scholarly journals Progression of Cognitive-Affective States During Learning in Kindergarteners: Bringing Together Physiological, Observational and Performance Data

2020 ◽  
Vol 10 (7) ◽  
pp. 177
Author(s):  
Priyashri Kamlesh Sridhar ◽  
Suranga Nanayakkara
Keyword(s):  
Data Sources ◽  
Physiological Data ◽  
Affective States ◽  
Multiple Sources ◽  
Multiple Data ◽  
Combining Data ◽  
And Performance ◽  
And Mathematics ◽  
Future Work ◽  
Physiological Markers

It has been shown that combining data from multiple sources, such as observations, self-reports, and performance with physiological markers offers better insights into cognitive-affective states during the learning process. Through a study with 12 kindergarteners, we explore the role of utilizing insights from multiple data sources, as a potential arsenal to supplement and complement existing assessments methods in understanding cognitive-affective states across two main pedagogical approaches—constructionist and instructionist—as children explored learning a chosen Science, Technology, Engineering and Mathematics (STEM) concept. We present the trends that emerged across pedagogies from different data sources and illustrate the potential value of additional data channels through case illustrations. We also offer several recommendations for such studies, particularly when collecting physiological data, and summarize key challenges that provide potential avenues for future work.

scholarly journals 3D Geometry-Based Indoor Network Extraction for Navigation Applications Using SFCGAL

2020 ◽  
Vol 9 (7) ◽  
pp. 417 ◽  
Author(s):  
Jernej Tekavec ◽  
Anka Lisec
Keyword(s):  
Integrated Approach ◽  
Indoor Navigation ◽  
Data Sources ◽  
Network Analyses ◽  
Multiple Data ◽  
3D Cadastre ◽  
3D Geometry ◽  
Building Information ◽  
And Performance ◽  
Simple Features

This study is focused on indoor navigation network extraction for navigation applications based on available 3D building data and using SFCGAL library, e.g. simple features computational geometry algorithms library. In this study, special attention is given to 3D cadastre and BIM (building information modelling) datasets, which have been used as data sources for 3D geometric indoor modelling. SFCGAL 3D functions are used for the extraction of an indoor network, which has been modelled in the form of indoor connectivity graphs based on 3D geometries of indoor features. The extraction is performed by the integration of extract transform load (ETL) software and the spatial database to support multiple data sources and provide access to SFCGAL functions. With this integrated approach, the current lack of straightforward software support for complex 3D spatial analyses is addressed. Based on the developed methodology, we perform and discuss the extraction of an indoor navigation network from 3D cadastral and BIM data. The efficiency and performance of the network analyses were evaluated using the processing and query execution times. The results show that the proposed methodology for geometry-based navigation network extraction of buildings is efficient and can be used with various types of 3D geometric indoor data.

scholarly journals Extending density surface models to include multiple and double-observer survey data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12113
Author(s):  
David L. Miller ◽  
David Fifield ◽  
Ewan Wakefield ◽  
Douglas B. Sigourney
Keyword(s):  
Spatial Model ◽  
Distance Sampling ◽  
Additive Model ◽  
Data Sources ◽  
Multiple Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Surface Models ◽  
Density Surface ◽  
Double Observer

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.

scholarly journals A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1822 ◽  
Author(s):  
Ana Claudia Sima ◽  
Christophe Dessimoz ◽  
Kurt Stockinger ◽  
Monique Zahn-Zabal ◽  
Tarcisio Mendes de Farias
Keyword(s):  
Query Language ◽  
Data Sources ◽  
Multiple Sources ◽  
Semantic Web Technologies ◽  
Genome Database ◽  
Web Technologies ◽  
Multiple Data ◽  
Hands On ◽  
Description Framework ◽  
Different Sources

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the SPARQL query language. In this article, we provide a hands-on introduction to querying evolutionary data across multiple sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different sources can be compared, through the use of federated SPARQL queries.

scholarly journals Reading Between the Lines: A Pursuit of Estimating the Population Prevalence of Mental Illness Using Multiple Data Sources

