops (4 pages, SoLAR companion proceedings template) provide an efficient forum for community building, sharing of perspectives, training, and idea generation for specific and emerging research topics or viewpoints. Successful proposals should be explicit regarding the kind of activities participants should expect, for example from interactive/generative participatory sessions to mini-conference or symposium sessions.
• Tutorials (4 pages, SoLAR companion proceedings template) aim to educate stakeholders on a specific learning analytics topic or stakeholder perspective. Proposals should be clear what the need is for particular knowledge, target audience and their prior knowledge, and the intended learning outcomes.

Review process

LAK21 will use a double-blind peer review process for all submissions except those for the doctoral consortium (as they include a letter of reference from the principal supervisor) and demos. Similar to the previous year, LAK21 will have a rebuttal phase for full and short research papers in which authors will be given five days to respond to remarks and comments raised by reviewers in a maximum of 500 words. Rebuttals are optional, and there is no requirement to respond. Authors should keep in mind that papers are being evaluated as submitted and thus, responses should not propose new results or restructuring of the presentation. Thus, rebuttals should focus on answering specific questions raised by reviewers (if any) and providing clarifications and justifications to reviewers. Finally, the conference timeline allows for rejected submissions to be re-submitted in revised form as workshop papers.

Proceedings publication

Accepted full and short research papers will be included in the LAK21 conference proceedings published and archived by ACM. Other types of submissions (posters, demos, workshops, tutorials, practitioner reports and doctoral consortium) will be included in the open access LAK companion proceedings, archived on SoLAR’s website. Please note at least one of the authors must register for the conference by the Early bird deadline before the paper can be included in the ACM Proceedings or LAK Companion Proceedings.