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Centre for Operational Excellence (COPE)

Faculty of Economics and Business
Projects Logistics in schools

Logistic knowledge applied to organising the educational process

We are in the midst of a great change in education: the transfer to personalised teaching and learning. It is not easy to organise this. How do you ensure that each child can learn at the right moment and according to their needs? Inspiration and knowledge derived from logistics may help solve this problem. In collaboration with students and a large network of secondary schools, researchers associated with the University of Groningen (RUG) are working on this issue.

The aim of the Educational Logistics for Personalised Learning project is to develop new logistic techniques together for the purpose of organising personalised teaching. Knowledge of logistics is demonstrated in many places in our society, for instance, in ports. ‘While it is possible to handle all the containers on a vessel separately, we can also cluster containers with similar properties’, Professor Iris Vis explained. These insights can also be placed in an educational context. This provides inspiration for the development of techniques which can identify clusters of pupils based on, for instance, speed of learning and progress. Such a group can then be coupled with the right teacher.

The added value of scientific research
The researchers cooperate with the Zo.Leer.Ik! schools network , which comprises representatives of secondary schools adopting personalised teaching and learning. Wim Kokx, Chairman of the Board of Governors of the Vlaardingen Schiedam public school group (Openbare Scholengroep Vlaardingen Schiedam) is the chairman of the Zo.Leer.Ik! network. He related: ‘The educational landscape has been the same for a very long period with regard to the way in which we organise instructions and go over and test learning content. Personalised teaching and learning is about the question: how can we bring teaching and learning better into line with the pupils? The cooperation between the schools’ network and RUG provides insight into how we should tackle this organisational-wise, for instance, where the use of timetables is concerned.’

Knowledge where human beings play a part
The question asked by schools is, by the same token, a request for a new fundamental research study into developing logistic models in which human beings are at the centre, because the issue of education is about pupils. A good example in this context is incorporating pupils' choice behaviour in making predictions about which class a pupil wishes to attend. ‘By the way, the Operations Research Group which conducts the research has more experience with research on logistics and the role of human beings in service-related sectors, such as the healthcare sector and libraries’, Prof. Vis said.

Sub projects
The introduction of personalised education involves educational and organisational change. The organisation of personalized learning requires a new paradigm for educational logistics in order to link student learning demands to a range of learning activities. In this research project we analyse how personalised learning can be facilitated by creating flexibility in planning learning activities, composing groups, assigning teachers and choosing working methods. Important building blocks of this research project are the conceptualisation and visualisation of personalized learning, data analyses and learning questions, and the simulation and development of new control rules. The preliminary conclusions show that personalised learning can be organised and that logistics knowledge can help. Many answers have already been found, but there are still plenty of questions for follow-up research projects in collaboration with schools in secondary education.

Below we give a brief overview of subprojects in each of the different themes.

Conceptualising lean

  • Conceptualising lean in secondary education: who is the customer in personalised learning?
    (Jorick Dam, 2016, Conceptualisating lean in secondary education: What is the pupil in personalised learning?, Master Thesis, University of Groningen).
  • Quality management in personalised learning
    (Rosa Vermeulen, 2016, Lean tailored quality management: defining the requirements of a quality management system in personalized learning, Master Thesis, University of Groningen).

Visualisation of personalised learning

  • Flow diagrams to analyse processes in personalised learning at a micro-level.
  • Value Stream Mapping to describe the current en desired situation to identify waste in the system.

Results have been described in the report (just in Dutch) of Spronk, C. et al. (2016).

Data analyses and learning demand

  • Analysing choices between a set of courses made by pupils during elective hours.
    (refer to Spronk, C. et al. (2016) and Tang, Q, 2016, Forecasting demand in pupils' lecture selection in personalized learning, Master Thesis, University of Groningen).
  • Designing a tool to identify student learning demand: Klunga
    (Hummel, J., 2017, Designing leagile methods for identification and clustering demand in personalized learning, Master Thesis, University of Groningen).

Design of new control policies

  • Making schedule adjustments
    (Veenstra, M., Vis, I.F.A., 2016, School timetabling problem under disturbances, Computers & Industrial Engineering 95, 175-186).
  • Flexible batching of student learning demand
    (Hahn, J. M., 2016, Flexible batching in personalized learning: a newly developed tool for secondary education. Master Thesis, University of Groningen).
  • Flexible planning per hour of learning activities based on student learning demand
    (Wouda, N.A., Aslan, A., Vis, I.F.A., 2020, An adaptive large neigbourhood search metaheuristic for hourly learning activity planning in personalised learning, working paper University of Groningen).
  • Flexible planning per week of learning activities based on student learning demand
    (Aslan, A., Bakir, I., Vis, I.F.A., 2020, A Dynamic Thompson Sampling Hyper-Heuristic Framework for Learning Activity Planning in Personalized Learning, European Journal of Operational Research, 286(2), 673-688).
  • Planning of elective hours (NRO-project)
    (Hakkens et al., 2020).
  • Staff planning in personalised learning
    (Aslan, A., Van Foreest, N., Bakir, I., Vis, I.F.A. (2020), Dynamic Resource Allocation for Flexible Group Instruction Services in Learner-Centered Schools, working paper, University of Groningen).

Simulation

  • simulation model of traditional learning for one specific school
  • simulation model of personalised learning for one specific school
  • generic simulation model for personalised learning

Researchers who have participated in designing the simulation models: Jose Lopez Alvarez, Niels Wouda, Jon Hummel, Tonny Romensen, Wim Kokx en Iris Vis. In the following reports (Dutch only) more information can be found on the models (Spronk, C. et al. (2016) en Hakkens et al., 2020).


Online workshops and a podcast

  • In this series of 13 mini-workshops, Iris Vis and Wim Kokx take you through how knowledge and inspiration from the logistics world can help design new logistics processes in education. This series (in Dutch) can be found here.

  • An earlier podcast made at Kennisnet about logistics for personalised learning.

Background

The aim of personalised teaching and learning in secondary education is that each pupil gets the appropriate type of education for each subject at the right time in order to prevent waste from occurring in the learning process. The coordination of the activities and the decision-making relating to the pupils' individual learning needs at the workplace take place in an interaction between teacher and pupil. In order to design a high-quality, flexible and, at the same time, cost-efficient system, it is to be expected that a new logistic paradigm is required for organising the core educational processes.

Research

The aim of the research is to develop methods which create flexibility in scheduling, group composition, teacher deployment and selection of working methods. This should result in all pupils being able to achieve their goals for each individual subject at any desired moment, based on their own learning speed, level and ambition. We develop state-of-the-art techniques to facilitate custom-made solutions based on real-time information in the learning process. These techniques will first be validated through simulation experiments using data from schools. This film shows an early version of a simulation model.

Building blocks

We distinguish the following building blocks in the research:

  • conceptualisation
  • visualisation
  • data analyses
  • simulation and
  • the development of new coordinating rules


So far, the research has produced the first findings about applying lean to the core educational processes in secondary education and about the challenges faced in developing new logistic tools for designing and managing personalised teaching and learning.

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