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 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 Topical directions of didactics development in the XXI century

2021 ◽  
Vol 101 ◽  
pp. 03016
Author(s):  
Elena Ivanova ◽  
Mikhail Klarin ◽  
Irina Osmolovskaya
Keyword(s):  
Higher Education ◽  
Interdisciplinary Research ◽  
Modern Society ◽  
Family Education ◽  
Evidence Based ◽  
Learning Research ◽  
Methodological Approaches ◽  
Research In Education ◽  
Education Of Students

The paper analyzes challenges of modern society affecting changes in education that determines the current directions of didactics development. The authors establish that in didactics there are: 1) expansion of the set of objects studied by didactics (the study of general secondary education is supplemented by the study of higher, corporate, family education, education of students with different educational needs), consideration of the learning process in the context of digitalization; 2) the development of methodological approaches to didactic research (activation of empirical research, an increase in the role of research methods of the humanities in didactics, setting the task of developing evidence-based learning research); 3) activation of interdisciplinary research in education (consideration of didactic objects from the standpoint of related sciences - didactics, psychology, cognitive science, sociology). The paper consideres conceptual provisions in the development of didactics of higher education.

scholarly journals Designing in Context: Reaching Beyond Usability in Learning Analytics Dashboard Design

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
June Ahn ◽  
Fabio Campos ◽  
Maria Hays ◽  
Daniela Digiacomo
Keyword(s):  
Visual Analytics ◽  
Learning Analytics ◽  
The United States ◽  
Pedagogical Practices ◽  
Research Practice ◽  
Design Practice ◽  
Improvement Science ◽  
District Improvement ◽  
Contextual Design ◽  
Science And Research

Researchers and developers of learning analytics (LA) systems are increasingly adopting human-centred design (HCD) approaches, with growing need to understand how to apply design practice in different educational settings. In this paper, we present a design narrative of our experience developing dashboards to support middle school mathematics teachers’ pedagogical practices, in a multi-university, multi-school district, improvement science initiative in the United States. Through documentation of our design experience, we offer ways to adapt common HCD methods — contextual design and design tensions — when developing visual analytics systems for educators. We also illuminate how adopting these design methods within the context of improvement science and research–practice partnerships fundamentally influences the design choices we make and the focal questions we undertake. The results of this design process flow naturally from the appropriation and repurposing of tools by district partners and directly inform improvement goals.

scholarly journals Learning Analytics for Blended Learning: A Systematic Review of Theory, Methodology, and Ethical Considerations

2020 ◽  
Vol 2 (2) ◽  
pp. 46
Author(s):  
Nina Bergdahl ◽  
Jalal Nouri ◽  
Thashmee Karunaratne ◽  
Muhammad Afzaal ◽  
Mohammed Saqr
Keyword(s):  
Blended Learning ◽  
Learning Analytics ◽  
Future Research ◽  
Journal Articles ◽  
Ethical Considerations ◽  
Critical Issues ◽  
Ethical And Legal Considerations ◽  
General Data ◽  
Methodological Approaches ◽  
Conference Papers

<p>Learning Analytics (LA) approaches in Blended Learning (BL) research is becoming an established field. In the light of previous critiqued toward LA for not being grounded in theory, the General Data Protection and a renewed focus on individuals’ integrity, this review aims to explore the use of theories, the methodological and analytic approaches in educational settings, along with surveying ethical and legal considerations. The review also maps and explores the outcomes and discusses the pitfalls and potentials currently seen in the field. Journal articles and conference papers were identified through systematic search across relevant databases. 70 papers met the inclusion criteria:  they applied LA within a BL setting, were peer-reviewed, full-papers, and if they were in English. The results reveal that the use of theoretical and methodological approaches was disperse, we identified approaches of BL not included in categories of BL in existing BL literature and suggest these may be referred to as hybrid blended learning, that ethical considerations and legal requirements have often been overlooked. We highlight critical issues that contribute to raise awareness and inform alignment for future research to ameliorate diffuse applications within the field of LA.</p>

scholarly journals Learning Analytics for Networked Learning Models

2014 ◽  
Vol 1 (3) ◽  
pp. 191-194
Author(s):  
Srecko Joksimovic ◽  
Dragan Gasevic ◽  
Marek Hatala
Keyword(s):  
Theoretical Model ◽  
Learning Analytics ◽  
Research Method ◽  
Teaching And Learning ◽  
Networked Learning ◽  
Learning Models ◽  
Dimensional Approach ◽  
Research Outcomes ◽  
Learning Research ◽  
Scalable Analysis

Teaching and learning in networked setting has attained a significant amount of attention recently. The central topic of networked learning research is human-human and human-information interactions that occur within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach in analyzing their effects. Therefore, the main goal of this research is the development of a theoretical model that allows for a comprehensive and scalable analysis of how and why learners engage into collaboration in networked communities. The proposed research method, anticipated research outcomes and contributions to the learning analytics field are discussed.

scholarly journals Collaboration Analytics Need More Comprehensive Models and Methods: An Opinion Paper

2021 ◽  
Vol 8 (1) ◽  
pp. 13-29
Author(s):  
Areum Han ◽  
Florian Krieger ◽  
Samuel Greiff
Keyword(s):  
Learning Analytics ◽  
Contextual Information ◽  
Future Research ◽  
Classroom Setting ◽  
Research And Practice ◽  
Learning Research ◽  
Technology Advances

As technology advances, learning analytics is expanding to include students’ collaboration settings. Despite their increasing application in practice, some types of analytics might not fully capture the comprehensive educational contexts in which students’ collaboration takes place (e.g., when data is collected and processed without predefined models, which forces users to make conclusions without sufficient contextual information). Furthermore, existing definitions and perspectives on collaboration analytics are incongruent. In light of these circumstances, this opinion paper takes a collaborative classroom setting as context and explores relevant comprehensive models for collaboration analytics. Specifically, this paper is based on Pei-Ling Tan and Koh’s ecological lens (2017, Situating learning analytics pedagogically: Towards an ecological lens. Learning: Research and Practice, 3(1), 1–11. https://doi.org/10.1080/23735082.2017.1305661), which illustrates the co-emergence of three interactions among students, teachers, and content interwoven with time. Moreover, this paper suggests several factors to consider in each interaction when executing collaboration analytics. Agendas and recommendations for future research are also presented.

A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining

2015 ◽  
pp. 1044-1063
Author(s):  
Chengcui Zhang
Keyword(s):  
Data Mining ◽  
Temporal Analysis ◽  
Volume Estimation ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Traffic Surveillance ◽  
Surveillance Videos ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Surveillance Applications

The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.

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