Abstract:

The class composition in master craftsman training is particularly heterogeneous due to the different professional experience, educational backgrounds and ages of the participants. The basic mathematical knowledge differs greatly before the start of the course, which is the basis for problem solving, especially in technical fields of action. Thus, courses in master craftsman training are faced with balancing out different levels of prior knowledge and enabling cognitive connectivity. The aim of this research is therefore to investigate whether e-learning-based mathematics courses can replace face-to-face courses for prospective master craftsmen in terms of motivation, mathematical knowledge and cognitive load.

To counteract heterogeneity of learning groups, methods for internal differentiation of learning material can be combined with digital learning modules. Through self-regulated processing of mathematical tasks and problems, learners become aware of knowledge gaps through automatic feedback. However, digital internal differentiation is still a research gap in vocational education and training. As learners in master craftsman training are adults who have been working for a longer period, differences in the achievement of cognitive learning goals or acceptance of the training are to be expected compared to pupils and students. Both groups receive a one-week basic mathematics course with the same content. In the experimental group (EG), this course is held in self-directed digital modules and in the control group (CG) in a classroom setting.

This paper compares the EG, which consists of automotive engineering master craftsmen students, with the CG, which is composed of precision engineering master craftsmen students. Overall, a mixed-methods approach was chosen, which uses qualitative guided interviews in addition to quantitative data collection in the form of a two-group plan pre-post test. The qualitative data is analysed using structured qualitative content analysis according to Mayring and Fenzl (2019). Furthermore, in the quantitative analysis, a random sample is drawn from the quasi-experimental sample and analysed by the same parameters to compensate for demographic differences between the groups. To determine the differences between the EG and CG, T-tests and U-tests were used.

There are significant differences in overall knowledge (t(9)=-4.247, p=0.004) and one sub-scale of motivation (probability of success, t(6)=-2.486, p=0.047) in favour of the CG. One possible explanation are the problems with the technology of the digital learning modules mentioned in the interviews. One approach to solving this could be additional training in using the learning platform. In the qualitative interviews both groups perceived a subjective learning success. Significant differences in the cognitive load of the participants were only found in the pretest of the controlled sample. The EG has a higher intrinsic cognitive load (t(6)=2.935, p=0.026), which can be attributed to a lack of prior knowledge. The results shows that the use of mathematical digital learning modules cannot replace the repetition of content in presence. Thus, to solve the problem of the heterogeneity in prior knowledge in master craftsman classes, digital learning modules do not suffice. In the future, it is advisable to try out a blended learning approach to combine the advantages of individualised digital learning opportunities and collaborative classroom phases.

Keywords:

Adult education, vocational education, e-learning, mathematical knowledge, master craftsmen, experimental study, mixed methods.