2021 ◽  
pp. 070674372110162
Author(s):  
Jordan Edwards ◽  
Katholiki Georgiades
Keyword(s):  
Mental Illness ◽  
Mental Health Policy ◽  
Public Mental Health ◽  
Population Level ◽  
Population Based ◽  
Service Planning ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Combining Data

Population-based prevalence estimates of mental illness are foundational to health service planning, strategic resource allocation, and the development and evaluation of public mental health policy. Generating valid, reliable, and context-specific population-level estimates is of utmost importance and can be achieved by combining various data sources. This pursuit benefits from the right combination of theory, applied statistics, and the conceptualization of available data sources as a collective rather than in isolation. We believe there is a need to read between the lines as theory, methodology, and context (i.e., strengths and limitations) are what determines the meaningfulness of a combined prevalence estimate. Currently lacking is a gold standard approach to combining estimates from multiple data sources. Here, we compare and contrast various approaches to combining data and introduce an idea that leverages the strengths of pre-existing individually linked population-based survey and health administrative data sources currently available in Canada.

scholarly journals Towards a Physiological Computing Infrastructure for Researching Students’ Flow in Remote Learning

2021 ◽  
Author(s):  
Maximilian Xiling Li ◽  
Mario Nadj ◽  
Alexander Maedche ◽  
Dirk Ifenthaler ◽  
Johannes Wöhler
Keyword(s):  
Supervised Machine Learning ◽  
Physiological Data ◽  
Raspberry Pi ◽  
Affective States ◽  
Real Time Measurement ◽  
Learning Journey ◽  
Browsing Behavior ◽  
Future Work ◽  
Computing Infrastructure ◽  
Remote Learning

AbstractWith the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students’ learning journey in remote learning. Specifically, our solution is based on the Raspberry Pi minicomputer and Polar H10 chest belt. In this work-in-progress paper, we present preliminary results and experiences we collected from a field study with medical students using our developed infrastructure. Our results do not only provide a new direction for more effectively capturing different types of data in remote learning by addressing the underlying challenges of remote setups, but also serve as a foundation for future work on developing a less obtrusive, (near) real-time measurement method based on the classification of cognitive-affective states such as flow or other learning-relevant constructs with the captured data using supervised machine learning.

The Student Self-Accommodation Strategy for Students with Visual Impairments

2021 ◽  
pp. 0145482X2110595
Author(s):  
Allison C. Nannemann
Keyword(s):  
High School Students ◽  
Visual Impairments ◽  
Metacognitive Knowledge ◽  
School Students ◽  
Post Secondary ◽  
Self Regulated Learning ◽  
Multiple Data ◽  
Education And Employment ◽  
And Performance ◽  
Future Work

Introduction Accommodations are essential for the successful participation of individuals with visual impairments in post-secondary education and employment. Passive experiences with accommodations in school, plus a complex advocacy process warrant the need to support students to engage in the accommodations process. Methods Four high school students with visual impairments were taught the Student Self-Accommodation Strategy. A parallel multiple-case design was used to determine how and how well the participants learned and used the strategy and to investigate their development of metacognitive knowledge and self-regulated learning (SRL) skills. Results The participants all learned the strategy to varying extents. The cross-case analysis revealed that recall and understanding the purpose of the strategy supported strategy performance but were not associated with in-class use of the strategy. Additionally, participants did not experience changes with metacognition or SRL; however, they did demonstrate metacognitive knowledge on multiple data sources, with few demonstrations of SRL. Discussion Findings indicate that the Student Self-Accommodation Strategy is accessible to students with visual impairments. Three factors seemed to be associated with the learning and use of the strategy: verbal and reasoning skills, achievement, and emotional-behavioral regulation. Metacognition and SRL can positively affect students with visual impairments. Implications Future work with the Student Self-Accommodation Strategy should incorporate in-class strategy coaching and an explicit investigation of the factors that seemed to influence strategy learning and performance. Research and practice should give greater attention to metacognition and SRL for students with visual impairments.

