Educational Data Mining and Learning Analytics for Improving Online Learning Environments

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.

Learning Analytics

2018 ◽  
pp. 5158-5168
Author(s):  
Constanţa-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Radu Ioan Mogos ◽  
Stelian Stancu
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Future Research ◽  
Educational Systems ◽  
Main Section ◽  
Research Directions ◽  
Data Intensive ◽  
Academic Analytics ◽  
Future Research Directions

Reinforcement of the technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The article starts by defining LA, with relevant views on the literature. A discussion about the relationships between LA, educational data mining and academic analytics is included in the background section. In the main section of the article, the learning analytics, as an emerging trend in the educational systems is describe, by discussing the main issues, controversies, problems on this topic. Final part of the article presents the future research directions and the conclusion.

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

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.

Learning Analytics

2019 ◽  
pp. 739-750
Author(s):  
Constanţa-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Radu Ioan Mogos ◽  
Stelian Stancu
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Future Research ◽  
Educational Systems ◽  
Main Section ◽  
Research Directions ◽  
Data Intensive ◽  
Academic Analytics ◽  
Future Research Directions

Reinforcement of technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The chapter starts by defining learning analytics (LA), with relevant views on the literature. A discussion about the relationships between LA, educational data mining, and academic analytics is included in the background section. In the main section of the chapter, the learning analytics, as an emerging trend in the educational systems is described by discussing the main issues, controversies, and problems on this topic. The final part of the chapter presents the future research directions and the conclusion.

scholarly journals The 2010 KDD Cup Competition Dataset: Engaging the machine learning community in predictive learning analytics

2016 ◽  
Vol 3 (2) ◽  
pp. 312-316 ◽  
Author(s):  
John Stamper ◽  
Zachary A Pardos
Keyword(s):  
Data Mining ◽  
Educational Technology ◽  
Learning Community ◽  
Learning Analytics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Educational Data Mining ◽  
Computing Machinery ◽  
Predictive Learning ◽  
Cognitive Tutors

In the spring of 2010, the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data-mining (KDD) selected a dataset from an educational technology for its annual competition. The competition, titled “Educational Data Mining Challenge”, tasked participants with predicting the correctness of student answers to questions within an Intelligent Tutoring System (ITS) from The Cognitive Tutors suite of tutors. This challenge was hosted by the PSLC DataShop, and included data provided by the Carnegie Learning Inc., producers of The Cognitive Tutors. Consisting of over 9GB of student data this was the largest KDD Cup dataset up to that point in time. The competition brought in 655 competitors submitting 3,400 solutions. Five years later, we believe the competition dataset has been the most often cited from an educational technology platform.

The Invisible Breadcrumbs of Digital Learning

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.

scholarly journals Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

2016 ◽  
Vol 3 (2) ◽  
pp. 220-238 ◽  
Author(s):  
Paulo Blikstein ◽  
Marcelo Worsley
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Online Courses ◽  
Educational Data Mining ◽  
Multimodal Learning ◽  
Learning Tasks ◽  
Education Data ◽  
Multimodal Learning Analytics ◽  
High Degree ◽  
Learning Analysis

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.

Data Mining to Identify Project Management Strategies in Learning Environments

2019 ◽  
pp. 532-545
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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