arXiv subjects

Paulo F. Carvalho

Publications and source records attributed to Paulo F. Carvalho.

10 recordsLinked to original sources

arXiv2026

Self-Efficacy and Favorability Shape Learning from Tutoring Systems and Paper Practice

Motivational factors such as self-efficacy and how favorably students feel toward practice play a crucial role in shaping learning, particularly in technology-supported environments. Yet, educational interventions often overlook how these factors interact with practice format. This paper examines the influence of self-efficacy and favorability on learning outcomes across two common practice formats: paper-based and system-based tutoring practice. Using a counterbalanced within-subject design with matched problem sets, we isolate the effect of practice format while modeling motivational differences. Results indicate that students with lower baseline self-efficacy achieved greater learning gains regardless of practice format. Among students with lower baseline self-efficacy, greater favorability toward the tutor was associated with greater learning gains during tutor practice, whereas the pattern differed in paper-based practice. Intelligent Tutoring System (ITS)-based practice did not significantly improve post-training self-efficacy relative to paper-based methods. These findings underscore the potential value of tailoring practice format to students' motivational profiles, as the benefits of tutor- and paper-based practice varied with baseline self-efficacy and favorability. They lay the groundwork for future research on how instructional formats can be aligned more effectively with learners' motivational needs.

cs.CY↗

arXiv2026

Language Preferences and Practices in Multilingual EdTech: Flexible Primary Language Use with Secondary Language Support

The benefits of learning in one's mother tongue are well documented, yet colonial languages dominate education, marginalizing local languages and limiting access for learners who rely on their mother tongue for understanding. With the rapid growth of educational technology, there is potential to integrate multilingual instruction supporting both colonial and local languages. This study is part of a larger quasi-experiment conducted in Uganda, where learners could choose to learn in English, Leb-Lango (a local language), or in Hybrid mode (a combination of both) in a remote EdTech course. We examined how learners who chose the Hybrid option navigated English and Leb-Lango. While many Hybrid learners did not consistently use both languages, those who did persisted longer in the course. Learners also shared how they managed language complexities. We provide the first empirical evidence of learner agency in bilingual remote EdTech instruction and offer insights for designing inclusive multilingual learning solutions.

cs.HC↗

arXiv2026

Evaluating a Data-Driven Redesign Process for Intelligent Tutoring Systems

Past research has defined a general process for the data-driven redesign of educational technologies and has shown that in carefully-selected instances, this process can help make systems more effective. In the current work, we test the generality of the approach by applying it to four units of a middle-school mathematics intelligent tutoring system that were selected not based on suitability for redesign, as in previous work, but on topic. We tested whether the redesigned system was more effective than the original in a classroom study with 123 students. Although the learning gains did not differ between the conditions, students who used the Redesigned Tutor had more productive time-on-task, a larger number of skills practiced, and greater total knowledge mastery. The findings highlight the promise of data-driven redesign even when applied to instructional units *not* selected as likely to yield improvement, as evidence of the generality and wide applicability of the method.

cs.HC↗

arXiv2026

Inclusive Mobile Learning: How Technology-Enabled Language Choice Supports Multilingual Students

Most learners worldwide are multilingual, yet implementing multilingual education remains challenging in practice. EdTech offers an opportunity to bridge this gap and expand access for linguistically diverse learners. We conducted a quasi-experiment in Uganda with 2,931 participants enrolled in a non-formal radio- and mobile-based engineering course, where learners self-selected instruction in Leb Lango (a local language), English, or a Hybrid option combining both languages. The Leb Lango version of the course was used disproportionately by learners from rural areas, those with less formal education, and those with lower prior knowledge, broadening participation among disadvantaged learners. Moreover, the availability of Leb Lango instruction was associated with higher active participation, even among learners who registered for English instruction. Although Leb Lango learners began with lower performance, they demonstrated faster learning gains and achieved comparable final examination outcomes to English and Hybrid learners. These results suggest that providing local language options to learners is an effective way to make EdTech more accessible.

cs.HC↗

arXiv2026

Generate-Then-Validate: A Novel Question Generation Approach Using Small Language Models

We explore the use of small language models (SLMs) for automatic question generation as a complement to the prevalent use of their large counterparts in learning analytics research. We present a novel question generation pipeline that leverages both the text generation and the probabilistic reasoning abilities of SLMs to generate high-quality questions. Adopting a "generate-then-validate" strategy, our pipeline first performs expansive generation to create an abundance of candidate questions and refine them through selective validation based on novel probabilistic reasoning. We conducted two evaluation studies, one with seven human experts and the other with a large language model (LLM), to assess the quality of the generated questions. Most judges (humans or LLMs) agreed that the generated questions had clear answers and generally aligned well with the intended learning objectives. Our findings suggest that an SLM can effectively generate high-quality questions when guided by a well-designed pipeline that leverages its strengths.

cs.CL↗

arXiv2024

Does the Doer Effect Exist Beyond WEIRD Populations? Toward Analytics in Radio and Phone-Based Learning

