scholarly journals Critical Issues in Designing and Implementing Temporal Analytics

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Bodong Chen ◽  
Simon Knight ◽  
Alyssa Friend Wise
Keyword(s):  
Learning Analytics ◽  
Special Section ◽  
Temporal Data ◽  
Data Types ◽  
Temporal Features ◽  
Critical Issues ◽  
Potential Impact ◽  
Future Work ◽  
Temporal Learning ◽  
Learning Data

The importance of temporality in learning has been long established, but it is only recently that serious attention has begun to be paid to the precise identification, measurement, and analysis of the temporal features of learning. From 2009 to 2016, a series of temporality workshops explored temporal concepts and data types, analysis methods for exploiting temporal data, techniques for visualizing temporal information, and practical considerations for the use of temporal analyses in particular contexts of learning. Following from these efforts, this two-part Special Section serves to consolidate research working to progress conceptual, technical and practical tools for temporal analyses of learning data. In addition, in this second and final editorial, we aim to make four contributions to the ongoing dialogue around temporal learning analytics to help us move towards a clearer mapping of the research space. First, the editorial presents an overview of the five papers in Part 2 of the Special Section on Temporal Analyses, highlighting the dimensions of data types, learning constructs, analysis approaches, and potential impact. Second, it draws on the fluid relationship between ‘analyzed time’ and ‘experienced time’ to highlight the need for caution and criticality in the purposes temporal analyses are mobilized to serve. Third, it offers a guide for future work in this area by outlining important questions that all temporal analyses should intentionally address. Finally, it proposes next steps learning analytics researchers and practitioners can take collectively to advance work on the use of temporal analyses to support learning

scholarly journals Time for Change: Why Learning Analytics Needs Temporal Analysis

2017 ◽  
Vol 4 (3) ◽  
Author(s):  
Simon Knight ◽  
Alyssa Friend Wise ◽  
Bodong Chen
Keyword(s):  
Learning Analytics ◽  
Temporal Analysis ◽  
Temporal Data ◽  
Research Practice ◽  
Process Data ◽  
Applied Learning ◽  
Privileged Access ◽  
Learning Research ◽  
Methodological Approaches ◽  
Temporal Learning

Learning is a process that occurs over time: We build understanding, change perspectives, and develop skills over the course of extended experiences. As a field, learning analytics aims to generate understanding of, and support for, such processes of learning. Indeed, a core characteristic of learning analytics is the generation of high-resolution temporal data about various types of actions. Thus, we might expect study of the temporal nature of learning to be central in learning analytics research and applications. However, temporality has typically been underexplored in both basic and applied learning research. As Reimann (2009) notes, although “researchers have privileged access to process data, the theoretical constructs and methods employed in research practice frequently neglect to make full use of information relating to time and order” (p. 239). Typical approaches to analysis often aggregate across data due to a collection of conceptual, methodological, and operational challenges. As described below, insightful temporal analysis requires (1) conceptualising the temporal nature of learning constructs, (2) translating these theoretical propositions into specific methodological approaches for the capture and analysis of temporal data, and (3) practical methods for capturing temporal data features and using analyses to impact learning contexts. There is a pressing need to address these challenges if we are to realize the exciting possibilities for temporal learning analytics.

scholarly journals Temporal Learning Analytics for Adaptive Assessment

2014 ◽  
Vol 1 (3) ◽  
pp. 165-168 ◽  
Author(s):  
Zacharoula Papamitsiou ◽  
Anastasios A. Economides
Keyword(s):  
Latent Variables ◽  
Learning Analytics ◽  
Research Work ◽  
Assessment System ◽  
Temporal Behaviour ◽  
Adaptive Assessment ◽  
Pls Analysis ◽  
Improved Learning ◽  
Future Work ◽  
Temporal Learning

