scholarly journals Logical word learning: The case of kinship

2019 ◽  
Author(s):  
Francis Mollica ◽  
Steven T. Piantadosi
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Conceptual Development ◽  
Learning Model ◽  
Developmental Model ◽  
Learning Trajectories ◽  
Model Framework ◽  
Model Simulations ◽  
Linguistic Data ◽  
Kinship Term

In this paper, we propose a framework for conceptual development through the lens of program induction. We implement this framework to model the acquisition of kinship term concepts, resulting in the first formal developmental model for kinship acquisition. We demonstrate that our model can learn several kinship systems of varying complexity using cross-linguistic data from English, Pukapuka, Turkish and Yanomamö. More importantly, the behavioral patterns observed in children learning kinship terms, under-extension and over-generalization, fall out naturally from our learning model. We conducted interviews to simulate realistic learning environments and demonstrate that the characteristic-to-defining shift is a consequence of our learning model in naturalistic contexts containing abstract and concrete features. We use model simulations to discuss the influence of simplicity and learning environment on the order of acquisition of kinship terms, positing novel predictions for the learning trajectories of kinship terms. We conclude the paper with a discussion of how this model framework generalizes beyond kinship terms and the limitations of our model.

scholarly journals Logical word learning: The case of kinship

2021 ◽  
Author(s):  
Francis Mollica ◽  
Steven T. Piantadosi
Keyword(s):  
Learning Environment ◽  
Computational Model ◽  
Learning Environments ◽  
Conceptual Development ◽  
Learning Model ◽  
Learning Trajectories ◽  
Model Framework ◽  
Model Simulations ◽  
Linguistic Data ◽  
Kinship Term

AbstractWe examine the conceptual development of kinship through the lens of program induction. We present a computational model for the acquisition of kinship term concepts, resulting in the first computational model of kinship learning that is closely tied to developmental phenomena. We demonstrate that our model can learn several kinship systems of varying complexity using cross-linguistic data from English, Pukapuka, Turkish, and Yanomamö. More importantly, the behavioral patterns observed in children learning kinship terms, under-extension and over-generalization, fall out naturally from our learning model. We then conducted interviews to simulate realistic learning environments and demonstrate that the characteristic-to-defining shift is a consequence of our learning model in naturalistic contexts containing abstract and concrete features. We use model simulations to understand the influence of logical simplicity and children’s learning environment on the order of acquisition of kinship terms, providing novel predictions for the learning trajectories of these words. We conclude with a discussion of how this model framework generalizes beyond kinship terms, as well as a discussion of its limitations.

A Framework for Developing and Implementing u-Learning Models

2010 ◽  
pp. 310-323
Author(s):  
Des Casey ◽  
Janet Fraser
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
System Architecture ◽  
Learning Model ◽  
Learning Models ◽  
Learning Content

The advent of u-learning environments requires the development of appropriate u-learning models to inform the use of such environments. As there is no single u-learning model to suit all environments and learning situations, there is a need to develop a methodology for developing models appropriate to various environments and situations. This chapter outlines such a methodology as a useful framework on which to base the derivation of particular models for specific situations. The study then illustrates the use of this methodology to derive a particular model: a task-based u-learning model, incorporating well-bounded learning content. Following this, the study proposes a system architecture to embody this derived u-learning model, and, then describes the implementation of this architecture through the development and deployment of the Walkabout u-Learning Environment.

U-Learning

2011 ◽  
pp. 381-394 ◽  
Author(s):  
Des Casey
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Mobile Devices ◽  
System Architecture ◽  
Learning Model ◽  
Human Learning ◽  
Ubiquitous Learning ◽  
Proposed Model ◽  
E Learning ◽  
Structured Knowledge

Increasingly powerful, handheld, networked devices provide an opportunity for educators to explore and implement ubiquitous learning (u-learning) environments through the extension of e-learning environments to include applications and services delivered to mobile devices. U-learning environments should be developed with both an understanding of human learning in general, and the learning of formally structured knowledge through de-structured u-learning environments. This study proposes a learning model for u-learning environments and a system architecture based on this model. The study outlines the features of the Walkabout u-Learning Environment, which has been implemented and trialled in accordance with the proposed model and architecture.

