The rich-get-richer effect: Prior knowledge predicts new learning of domain-relevant information.

2021 ◽  
Author(s):  
Amber E. Witherby ◽  
Shana K. Carpenter
Keyword(s):  
Prior Knowledge ◽  
Relevant Information ◽  
New Learning ◽  
The Rich

Introduction

2019 ◽  
pp. 1-8
Author(s):  
Christopher Berg
Keyword(s):  
Prior Knowledge ◽  
Practice Guidelines ◽  
Classical Guitar ◽  
Advanced Technique ◽  
New Learning ◽  
The Rich ◽  
Foundational Work ◽  
Intended Use

This chapter lays out the scope and intended use of The Classical Guitar Companion. It explores how guitarists and teachers can create unique and distinctive curricula of study for themselves or their students to help them acquire and develop fundamental techniques and foundational knowledge. Included are practice guidelines applicable to each chapter of the book. Because all new learning requires a finely honed foundation of prior knowledge and skill, the introduction presents advice that will help guitarists transform their foundational work into advanced technique and a deeper understanding of the instrument. This work will help guitarists meet the artistic and technical demands of advanced guitar literature. The introduction also explores those best served by the book: those studying the classical guitar (whether novice or advanced); guitarists wishing to fill gaps in their background; new guitar teachers looking for guidance with curricula; and guitarists interested in the rich pedagogical heritage of the instrument.

Using Concept Maps to Enhance Students' Prior Knowledge in Complex Learning

2010 ◽  
pp. 163-181 ◽  
Author(s):  
Robert Z. Zheng ◽  
Laura B. Dahl
Keyword(s):  
Empirical Study ◽  
Prior Knowledge ◽  
Concept Mapping ◽  
Concept Maps ◽  
Cognitive Styles ◽  
Future Research ◽  
Instructional Tool ◽  
Cognitive Tool ◽  
New Learning ◽  
Before And After

As an instructional tool, concept map has been widely used to teach complex subjects in schools. Research suggests that concept mapping can help bridge learners’ prior knowledge with new learning, reduce the cognitive load involved in learning and improve comprehension, content retention, and knowledge transfer. Existing literature focuses on cognitive features, cognitive styles and differences between instructor provided and student generated concepts. However, little is known about the effects of concept maps as a cognitive tool to influence learners’ learning, specifically before and after the learning takes place. This chapter offers a discussion of general research in concept mapping and theories that support such instruction. Finally, an empirical study is presented with suggestions for future research in concept mapping.

Neurons and the Process Standards

2011 ◽  
Vol 18 (4) ◽  
pp. 244-250
Author(s):  
Ron Zambo ◽  
Debby Zambo
Keyword(s):  
Prior Knowledge ◽  
Mathematical Structure ◽  
New Learning

A series of tasks with the mathematical structure of a Chickens and Pigs problem can change children's brains as they connect new learning to prior knowledge.

scholarly journals Information effect on consumer adoption for a new beef brand in the Vietnamese market: prior knowledge, appealing the brand distinction, differentiation and similarity

2018 ◽  
Vol 13 (3) ◽  
pp. 1014-1034
Author(s):  
Thu Tran ◽  
Masahiro Moritaka ◽  
Ran Liu ◽  
Susumu Fukuda
Keyword(s):  
Prior Knowledge ◽  
Consumer Preferences ◽  
Early Stage ◽  
Relevant Information ◽  
Added Value ◽  
Ho Chi Minh City ◽  
Consumer Adoption ◽  
Information Bias ◽  
Knowledge Usage ◽  
Similarity Information

Abstract The purpose of this study is to evaluate the effects of information on consumer adoption when introducing a new beef brand to the Vietnamese markets. Three variables proxy the impacts of information are prior knowledge, usage experience, and price. This study developed three pieces of advertised information and combined them with three levels of price to indicate the relevant information to diffuse at the introduction of a new brand. Three kinds of information include: (1) distinction information, which defines a new brand to be distinct from existing competitive brands; (2) differentiation information, which identifies a new brand to be different from one existing brand; (3) similarity information, which defines a new brand to be similar to one existing brand. The survey was conducted via direct interviews with 480 customers at the food outlets in Ho Chi Minh City, Vietnam. The ordered logit model was applied to examine the influence of each kind of information on consumer preferences for a new beef brand. The results indicated that (1) the effect of information on consumer adoption for a new brand at early stage depends on how that information defines the new brand in consumers’ perception; (2) the distinction information generates the highest economic added value; (3) the similarity information creates the information bias at introduction; (4) the usage experience can be diagnostic for the information bias.

