Publisher: NPJ Science of Learning Link>
ABSTRACT
Young children’s linguistic and communicative abilities are foundational for their academic achievement and overall well-being. We present the positive outcomes of a brief tablet-based intervention aimed at teaching toddlers and preschoolers new word-object and letter-sound associations. We conducted two experiments, one involving toddlers ( ~ 24 months old, n = 101) and the other with preschoolers ( ~ 42 months old, n = 152). Using a pre-post equivalent group design, we measured the children’s improvements in language and communication skills resulting from the intervention. Our results showed that the intervention benefited toddlers’ verbal communication and preschoolers’ speech comprehension. Additionally, it encouraged vocalizations in preschoolers and enhanced long-term memory for the associations taught in the study for all participants. In summary, our study demonstrates that the use of a ludic tablet-based intervention for teaching new vocabulary and pre-reading skills can improve young children’s linguistic and communicative abilities, which are essential for future development.
Publisher: Journal of Research in Science Teaching Link>
ABSTRACT
Artificial intelligence (AI) technologies generate increasingly sophisticated non-human cognition; however, foundational learning theories only contemplate human cognition, and current research conceptualizes AI as a pedagogical tool. We argue that the incipient abilities of AI for mutual engagement with people could allow AI to participate as a legitimate member in social constructivist learning environments and suggest some potential structures and activities to explore AI's capabilities for full participation.
"Participation is an active process, but I will reserve the term for actors who are members of social communities. For instance, I will not say that a computer “participates” in a community of practice…. (Wenger, 1998, p. 56)"
Twenty-five years ago, Etienne Wenger published his influential book Communities of practice: Learning, meaning, and identity (Wenger, 1998), where he specifically discounted computers as potential members of a community of practice (CoP). Recently, however, the abilities of computational systems like generative artificial intelligence (AI) oblige us to reconsider the roles non-human cognition could play in communities of practice centered on learning. Recently, the editorial article “Artificial Intelligence and the Journal of Research in Science Teaching” (Sadler et al., 2024) describes the potential for AI technology to transform science education, but notes that “the science education research community is not as far along as it needs to be in terms of understanding, theorizing, and studying the intersections of AI and science education.” (p. 742). In response, this commentary presents our theorization and conceptualization of AI in science education. We apply the lens of social constructivism (Wenger, 1998) to theorize about this question and we argue that the nature of generative AI allows it to transcend an instrumental role and achieve full participation in a CoP. We are convinced that socio-constructivist theory in general, and CoP specifically, can provide conceptual tools and theoretical underpinnings to guide the use of AI in education. In this commentary, we synthesize ideas from current literature to construct a theoretical framework and offer suggestions for the transformative use of generative AI.
Publisher: Journal of Engineering Education Link>
ABSTRACT
Background
We examine the efficacy of an online collaborative problem-solving (CPS) teaching approach in academic performance and student connections with other peers, among first-year engineering calculus students at a Latin American university. Our research uses communities of practice (CoP) to emphasize the social nature of learning and the importance of participation and interaction within a community.Methods
The work applies a quasi-experimental design and social network analysis (SNA). A total of 202 engineering students were instructed using CPS methodology (experimental group), while 380 students received traditional online teaching methods (control group) during one semester in the first calculus class for engineers.Results
Results show no significant difference in the grades obtained between the experimental and control groups. However, students exposed to CPS reported a statistically significant higher passing rate, as well as larger and more significant academic and social connections. Additionally, SNA results suggest that CPS facilitated stronger peer connections and promoted a more equitable distribution of participation among students, particularly women, compared to students taught under traditional online teaching methods.Conclusions
The study underscores the importance of fostering collaborative learning environments and highlights CPS as a strategy to enhance student performance and network formation. Findings suggest that CPS can improve academic outcomes and promote more equitable learning practices, potentially reducing dropout rates among women engineering students. These findings contribute to the ongoing efforts to address systematic biases and enhance learning experiences in engineering education.
