Short answer
Incorporate predictive analytics into educational tools to identify struggling students early and offer tailored support.
- Field
- Human Factors
- Source
- Educational Technology Research and Development (2020)
- Method
- Systematic Review
- Sample
- 46 publications
- Evidence
- Moderate effect
Learning analytics, by analyzing student data, can identify individuals at risk of academic failure and provide timely interventions. This human factors research insight is drawn from a 2020 study published in Educational Technology Research and Development. Using Systematic review with 46 publications, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Incorporate predictive analytics into educational tools to identify struggling students early and offer tailored support.
Learning Analytics Can Predict and Support Student Success by Identifying At-Risk Individuals
Learning analytics, by analyzing student data, can identify individuals at risk of academic failure and provide timely interventions.
Educational Technology Research and Development · 2020
Key Findings
- 01Learning analytics approaches can effectively support study success and identify students at risk of dropping out.
- 02There is a lack of rigorous, large-scale evidence demonstrating the definitive effectiveness of learning analytics in supporting study success.
Application
Design takeaway
Incorporate predictive analytics into educational tools to identify struggling students early and offer tailored support.
How to apply
Develop dashboards for educators that highlight students exhibiting patterns associated with academic risk, prompting timely outreach.
Project actions
- 01When designing an educational tool, consider how data can be collected and analyzed to understand user behavior.
- 02Think about what 'study success' means in your specific context and how it can be measured.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Comprehensive systematic review methodology.
- +Focus on empirical evidence.
Limitations
The effectiveness of learning analytics can depend heavily on the quality and completeness of the data collected, as well as the algorithms used for analysis.
Reliability & validity
The reliability of the findings depends on the quality of the systematic review process (e.g., clear inclusion/exclusion criteria, multiple reviewers) and the validity of the original studies included (e.g., robust methodologies, appropriate statistical analysis).
Think critically
Given the limitations in rigorous evidence, how can designers ensure that learning analytics are implemented ethically and do not inadvertently create new barriers for students?
Design Principles
"Proactive support through data-driven insights enhances learner outcomes."
Understanding the factors that contribute to student success or failure is crucial for educational institutions. Learning analytics offers a data-driven approach to proactively support learners, moving beyond reactive measures to foster a more inclusive and effective learning environment.
What This Means for Your Design
By looking at how students use online learning tools, we can guess who might need extra help and give it to them before they fall too far behind.
How to use in your project
- 1.Use this review to justify the importance of data analysis in understanding user behavior and improving outcomes in your design project.
Add to My Project
Quick Cite
Paragraph starter
This systematic review highlights the potential of learning analytics to significantly enhance student success in higher education by identifying at-risk individuals and providing timely support. While the evidence base is still developing, the reviewed approaches demonstrate how analyzing learner data can lead to more effective interventions and improved continuation rates, offering valuable insights for the design of supportive educational technologies.
Source
Educational Technology Research and Development
Utilising learning analytics to support study success in higher education: a systematic review
journal · 2020
View sourceQuestions About This Research
- What does the research say about learning analytics can predict and support student success by identifying at-risk individuals?
- Incorporate predictive analytics into educational tools to identify struggling students early and offer tailored support. Evidence: Educational Technology Research and Development (2020).
- Why does "Learning Analytics Can Predict and Support Student Success by Identifying At-Risk Individuals" matter for design?
- Understanding the factors that contribute to student success or failure is crucial for educational institutions. Learning analytics offers a data-driven approach to proactively support learners, moving beyond reactive measures to foster a more inclusive and effective learning environment.
- How can designers apply this research?
- Incorporate predictive analytics into educational tools to identify struggling students early and offer tailored support.
- What were the main findings?
- Learning analytics approaches can effectively support study success and identify students at risk of dropping out.. There is a lack of rigorous, large-scale evidence demonstrating the definitive effectiveness of learning analytics in supporting study success.
- What research method was used?
- Systematic Review with 46 publications.
- How strong is the evidence?
- Evidence strength is rated Moderate effect, based on a 2020 journal from Educational Technology Research and Development.
- What should I do differently in my next project?
- Develop dashboards for educators that highlight students exhibiting patterns associated with academic risk, prompting timely outreach.
- What are the limitations?
- The review highlights a need for more rigorous, large-scale evidence, suggesting that current findings may be based on smaller or less controlled studies.