Gesture enhances learning of a complex statistical concept.
Ontology highlight
ABSTRACT:
SUBMITTER: Rueckert L
PROVIDER: S-EPMC5281660 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In this article, we begin to lay out a framework and approach for studying how students come to understand complex concepts in rich domains. Grounded in theories of embodied cognition, we advance the view that understanding of complex concepts requires students to practice, over time, the coordination of multiple concepts, and the connection of this system of concepts to situations in the world. Specifically, we explore the role that a teacher's gesture might play in supporting students' coordination of two concepts central to understanding in the domain of statistics: mean and standard deviation. In Study 1 we show that university students who have just taken a statistics course nevertheless have difficulty taking both mean and standard deviation into account when thinking about a statistical scenario. In Study 2 we show that presenting the same scenario with an accompanying gesture to represent variation significantly impacts students' interpretation of the scenario. Finally, in Study 3 we present evidence that instructional videos on the internet fail to leverage gesture as a means of facilitating understanding of complex concepts. Taken together, these studies illustrate an approach to translating current theories of cognition into principles that can guide instructional design.
| S-EPMC5780541 | biostudies-literature
Project description:KLF4 and ZNF611 ChIP-seq when overexpressed in primed or in naive respectively
2023-10-03 | GSE208403 | GEO
Project description:Statistical learning and the social contexts of language addressed to infants are hypothesized to play important roles in early language development. Previous behavioral work has found that the exaggerated prosodic contours of infant-directed speech (IDS) facilitate statistical learning in 8-month-old infants. Here we examined the neural processes involved in on-line statistical learning and investigated whether the use of IDS facilitates statistical learning in sleeping newborns. Event-related potentials (ERPs) were recorded while newborns were exposed to12 pseudo-words, six spoken with exaggerated pitch contours of IDS and six spoken without exaggerated pitch contours (ADS) in ten alternating blocks. We examined whether ERP amplitudes for syllable position within a pseudo-word (word-initial vs. word-medial vs. word-final, indicating statistical word learning) and speech register (ADS vs. IDS) would interact. The ADS and IDS registers elicited similar ERP patterns for syllable position in an early 0-100 ms component but elicited different ERP effects in both the polarity and topographical distribution at 200-400 ms and 450-650 ms. These results provide the first evidence that the exaggerated pitch contours of IDS result in differences in brain activity linked to on-line statistical learning in sleeping newborns.
| S-EPMC5019490 | biostudies-literature
Project description:In gesture-aided learning (GAL), learners perform specific body gestures while rehearsing the associated learning content. Although this form of embodiment has been shown to benefit learning outcomes, it has not yet been incorporated into e-learning. This work presents a generic system design for an online GAL platform. It is comprised of five modules for planning, administering, and monitoring remote GAL lessons. To validate the proposed design, a reference implementation for word learning was demonstrated in a field test. 19 participants independently took a predefined online GAL lesson and rated their experience on the System Usability Scale and a supplemental questionnaire. To monitor the correct gesture execution, the reference implementation recorded the participants' webcam feeds and uploaded them to the instructor for review. The results from the field test show that the reference implementation is capable of delivering an e-learning experience with GAL elements. Designers of e-learning platforms may use the proposed design to include GAL in their applications. Beyond its original purpose in education, the platform is also useful to collect and annotate gesture data.
