Personalized strategies for academic success in learning anatomy: Exploring metacognitive and technological adaptation in medical students.

Personalization of learning is an educational strategy rooted in metacognition and is significant in academic training. This is especially true in medical contexts. This study explored the relationship between the metacognitive profile of students of human anatomy, the classification of questions according to their difficulty, and the anatomical domain. It also covered the integration of educational technologies to create personalized learning environments. The identification of metacognitive profiles ("Active", "Pragmatic", "Theoretical", and "Reflective") has been highlighted as a critical influence on students' responses to different pedagogical approaches. Personalized adaptation based on these profiles has shown potential for improving grades and increasing student satisfaction and engagement with learning. The results revealed variations in student performance in relation to different pedagogical approaches, learning units, and evaluation modalities. The "Experience" evaluation modality, personalized according to metacognitive profiles, level of competence, and learning objectives, resulted in higher average scores. However, there was significant variability in the results. Those findings confirm the effectiveness of metacognitive adaptation in improving academic performance. Furthermore, they provide a solid basis for formulating personalized and effective pedagogical strategies in medical education. They recognize the influence of metacognitive profiles on student performance and contribute to advancing medical pedagogy.

Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation.

Gamification has appeared as an alternative educational methodology to traditional tools. Specifically, in anatomy teaching, multiple technological applications have emerged in response to the difficulties of accessing cadaveric material; however, there is insufficient information about the effects of these applications on the performance achieved by students, or about to the best way to adapt learning to meet their educational needs. In this study, we investigated how teaching human anatomy through a mobile gamified technological tool containing recommendation systems can be combined with a virtual assistant to improve the learning and academic performance of medical students in the Anatomy Department at the Universidad de La Frontera in Temuco, Chile and the Anatomy Department at the Pontificia Universidad Católica de Chile. In total, 131 students participated in the experiment, which was divided into two case studies. The main findings led to the conclusion that gamified components support students in learning anatomy. In addition, the predictions and recommendations provided by the virtual assistant enabled the academic aspects that the students needed to improve to be extracted adequately. Future work is expected to support adaptive learning by incorporating new artificial intelligence in education elements that can generate personalized scenarios for studying anatomy based on the application.

ABENEARIO: A system for learning early maths with ABN.

ABN (Abierto Basado en Números-Open Calculation Based on Numbers) is a method for teaching basic arithmetic operations in primary education that has become popular in recent years and that is based on the decomposition of numbers through manipulative materials that encourage mental calculation. Currently there is limited number of tools that can be used to support the ABN method and so this article presents the design and development of two tools that facilitate learning with this method, a physical device, ABENEARIO-P, and a virtual device (web application), ABENEARIO-V, that complements the physical device. In addition, a study of the use of these tools was carried out with 80 learners (ages 7 and 9) and 9 teachers with a focus on ABENEARIO-V. The results of this study showed a positive evaluation of the tool by both learners and teachers, an adequate time to complete the mathematical operations assigned to learners and an improvement in performance as the tool was used. As a conclusion, it is important to provide adequate tools that can support teachers and learners in the practice with the ABN method as in the case of ABENEARIO-P and ABENEARIO-V. Limitations refer mainly to the context of the study, which was conducted at a time of severe social distance restrictions during the COVID-19 pandemic on touching physical devices or being able to gather a larger number of learners in the classroom.

A cost-effective IoT learning environment for the training and assessment of surgical technical skills with visual learning analytics.

BACKGROUND: Surgeons need to train and certify their technical skills. This is usually done with the intervention of experts who monitor and assess trainees. Nevertheless, this is a time-consuming task that is subject to variations among evaluators. In recent decades, subjectivity has been significantly reduced through 1) the introduction of standard curricula, such as the Fundamentals of Laparoscopic Surgery (FLS) program, which measures students' performance in specific exercises, and 2) rubrics, which are widely accepted in the literature and serve to provide feedback about the overall technical skills of the trainees. Although these two elements reduce subjectivity, they do not, however, eliminate the figure of the expert evaluator, and so the process remains time consuming. OBJECTIVES: The objective of this work is to automate those parts of the work of the expert evaluator that the technology can measure objectively, using sensors to collect evidence, and visualizations to provide feedback. We designed and developed 1) a cost-effective IoT (Internet of Things) learning environment for the training and assessment of surgical technical skills and 2) visualizations supported by the literature on visual learning analytics (VLA) to provide feedback about the exercises (in real time) and overall performance (at the end of the training) of the trainee. METHODS: A hybrid approach was followed based on previous research for the design of the sensor based IoT learning environment. Previous studies were used as the basis for getting best practices on the tracking of surgical instruments and on the detection of the force applied to the tissue, with a focus on reducing the costs of data collection. The monitoring of the specific exercises required the design of sensors and collection mechanisms from scratch as there is little existing research on this subject. Moreover, it was necessary to design the overall architecture to collect, process, synchronize and communicate the data coming from the different sensors to provide high-level information relevant to the end user. The information to be presented was already validated by the literature and the focus was on how to visualize this information and the optimal time for its presentation to end users. The visualizations were validated with 18 VLA experts assessing the technical aspects of the visualizations and 4 medical experts assessing their functional aspects. RESULTS: This IoT learning environment amplifies the evaluation mechanisms already validated by the literature, allowing automatic data collection. First, it uses IoT sensors to automatically correct two of the exercises defined in the FLS (peg transfer and precision cutting), providing real-time visualizations. Second it monitors the movement of the surgical instruments and the force applied to the tissues during the exercise, computing 6 of the high-level indicators used by expert evaluators in their rubrics (efficiency, economy of movement, hand tremor, depth perception, bimanual dexterity, and respect for tissue), providing feedback about the technical skills of the trainee using a radar chart with these six indicators at the end of the training (summative visualizations). CONCLUSIONS: The proposed IoT learning environment is a promising and cost-effective alternative to help in the training and assessment of surgical technical skills. The system shows the trainees' progress and presents new indicators about the correctness of each specific exercise through real-time visualizations, as well as their general technical skills through summative visualizations, aligned with the 6 more frequent indicators in standardized scales. Early results suggest that although both types of visualizations are useful, it is necessary to reduce the cognitive load of the graphs presented in real time during training. Nevertheless, an additional evaluation is needed to confirm these results.

Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review.

The assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT (Internet of Things), the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a more objective and automated assessment of surgical technical skills. This paper presents a systematic literature review of papers published after 2013 discussing the objective and automated assessment of surgical technical skills. 101 out of an initial list of 537 papers were analyzed to identify: 1) the sensors used; 2) the data collected by these sensors and the relationship between these data, surgical technical skills and surgeons' levels of expertise; 3) the statistical methods and algorithms used to process these data; and 4) the feedback provided based on the outputs of these statistical methods and algorithms. Particularly, 1) mechanical and electromagnetic sensors are widely used for tool tracking, while inertial measurement units are widely used for body tracking; 2) path length, number of sub-movements, smoothness, fixation, saccade and total time are the main indicators obtained from raw data and serve to assess surgical technical skills such as economy, efficiency, hand tremor, or mind control, and distinguish between two or three levels of expertise (novice/intermediate/advanced surgeons); 3) SVM (Support Vector Machines) and Neural Networks are the preferred statistical methods and algorithms for processing the data collected, while new opportunities are opened up to combine various algorithms and use deep learning; and 4) feedback is provided by matching performance indicators and a lexicon of words and visualizations, although there is considerable room for research in the context of feedback and visualizations, taking, for example, ideas from learning analytics.