scholarly journals Validating an Instrument to Measure School Culture in Project-Based Learning Environments

2020 ◽  
Vol 2 (4) ◽  
Keyword(s):  
School Culture ◽  
Learning Environment ◽  
Learning Environments ◽  
Student Outcomes ◽  
Learning Experiences ◽  
International Comparisons ◽  
Project Based Learning ◽  
Instructional Model ◽  
Educational Environments ◽  
Common School

Measuring school culture and analyzing student learning experiences is a rapidly growing practice, with a notable uptick following the increased forcus on learning experiences spurred by international comparisons of educational environments and resulting student outcomes. The literature documents common constructs that are often included in school culture surveys. However, often all learning environments are organized together and offered the same school culture survey. This is problematic because a common school culture survey construct is “learning environment” and the items that form this construct will be significantly different based on the instructional model. Therefore, providing educators with a one size fits all culture survey does not meet the needs of schools offering problem-based learning (PrBL) and project-based learning (PBL) environments. This research examines the process for revising, designing, and validating a school culture survey aligned to PrBL and PBL environments.

scholarly journals CORRELATION BETWEEN PERCEPTION ON CLINICAL LEARNING ENVIRONMENTS AND CAREER CHOICE ON CLERKSHIP STUDENTS IN FACULTY OF MEDICINE UNIVERSITAS SEBELAS MARET

2019 ◽  
Vol 8 (1) ◽  
pp. 31
Author(s):  
Adinda Kharisma Apriliani ◽  
Eti Poncorini Pamungkasari ◽  
Amandha Boy Timor Randita
Keyword(s):  
Medical Students ◽  
Learning Environment ◽  
Career Choice ◽  
Learning Environments ◽  
Career Choices ◽  
Health Workers ◽  
Observational Research ◽  
Clinical Learning ◽  
Weak Correlation ◽  
Clinical Learning Environment

Background: Needs of health workers, especially general practitioners, relatively high in Indonesia. Career choices among medical students are various, such as general practitioner, specialist, medical researcher, etc. Many factors affect medical students’ career choices, one of them is learning environments. This study aims to prove the correlation between clerkship students’ perceptions of clinical learning environments and their career choices.Methods: This research was an analytical observational research with cross sectional approach. The subjects were clerkship students who underwent clinical rotation. The samples were 178 clerkship students from all departments. They were chosen by probability proportional to size sampling. Every respondent was given career choices questionnaire and PHEEM questionnaire which has analyzed for its validity and reliability with α≥0,6 (α=0,826) and r≥0,30 (r=0,442). The result of this study was analyzed by Chi-square test and followed by Contingency Coefficient with 95% confidence level (α = 0,05). Result: The result showed that students’ perception of clinical learning environment “good but still need improvement” category was nearly the same as “plenty of problems” category. The result on the students’ career choices, most students choose clinical career. There was significant correlation between perception of clinical learning environment and career choices on clerkship students of medical faculty, Sebelas Maret University with p <0,05 (p= 0,018), x2 count (x2=5,625) > x2 table (x2=3,841) and also very weak correlation (C= 0,189).Conclusion: There was very weak correlation between perception of clinical learning environments and career choice on clerkship students. 

scholarly journals A scale development study for assessing the effectiveness of blended learning environments

2013 ◽  
Vol 3 (3) ◽  
pp. 11-26 ◽  
Author(s):  
Emine Cabı ◽  
Yasemin Gülbahar
Keyword(s):  
Online Learning ◽  
Scale Development ◽  
Learning Environment ◽  
Blended Learning ◽  
Learning Environments ◽  
Reliability And Validity ◽  
Development Study ◽  
Online Learning Environments ◽  
Face To Face ◽  
Development Data

This study is conducted to develop a scale for assessing the effectiveness of blended learning environments based on the features of both face-to-face and online learning environments and provide suggestions for stakeholders. In the process of scale development, data gathered from 314 students were analyzed. The reliability and validity results for collected data were found to be acceptable since they were between or above the expected value. Based on the analysis it is found that the scale is composed of 55 items having a structure of 4 factors. Hence, it can be concluded that "Effectiveness of Blended Learning Environments Scale" is found as reliable and valid, and can measure what it aims to measure. Blended Learning Environment Scale, which was developed and analyzed for reliability and validity throughout this study, is expected to facilitate the further research studies that focused on blended learning environments.

