Abstract
Assessment plays a central role in any educational process as a way of evaluating the students' knowledge on the concepts associated with learning objectives. The assessment of free-text answers is a process that, besides being very costly in terms of time spent by teachers, may lead to inequities due to the difficulty in applying the same evaluation criteria to all answers. This paper describes a system composed by several modules whose main goal is to work as a formative assessment tool for students and to help teachers creating and assessing exams as well monitoring students' progress. The system automatically creates training exams for students to practice based on questions from previous exams and assists teachers in the creation of evaluation exams with various kinds of information about students' performance. The system automatically assesses training exams to give automatic feedback to students. The correction of free-text answers is based on the syntactic and semantic similarity between the student answers and various reference answers, thus going beyond the simple lexical matching. For this, several pre-processing tasks are performed in order to reduce each answer to its more manageable canonical form. Besides the syntactic and semantic similarity between answers, the way the teacher evaluates the answers is also acquired. To accomplish that, the assessment is done using sub scores defined by the teacher concerning parts of the answer or its subgoals. The system has been trained and tested on exams manually graded by History teachers. There is a good correlation between the evaluation of the instructors and the evaluation performed by our system.

Abstract
This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly. © 2024 The Authors. Published by ELSEVIER B.V.

Abstract
The intrinsic challenges of contemporary marketing encourage discovering new approaches to engage and retain customers effectively. As the main channels of interactions between customers and brands pivot between the physical and the digital world, analyzing the outcome behavioral patterns must be achieved dynamically with the stimulus performed in both poles. This systematic review investigates the collaborative impact of adopting multidisciplinary fields of Affective Computing to evaluate current marketing strategies, upholding the process of using multimodal information from consumers to perform and integrate Sentiment Analysis tasks. The adjusted representation of modalities such as textual, visual, audio, or even psychological indicators enables prospecting a more precise assessment of the advantages and disadvantages of the proposed technique, glimpsing future applications of Multimodal Artificial Intelligence in Marketing. Embracing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as the research method to be applied, this article warrants a rigorous and sequential identification and interpretation of the synergies between the latest studies about affective computing and marketing. Furthermore, the robustness of the procedure is deepened in knowledge-gathering concerning the current state of Affective Computing in the Marketing area, their technical practices, ethical and legal considerations, and the potential upcoming applications, anticipating insights for the ongoing work of marketers and researchers.