Workshop on Designing Learning Technologies for Equality, Diversity and Inclusion

This workshop addresses the extent to which learning technologies contribute to Equality, Diversity and Inclusion (EDI) objectives in and outside of learning institutions. Some of the themes that we will discuss at the workshop include:
  • The “prioritization” of normative learning experiences with technology (Normativity)
  • Learning technologies and their utility for students belonging to ethnic or cultural minorities and underserved populations (EDI)
  • The intersection of multiple identities in learning (Intersectionality)
  • Fairness and Equity in learning technology (Bias, Fairness, and Equity)
  • Accessibility in the pipeline (Design Justice)
  • Involving learners in the development of learning technology, in particular the evaluation of impact (Co-Creation, Co-design)
  • Critical educational research and reflection
Participants are asked to submit a short evaluation of their own previous or current work that addresses the provision of learning technology within an educational context. These statements may be of any length (within reason) but should address the following questions:
  • What was the aim of the project/study?
  • What learner data did you use? (This could be qualitative data, data from learning analytics, data from educators, etc.)
  • Which learners were involved in evaluating your project/study (if any)?
  • What are the implications of your work for different subgroups?
    • For example, you might have deployed the educational intervention with students but did not investigate the impact across different subgroups, such as learners with a different ethnic background, racial identity, gender identity, sexuality, ability, etc., or combined intersections of these identities.
  • What are the implications of your work for equality, diversity and inclusion?
  • What are the open questions you still have about equality, diversity and inclusion in learning technology?

Topics of interest

  • Learning analytics
  • Artificial intelligence
  • Recommender systems
  • Large-scale learning systems
  • Inclusive, equitable, and accessible learning
  • Learning of students of underrepresented groups, including students with disabilities
  • General info

    Workshop goals

    The objectives of the workshop are as follows:
  • Guide participants in reflecting on their own work critically
  • Promote knowledge and understanding of EDI in research
  • Generate innovative ideas for including EDI perspectives in educational research
  • Create a set of lessons learned for publication
  • Motivation

    Sociologist Patricia Hill Collins and Columbia Law School Professor Kimberlé Crenshaw developed the notion of intersectionality to describe the ways in which multiple, multi-faceted identities associated with race, ethnicity, gender, class, sexuality and ability (among others) compound marginalisation (and privilege) in various contexts [1]. In contexts where an individual’s identities are in alignment with the normative or typical profile, all of the goods and services created in that context are likely to be accessible and useful for that individual. When the opposite is true, and an individual’s identities are not in alignment with the norm, one can expect that the goods and services created for the normative profile may not be as accessible or useful for that individual [2]. What are the dangers of serving the normative profile in education? How do we get past it? What are some of the limitations of those solutions and what should we be designing for the future? In this workshop, we are asking participants to consider the implications of their work for the goals and objectives of equality, diversity and inclusion in education.

    For example, several recent studies evaluate the impact of delivering learning analytics based interventions with students, which shows a promising progress beyond pure development of predictive models [4,5,6]. However, what we don’t know is how these models impact different subgroups and whether they decrease or highlight existing gaps [3]. This is important because divergences in the learning and behavioural patterns of minority groups (students of a minority ethnicity, for example, or students with disabilities) may not be given the same relevance than those of the majority group. One of the first studies analysed the impact of using Predictive Learning Analytics by teachers on students coming from different socio-ecocomic backgrounds and students from different ethnicities [10].

    Another emerging topic in the Machine Learning community is investigation of fairness of predictive models. Several papers from the Learning Analytics community have been published in the last two years [7, 9], describing methodologies for making models fair. But the number of fairness metrics is huge and sometimes contradictory, and various papers select different metrics to evaluate fairness. On the other hand, no research has discussed implications of deploying models that are fair on decreasing the existing gaps or on equity. For example, what is the impact of removing the ethnicity/race factor from the predictive models used to target at-risk students on the completion rates, overall score or student satisfaction ?