Accurate and early predictions of students’ performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research work, we seek to identify and formalize temporal parameters as predictors of performance (“temporal learning analytics”-TLA) and examine students' temporal behaviour (i.e. in terms of time-spent) during testing. The goal is to specify a functional set of parameters that will be embedded in an adaptive assessment system in order to contribute to personalization of feedback services. We adopted the Partial Least-Squares (PLS) analysis method for formulating the causal dependencies between latent variables and the relations to their indicators. In this paper we present the motivation and rationale of our work, along with the followed methodology, initial results and contributions so far, and our plans on future work.

scholarly journals Learning Analytics for Primary and Secondary Schools

2021 ◽  
Vol 8 (2) ◽  
pp. 1-5
Author(s):  
Vitomir Kovanovic ◽  
Claudia Mazziotti ◽  
Jason Lodge
Keyword(s):  
Learning Analytics ◽  
Tertiary Education ◽  
Special Section ◽  
School Context ◽  
School Settings ◽  
Opportunities For Learning ◽  
Challenges And Opportunities ◽  
Improving Student Learning ◽  
Data Driven Decisions ◽  
Future Work

Over the past decade, the increasing use of learning analytics opened the possibility of making data-driven decisions for improving student learning. Driven by the strong university adoption of learning analytics, most early learning analytics research focused on issues specific to tertiary education. With the broader adoption of educational technologies in primary and secondary education and the emergence of new classroom-focused technologies, there has been a growing awareness of the potentials of learning analytics for supporting students and diagnosing their learning progress in pre-university contexts. This special section focused on investigating, developing, and evaluating state-of-the-art learning analytics approaches within primary and secondary school settings. In this editorial, we summarize the papers of the special section and discuss the challenges and opportunities for learning analytics within the school context. We conclude with the discussion around the opportunities for future work and the implications of this special section for the field of learning analytics.

What are the risks of Virtual Reality data? Learning Analytics, Algorithmic Bias and a Fantasy of Perfect Data

2021 ◽  
pp. 146144482110127
Author(s):  
Marcus Carter ◽  
Ben Egliston
Keyword(s):  
Virtual Reality ◽  
Learning Process ◽  
Learning Analytics ◽  
Soft Skills ◽  
Education Technology ◽  
Skills Training ◽  
Emerging Technology ◽  
Critical Issues ◽  
Vr Training ◽  
Algorithmic Bias

Virtual reality (VR) is an emerging technology with the potential to extract significantly more data about learners and the learning process. In this article, we present an analysis of how VR education technology companies frame, use and analyse this data. We found both an expansion and acceleration of what data are being collected about learners and how these data are being mobilised in potentially discriminatory and problematic ways. Beyond providing evidence for how VR represents an intensification of the datafication of education, we discuss three interrelated critical issues that are specific to VR: the fantasy that VR data is ‘perfect’, the datafication of soft-skills training, and the commercialisation and commodification of VR data. In the context of the issues identified, we caution the unregulated and uncritical application of learning analytics to the data that are collected from VR training.

scholarly journals Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

2020 ◽  
Vol 21 (24) ◽  
pp. 9461
Author(s):  
Aurora Savino ◽  
Paolo Provero ◽  
Valeria Poli
Keyword(s):  
Cancer Biology ◽  
Regulatory Networks ◽  
Tumour Progression ◽  
External Perturbation ◽  
Data Types ◽  
Differential Gene ◽  
Gene Regulatory ◽  
Experimental Validations ◽  
Definition Of ◽  
Future Work

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

Study of the Spatio-Temporal Data Model in Sea Battlefield

2012 ◽  
Vol 246-247 ◽  
pp. 744-748
Author(s):  
Yue Lin Sun ◽  
Lei Bao ◽  
Yi Hang Peng
Keyword(s):  
Data Model ◽  
Object Oriented ◽  
Space Time ◽  
Comprehensive Analysis ◽  
Temporal Data ◽  
Data Types ◽  
Spatio Temporal ◽  
The Right ◽  
Effective Analysis

An effective analysis of the battlefield situation and spatio-temporal data model in a sea battlefield has great significance for the commander to perceive the battlefield situation and to make the right decisions. Based on the existing spatio-temporal data model, the present paper gives a comprehensive analysis of the characteristics of sea battlefield data, and chooses the object-oriented spatio-temporal data model to modify it; at the same time this paper introduces sea battlefield space-time algebra system to define various data types formally, which lays the foundation for the establishment of the sea battlefield spatio-temporal data model.

scholarly journals Strong Copyright + DRM + Weak Net Neutrality = Digital Dystopia?

