Conference Agenda

Paper Session 5B: Teacher skills
Thursday, 03/Feb/2022:
4:15pm - 5:45pm

Session Chair: Marina FIORI
Location: Room 2


From a craftsman to a teacher: important competence for vocational teachers


Stockholm University, Sweden

The high demand for vocational teachers in the Swedish VET has led to their recruitment in the early stages of vocational teacher studies or sometimes even before that. Entering this new community of practice, vocational teachers bring in their previous experience from the occupational practice and other prior learning. While teachers crossing boundaries between their previous occupation and their teaching job, they constantly transfer competence from one context to another. In this context, this study aims to explore vocational teachers’ competence through their own perceptions, addressing important competence areas and how they are acquired by teachers, as well as how is competence understood and defined by them. The research design is qualitative, collecting data through semi-structured interviews. Drawing on the perspective of 12 vocational teachers, the study adopts an inductive approach, generating themes from a thematic data analysis. Preliminary findings indicate that competence in vocational teaching, competence in supporting the social aspect of learning, competence relating to the student work placement and the attitude for lifelong learning are crucial in the work of vocational teachers. Competence in these areas is developed through informal continuous professional development (e.g. reading), but also by the connection to the world outside of the school. More specifically, the communication with students´ workplaces and past colleagues have been sources of updating vocational and teaching competence. Regarding professional competence definition, teachers understand competence as an applied form of knowledge, which in their case is the transfer of their prior occupational experience to their students. In other words, competence is action and thus performance seen and evaluated by others, bonding competence to the context is it performed. The study contributes in defining the regime of competence of the vocational teaching profession, supporting teachers´ future development whether it is to be performed in a formal or informal way.

Digital transformation in vocational education: What technology vocational teachers use and what digital competence they need?

Chiara Antonietti, Francesca Amenduni, Alberto Cattaneo

Swiss Federal University for Vocational Education and Training (SFUVET), Switzerland

Vocational learners are expected to develop digital skills to cope with digital transformation and the spread of technologies in different professions. Since regular use of digital tools can positively affect the development of students' digital competence, vocational teachers need to address an effective integration of technology into teaching-and-learning practices. Despite several research contributions emphasize the effectiveness of student-centered pedagogies – supporting an active use of technology by students for interactive and collaborative learning activities –, teachers still seem to use technology mainly in a teacher-centered approach (e.g. personal administrative tasks, preparing lessons, presenting instructional materials). This may be due to the lack of teachers’ digital competence in using interactive and collaborative software in education and in designing learning activities that require students to use these technologies. However, the specific competencies needed to enact a student-centered digital pedagogy remains to be clarified.

The aim of the present study is twofold: 1) to assess the frequency of utility technologies use (e.g., word-processor software, presentation software, e-mail) versus collaborative-interactive technologies use (e.g., collaborative software, interactive digital learning software) for teaching and learning; 2) to evaluate the relationship between teachers’ digital competence and different types of technology use. Results coming from a sample of 2261 vocational teachers show that teachers use collaborative-interactive technologies significantly less frequently than utility ones. Regression analysis further reveals that teachers’ digital competence explains the variance of the technology used by teachers well. More in detail, the Teaching and Learning and Facilitating Learners’ Digital Competence are the two areas of digital competence that mainly predict the use of collaborative-interactive technologies. These two areas are also among the three lowest developed areas in our vocational teachers’ sample. Thus, basic and continuing training should pay stronger attention to stimulating the development of these digital skills among Swiss vocational teachers.

Computer or teacher; who predicts dropout best?

Irene Eegdeman, Chris van Klaveren, Martijn Meeter

Vrije Universiteit Amsterdam, Netherlands, The

Most vocational teachers have their practical theory about signaling at-risk students. They can generate a long list of clues which they use to identify these early dropouts and combine those signals with everyday practice. Nowadays, instead of using a rather standard set of predictors (e.g. preceding GPA, grades, attendance, achievement, ability and personality), more and more studies use machine learning techniques to increase predictive performance.

To predict at-risk students as soon as possible it is necessary to find out if the combination of unobserved factors and practical theories of the teachers can be used to predict dropout. In addition, the predictions of teachers may also be of value to enhance the machine learning algorithms. This study puts the teachers to the test: Are teachers capable of making better predictions than new developed machine learning algorithms?

At the start of the program, after the first 10 weeks and after 20 weeks teachers were asked to estimate the probability that a student will drop out or be successful. In this study we compare the ‘student at-risk’ estimations of teachers with the estimations of two machine learning algorithms (Support Vector Machine and LASSO regression).

As prediction accuracy measure, we calculated the nonparametric Kendall’s τ coefficient between predictions and outcome. We also calculated precision (relevant proportion of the selected dropouts) and sensitivity (correctly identified dropouts) by comparing the estimations of the teachers (or the algorithms) with the actual dropout.

We can conclude that teachers (as a group) are indeed capable of making a better assessment of the at-risk students than the machine learning algorithms at the start of the program which indicates that the input of teachers can be useful to increase the performance of the machine learning algorithms.

Socio-emotional competences in vocational education and training: state of the art and guidelines for interventions

Matilde WENGER, Florinda SAULI, Marina FIORI

Swiss Federal University for Vocational Education and Training, Switzerland

Interventions and scientific contributions on social and emotional competences are flourishing in the educational contest. In contrast, we observe few programs in Europe and a dearth of scientific contributions regarding socio-emotional interventions in the vocational education and training (VET) literature. Our purpose with this paper is twofold: a) we provide the state of the art on existing scientific publications about socio-emotional training interventions in VET and a summary of existing programs at the European level; b) by relying on the analysis of relevant cases in the educational literature, we provide guidelines about how scientifically-based interventions on socio-emotional competences in VET could be developed. Ultimately our goal is to open a discussion around how socio-emotional training may be regarded as a novel domain of research for VET scholars and practitioners.