Data Mining to Identify Project Management Strategies in Learning Environments

Projects have become a key strategic working form. It is agreed that project performance must achieve its objective and be aligned with criteria that the project stakeholders establish. The usual metrics that are considered are cost, schedule, and quality. Configuration for the management of projects is a matter of decision that influences the project's evolution. There also are factors like virtual teamwork and team building processes that are relevant to that evolution. Effectiveness in managing projects depends on these factors and is investigated in this work by means of educational data mining as they can help to build more effective learning and operating procedures. The conclusions from this study can help higher education course designers as well as teachers and students by making apparent the influence of smarter strategies in the learning process. In fact, the same benefits will help practitioners too, as they can improve their continuous learning procedures and adjust their project management policies and strategies.

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Data Mining to Identify Project Management Strategies in Learning Environments

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch168 ◽

2018 ◽

pp. 1934-1946

Author(s):

Ana González-Marcos ◽

Joaquín Ordieres-Meré ◽

Fernando Alba-Elías

Keyword(s):

Data Mining ◽

Project Management ◽

Learning Environments ◽

Team Building ◽

Management Strategies ◽

Educational Data Mining ◽

Effective Learning ◽

Virtual Teamwork ◽

Teachers And Students ◽

Management Policies

Projects have become a key strategic working form. It is agreed that project performance must achieve its objective and be aligned with criteria that the project stakeholders establish. The usual metrics that are considered are cost, schedule and quality. Configuration for the management of projects is a matter of decision that influences the project's evolution. There also are factors like virtual teamwork and team building processes that are relevant to that evolution. Effectiveness in managing projects depends on these factors and is investigated in this work by means of Educational Data Mining as they can help to build more effective learning and operating procedures. The conclusions from this study can help higher education course designers as well as teachers and students, by making apparent the influence of smarter strategies in the learning process. In fact, the same benefits will help practitioners too, as they can improve their continuous learning procedures and adjust their project management policies and strategies.

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A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments

Advances in Early Childhood and K-12 Education - Handbook of Research on Equity in Computer Science in P-16 Education ◽

10.4018/978-1-7998-4739-7.ch016 ◽

2021 ◽

pp. 278-292

Author(s):

Eric Araka ◽

Robert Oboko ◽

Elizaphan Maina ◽

Rhoda K. Gitonga

Keyword(s):

Data Mining ◽

Online Learning ◽

Learning Environments ◽

Domain Knowledge ◽

Educational Data Mining ◽

Online Learning Environments ◽

Student Activities ◽

Self Regulated Learning ◽

Regulated Learning ◽

E Learning

Self-regulated learning is attracting tremendous researches from various communities such as information communication technology. Recent studies have greatly contributed to the domain knowledge that the use self-regulatory skills enhance academic performance. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Modern learning management systems (LMS) allow storage of datasets on student activities. Subsequently, it is now possible to use Educational Data Mining to extract learner patterns which can be used to support SRL. This chapter discusses the current tools for measuring and promoting SRL on e-learning platforms and a conceptual model grounded on educational data mining for implementation as a solution to promoting SRL strategies.

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Analysing Ongoing Learning Experience with Educational Data Mining for Interactive Learning Environments

2018 Global Wireless Summit (GWS) ◽

10.1109/gws.2018.8686580 ◽

2018 ◽

Author(s):

Charoenchai Wongwatkit ◽

Pakpoom Prommool

Keyword(s):

Data Mining ◽

Learning Environments ◽

Interactive Learning ◽

Learning Experience ◽

Educational Data Mining ◽

Interactive Learning Environments

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Educational Data Mining: User Categorization in Virtual Learning Environments

Soft Computing for Business Intelligence - Studies in Computational Intelligence ◽

10.1007/978-3-642-53737-0_16 ◽

2014 ◽

pp. 225-237 ◽

Cited By ~ 2

Author(s):

Angel Cobo Ortega ◽

Rocío Rocha Blanco ◽

Yurlenis Álvarez Diaz

Keyword(s):

Data Mining ◽

Learning Environments ◽

Educational Data Mining ◽

Virtual Learning ◽

Virtual Learning Environments

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Educational Data Mining and Learning Analytics for Improving Online Learning Environments

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641113 ◽

2020 ◽

Vol 64 (1) ◽

pp. 500-504

Author(s):

Yancy Vance Paredes ◽

Robert F. Siegle ◽

I-Han Hsiao ◽

Scotty D. Craig

Keyword(s):

Data Mining ◽

Educational Technology ◽

Human Factors ◽

Learning Environments ◽

Learning Analytics ◽

Educational Data Mining ◽

Educational Systems ◽

User Centered Design ◽

High Quality ◽

Online Learning Environments

The proliferation of educational technology systems has led to the advent of a large number of datasets related to learner interaction. New fields have emerged which aim to use this data to identify interventions that could help the learners become efficient and effective in their learning. However, these systems have to follow user-centered design principles to ensure that the system is usable and the data is of high quality. Human factors literature is limited on the topics regarding Educational Data Mining (EDM) and Learning Analytics (LA). To develop improved educational systems, it is important for human factors engineers to be exposed to these data-oriented fields. This paper aims to provide a brief introduction to the fields of EDM and LA, discuss data visualization and dashboards that are used to convey results to learners, and finally to identify where human factors can aid other fields.

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User-centred design and educational data mining support during the recommendations elicitation process in social online learning environments

Expert Systems ◽

10.1111/exsy.12041 ◽

2013 ◽

Vol 32 (2) ◽

pp. 293-311 ◽

Cited By ~ 21

Author(s):

Olga C. Santos ◽

Jesus G. Boticario

Keyword(s):

Data Mining ◽

Online Learning ◽

Learning Environments ◽

Educational Data Mining ◽

Online Learning Environments ◽

User Centred Design ◽

Elicitation Process

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The Invisible Breadcrumbs of Digital Learning

Handbook of Research on Digital Learning - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-9304-1.ch019 ◽

2020 ◽

pp. 302-316

Author(s):

Luc Paquette ◽

Nigel Bosch

Keyword(s):

Data Mining ◽

Learning Environments ◽

Test Scores ◽

Learning Analytics ◽

Educational Data Mining ◽

Digital Learning ◽

Fine Grained ◽

Digital Learners ◽

High Level ◽

Digital Learning Environments

A main opportunity provided by digital learning environments is the ability to not only examine the final products of learning activities (e.g., essays, test scores, final answers to problems), but also the detailed logs of how learners interact with the environment itself. Those logs of the learners' actions serve as breadcrumbs marking the path they take as they engage with the environment, providing fine-grained information about when and how they interact with specific components of its user interface. The emerging fields of learning analytics and educational data mining have taken a particular interest in studying how we can make sense of those fine-grained interactions to better inform us of digital learners' experiences and how we can provide new opportunities to better support learners as they engage with digital learning environments. This chapter discusses how those fine-grained logs can be analyzed to identify high-level behaviors, investigate their relationships with learning, and provide us with insights about how to adapt learning environments to learners' needs.

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