scholarly journals Distilling Knowledge from Well-Informed Soft Labels for Neural Relation Extraction

2020 ◽  
Vol 34 (05) ◽  
pp. 9620-9627 ◽  
Author(s):  
Zhenyu Zhang ◽  
Xiaobo Shu ◽  
Bowen Yu ◽  
Tingwen Liu ◽  
Jiapeng Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Bipartite Graph ◽  
Prior Knowledge ◽  
Semantic Information ◽  
Relation Extraction ◽  
Important Task ◽  
Experimental Results ◽  
Plain Text ◽  
Knowledge Distillation ◽  
The Rich

Extracting relations from plain text is an important task with wide application. Most existing methods formulate it as a supervised problem and utilize one-hot hard labels as the sole target in training, neglecting the rich semantic information among relations. In this paper, we aim to explore the supervision with soft labels in relation extraction, which makes it possible to integrate prior knowledge. Specifically, a bipartite graph is first devised to discover type constraints between entities and relations based on the entire corpus. Then, we combine such type constraints with neural networks to achieve a knowledgeable model. Furthermore, this model is regarded as teacher to generate well-informed soft labels and guide the optimization of a student network via knowledge distillation. Besides, a multi-aspect attention mechanism is introduced to help student mine latent information from text. In this way, the enhanced student inherits the dark knowledge (e.g., type constraints and relevance among relations) from teacher, and directly serves the testing scenarios without any extra constraints. We conduct extensive experiments on the TACRED and SemEval datasets, the experimental results justify the effectiveness of our approach.

Brief Report: Teachers' Awareness of the Relationship Between Prior Knowledge and New Learning

2016 ◽  
Vol 47 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Charles Hohensee
Keyword(s):  
Professional Development ◽  
Prior Knowledge ◽  
Teacher Professional Development ◽  
Experienced Teachers ◽  
Future Research ◽  
New Learning ◽  
Teacher Professional ◽  
The Relationship

In this study, I examined the degree to which experienced teachers were aware of the relationship between prior knowledge and new learning. Interviews with teachers revealed that they were explicitly aware of when students made connections between prior knowledge and new learning, when they applied their prior knowledge to new contexts, and when they developed their prior knowledge as a result of applying that knowledge to new contexts. However, teachers were not explicitly aware of backwardtransfer effects. Results from this study have implications for future research on backward transfer, as well as for teacher professional development.

scholarly journals Cross-Representational Signaling and Cohesion Support Inferential Comprehension of Text–Picture Documents

2021 ◽  
Vol 11 ◽  
Author(s):  
Juliette C. Désiron ◽  
Mireille Bétrancourt ◽  
Erica de Vries
Keyword(s):  
Prior Knowledge ◽  
Struggling Readers ◽  
Text Comprehension ◽  
Visual Cues ◽  
Text Processing ◽  
Relevant Information ◽  
Pictorial Representation ◽  
Reading Abilities ◽  
Text Cohesion ◽  
Positive Effect

Learning from a text–picture multimedia document is particularly effective if learners can link information within the text and across the verbal and the pictorial representations. The ability to create a mental model successfully and include those implicit links is related to the ability to generate inferences. Text processing research has found that text cohesion facilitates the generation of inferences, and thus text comprehension for learners with poor prior knowledge or reading abilities, but is detrimental for learners with good prior knowledge or reading abilities. Moreover, multimedia research has found a positive effect from adding visual representations to text information, particularly when implementing signaling, which consists of verbal or visual cues designed to guide attention to the pictorial representation of relevant information. We expected that, as with text-only documents, struggling readers would benefit from high text cohesion (Hypothesis 1) and that signaling would foster inference generation as well (Hypothesis 2). Further, we hypothesized that better learning outcomes would be observed when text cohesion was low and signaling was present (Hypothesis 3). Our first experimental study investigated the effect of those two factors (cohesion and signaling) on three levels of comprehension (text based, local inferences, global inferences). Participants were adolescents in prevocational schools (n = 95), where some of the students are struggling readers. The results showed a trend in favor of high cohesion, but with no significant effect, a significant positive effect of cross-representational signaling (CRS) on comprehension from local inferences, and no interaction effect. A second experiment focused on signaling only and attention toward the picture, with collection of eye-tracking data in addition to measures of offline comprehension. As this study was conducted with university students (n = 47), who are expected to have higher reading abilities and thus are less likely to benefit from high cohesion, the material was presented in its low cohesive version. The results showed no effect of conditions on comprehension performances but confirmed differences in processing behaviors. Participants allocated more attention to the pictorial representation in the CRS condition than in the no signaling condition.

