This study investigates sensor-free emotion detection techniques in Intelligent Learning Environments, focusing specifically on detecting engagement and confusion. Traditional sensor-based methods of emotion detection face challenges, especially in low- and middle-income countries due to resource constraints. Consequently, our research utilizes data mining and machine learning algorithms to develop models that accurately detect student emotions using only interaction data. We also examine the potential of incorporating personality traits to boost the effectiveness of these models. Our analysis involves a dataset of interactions and annotated emotions from students using a Math step-based Intelligent Tutoring System. We implemented a structured machine learning pipeline designed to prevent data leakage and ensure robust model development. The findings indicate that while the baseline models for detecting engagement and confusion are reliable and show improvements over previous models tested on the same dataset, the inclusion of personality traits did not significantly enhance model performance. This research highlights the complexity of emotion detection in learning environments and discusses the implications of using personal characteristics to improve emotion detection accuracy.

Abstract A multi‐tenant microservice architecture involving components with asynchronous interactions and batch jobs requires efficient strategies for managing asynchronous workloads. This article addresses this issue in the context of a leading company developing tax software solutions for many national and multi‐national corporations in Brazil. A critical process provided by the company's cloud‐based solutions encompasses tax integration, which includes coordinating complex tax calculation tasks and needs to be supported by asynchronous operations using a message broker to guarantee order correctness. We explored and implemented two approaches for managing asynchronous workloads related to tax integration within a multi‐tenant microservice architecture in the company's context: (i) a polling‐based approach that employs a queue as a distributed lock (DL) and (ii) a push‐based approach named single active consumer (SAC) that relies on the message broker's logic to deliver messages. These approaches aim to achieve efficient resource allocation when dealing with a growing number of container replicas and tenants. In this article, we evaluate the correctness and performance of the DL and SAC approaches to shed light on how asynchronous workloads impact the management of multi‐tenant microservice architectures from delivery and deployment perspectives.

Academia and industry have recently acknowledged the many benefits of architecting with microservices. Nonethe-less, adopting a microservice architecture does not immediately guarantee the achievements promised by such an architectural style. A crucial architectural decision is why and where multi-tenancy should be adopted in microservice architectures. This paper presents a multi-tenant microservice architecture conceived to meet the requirements of a company to serve multiple customer applications (tenants). By reporting the migration and modern-ization of a monolith legacy architecture towards microservices, this paper discusses how a multi-tenant microservice architecture could benefit scalability, management, and third-party system integration. This paper also presents empirical results of a scalability evaluation to assess the adoption of multi-tenancy in the proposed architecture and quantify its impact on the system's performance.

Intelligent Tutoring Systems (ITSs) for Math still use traditional methods of input, such as computer keyboard and mouse. This strategy exposes students to a high extraneous cognitive load, due to the way the students are used to work, what could impair their learning. This paper presents a tool for Math ITSs that uses the student handwriting as data input. This tool was integrated into PAT2Math, an algebraic ITS, for an experimental evaluation in a public school with 48 students. It followed a design of control and experimental groups with pre and post-tests and a post questionnaire. Although our results have shown statistically significant improvement in student learning gains in both groups, no difference between groups was found. However, questionnaire responses suggest that handwriting provides a greater facility to the insertion of equations into ITSs for Math if compared with traditional methods (i.e., keyboard and mouse).