| S-EPMC8659482 | biostudies-literature
Project description:Repetitive practice is one of the most important factors in improving the performance of motor skills. This paper focuses on the analysis and classification of forearm gestures in the context of violin playing. We recorded five experts and three students performing eight traditional classical violin bow-strokes: martelé, staccato, detaché, ricochet, legato, trémolo, collé, and col legno. To record inertial motion information, we utilized the Myo sensor, which reports a multidimensional time-series signal. We synchronized inertial motion recordings with audio data to extract the spatiotemporal dynamics of each gesture. Applying state-of-the-art deep neural networks, we implemented and compared different architectures where convolutional neural networks (CNN) models demonstrated recognition rates of 97.147%, 3DMultiHeaded_CNN models showed rates of 98.553%, and rates of 99.234% were demonstrated by CNN_LSTM models. The collected data (quaternion of the bowing arm of a violinist) contained sufficient information to distinguish the bowing techniques studied, and deep learning methods were capable of learning the movement patterns that distinguish these techniques. Each of the learning algorithms investigated (CNN, 3DMultiHeaded_CNN, and CNN_LSTM) produced high classification accuracies which supported the feasibility of training classifiers. The resulting classifiers may provide the foundation of a digital assistant to enhance musicians' time spent practicing alone, providing real-time feedback on the accuracy and consistency of their musical gestures in performance.
| S-EPMC7813937 | biostudies-literature
Project description:Statistical learning quantifies transposable element-mediated cis-regulation
| PRJNA859814 | ENA
Project description:In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then extracted from each channel. Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods.
| S-EPMC9243223 | biostudies-literature
Project description:We followed established best practices in concept inventory design and developed a 12-item inventory to assess student ability in statistical reasoning in biology (Statistical Reasoning in Biology Concept Inventory [SRBCI]). It is important to assess student thinking in this conceptual area, because it is a fundamental requirement of being statistically literate and associated skills are needed in almost all walks of life. Despite this, previous work shows that non-expert-like thinking in statistical reasoning is common, even after instruction. As science educators, our goal should be to move students along a novice-to-expert spectrum, which could be achieved with growing experience in statistical reasoning. We used item response theory analyses (the one-parameter Rasch model and associated analyses) to assess responses gathered from biology students in two populations at a large research university in Canada in order to test SRBCI's robustness and sensitivity in capturing useful data relating to the students' conceptual ability in statistical reasoning. Our analyses indicated that SRBCI is a unidimensional construct, with items that vary widely in difficulty and provide useful information about such student ability. SRBCI should be useful as a diagnostic tool in a variety of biology settings and as a means of measuring the success of teaching interventions designed to improve statistical reasoning skills.
| S-EPMC4803094 | biostudies-literature
Project description:The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1-dog1, bird2-dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1-dog_picture1, bird_picture2-dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation.
| S-EPMC4905776 | biostudies-literature
Project description:BackgroundCommunity learning and e-mentoring, learning methods used in higher education, are not used to any extent in residency education. Yet both have the potential to enhance resident learning and, in the case of community learning, introduce residents to basic lifelong learning skills. We set out to determine whether residents participating in an Internet based e-mentoring program would, with appropriate facilitation, form a community of learners (CoL) and hold regular community meetings. We also determined resident and faculty perceptions of CoL and Internet sessions as effective learning experiences.MethodsA six-month e-mentoring pilot was offered to 10 Radiology residents in the Aga Khan University Postgraduate Medical Education Program in Nairobi, Kenya (AKUHN) with a Professor of Radiology, located at University of Virginia, USA, acting as the e-mentor. Monthly Internet case-based teaching sessions were facilitated by the e-mentor. In addition, residents were coached by a community facilitator to form CoL and collectively work through clinical cases at weekly face-to-face CoL sessions.Event logs described observed resident activity at CoL sessions; exit survey and interviews were used to elicit perceptions of CoL and Internet sessions as effective learning experiences.ResultsResident adoption of CoL behaviors was observed, including self-regulation, peer mentoring and collaborative problem solving. Analysis revealed high resident enthusiasm and value for CoL. Surveys and interviews indicated high levels of acceptance of Internet learning experiences, although there was room for improvement in audio-visual transmission technologies. Faculty indicated there was a need for a larger multi-specialty study.ConclusionsThe pilot demonstrated resident acceptance of community building and collaborative learning as valued learning experiences, addressing one barrier to its formal adoption in residency education curricula. It also highlighted the potential of e-mentoring as a means of expanding faculty and teaching materials in residency programs in developing countries.
| S-EPMC3041783 | biostudies-literature