scholarly journals Student Preferences within a Holistic Blended Learning Environment

2019 ◽  
pp. 276-290
Author(s):  
Bernice Beukes ◽  
Karin Barac ◽  
Lynette Nagel
Keyword(s):  
Academic Performance ◽  
Learning Environment ◽  
Blended Learning ◽  
Learning Environments ◽  
Mixed Method ◽  
Student Preferences ◽  
Performance Levels ◽  
Residential University ◽  
The Subject ◽  
Method Approach

Extant research shows that blended learning environments are widely accepted by students mainly because of the flexibility it offers. However, there is very little research that focuses on students’ preferences within a holistic blended learning environment and the contribution that a component makes to the learning of the subject matter, especially in large class settings. The purpose of this study is to investigate students’ perceptions of blended learning components in a holistic blended learning environment and whether these perceptions vary for students with different academic performance levels. A mixed method approach was used in this study performed at a residential university in South Africa and the results indicate that auditing students do have a clear preference for specific components within the environment and significant differences exist between the preferences of different academic performance levels. Such insights allow lecturers to adjust the resources and focus of the different components implemented in a blended learning environment.

scholarly journals Making ‘Math Making’ Virtual

2020 ◽  
Vol 29 (2) ◽  
pp. 30
Author(s):  
Candace Figg ◽  
Anjali Khirwadkar ◽  
Shannon Welbourn
Keyword(s):  
Preservice Teachers ◽  
Learning Environment ◽  
Learning Environments ◽  
Mathematics Learning ◽  
Computational Thinking ◽  
Teacher Preparation Programs ◽  
Preparation Programs ◽  
University Professors ◽  
Positive Attitudes ◽  
Hands On

Due to the COVID-19 pandemic, university professors are challenged to re-envision mathematics learning environments for virtual delivery. Those of us teaching in elementary teacher preparation programs are exploring different learning environments that not only promote meaningful learning but also foster positive attitudes about mathematics teaching. One learning environment that has been shown to be effective for introducing preservice teachers to the creative side of mathematics—the mathematics makerspace—promotes computational thinking and pedagogical understandings about teaching mathematics, but the collaborative, hands-on nature of such a learning environment is difficult to simulate in virtual delivery. This article describes the research-based design decisions for the re-envisioned virtual mathematics makerspace.

scholarly journals Factors Affecting Students’ Preferences for Online and Blended Learning: Motivational Vs. Cognitive

2020 ◽  
Vol 22 (2) ◽  
pp. 72-86 ◽  
Author(s):  
Sinan Keskin ◽  
Halil Yurdugül
Keyword(s):  
Learning Environment ◽  
Blended Learning ◽  
Learning Environments ◽  
Self Efficacy ◽  
Teaching And Learning ◽  
Learning Motivation ◽  
Task Value ◽  
Individual Factors ◽  
E Learning ◽  
The Individual

AbstractToday’s educational institutions are expected to create learning opportunities independent of time and place, to offer easily accessible learning environments and interpersonal communication opportunities. Accordingly, higher education institutions develop strategies to meet these expectations through teaching strategies, such as e-learning, blended learning, mobile learning, etc., by using teaching technologies. These new technology-based teaching strategies are mainly shaped by decision-makers in education. This study seeks to analyse the individual factors that affect learners’ mode of teaching and learning delivery preferences. In this study, blended and online learning is considered as preferences of learners’ mode of teaching and learning delivery. The individual factors discussed in this research are cognitive learning strategies, e-learning readiness, and motivation. The data were obtained from the pre-service teachers at the end of the academic semester when they experienced online and blended learning. Data were analysed using optimal scaling analysis. The analysis method provides a two-dimensional centroid graph which shows the correlations between the variable categories. According to study findings, there is a correlation between the preferences of the learning environment, and the constructs of self-efficacy, e-learning motivation, and task value. It can be said that the motivational variables are more effective in the learning environment preference. The students with high task value, e-learning motivation, and self-efficacy preferred studying in blended learning environments. Cognitive strategies, self-directed learning, learner control, and test anxiety factors are independent of the learners’ learning delivery preferences.

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