Differences in Perception Matter – How Differences in the Perception of Training Quality of Trainees and Trainers Affect Drop-Out in VET

Abstract

AbstractThe dual system of vocational education and training (VET) and its quality have recently been receiving scientific attention, partly due to high drop-out rates and to politically-motivated efforts to increase participation in the system (Le Mouillour, 2018). However, it remains controversial as to how quality within training companies should be measured, and from whose perspective, and how the quality of training affects drop-out. Empirical studies mostly target the quality perceptions of trainees or of trainers in isolation. The extent to which output factors such as drop-out rates are influenced by variations in perceptions of quality between both these parties has to this point not been tested. The object of the present study was to present a novel bilateral approach to assessing training quality. For this reason, the effects on drop-out intention of differences in the perception of in-company training quality (incongruences) between trainees and trainers from the same company were examined. An online survey was conducted involving 311 commercial trainees and training officers from 30 German companies. A framework model of workplace learning (Tynjälä, Vocations and Learning,6(1), 11–36, 2013) and a short questionnaire “VET-LQI” (Böhn and Deutscher, Zeitschrift für pädagogische Psychologie: ZfPP, 33, 49–70, 2020) served as the theoretical basis and test instrument respectively. Responses from trainees were matched to those of their corresponding trainers, in order to determine possible differences for every item and scale. Following a weighting method regarding the absolute rating level, 15 input- and process-quality factors of divergence, so called “difference accounting multi-perspective scores” were used as independent variables in multiple regression analyses. The results show that differences in perception of quality not only have a significant effect on drop-out intentions, but also explain drop-out intentions more generally, and with a higher predictive power than the conventional method of merely focusing on the quality perceptions of trainees (adjusted R2 = .439 > .333).