Blended Learning (BL) is defined as a combination of face‐to‐face and digital activities that, in recent years, has been adopted more and more frequently by Higher Educational Institutions (HEIs). In the engineering field, the adoption of BL allows creating challenging situations for students with industry‐like problems to foster the acquisition of advanced problem‐solving skills. Thus, it can be used to enhance traditional learning by enriching it with new aspects, allowing to update the Intended Learning Outcomes traditionally defined by teachers. Although prior coronavirus disease 2019 (COVID‐19) teachers had the time to prepare and programme the transition to BL, during the pandemic they had to abruptly move to the full digital delivery of the content, requiring technological and organizational adaptation, as well as change in the content teaching and assessment methods. Through a systematic literature review, this paper aims to understand how BL has been implemented in the engineering field by HEIs, discussing if and how the learning expectations of teachers (evaluated through Bloom's Taxonomy) change when using different mixes of face‐to‐face and digital activities and when the target audience changes. More specifically, the investigation addresses how content and learning expectations are split and set in face‐to‐face and digital settings. Additionally, the interest is towards understanding how COVID‐19 impacted the adoption of BL, not only during the pandemic but also after.

Predicting the optimal recovery time following a soccer player’s injury is a complex task with heavy implications on team performance. While most current decision-based models rely on the physician’s perspective, this study proposes a machine learning (ML)-based approach to predict recovery duration using three modeling techniques: linear regression, decision tree, and extreme gradient boosting (XGB). Performance is compared between the models, against the expert, and together with the expert. The results demonstrate that integrating the expert’s predictions as a feature improves the performance of all models, with XGB performing best with a mean $R^2$ score of $0.72$, outperforming the expert’s predictions with an $R^2$ score of $0.62$. This approach has significant implications for sports medicine, as it could help teams make better decisions on the return-to-play of their players, leading to improved performance and reduced risk of re-injury.

Every soccer game influences each player’s performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player’s energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players’ responses to various game situations.

In the field of muscle fatigue models (MFMs), the prior research has demonstrated success in fitting data in specific contexts, but it falls short in addressing the diverse efforts and rapid changes in exertion typical of soccer matches. This study builds upon the existing model, aiming to enhance its applicability and robustness to dynamic demand shifts. The objective is to encapsulate the complexities of soccer dynamics with a streamlined set of parameters. Our refined model achieved a slight improvement in the $R^2$ score in the maximum hand-grip test, increasing from $0.87$ to $0.89$ compared to the existing model. It also demonstrated dynamic change robustness in a soccer-specific 1 min drill and 15 min treadmill protocol extracted from the literature. Through individualized fitting on a 10-repetition 80 m sprint test for a soccer player, the model exhibited $R^2$ scores between $0.62$ and $0.80$. Furthermore, when tested with actual soccer match data, it maintained a robust performance, with the average $R^2$ scores ranging from $0.70$ to $0.72$. The proposed approach holds the potential to advance the understanding of tactical decisions by correlating them with real-time physical performance, offering opportunities for more informed strategies and ultimately enhancing team performance.

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players’ energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder–Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of $396.87\pm 61.42$ and root mean squared error (RMSE) of $520.69\pm 88.66$ achieved by our model, as opposed to the $B_1$ MAE of $429.04\pm 84.87$ and RMSE of $581.34\pm 185.84$, and $B_2$ MAE of $421.57\pm 95.96$ and RMSE of $613.47\pm 300.11$ observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players’ responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics.