Learning electronics in the fourth-wave industrial age requires an extra effort to motivate students. They often feel frustrated so they do not spend enough time to acquire in depth-knowledge. To enhance student's motivation and engagement, instructional methods of teaching and learning are being tested in some Universities [1]. Among these methods, project based learning (PBL) has proven to be useful to overcome frustration and improve student engagement. This methodology is even more successful if it is implemented using an agile approach, like Scrum. This paper describes a new way to teach electronics based on the use of PBL and Scrum and presents results of its application regarding to the acquisition level of some competences.

Background/Objectives: Conventional psychometric instruments generally show limited ability to correlate with circulating biological markers. ADNe® is a 29-item psychogenic instrument based on a neurotransmitter-weighted response framework, designed to model functional patterns of neurobiological activation rather than classify personality traits. This study aimed to explore whether ADNe® neurofactor scores are associated with plasma concentrations of cortisol, interleukin-6 (IL-6), and testosterone in healthy adults. Methods: Twenty healthy volunteers (15 women, 5 men; mean age 30.3 ± 9.6 years, range 22–61) completed the ADNe® questionnaire and provided a peripheral blood sample on the same day. Plasma was isolated by centrifugation (400 × g, 5 min), and biomarker levels were quantified using ELISA. Statistical analyses included Spearman rank correlations and exploratory multiple linear regression models using the seven neurofactors as simultaneous predictors. Results: Significant associations were identified between selected neurofactors and biomarkers. Sm (attentional amplitude) showed an inverse correlation with testosterone (ρ = −0.567; p = 0.022), while Mn (strategic acuity) was inversely associated with IL-6 (ρ = −0.489; p = 0.046). Exploratory multivariate models showed high apparent explanatory capacity within this dataset (R² = 0.848 for IL-6, 0.735 for testosterone, and 0.569 for cortisol). However, given the small sample size and the number of predictors included, these estimates should be interpreted with caution. Conclusions: In this pilot study, ADNe® neurofactor profiles showed associations with circulating biomarkers, suggesting that this approach may capture dimensions related to physiological regulation. Nevertheless, the findings are exploratory and may be influenced by overfitting and unmeasured confounding variables. Independent replication in larger, pre-registered studies with appropriate validation strategies will be necessary to confirm the robustness and generalizability of these results.