In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index q; (2) performance testing of the best entropic indices on new datasets; (3) a comparison made with Shannon entropy and (4) generalized entropies; (5) an investigation of the Tsallis entropy in the context of dimensionality reduction. As a result, our proposal proved to be effective, being superior to Shannon entropy and robust in terms of generalization, and also potentially representative for collecting information in fewer dimensions compared with methods such as Singular Value Decomposition and Uniform Manifold Approximation and Projection.

Gamification in education has received significant attention for its potential to enhance student engagement and motivation. However, challenges arise from the excessive use of reward-oriented game elements, which are assumed to have negative effects on learning outcomes in many cases. In contrast, while the literature argues immersion-based gamification holds great potential, there is little research on how such an approach affects learning experiences. The Sensation game element, for instance, might contribute to students' experiences by providing sensory queues, such as auditory feedback based on background music, to foster concentration, engagement, and immersive learning experiences. Nevertheless, past studies have not sufficiently investigated how the Sensation game element affects gamified learning experiences. Therefore, this paper implements the Sensation game element in two studies: Study 1 introduced background music during a reading activity, while Study 2 implemented multiple background music tracks aligned with different learning stages to drive students' experiences. Accordingly, we evaluated this game element through usability tests, based on high-fidelity prototypes of online learning environments, followed by semi-structured interviews that were analyzed through thematic analysis with the help of ChatGPT. Overall, we found that Sensation, particularly instrumental music, positively influenced concentration but requires careful design to maintain engagement. The findings highlight the importance of tailoring Sensation's implementation to consider individual preferences and contextual factors and the need for thoughtful selection and management of sensory queues to optimize learning environments effectively. Additionally, we provide evidence emphasizing the value of using tools like ChatGPT to optimize qualitative data analysis, although human oversight remains prominent to ensure robust research outcomes. Overall, this study contributes insights for designing personalized and effective gamified learning experiences based on the Sensation game element in enhancing learning experiences.

Gamified learning has the potential to improve learning outcomes, but most strategies are focused on using Points-Badges-Leaderboards (PBL) while neglecting other game elements. Drawing upon research discussing nostalgia’s potential to improve user experience and engagement, we hypothesized affective memory could aid in designing gamification’s visual aesthetics. To start testing that hypothesis, we conducted a moderated usability study aiming to understand how Pokémon-based gamification compares to a trophy-based approach. Nine undergraduate students used two versions of a gamified quiz prototype (Pokémon and trophy-based) to complete multiple-choice items, then discussed their experiences in a semi-structured interview. Our findings suggest affective memory plays a significant role in gamified learning and indicate Pokémon-based gamification might maximize intrinsic motivation. Thus, informing practitioners that gamification targeting affective memory can enhance learning by increasing engagement and intrinsic motivation, and calling researchers to further investigate affective memory and other brands in gamification research.

Mathematical problem solving is essential for developing analytical skills and critical thinking among learners. Traditional methods of detecting and correcting errors in mathematical problem-solving are manual and time-consuming, leading to delayed feedback, which can hinder effective learning. Intelligent Tutoring Systems (ITS) provide real-time, error-specific feedback but require direct interaction with computational devices, limiting their use in classrooms with insufficient technological infrastructure. ITS unplugged (ITS-U) have been proposed to address these technological barriers, but no research has investigated how to design a lightweight error classificatin systems aligned to the resource-constrained setting of ITS-U. Therefore, this paper presents the Mathematical Solutions Error Classification (MSEC) API, a lightweight, knowledge-based tool designed to automate the classification of errors in mathematical solutions. This API categorizes errors into theoretically-grounded types, such as syntactical mistakes, conceptual misunderstandings, and calculation errors. MSEC is particularly suited for low-tech educational environments, aligning with the principles of ITS-U. We implemented MSEC within an ITS-U and conducted a case study involving 49 students, demonstrating its practical applicability and positive contribution to teaching and learning. The study highlights MSECs efficiency, ability to provide real-time feedback, and adaptability to various educational contexts, offering a significant advancement in automated error detection for ITS-U.