Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry; the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria: mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy: R² > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8.

This study aims to assess learning outcomes and identify students’ misconceptions in plant classification. We conducted a questionnaire survey with undergraduate and master’s students. The qualitative analysis of the students’ responses made it possible to shed light on the difficulties of assimilation of many notions and also to identify the different misconceptions constructed during their learning courses about plant organisms. The findings indicate that some students are not motivated to take the course on plant classification. This demotivation is reinforced further by students’ perceptions of plant classification, especially that it is not important and not useful for learning other biology specialities. The findings also show that more than half of the students who participated in this study consider plant systematics a difficult subject. We also note that some of the students surveyed seem not to have acquired many concepts of plant biology including concepts related to the biology, reproduction and evolution of plants. Thanks to this, we could see different types of problems in plant classification, which constitute misconceptions hindering learning. Initial training in plant biology does not appear to have a significant effect in modifying students’ misconceptions related to plant classification.

Given the recognized advantages of additive manufacturing (AM) printing systems in comparison with conventional subtractive manufacturing systems, AM technology has become increasingly adopted in 3D manufacturing, with usage rates increasing dramatically. This strong growth has had a significant and direct impact not only on energy consumption but also on manufacturing time, which in turn has generated significant costs. As a result, this problem has attracted the attention of industry actors and the research community, and several studies have focused on predicting and reducing energy consumption and additive manufacturing time, which has become one of the main objectives of research in this field. However, there is no effective model yet for predicting and optimizing energy consumption and printing time in a fused deposition modeling (FDM) process while taking into account the correct part orientation that minimizes both of these costs. In this paper, a neural-network-based model has been proposed to solve this problem using experimental data from isovolumetrically shaped mechanical parts. The data will serve as the basis for proposing the appropriate model using a specific methodology based on five performance criteria with the following statistical values: R2-squared > 99%, explained variance > 99%, MAE < 0.99%, MSE < 0.02% and RMSE < 1.36%. These values show just how effective the proposed model will be in estimating energy consumption and FDM printing time, taking into account the best choice of part orientation for the lowest cost. This model provides a global understanding of the primary energy and time requirements for manufacturing while also improving the system’s cost efficiency. The results of this work can be extended and applied to other additive manufacturing processes in future work.

The teaching of Earth Sciences (ES) is particularly delicate and seems to be problematic for both learners and Moroccan teachers for multiple reasons. Based on this observation, this study aims at identifying the difficulties related to the teaching of ES by exploring the points of view of the Moroccan teachers toward this field. As an investigative tool, we used a questionnaire and semi-directive interviews with nearly 122 secondary school teachers of Life and Earth Sciences (LES). The results of our survey revealed that the major difficulties that hinder the teaching of natural sciences are mainly related to the teachers’ university studies. Most of them had training in biology as well as in the relationship that the natural sciences maintain within time and space, the limited abstraction capacity of unmotivated learners, and the inadequacy of their prerequisites in these sciences. On the other hand, they were aware of the demotivating geological knowledge taught to the learners and the lack of initial and continuous training for teachers, especially for those who specialized in natural sciences.

The preservation of biodiversity has become a major concern in contemporary society. It is essential to understand teachers’ representations of biodiversity to effectively guide educational efforts because of the significant role of education in raising awareness and promoting sustainable behavior. Through a survey of a sample of 118 teachers, we adopted a mixed-method approach combining both a closed questionnaire and interviews. We used these methods to explore teachers’ representations of the concept of biodiversity as an integral part of the school curriculum, as well as to gather their views on certain features of its teaching. The results state that the majority of teachers perceive biodiversity as a whole and in its most complex relationships. During their professional experience, they have developed a biocentric and anthropocentric conception of biodiversity. This study provides valuable information to guide future pedagogical practices and educational policies in sustainable education.