Publisher: Remote Sensing of Environment Link>
ABSTRACT
Accurate early-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenges. While current deep learning-based methods show promise in crop type classification from single- and multi-modal time series, most existing methods rely on a single modality, such as satellite optical remote sensing data or crop rotation patterns. We propose a novel approach to fuse multimodal information into a model for improved accuracy and robustness across multiple crop seasons and countries. The approach relies on three modalities used: remote sensing time series from Sentinel-2 and Landsat 8 observations, parcel crop rotation and local crop distribution. To evaluate our approach, we release a new annotated dataset of 7.4 million agricultural parcels in France (FR) and the Netherlands (NL). We associate each parcel with time-series of surface reflectance (Red and NIR) and biophysical variables (LAI, FAPAR). Additionally, we propose a new approach to automatically aggregate crop types into a hierarchical class structure for meaningful model evaluation and a novel data-augmentation technique for early-season classification. Performance of the multimodal approach was assessed at different aggregation levels in the semantic domain, yielding to various ranges of the number of classes spanning from 151 to 8 crop types or groups. It resulted in accuracy ranging from 91% to 95% for the NL dataset and from 85% to 89% for the FR dataset. Pre-training on a dataset improves transferability between countries, allowing for cross- domain and label prediction, and robustness of the performances in a few-shot setting from FR to NL, i.e., when the domain changes as per with significantly new labels. Our proposed approach outperforms comparable methods by enabling deep learning methods to use the often overlooked spatio-temporal context of parcels, resulting in increased precision and generalization capacity.
Publisher: PeerJ Computer Science Link>
ABSTRACT
Emergency remote teaching is a temporary change in the way education occurs, whereby an educational system unexpectedly becomes entirely remote. This article analyzes the motivation of students undertaking a university course over one semester of emergency remote teaching in the context of the COVID-19 pandemic. University students undertaking a programming course were surveyed three times during one semester, about motivation and COVID concern. This work explores which student motivation profiles existed, how motivation evolved, and whether concern about the pandemic was a factor affecting motivation throughout the course. The most adaptive profile was highly motivated, more prepared and less frustrated by the conditions of the course. However, this cluster experienced the highest levels of COVID-19 concern. The least adaptive cluster behaved as a mirror image of the most adaptive cluster. Clear differences were found between the clusters that showed the most and least concern about COVID-19.
Publisher: Alzheimer's Association Link>
ABSTRACT
INTRODUCTION
Age-related hearing loss is an important risk factor for cognitive decline. However, audiogram thresholds are not good estimators of dementia risk in subjects with normal hearing or mild hearing loss. Here we propose to use distortion product otoacoustic emissions (DPOAEs) as an objective and sensitive tool to estimate the risk of cognitive decline in older adults with normal hearing or mild hearing loss.METHODS
We assessed neuropsychological, brain magnetic resonance imaging, and auditory analyses on 94 subjects > 64 years of age.RESULTS
We found that cochlear dysfunction, measured by DPOAEs—and not by conventional audiometry—was associated with Clinical Dementia Rating Sum of Boxes (CDR-SoB) classification and brain atrophy in the group with mild hearing loss (25 to 40 dB) and normal hearing (<25 dB).DISCUSSION
Our findings suggest that DPOAEs may be a non-invasive tool for detecting neurodegeneration and cognitive decline in the older adults, potentially allowing for early intervention.
Publisher: Scientific Reports Link>
ABSTRACT
The rise of bots that mimic human behavior represents one of the most pressing threats to healthy information environments on social media. Many bots are designed to increase the visibility of low-quality content, spread misinformation, and artificially boost the reach of brands and politicians. These bots can also disrupt civic action coordination, such as by flooding a hashtag with spam and undermining political mobilization. Social media platforms have recognized these malicious bots’ risks and implemented strict policies and protocols to block automated accounts. However, effective bot detection methods for Spanish are still in their early stages. Many studies and tools used for Spanish are based on English-language models and lack performance evaluations in Spanish. In response to this need, we have developed a method for detecting bots in Spanish called Botcheck. Botcheck was trained on a collection of Spanish-language accounts annotated in Twibot-20, a large-scale dataset featuring thousands of accounts annotated by humans in various languages. We evaluated Botcheck’s performance on a large set of labeled accounts and found that it outperforms other competitive methods, including deep learning-based methods. As a case study, we used Botcheck to analyze the 2021 Chilean Presidential elections and discovered evidence of bot account intervention during the electoral term. In addition, we conducted an external validation of the accounts detected by Botcheck in the case study and found our method to be highly effective. We have also observed differences in behavior among the bots that are following the social media accounts of official presidential candidates.
