Smart Learning in Logistics

Background and Problem

The high degree of opacity in the continuing training market means that it is becoming ever more difficult for individual employees to identify suitable provision that is relevant to their professional development. This applies in particular to so-called ‘educationally disadvantaged’ people, which in turn reinforces the selectivity of participation in continuing training. Taking up this problem, the project aims to develop an AI-based platform in the sense of a digital learning ecosystem and an open learning setting. In SMALO, AI-supported search algorithms display continuing training provision in the logistics sector to those interested in continuing training in a personalised way. The intention is to reduce the confusing array of offers, to improve the matching between those interested in continuing training and continuing training providers, and to make it easier for those interested in continuing training to access educational provision.

Research question

• How must platforms be designed in order to enable individual, flexible learning?

• How must digital continuing training provision be designed in order to increase participation in job-related continuing training, whether analogue or digital?

Approach and Methods

Desk research, iterative prototype development, user tests.

• Guideline-based expert interviews (N = 14),

• a standardised questionnaire administered in continuing vocational training courses (N = 122). This restricted the sample to people taking part in continuing training, in line with the guiding research question concerning the optimal continuing training format from the employees’ perspective and the question of the forms of support in the learning process, so that information for the design of a continuing training platform could be derived.

Results

1. SMALO set out to adapt the approach of a ‘digital learning ecosystem’ (Seufert 2018), originally developed for vocational education and training, to continuing vocational training. The SMALO learning ecosystem was based on the following design features.

2. Creation of learning pathways for the warehouse logistics specialist (Fachkraft für Lagerlogistik), the IHK-certified logistics master (Geprüfter Logistikmeister) and the Hanselogistiker. The learning pathways distinguished between adaptation-oriented continuing training (maintaining employability) and advancement-oriented continuing training.

3. As the various learning formats became more differentiated, the demands on the possibilities for – and the need for – support in the learning process also increased. Among industrial and technical employees, our survey showed a clear preference for face-to-face provision and the wish for a fixed contact person during the learning process.

Dissemination and Transfer Activities

Ahrens, D. (2022): Risiken eines neuen digital divide im Zuge der Entgrenzung des Lernens durch Plattformen. In: bwp@ Berufs- und Wirtschaftspädagogik – online, Ausgabe 43, 1-16. Online: https://www.bwpat.de/ausgabe43/ahrens_bwpat43.pdf

Ahrens, D. (2023). Haben wir die richtigen Angebote für Geringqualifizierte? Herausforderungen bei der Planung und Weiterentwicklung neuer Weiterbildungsangebote. DENK-doch-MAL.de. Das Online-Magazin (03-23). https://denk-doch-mal.de/daniela-ahrens-haben-wir-die-richtigen-angebote-fuer-geringqualifizierte-herausforderungen-bei-der-planung-und-weiterentwicklung-neuer-weiterbildungsangebote/

Continuing training as a strategy for securing skilled labour in logistics – workshop for companies and those responsible for continuing training (20 April 2023)

Preparation of handouts on didactic design and human resource development measures (https://www.invite-toolcheck.de/html/de/SMALO.php)

Ahrens, D. & Siemer, C. (2022). Smartes Lernen in der Logistik. Thesenpapier zu den empirischen Zwischenergebnissen, (Hrsg.) Institut Technik und Bildung (ITB), Universität Bremen.