Paulo Carvalho, Ph.D. - US grants

A distinctive characteristic of human learning is its capability to flexibly acquire a wide range of rich and complex forms of knowledge (e.g., first and second languages) and acquiring new and accumulated knowledge (e.g., learning physics is easier after having learned algebra). An adequate explanation of human learning must address how existing knowledge changes the way we learn so that we achieve knowledge goals and in specific contexts. This project aims to discover and specify how human learning processes operate differently under different contexts, depending upon what content is being learned. This research contributes to improved educational practices by specifying how learning processes are influenced by knowledge acquisition in a systematic and replicable way. This research will enhance our understanding of successful learning and optimal performance.

The project explores learning processes by contrasting learning from retrieval practice and learning from studying examples. The goal of this project is to resolve and clarify how these processes compete for cognitive resources, including attention and working memory, in ways that depend on the knowledge content to be learned. This research examines (1) the learning processes involved in learning from retrieval practice and from worked examples, (2) how these learning processes work differently when applied to different knowledge content, and (3) the computational mechanisms of learning that give rise to learning different content. The researchers use a combination of experiments in which the learning approach is varied along with the materials being studied and machine learning models. It will demonstrate knowledge-learning dependence by showing that one learning process (e.g., retrieval practice) produces better learning outcomes than another (e.g., example encoding) in some knowledge contexts but the reverse occurs in other knowledge contexts. By implementing the studies as part of a machine learning architecture, this research will provide computational evidence and theoretical insight into the hypothesized knowledge-learning dependence framework. The machine learning architecture developed may be used as an educational and research tool in learning sciences, and the project involves training new learning scientists.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Malaria, a parasitic mosquito-borne disease, is a major concern worldwide, with 219 million cases occurring in 2017 (WHO 2018 report), causing 435,000 deaths of which 61% were children under 5. Plasmodium falciparum is the causative agent of the deadliest form of malaria and current treatment guidelines include artemisinin-based combination therapies (ACTs), combining one artemisinin derivative (artemether, artesunate or dihydroartemisinin) with one or two different drugs. Most studies have demonstrated that ACTs remain effective, but partial resistance has been reported in southeast Asia, linked to the development of the parasite?s ability to remain dormant at the ring stage long enough for clearance of artemisinin-based drugs so the parasites can re-emerge. Virtually all derivatives of artemisinin currently available are the result of chemical modifications at ?C-10?, or carbon number 10 on its structure (IUPAC numbering). The fungus Cunninghamella elegans can add a hydroxyl group to carbon number 7 (C7) which, until the use of fungal transformation, was inaccessible except through extensive and costly total synthesis. The overall objective for this application is to 1) prepare C-7 derivatives of artemisinin linked with hydrophilic groups and fluorescent probes; 2) test those derivatives against a) standard P. falciparum strains (3D7, W2mef, HB3); b) at least one artemisinin-resistant phenotype (C2A) and c) test for possible anti-gametocyte action and transmission blocking activity by Standard Membrane-Feeding Assay; 3) chemogenomic profiling studies of P. falciparum piggyBac single insertion mutants seeking better understanding of the interaction of these new C7 derivatives of artemisinin with druggable targets and pathways. The central hypothesis is that derivatives of artemisinin without any steric hindrance to the peroxide group will allow full interaction with cellular targets, precisely tagging cellular structures bound to the artemisinin scaffold and enhancing inhibitory effect. These new semi-synthetic derivatives of artemisinin, built for the first time using functional groups placed structurally on the opposite side of the peroxide bridge, are expected to have enhanced antimalarial activity, present better pharmacokinetic profiles and work better as molecular probes for elucidation of mechanisms of action and drug resistance.