arXiv subjects

Pedro Cardoso-Leite

Publications and source records attributed to Pedro Cardoso-Leite.

4 recordsLinked to original sources

arXiv2026

Can Theory-Informed Message Framing Drive Honest and Motivated Performance with Better Assessment Experiences in a Remote Assessment?

Remote unproctored assessments increasingly use messaging interventions to reduce cheating, but existing approaches lack theoretical grounding, focus narrowly on cheating suppression while overlooking performance and experience, and treat cheating as binary rather than continuous. This study examines whether messages based on 15 psychological concepts from self-determination, cognitive dissonance, social norms, and self-efficacy theories can reduce cheating while preserving performance and experience. Through an expert workshop (N=5), we developed 45 theory-informed messages and tested them with online participants (N=1232) who completed an incentivized anagram task. Participants were classified as non-cheaters (0% items cheated), partial-cheaters (1-99% cheated), or full-cheaters (100% cheated). Results show that concept-based messages reduced full-cheating occurrence by 42% (33% to 19%), increased non-cheating by 19% (53% to 63%), with no negative effects on performance or experience across integrity groups. Surprisingly, messages grounded in different theoretical concepts produced virtually identical effects. Analyses of self-rated psychological mechanisms revealed that messages influenced multiple mechanisms simultaneously rather than their intended targets, though these mechanisms predicted behavior, performance, and experience. These findings show that causal pathways are more complex than current theories predict. Practically, integrity interventions using supportive motivation rather than rule enforcement can reduce cheating without impairing performance or experience.

cs.HC↗

arXiv2024

Balancing The Perception of Cheating Detection, Privacy and Fairness: A Mixed-Methods Study of Visual Data Obfuscation in Remote Proctoring

Remote proctoring technology, a cheating-preventive measure, often raises privacy and fairness concerns that may affect test-takers' experiences and the validity of test results. Our study explores how selectively obfuscating information in video recordings can protect test-takers' privacy while ensuring effective and fair cheating detection. Interviews with experts (N=9) identified four key video regions indicative of potential cheating behaviors: the test-taker's face, body, background and the presence of individuals in the background. Experts recommended specific obfuscation methods for each region based on privacy significance and cheating behavior frequency, ranging from conventional blurring to advanced methods like replacement with deepfake, 3D avatars and silhouetting. We then conducted a vignette experiment with potential test-takers (N=259, non-experts) to evaluate their perceptions of cheating detection, visual privacy and fairness, using descriptions and examples of still images for each expert-recommended combination of video regions and obfuscation methods. Our results indicate that the effectiveness of obfuscation methods varies by region. Tailoring remote proctoring with region-specific advanced obfuscation methods can improve the perceptions of privacy and fairness compared to the conventional methods, though it may decrease perceived information sufficiency for detecting cheating. However, non-experts preferred conventional blurring for videos they were more willing to share, highlighting a gap between the perceived effectiveness of the advanced obfuscation methods and their practical acceptance. This study contributes to the field of user-centered privacy by suggesting promising directions to address current remote proctoring challenges and guiding future research.

cs.HC↗

arXiv2022

Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control

Traditionally, theory and practice of Cognitive Control are linked via literature reviews by human domain experts. This approach, however, is inadequate to track the ever-growing literature. It may also be biased, and yield redundancies and confusion. Here we present an alternative approach. We performed automated text analyses on a large body of scientific texts to create a joint representation of tasks and constructs. More specifically, 385,705 scientific abstracts were first mapped into an embedding space using a transformers-based language model. Document embeddings were then used to identify a task-construct graph embedding that grounds constructs on tasks and supports nuanced meaning of the constructs by taking advantage of constrained random walks in the graph. This joint task-construct graph embedding, can be queried to generate task batteries targeting specific constructs, may reveal knowledge gaps in the literature, and inspire new tasks and novel hypotheses.

cs.AI↗

arXiv2020

The structure of behavioral data

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral data is fundamental to many scientific fields, there is currently no widely adopted standard for formatting, naming, organizing, describing or sharing such data. This lack of standardization is a major bottleneck for scientific progress. Not only does it prevent the effective reuse of data, it also affects how behavioral data in general are processed, as non-standard data calls for custom-made data analysis code and prevents the development of efficient tools. To address this problem, we develop the Behaverse Data Model (BDM), a standard for structuring behavioral data. Here we focus on major concepts in behavioral data, leaving further details and developments to the project's website (https://behaverse.github.io/data-model/).

q-bio.NC↗