This report presents, analyzes and critically discusses learning analytics on the extent to which it supports learning. It will introduce the learning analytics and provide its background to the current research and development in e-learning, explaining how they relate to, and differ from, other uses of data in education.
The report shall also describe in details the implications associated with the use of learning analytics in the context of international training organizations and the recommendable success load map. In the same context, a justification on how the most important features of learning analytics support learning will be provided, setting out the key benefits and risks. Finally, two examples illustrating the use of learning analytics will be evaluated against how they enhances learning in relevant contexts. As far as resourcing will be given account, the continuous use and innovation in learning analytics is recommended.
Table of Contents
I. Introduction to Learning Analytics
1.1. Definition
1.2. Implications
1.2.1. Context
1.2.2. Implication
1.2.3. Success tips
II. Justification
III. Evaluation
3.1. Save the children’s onenet platform
3.2. Open University’s VLE
Research Objectives and Core Themes
This report investigates the efficacy of learning analytics (LA) in supporting educational outcomes, exploring its integration within digital training environments to optimize learner experiences through data-driven insights.
- The theoretical foundations and diverse definitions of learning analytics.
- The implications of deploying data-driven systems in humanitarian training organizations.
- Methods for identifying learners' performance trends and providing personalized feedback.
- Strategic frameworks for overcoming technological and human challenges in LA implementation.
- Practical evaluations of existing platforms, such as Onenet and the Open University’s VLE.
Excerpt from the Book
1.2.3. Success tips
While the organization has not started to implement Learning Analytics project, it’s important to note that in order to be successful there may be a range of technological and human challenges that need to be addressed (Jose, 2018) such as incomprehensiveness, complexity, poor data quality, etc.
One of the main concerns with learning analytics is the collection of partial data because certain interactions may not be detected by the analytic engine once they do not occur in digital environment. The use of simple web application would help collect these missing information. Furthermore, it’s advisable to used data collected once a significant number of participants have used the application.
The use of student dashboard with comprehensive features presented in simple format would give feedback to students about their learning status/progress, hence dashboard should be informative enough and if possible indicate some immediate improvement actions. For example, displaying the student status within the group so they have a clear reference on where they are compared to their peers or using a red or green traffic light could show the students’ progress status in a course.
Since learning analytic projects are mostly about finding factors that contribute to learners’ failures or successes in order to design intervention strategies that work in a given learning context, periodic follow-up meetings with relevant stakeholders beyond the pure implementation team are recommended. This goes hand in hand with clearing ethical concerns, for example by creating ethical project charter, and engage all stakeholders in the ethical committee. In addition, the use of more data sources and multiple mathematical models would enhance the learning analytics model, allow the collection of quality data and improve the predictions.
Summary of Chapters
I. Introduction to Learning Analytics: Provides a definition and background of learning analytics, contextualizing its role in modern education technology.
II. Justification: Outlines the benefits of using predictive models and behavioral data to improve engagement, retention, and academic outcomes.
III. Evaluation: Analyzes the real-world application of learning analytics via the Onenet platform and the Open University's Virtual Learning Environment.
Keywords
Learning Analytics, Virtual Learning Environment, Learning Management System, Students Behaviour in Online Environment, Online Education, Students Retention, Predictive Modelling, Data-driven Education, Educational Data Mining, Academic Analytics, Personalized Learning, Student Engagement, Digital Training, Pedagogical Simulation, Learning Outcomes.
Frequently Asked Questions
What is the core focus of this report?
The report examines how learning analytics can be deployed to support study success, specifically analyzing the intersection of learner behavior data and educational outcomes.
Which fields are considered central to the work?
The central fields include educational technology, learning analytics, academic analytics, and educational data mining within both higher education and international training contexts.
What is the primary research goal?
The goal is to provide a comprehensive analysis of how learning analytics enhances learning and to offer a roadmap for organizations to successfully adopt these tools.
Which scientific methodology is applied here?
The report utilizes a literature-based analysis and a comparative case study approach, evaluating existing platforms against defined criteria for effective learning analytics.
What topics are covered in the main section?
The main sections cover the definition and implications of LA, the justification for its deployment, and practical evaluation examples from specific institutions.
How is this work characterized by its keywords?
The work is characterized by terms like Students Retention, Predictive Modelling, and Student Behaviour, which highlight the focus on data-driven intervention strategies.
What role does the 'Onenet' platform play in this study?
Onenet serves as a case study for an adaptive learning environment that effectively utilizes learning analytics for staff training without traditional teacher intervention.
Why is a pilot project recommended for adoption?
A pilot project is suggested to minimize risks, limit initial costs, and allow the organization to explore the technology's potential before full-scale implementation.
How can dashboards assist in the learning process?
Dashboards provide students with clear, visual feedback on their progress, allowing them to compare their status against peers and take immediate action to improve performance.
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- Dr. Sixbert Sangwa (Autor:in), 2019, Openness and Innovation in E-Learning. Deploying Learning Analytics to Support Study Success in Higher Education, München, GRIN Verlag, https://www.grin.com/document/1012814
