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Pedagogy for conceptual thinking and meaning equivalence : emerging research and opportunities
\"This book explores enhancing learning outcomes with pedagogy for conceptual thinking and meaning equivalent reusable learning objects\"-- Provided by publisher.
Book
International handbook of research on conceptual change
Conceptual change research investigates the processes through which learners substantially revise prior knowledge and acquire new concepts. Tracing its heritage to paradigms and paradigm shifts made famous by Thomas Kuhn, conceptual change research focuses on understanding and explaining learning of the most the most difficult and counter-intuitive concepts. Now in its second edition, the International Handbook of Research on Conceptual Change provides a comprehensive review of the conceptual change movement and of the impressive research it has spawned on students' difficulties in learning.
In thirty-one new and updated chapters, organized thematically and introduced by Stella Vosniadou, this volume brings together detailed discussions of key theoretical and methodological issues, the roots of conceptual change research, and mechanisms of conceptual change and learner characteristics. Combined with chapters that describe conceptual change research in the fields of physics, astronomy, biology, medicine and health, and history, this handbook presents writings on interdisciplinary topics written for researchers and students across fields.
eBook
A review of abstract concept learning in embodied agents and robots
This paper reviews computational modelling approaches to the learning of abstract concepts and words in embodied agents such as humanoid robots. This will include a discussion of the learning of abstract words such as ‘use’ and ‘make’ in humanoid robot experiments, and the acquisition of numerical concepts via gesture and finger counting strategies. The current approaches share a strong emphasis on embodied cognition aspects for the grounding of abstract concepts, and a continuum, rather than dichotomy, view of concrete/abstract concepts differences.
This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.
Journal Article
Second verse, same as the first: learning generalizable relational concepts through functional repetition
The ability to learn and flexibly apply sophisticated concepts is thought by many to be what differentiates humans from all other animals. A basic assumption underlying this belief is that some “lower-order” associative learning mechanisms link perceptual events to specific reactions, whereas the kinds of verbalizable concepts that humans form depend on “higher-order” cognitive processes that rely less on perception and more on rational thought. Evidence in support of this interpretation comes largely from experiments in which animals either fail to learn or generalize concepts that humans readily learn, or learn them with great difficulty. Here, we argue that the formation of generalizable relational concepts may depend more on an individual’s capacity to shift attention than on the possession of representational processes that are unique to humans. Studies of relational concept learning in non-human animals show that they can learn generalizable concepts when conditions are favorable. In particular, repetition of similar training experiences appears to facilitate attentional redirection, thereby enabling animals to flexibly reenact past events and to judge the similarity of items within stimulus sets. The conditions that promote concept learning in humans may differ substantially from those experienced by most other animals. This does not imply, however, that either (1) conceptual learning mechanisms differ qualitatively from other learning mechanisms, or (2) that the processes that lead to concept formation in humans differ significantly from those present in other species.
Journal Article
Concept learning based on improved FCM- BiLSTM for fuzzy data classification and fusion
Concept-Cognitive Learning (CCL) is an effective concept learning approach that simulates human cognitive processes to facilitate knowledge discovery. However, existing CCL methods face two significant challenges. One is that existing models often assume accurate labels and ignore the possibility of inaccuracy, which may lead to decisions based on incorrect information. The other one is that the cognitive mechanisms in current CCL models do not account for the dependency relationships between objects, which limits the model’s ability to adapt to diverse datasets and handle complex relational patterns. Inspired by both fuzzy clustering and deep learning, this paper proposes a novel Concept-Cognitive Learning Model (FCLSCL), which integrates an improved Fuzzy C-means (FCM) and Bidirectional Long Short-Term Memory Network (BiLSTM). Specifically, an improved FCM is designed under the framework of fuzzy concept clustering to learn pseudo-concepts for each category and obtain the membership degree of each object to every category. Then, the weighted fuzzy concepts are introduced to capture the uncertainty of datasets by taking both membership degree and pseudo-concepts into account. For achieving concept classification tasks, the intents of the weighted fuzzy concept and fuzzy concept are concatenated together as inputs for BiLSTM to achieve bidirectional concept learning. Finally, experimental results, compared with several popular machine learning models, CCL methods and LSTM, demonstrate the effectiveness of the proposed FCLSCL.
Journal Article
Things that go
Filled with colorful photographs inspired by \"National Geographic Little Kids\" magazine, curious children are introduced to vehicles on the move--trains, planes, and trucks.
Book
Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects
Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.
Journal Article