Call for papers

Enhancing Impact: Convergence of Communities for Grounding, Implementation and Validation

The 6th International Conference on Learning Analytics and Knowledge (LAK16) will be held in the beautiful city of Edinburgh, Scotland, April 25-29 and for the first time, LAK16 will be co-located with ACM Learning @ Scale 2016. The LAK16 conference is organised by the Society for Learning Analytics Research (SOLAR) and will be hosted by the University of Edinburgh, a university with a long and rich history of innovation and research in teaching, learning and technologies. Building on the momentum generated from previous LAK conferences, we extend invitations to practitioners, researchers, administrators, government and industry groups alike, interested in the field of learning analytics and related disciplines. This annual conference provides a multidisciplinary forum for addressing the critical issues and challenges confronting the education sector today. A particular emphasis of this year’s program is enhancing our impact through synergistic connections with other related research communities.

The field of learning analytics is rapidly growing in all facets of its research, application into practice and theoretical contributions. The theme for the 6th International Learning Analytics and Knowledge (LAK16) conference aims to explore the multidisciplinary connections that effectively illustrate how learning analytics can provide critical insights into the individual and collective learning process. This year’s theme particularly highlights the multidisciplinary nature of the field and embraces the convergence of these disciplines to provide theoretical and practical insights that will further advance the field – through research, adoption and implementation and ultimately provide a foundation for informing government and institutional policy. We invite research and practice papers that address the “convergence of communities” in LAK and bring a novel perspective and approach for reflecting on the field.

Research Track

  • Focus: Rigorous academic research findings
  • Review Process: Formal peer-review process
  • Included in ACM Proceedings?: Yes
  • Submission Types: Full paper, short paper, posters, research demonstrations, workshops and tutorials*
  • Awards: Best Paper, Best Poster, Best Demonstration

Research Submission Instructions

Practitioner Track

  • Focus: Innovative and impactful implementation of learning analytics
  • Review Process: General review by other learning analytics practitioners
  • Included in ACM Proceedings?: No, but will have its own proceedings on CEUR Workshop Proceedings
  • Submission Types: Presentation, panel, technology showcase
  • Awards: Best Presentation/Panel, Best Technology Showcase

Practitioner Submission Instructions


LAK 2016 is seeking contributions from a diversity of disciplines and topics (see below). In keeping with our theme, we particularly invite papers that highlight the core strength of the LAK community while demonstrating significant input from other synergistic research communities. Papers are welcome from any education context and setting – formal and informal learning; workplace, k-12 and tertiary education; including online, distance, blended, mobile or traditional modes of learning. We invite papers related to research, theory and practice – broadly focusing on innovations in the field, assessment of impact, policy, ethics and privacy alongside computational advances, tools and visualisation techniques.

The following keywords will be used to classify submissions, and convey the breadth of topics covered.

  • Analytic Approaches, Methods, and Tools for sensemaking in learning analytics, including: algorithms, architectures, behavior modeling, case studies, clustering, computational linguistics, concept mapping, crowdsourcing, data integration, data mining, data sharing, research about design, discourse analysis, educational research methods, ethnography, ethnomethodology, evaluation methods, frameworks, grounded theory, information visualization, interfaces for learning analytics, knowledge representation, machine learning, natural language processing, predictive analytics, recommendation engines, semantic web, sequential analysis, social network analysis, social network visualization, statistical analysis, surveys, text mining, visual learning analytics
  • Theories and Theoretical Concepts for understanding learning, including: activity theory, actor-network theory, learning sciences, conceptual models of learning enabled by analytics, distributed cognition, networked individualism, reflective learning, situated learning, social capital, social learning, sociocultural theory, structuration theory, symbolic interactionism
  • Measures of Learning, Change and Success, including: accreditation, affect, emotions, and flow, analytic patterns, attendance and retention (as predictors of learning), attention, attitudes, collaboration and cooperation, community structure, comprehension/understanding, conceptual change, degree of competence, educational performance, expectations, learner behavior modeling, learning dispositions, metacognition, misconceptions, motivation, off-task behavior, organizational dynamics, participation, satisfaction, social dynamics
  • Learning Activities, Applications, and Interventions: adaptation, analytic tools for learners, argumentation, assessment, awareness, big data applications and opportunities, classroom orchestration, collaborative learning, course management systems, decision-support systems for learning, informing policy, instructor support, intelligent tutoring systems, interventions based on analytics, knowledge work, language learning, learning communities, learning environments enhanced with analytics, learning how to learn, lifelong learning, management of learning interventions or settings, mentoring, open data and data access for learners, pedagogical adjustment/intervention, personalization, predicting failure, professional development, quantified self, reflection, scaffolding and scripting, self-management of learning, student monitoring, teacher analytics, teaching learning analytics
  • Data sources: blogging, chats, haptic media & tangible computing, microblogging (twitter), mobile platforms, wearable computing, immersive learning environments, tutors, intelligent agents, online discussion forums, shared workspaces, social networking media, video, whiteboards, wikis, and face-to-face interaction supported by technology


All conference submissions must be via EasyChair.

All the dates are hard deadlines. No extensions for submissions will be granted.

  • 31 October 2015 (HAST): Deadline for full/short papers, panel proposals, workshop proposals, tutorial proposals, demonstrations and posters (extensions will not be considered).
  • 30 November 2015 (HAST): Notification of acceptance for workshop and tutorial proposals.
  • 18 December 2015 (HAST): Notification of acceptance for full/short papers, panel proposals, demonstrations and posters
  • 5 February 2016 (HAST): Final version of accepted papers due
  • 25-29 April 2016: LAK conference
  • Archiving post conference: Conference proceedings will be published in ACM Digital Library


The conference accepts the following type of submissions:

All documents must follow the ACM Proceedings Format. Please check the conference website for detailed author instructions for each type of submission. Submissions will be received and processed with EasyChair. The conference proceedings will include all accepted conference submissions except tutorial proposals and workshop papers.


The conference will also include a doctoral consortium.