The Doer Effect states that completing more active learning activities, like practice questions, is more strongly related to positive learning outcomes than passive learning activities, like reading, watching, or listening to course materials. Although broad, most evidence has emerged from practice with tutoring systems in Western, Industrialized, Rich, Educated, and Democratic (WEIRD) populations in North America and Europe. Does the Doer Effect generalize beyond WEIRD populations, where learners may practice in remote locales through different technologies? Through learning analytics, we provide evidence from N = 234 Ugandan students answering multiple-choice questions via phones and listening to lectures via community radio. Our findings support the hypothesis that active learning is more associated with learning outcomes than passive learning. We find this relationship is weaker for learners with higher prior educational attainment. Our findings motivate further study of the Doer Effect in diverse populations. We offer considerations for future research in designing and evaluating contextually relevant active and passive learning opportunities including leveraging familiar technology, increasing the number of practice opportunities, and aligning multiple data sources.

cs.CY↗

arXiv2024

Beyond Repetition: The Role of Varied Questioning and Feedback in Knowledge Generalization

This study examines the effects of question type and feedback on learning outcomes in a hybrid graduate-level course. By analyzing data from 32 students over 30,198 interactions, we assess the efficacy of unique versus repeated questions and the impact of feedback on student learning. The findings reveal students demonstrate significantly better knowledge generalization when encountering unique questions compared to repeated ones, even though they perform better with repeated opportunities. Moreover, we find that the timing of explanatory feedback is a more robust predictor of learning outcomes than the practice opportunities themselves. These insights suggest that educational practices and technological platforms should prioritize a variety of questions to enhance the learning process. The study also highlights the critical role of feedback; opportunities preceding feedback are less effective in enhancing learning.

cs.HC↗

arXiv2024

Uncovering Name-Based Biases in Large Language Models Through Simulated Trust Game

Gender and race inferred from an individual's name are a notable source of stereotypes and biases that subtly influence social interactions. Abundant evidence from human experiments has revealed the preferential treatment that one receives when one's name suggests a predominant gender or race. As large language models acquire more capabilities and begin to support everyday applications, it becomes crucial to examine whether they manifest similar biases when encountering names in a complex social interaction. In contrast to previous work that studies name-based biases in language models at a more fundamental level, such as word representations, we challenge three prominent models to predict the outcome of a modified Trust Game, a well-publicized paradigm for studying trust and reciprocity. To ensure the internal validity of our experiments, we have carefully curated a list of racially representative surnames to identify players in a Trust Game and rigorously verified the construct validity of our prompts. The results of our experiments show that our approach can detect name-based biases in both base and instruction-tuned models.

cs.CY↗

arXiv2018

PCIe Hot Plug support standardization challenges in ATCA

Throughout the last decade, the Advanced Telecommunications Computing Architecture (ATCA) solidified its position as one of the main switched-based crate standards for advanced Physics instrumentation, offering not only highly performant characteristics in data throughput, channel density or power supply/dissipation capabilities, but also special features for high availability (HA), required for latest and upcoming large-scale endeavours, as is the case of ITER. Hot Swap is one of the main HA features in ATCA, allowing for Boards to be replaced in a crate (Shelf), without powering off the whole system. Platforms using the Peripheral Component Interconnect Express (PCIe) protocol on the Fabric Interface must be complemented, at the software level, with the PCIe Hot Plug native feature, currently not specified for the ATCA form-factor. From a customised Hot Plug support implementation for ATCA Node Boards, the paper presents an implementation extension for Hub Boards, allowing Hot Plug of PCIe switching devices, without causing bus enumeration problems. This paper further addresses the main issues concerning an eventual standardization of PCIe Hot Plug support in ATCA, such as the implementability of Hot Plug Elements and the generation and management of Hot Plug Events, aiming to stimulate the discussion within the PICMG community towards a long overdue standardized solution for Hot Plug in ATCA.

physics.ins-det↗

arXiv2018

FPGA code for the data acquisition and real-time processing prototype of the ITER Radial Neutron Camera

The main role of the ITER Radial Neutron Camera (RNC) diagnostic is to measure in real-time the plasma neutron emissivity profile at high peak count rates for a time duration up to 500 s. Due to the unprecedented high performance conditions and after the identification of critical problems, a set of activities have been selected, focused on the development of high priority prototypes, capable to deliver answers to those problems before the final RNC design. This paper presents one of the selected activities: the design, development and testing of a dedicated FPGA code for the RNC Data Acquisition prototype. The FPGA code aims to acquire, process and store in real-time the neutron and gamma pulses from the detectors located in collimated lines of sight viewing a poloidal plasma section from the ITER Equatorial Port Plug 1. The hardware platform used was an evaluation board from Xilinx (KC705) carrying an IPFN FPGA Mezzanine Card (FMC-AD2-1600) with 2 digitizer channels of 12-bit resolution sampling up to 1.6 GSamples/s. The code performs the proper input signal conditioning using a down-sampled configuration to 400 MSamples/s, apply dedicated algorithms for pulse detection, filtering and pileup detection, and includes two distinct data paths operating simultaneously: i) the event-based data-path for pulse storage; and ii) the real-time processing, with dedicated algorithms for pulse shape discrimination and pulse height spectra. For continuous data throughput both data-paths are streamed to the host through two distinct PCIe x8 Direct Memory Access (DMA) channels.

physics.ins-det↗