Horizon 2020 - ENLIVEN

Encouraging Lifelong Learning for an Inclusive and Vibrant Europe

Calling for workshop contributors: Developing IDSS: Societal Challenges and Technical Strategies

The European Symposium Series on Societal Challenges in Computational Social Science 2017: Inequality and Imbalance

15th November, 9.30 – 12.30
The Alan Turing Institute, London

The concept behind this workshop – ‘Developing Intelligent Decision Support Systems: Societal Challenges and Technical Strategies’– has resulted from the interplay of ideas and ways of thinking between two University of Nottingham colleagues, one from Computer Science and one from Education, working on the development of an Intelligent Decision Support System (IDSS) within a H2020 lifelong learning project, entitled Enliven.

Enliven (Encouraging Lifelong Learning for an Inclusive and Vibrant Europe) brings together an inter-disciplinary team at the University of Nottingham with social science colleagues across 9 countries in Europe. Its objective is to develop and provide an innovative model and mechanism to support policy debate, policy formation and policy evaluation in lifelong learning, with a particular focus on young adults from the most disadvantaged backgrounds, and those furthest from the education, training and labour market.

Dealing with issues of social exclusion, and the barriers that young people face, the ENLIVEN research combines bounded agency theory from the social sciences with case-based reasoning from computer science (in particular, artificial intelligence). Bounded agency is a key concept in Enliven which recognises the complex interplay between personal/individual motivation and the broader structural and cultural conditions in which a person has been raised – specifically the institutional and labour market settings and the social support available. The theory argues that such factors are as important in shaping a person’s decision to engage in lifelong learning/adult education as their individual drive or motivation (‘agency’).

An IDSS system “uses artificial intelligence, machine learning, taught algorithms and data analytics to help support decision-making in real-time, by setting out possible courses of action and evaluating the likely results of these proposed actions” [1] . An IDSS will suggest types of action which have been previously employed and enable them to be assessed against suitable criteria.

Bringing together the two theories and determining how they might meet the needs of policy makers and other end users of the research has been challenging. A starting position was to try to ascertain the outcome of policies across Europe in relation to interventions and programmes targeting disadvantaged young adults, both in terms of what worked and what did not. The process of finding programmes which have been well-evaluated, at the individual, the intervention/practitioner level and the policy maker level, has demonstrated that there is little commonality across countries or across programmes in terms of how interventions are evaluated, and at what level. Establishing the needs of end users for the research and the IDSS has also been problematic as policymakers and who they are/where they are located varies across Europe. We have also argued that practitioners are important end users as they develop and deliver programmes and that the ultimate end users are the young people themselves. Young people have a stake in how these policies are enacted that goes beyond systems and evaluations and has a direct impact on their lives. We already know, from previous research, that “people living in specially disadvantaged circumstances are less likely to engage in lifelong learning, in part because they lack the financial resources to fund their studies and believe that there will be few economic benefits. In addition, their life experiences may have reinforced a sense of powerlessness and inability to control risk” (Róbert 2012, p. 88) [2].

Our focus has become one of knowledge democracy and we are keen to explore the ontological differences between data expectations at policy maker level and at target community level and how data can serve knowledge creation for those most excluded in society.

To enable the Nottingham team to examine the needs of end users and to collect and analyse data, the team has been working with an independent community-based practitioner, Richard Hazledine, who is evaluating a project which supports NEET young people in Nottinghamshire and Derbyshire, called Young and Successful. This is a five year programme, managed by Groundwork Greater Nottingham, which is designed to provide support services to develop young peoples’ employability. The programme particularly emphasises the importance of trust building and ongoing communication and the heterogeneity of the young people who are targeted, offering a ‘person centred’ and individualised approach, and focusing on fostering autonomous thinking and decision making. It recognises that the journey to develop employability is not linear and consistent between individuals and that attempts to standardise interventions have a high probability of failure and can have unintended deleterious outcomes based on erroneous assumptions.

The work with Enliven allows for the application of conceptual approaches to using data to help identify the specific needs and situations facing young people. Though our partnership is still in its infancy, we ultimately aim to ‘road test’ data to provide opportunities to spot underlying trends and patterns which contribute to inequalities and bounded agency and to identify those which foster autonomy and emotional, social and economic well-being.

We are seeking to invite contributions for attendance at the workshop from people working in either the social sciences or computer science who have an interest in the interface between the two disciplines and the sharing of best practice. We have an open slot from 10.30 – 11.10 which allows for two sets of contributors, each offering a presentation of 20 minutes. We would like you to focus on issues emerging from the interface between computational science and social science and, in particular, how you have determined who the end users are and how to create data which meets their needs.

Please submit a brief outline of your proposed presentation of 200-500 words to the Workshop Committee by 5 pm on 31st October We will respond by 3rd November. Contact: sharon.clancy1@nottingham.ac.uk

**Please click HERE for full details of the proposal.**


[1] Field, J., the Learning Professor, blog, Mechanising education policy with intelligent decision support systems, Posted on January 18, 2015, Accessed 20 September 2017).

[2] RÓBERT, P. (2012) The sociodemographic obstacles to participating in lifelong learning across Europe. In RIDDELL, S., MARKOWITSCH, J. and WEEDON, E. (Eds.), (2012) Lifelong Learning in Europe: Equity and Efficiency in the Balance, Bristol: The Policy Press (pp. 87-101).


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