Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer. In this paper we discuss our programme and the lessons learned for industry and research.
Objective: To explore predictors of dropout of patients with chronic musculoskeletal pain from an interdisciplinary chronic pain management programme, and to develop and validate a multivariable prediction model, based on the Extended Common- Sense Model of Self-Regulation (E-CSM). Methods: In this prospective cohort study consecutive patients with chronic pain were recruited and followed up (July 2013 to May 2015). Possible associations between predictors and dropout were explored by univariate logistic regression analyses. Subsequently, multiple logistic regression analyses were executed to determine the model that best predicted dropout. Results: Of 188 patients who initiated treatment, 35 (19%) were classified as dropouts. The mean age of the dropout group was 47.9 years (standard deviation 9.9). Based on the univariate logistic regression analyses 7 predictors of the 18 potential predictors for dropout were eligible for entry into the multiple logistic regression analyses. Finally, only pain catastrophizing was identified as a significant predictor. Conclusion: Patients with chronic pain who catastrophize were more prone to dropout from this chronic pain management programme. However, due to the exploratory nature of this study no firm conclusions can be drawn about the predictive value of the E-CSM of Self-Regulation for dropout.
The quality of mentoring in teacher education is an essential component of a powerful learning environment for teachers. There is no single approach to mentoring that will work in the same way for every teacher in each context. Nevertheless, most mentor teachers hardly vary their supervisory behaviour in response to varying mentoring situations. Developing versatility in mentor teachers' use of supervisory skills, then, is an important challenge. In this chapter, we discuss the need for mentor teacher preparation and explain the focus, content, and pedagogy underlying a particular training programme for mentor teachers, entitled Supervision Skills for Mentor teachers to Activate Reflection in Teachers (SMART). Also, findings from several studies assessing mentor teachers' supervisory roles and use of supervisory skills in mentoring dialogues, before and after the SMART programme, are presented. In addition, implications and perspectives for mentor teacher development and preparation are discussed.