2006 ◽  
Vol 25 (3) ◽  
pp. 116 ◽  
Author(s):  
Charles W. Bailey Jr.
Keyword(s):  
Digital Rights Management ◽  
Internet Traffic ◽  
Digital Content ◽  
Net Neutrality ◽  
Digital Information ◽  
Digital Rights ◽  
Copyright Laws ◽  
Rights Management ◽  
Critical Issues ◽  
Potential Impact

Three critical issues—a dramatic expansion of the scope, duration, and punitive nature of copyright laws; the ability of Digital Rights Management (DRM) systems to lock-down digital content in an unprecedented fashion; and the erosion of Net neutrality, which ensures that all Internet traffic is treated equally—are examined in detail and their potential impact on libraries is assessed. How legislatures, the courts, and the commercial marketplace treat these issues will strongly influence the future of digital information for good or ill.

Temporal Data Types

1995 ◽  
pp. 123-152
Author(s):  
Michael D. Soo ◽  
Richard T. Snodgrass
Keyword(s):  
Temporal Data ◽  
Data Types

scholarly journals Omics in a Digital World: The Role of Bioinformatics in Providing New Insights Into Human Aging

2021 ◽  
Vol 12 ◽  
Author(s):  
Serena Dato ◽  
Paolina Crocco ◽  
Nicola Rambaldi Migliore ◽  
Francesco Lescai
Keyword(s):  
High Throughput ◽  
Aging Process ◽  
Single Cell Analysis ◽  
Physiological Age ◽  
Data Types ◽  
Aging Research ◽  
Digital World ◽  
Age Related ◽  
Critical Issues ◽  
The Individual

BackgroundAging is a complex phenotype influenced by a combination of genetic and environmental factors. Although many studies addressed its cellular and physiological age-related changes, the molecular causes of aging remain undetermined. Considering the biological complexity and heterogeneity of the aging process, it is now clear that full understanding of mechanisms underlying aging can only be achieved through the integration of different data types and sources, and with new computational methods capable to achieve such integration.Recent AdvancesIn this review, we show that an omics vision of the age-dependent changes occurring as the individual ages can provide researchers with new opportunities to understand the mechanisms of aging. Combining results from single-cell analysis with systems biology tools would allow building interaction networks and investigate how these networks are perturbed during aging and disease. The development of high-throughput technologies such as next-generation sequencing, proteomics, metabolomics, able to investigate different biological markers and to monitor them simultaneously during the aging process with high accuracy and specificity, represents a unique opportunity offered to biogerontologists today.Critical IssuesAlthough the capacity to produce big data drastically increased over the years, integration, interpretation and sharing of high-throughput data remain major challenges. In this paper we present a survey of the emerging omics approaches in aging research and provide a large collection of datasets and databases as a useful resource for the scientific community to identify causes of aging. We discuss their peculiarities, emphasizing the need for the development of methods focused on the integration of different data types.Future DirectionsWe critically review the contribution of bioinformatics into the omics of aging research, and we propose a few recommendations to boost collaborations and produce new insights. We believe that significant advancements can be achieved by following major developments in bioinformatics, investing in diversity, data sharing and community-driven portable bioinformatics methods. We also argue in favor of more engagement and participation, and we highlight the benefits of new collaborations along these lines. This review aims at being a useful resource for many researchers in the field, and a call for new partnerships in aging research.

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