scholarly journals Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences

2021 ◽  
pp. 027836492110416
Author(s):  
Erdem Bıyık ◽  
Dylan P. Losey ◽  
Malayandi Palan ◽  
Nicholas C. Landolfi ◽  
Gleb Shevchuk ◽  
...  
Keyword(s):  
Data Sources ◽  
Mobile Manipulator ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Reward Function ◽  
Multiple Data ◽  
Preference Queries ◽  
Passive Data ◽  
Reward Functions ◽  
Human Teachers

Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of forms: passive data sources include demonstrations (e.g., kinesthetic guidance), whereas preferences (e.g., comparative rankings) are actively elicited. Prior research has independently applied reward learning to these different data sources. However, there exist many domains where multiple sources are complementary and expressive. Motivated by this general problem, we present a framework to integrate multiple sources of information, which are either passively or actively collected from human users. In particular, we present an algorithm that first utilizes user demonstrations to initialize a belief about the reward function, and then actively probes the user with preference queries to zero-in on their true reward. This algorithm not only enables us combine multiple data sources, but it also informs the robot when it should leverage each type of information. Further, our approach accounts for the human’s ability to provide data: yielding user-friendly preference queries which are also theoretically optimal. Our extensive simulated experiments and user studies on a Fetch mobile manipulator demonstrate the superiority and the usability of our integrated framework.

scholarly journals Becoming Open Knowledge Institutions: Divergence, Dialogue and Diversity

2021 ◽  
pp. 431-440
Author(s):  
Katie Wilson ◽  
Lucy Montgomery ◽  
Cameron Neylon ◽  
Rebecca N. Handcock ◽  
Richard Hosking ◽  
...  
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Open Access ◽  
Cultural Change ◽  
Research Output ◽  
Data Sources ◽  
Global Approach ◽  
Multiple Data ◽  
And Performance ◽  
Bibliometric Measures

AbstractThe Curtin Open Knowledge Initiative (COKI) is an innovative research project that collects and analyses publicly available research output data to assist and encourage researchers, academics, administrators and executives to understand the actual and potential reach of openness in research, and to assess their progress on the path towards open knowledge institutions. By taking a broad global approach and using multiple data sources, the project diverges from existing approaches, methods and bibliometric measures in the scholarly research environment. It combines analysis of research output, citations, publication sources and publishers, funders, social media events, open and not open access to provide overviews of research output and performance at institutional, funder, consortial and country levels. The project collects and analyses personnel diversity data such as gender, focusing on widening the reach of data analysis to emphasise the importance and value of diversity in research and knowledge production. Interactive visual tools present research output and performance to encourage understanding and dialogue among researchers and management. The path towards becoming open knowledge institutions involves a process of cultural change, moving beyond dominant publishing and evaluation practices. This paper discusses how through divergence, diversity and dialogue the COKI project can contribute to this change, with examples of applications in understanding and embracing openness.

Relationships among the Ready or Not Parental Checklist for School Readiness, the Brigance Kindergarten and First Grade Screen, and Sra Scores

1991 ◽  
Vol 73 (3) ◽  
pp. 859-862 ◽  
Author(s):  
Elaine Campbell ◽  
Terri Schellinger ◽  
John Beer
Keyword(s):  
School Districts ◽  
School Readiness ◽  
Analysis Of Variance ◽  
First Grade ◽  
Composite Score ◽  
Screening Instruments ◽  
Multiple Sources ◽  
North Central ◽  
And Performance ◽  
And Mathematics

73 children (38 boys, 35 girls) from rural north central Kansas school districts participated in a study comparing measures of school readiness and performance. The children's parents completed the Ready or Not checklist while all 73 children were given the Brigance K & 1 Screen. Once in kindergarten all children were administered the SRA Survey of Basic Skills. Analysis of variance of the Composite score and scores on reading and mathematics as well as the educational ability quotient indicated no significant associations of gender. Parents rated the readiness of children as very probable while on the Brigance the children performed in the higher-than-average range. These two scores gave similar results over-all and correlated significantly with the SRA scores (the better children scored on the Brigance or were rated by a parent on the Ready or Not, the better they performed on the SRA survey). These devices can be used as screening instruments to collect information about children's readiness from more than one source (parental checklist and observation of a child's performance). When multiple sources having nonidentical bases are used, more valid judgments can be made about children's readiness for school.

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