Publisher: Cognitive Systems Research, Elsevier Link>
ABSTRACT
This article presents a transdisciplinary analysis of the challenges in fusing neuroscience concepts with artificial intelligence (AI) to create AI systems inspired by biological cognition. We explore the structural and functional disparities between the neocortex’s canonical microcircuits and existing AI models, focusing on architectural differences, learning mechanisms, and energy efficiency. The discussion extends to adapting non-goal-oriented learning and dynamic neuronal connections from biological brains to enhance AI’s flexibility and efficiency. This work underscores the potential of neuroscientific insights to revolutionize AI development, advocating for a paradigm shift towards more adaptable and brain-like AI systems. We conclude that there is major room for bioinspiration by focusing on developing architecture, objective functions, and learning rules using a local instead of a global approach.
Publisher: IEEE Computational Intelligence Magazine Link>
ABSTRACT
This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predictions of feed-forward neural networks operating on structured classification problems. In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron. Composition vectors are initialized using a given input instance and subsequently propagated through the whole network until reaching the output layer. The sign of each composition value indicates whether the corresponding feature excites or inhibits the neuron, while the absolute value quantifies its impact. The FCP algorithm is executed on a post-hoc basis, i.e., once the learning process is completed. Aiming to illustrate the FCP algorithm, this paper develops a case study concerning bias detection in a fairness problem in which the ground truth is known. The simulation results show that the composition values closely align with the expected behavior of protected features. The source code and supplementary material for this paper are available at https://github.com/igraugar/fcp.
lisher: Advances in Information Retrieval Link>
ABSTRACT
Bifidobacterium longum subsp. infantis is a representative and dominant species in the infant gut and is considered a beneficial microbe. This organism displays multiple adaptations to thrive in the infant gut, regarded as a model for human milk oligosaccharides (HMOs) utilization. These carbohydrates are abundant in breast milk and include different molecules based on lactose. They contain fucose, sialic acid, and N-acetylglucosamine. Bifidobacterium metabolism is complex, and a systems view of relevant metabolic pathways and exchange metabolites during HMO consumption is missing. To address this limitation, a refined genome-scale network reconstruction of this bacterium is presented using a previous reconstruction of B. infantis ATCC 15967 as a template. The latter was expanded based on an extensive revision of genome annotations, current literature, and transcriptomic data integration. The metabolic reconstruction (iLR578) accounted for 578 genes, 1,047 reactions, and 924 metabolites. Starting from this reconstruction, we built context-specific genome-scale metabolic models using RNA-seq data from cultures growing in lactose and three HMOs. The models revealed notable differences in HMO metabolism depending on the functional characteristics of the substrates. Particularly, fucosyl-lactose showed a divergent metabolism due to a fucose moiety. High yields of lactate and acetate were predicted under growth rate maximization in all conditions, whereas formate, ethanol, and 1,2-propanediol were substantially lower. Similar results were also obtained under near-optimal growth on each substrate when varying the empirically observed acetate-to-lactate production ratio. Model predictions displayed reasonable agreement between central carbon metabolism fluxes and expression data across all conditions. Flux coupling analysis revealed additional connections between succinate exchange and arginine and sulfate metabolism and a strong coupling between central carbon reactions and adenine metabolism. More importantly, specific networks of coupled reactions under each carbon source were derived and analyzed. Overall, the presented network reconstruction constitutes a valuable platform for probing the metabolism of this prominent infant gut bifidobacteria.
Edificio de Innovación UC, Piso 2
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Macul, Chile
Vicuña Mackenna 4860
Macul, Chile