Abstract
There are several educational software (ES) used in the classroom environment for the teaching and learning of geometric contents that are part of the Portuguese basic education mathematics program. There are studies that show that the use of this type of artifact has a fundamental role in the behavior of students, raising, among other aspects, a greater motivation for learning mathematics. The aim of this work is to explore and describe implications for the behavior and learning of students in the 7th grade of Portuguese basic education, in face of a pedagogical practice that involves carrying out tasks using ES Plickers, in the theme similarities of the domain geometry and measurement, throughout intervention carried out. The adopted methodology presents characteristics of a quasi-experimental study. The participants were 61 students from three classes of a school in the north of Portugal, followed during eight consecutive classes. A set of tasks using Plickers, tests and a questionnaire survey were used as instruments for data collection. The results point to positive increments, at a behavioral level, as well as in the evolution of learning, in view of the use of this methodology in the classroom.

Abstract

Abstract
Virtual reality (VR) technologies have been evolving in recent decades, allowing simulating real-life situations in controlled and safe virtual environments, where they reveal increasingly realistic details. There is an increase in the number of publications on virtual reality interventions in different areas, especially in Education, particularly in interventions with children diagnosed with Autism Spectrum Disorders (ASD). The lack of social skills prevents these children diagnosed with ASD to respond appropriately and adapt to the most diverse daily social situations. On this basis, VR has revealed a set of evidences that present promising results and show great acceptance among the diversified population with ASD. In order to understand how VR may contribute to the improvement of skills, allowing their inclusion, we conducted a systematic review of the literature. We present considerations on the selected studies, identifying the main gaps and pointing out possible directions for future research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Learning environments unquestionably enable learners to develop their pedagogical and scientific processes efficiently and effectively. Thus, considering the impossibility of not having conditions of autonomy over the routine underlying the studies and, consequently, not having guarantees of the learning carried out makes the learners experience gaps in the domain of materials adequate to their actual needs. The paper’s objective is to present the relevance of the applicability of Artificial Intelligence in Recommendation Systems, reinforced through the Assurance of Learning, oriented towards adaptive-personalized practice in corporate e-learning contexts. The research methodology underlying the work fell on Design Science Research, as it is considered adequate to support the research, given the need to carry out the design phases, development, construction, evaluation, validation of the artefact and, finally, communication of the results. The main results instigate the development of an Adaptive-Personalized Learning framework for corporate e-learning, provided with models of Artificial Intelligence and guided using the Assurance of Learning process. It becomes central that learners can enjoy adequate academic development. In this sense, the framework has an implicit structure that promotes the definition of personalized attributes, which involves recommendations and customizations of content per profile, including training content that will be suggested and learning activity content that will be continuously monitored, given the specific needs of learners. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
To tackle wildfires and improve forest biomass management, cost effective and reliable mowing and pruning robots are required. However, the development of visual perception systems for forestry robotics needs to be researched and explored to achieve safe solutions. This paper presents two main contributions: an annotated dataset and a benchmark between edge-computing hardware and deep learning models. The dataset is composed by nearly 5,400 annotated images. This dataset enabled to train nine object detectors: four SSD MobileNets, one EfficientDet, three YOLO-based detectors and YOLOR. These detectors were deployed and tested on three edge-computing hardware (TPU, CPU and GPU), and evaluated in terms of detection precision and inference time. The results showed that YOLOR was the best trunk detector achieving nearly 90% F1 score and an inference average time of 13.7ms on GPU. This work will favour the development of advanced vision perception systems for robotics in forestry operations.

Abstract
Bin picking is a challenging task involving many research domains within the perception and grasping fields, for which there are no perfect and reliable solutions available that are applicable to a wide range of unstructured and cluttered environments present in industrial factories and logistics centers. This paper contributes with research on the topic of object segmentation in cluttered scenarios, independent of previous object shape knowledge, for textured and textureless objects. In addition, it addresses the demand for extended datasets in deep learning tasks with realistic data. We propose a solution using a Mask R-CNN for 2D object segmentation, trained with real data acquired from a RGB-D sensor and synthetic data generated in Blender, combined with 3D point-cloud segmentation to extract a segmented point cloud belonging to a single object from the bin. Next, it is employed a re-configurable pipeline for 6-DoF object pose estimation, followed by a grasp planner to select a feasible grasp pose. The experimental results show that the object segmentation approach is efficient and accurate in cluttered scenarios with several occlusions. The neural network model was trained with both real and simulated data, enhancing the success rate from the previous classical segmentation, displaying an overall grasping success rate of 87.5%.