    Additional materials


    1. Crenshaw, K., 1989. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. u. Chi. Legal f., p.139.
    2. Costanza-Chock, S., 2018. Design justice: Towards an intersectional feminist framework for design theory and practice. Proceedings of the Design Research Society.
    3. Reich, J., Ito, M., Team, M.S.: From good intentions to real outcomes
    4. Herodotou C., Rienties B., Hlosta M., Boroowa A., Mangafa C., Zdrahal Z. The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study The Internet and Higher Education, Volume 45, April 2020
    5. De Laet, T., Millecamp, M., Ortiz-Rojas, M., Jimenez, A., Maya, R., Verbert, K.: Adoption and impact of a learning analytics dashboard supporting the ad- visor—student dialogue in a higher education institute in latin america. British journal of educational technology 51(4), 1002–1018, 2020
    6. Dawson, S., Jovanovic, J., Gasevic, D., Pardo, A.: From prediction to impact: Evaluation of a learning analytics retention program. In: Proceedings of the seventh international learning analytics & knowledge conference. pp. 474–478, 2017
    7. Bayer V., Hosta M., Fernandez M. Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally? In: AIED’21
    8. Hlosta M., Herodotou C., Fernandez M., Bayer V. Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM, In: AIED’21
    9. Kizilcec RF, Lee H. Algorithmic Fairness in Education. arXiv preprint arXiv:2007.05443. 2020.


    • The submissions need will be published in Companion Proceedings, e.g., via CEUR Workshop Proceedings (CEUR-WS) and authors have to comply with the CEUR template  (which is different from the LNCS template). We will only publish a CEUR proceedings if we meet the threshold of 40 pages (6 papers).
    • Participants are asked to submit a short evaluation of their own previous or current work that addresses the provision of learning technology within an educational context.
    • The recommended format for submissions, to ensure continuity for future proceedings is the following:
      1. Introduction to the specific EDI topic that is relevant to your work
      2. Description of the work(s) your paper will evaluate from the field of Educational Technology
      3. Evaluation of your work relative to the EDI topic including opportunities, challenges and recommendations for the future.
    • We accept both short (5-9 pages) and regular (10 and more pages) papers.
    • The submissions should be done using EasyChair system using the link below.
    • Each of the submitted paper will be reviewed by at least two members if the Program Committee.

    Submit paper

    *All submissions should be made through Easychair

    Important dates

    • July 31st: Submission deadline for applications/submissions
    • August 1st: Send out notifications of acceptance
    • September 20th - 21st: Workshops are held online


    Tracie Farrell  

    KMi, The Open University, United Kingdom

    Martin Hlosta  

    KMi, The Open University, United Kingdom

    Vaclav Bayer  

    KMi, The Open University, United Kingdom

    Programme Committee.

    • Venetia Brown, Knowledge Media Institute, The Open University, UK
    • Christothea Herodotou, Institute of Education, The Open University, UK
    • Mirko Marras, EPFL, Switzerland
    • Jiten Patel, Diversync Limited, UK
    • Maren Scheffel, ‎Ruhr-Universität Bochum, Germany
    • Angela Stewart, Carnegie Mellon University, USA
    • Allan Third, Knowledge Media Institute, The Open University, UK

    Accepted papers and Schedule

    Accepted papers


    • 9:00 - Ice-breaker and Introductions
    • 9:15 - 9:30 - Keynote (Jiten Patel) - What inclusion really is and why it's important in education

      Jiten Patel is an author and award winning Inclusion and Diversity strategist and practitioner, helping to maximise the value of diversity through conscious inclusion strategies designed to be integrated with:

      • Business planning
      • Policy development
      • Operational Implementation
      • Leadership and Management Development

      His book, Demystifying Diversity (co-authored by Gamiel Yafai), was described by People Management as an important contribution to the field.

    • 9:30 - 10:20 - Paper session 1 (each - 15min presentation, 5-10min discussion)
      • 9:30 - 9:55 - Hildo Bijl
      • 9:55 - 10:20 - Vaclav Bayer, Tracie Farrell, Martin Hlosta
    • 10:20 - 10:35 - Break
    • 10:35 - 11:00 - Activity – Do our main tools work from an EDI perspective? In this session, we will be asking participants to put some of our most common learning technologies under the microscope.
    • 11:00 - 10:50 - Paper session 2 (each - 15min presentation, 5-10min discussion)
      • 11:00 - 11:25 - Amanda Buddemeyer, Erin Walker and Malihe Alikhani
      • 11:25 - 11:50 - Saman Rizvi, Bart Rienties, Jekaterina Rogaten and Rene Kizilcec
    • 11:50 - 12:00 - Participant Feedback



    In case of any questions regarding the workshop, please contact : Tracie Farell   and Martin Hlosta   and mark the subject as LearnTec4EDI.


    The workshop will be in conjunction with the 16th European Conference on Technology Enhanced Learning.