An apparent inconsistency arising from tests of significance based on fiducial distributions of unknown parameters

1939 ◽  
Vol 35 (4) ◽  
pp. 579-591 ◽  
Author(s):  
F. Yates
Keyword(s):  
Prior Knowledge ◽  
Relevant Information ◽  
Small Samples ◽  
Weighted Mean ◽  
Unknown Parameters ◽  
Fisher Test ◽  
Tests Of Significance ◽  
Two Samples ◽  
Apparent Inconsistency ◽  
The Difference

It has been observed that the Behrens and Fisher test of the difference of the means of two samples gives a smaller percentage of significant results than might be expected on the analogy of the ordinary t test with a pooled estimate of variance. The cause of this apparent anomaly is explained, and it is shown that the criticisms of the test to which the anomaly has given rise have their origin in (a) neglect of the relevant information provided by the estimated values of the variances, and (b) an insufficient appreciation of the fiducial basis of all tests of significance (including the ordinary t test) on small samples.It is pointed out that Sukhatme's table (constructed for the Behrens and Fisher test) also provides a test for the weighted mean of the means of two sets of observations, concerning whose relative accuracy no prior knowledge is available.

scholarly journals Deep data: discovery and visualization Application to hyperspectral ALMA imagery

2016 ◽  
Vol 12 (S325) ◽  
pp. 281-290 ◽  
Author(s):  
Erzsébet Merényi ◽  
Joshua Taylor ◽  
Andrea Isella
Keyword(s):  
Leading Edge ◽  
Large Data ◽  
Relevant Information ◽  
Moment Maps ◽  
Machine Learning Approach ◽  
Rich Information ◽  
The Rich ◽  
Near Future ◽  
Spectral Channels ◽  
Visualization Application

AbstractLeading-edge telescopes such as the Atacama Large Millimeter and sub-millimeter Array (ALMA), and near-future ones, are capable of imaging the same sky area at hundreds-to-thousands of frequencies with both high spectral and spatial resolution. This provides unprecedented opportunities for discovery about the spatial, kinematical and compositional structure of sources such as molecular clouds or protoplanetary disks, and more. However, in addition to enormous volume, the data also exhibit unprecedented complexity, mandating new approaches for extracting and summarizing relevant information. Traditional techniques such as examining images at selected frequencies become intractable while tools that integrate data across frequencies or pixels (like moment maps) can no longer fully exploit and visualize the rich information. We present a neural map-based machine learning approach that can handle all spectral channels simultaneously, utilizing the full depth of these data for discovery and visualization of spectrally homogeneous spatial regions (spectral clusters) that characterize distinct kinematic behaviors. We demonstrate the effectiveness on an ALMA image cube of the protoplanetary disk HD142527. The tools we collectively name “NeuroScope” are efficient for “Big Data” due to intelligent data summarization that results in significant sparsity and noise reduction. We also demonstrate a new approach to automate our clustering for fast distillation of large data cubes.

Introduction

2017 ◽  
pp. 1-2
Author(s):  
Bracha Yaniv
Keyword(s):  
Twentieth Century ◽  
Jewish Life ◽  
Geographical Location ◽  
Visual Presentation ◽  
Relevant Information ◽  
Second World War ◽  
World War ◽  
Art Historians ◽  
The Rich ◽  
Second World

This chapter gives an overview of installed arks and synagogues that still existed at the beginning of the twentieth century but were destroyed in the two world wars. It mentions surviving ark doors of synagogues that are now kept as museum items, which are considered silent witnesses to the rich tradition that was once part of Jewish life. It also reviews photographs of the arks that were taken before the Second World War and a few pre-war academic works and publications that were written mainly by architects and art historians, whose interest did not focus on Torah arks. The chapter refers to arks according to the name of their city or town of provenance, including their wider geographical location and other relevant information. It describes unique visual presentation of motifs that were never seen on arks, which characterized the period between the seventeenth and nineteenth centuries.

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