What are effective data collection methods for identifying at-risk students in digital learning?

1 Learning analytics

Learning analytics is the process of measuring, collecting, analyzing, and reporting data about learners and their contexts, to understand and optimize learning and the environments in which it occurs. Learning analytics can help identify at-risk students in digital learning by tracking and visualizing their interactions, behaviors, and outcomes in online platforms and courses. For example, learning analytics can reveal how often and how long students log in, how they navigate through the content, how they participate in discussions and assessments, and how they perform on learning objectives and competencies.

2 Early warning systems

Early warning systems are tools that use data to identify students who are showing signs of academic or behavioral difficulties, and provide timely interventions to prevent them from dropping out or failing. Early warning systems can help identify at-risk students in digital learning by using predictive models and indicators based on historical and current data, such as attendance, grades, test scores, course completion, and feedback. For example, early warning systems can alert educators and administrators when students are missing assignments, falling behind on credits, or expressing dissatisfaction or frustration with their learning experience.

3 Surveys and feedback

Surveys and feedback are methods that allow students to share their opinions, perceptions, and experiences of their learning process, environment, and outcomes. Surveys and feedback can help identify at-risk students in digital learning by capturing their voice, preferences, needs, and challenges in online learning. For example, surveys and feedback can measure students' satisfaction, motivation, engagement, self-efficacy, and sense of belonging in digital learning, as well as their access to resources, support, and guidance.

4 Portfolios and reflections

Portfolios and reflections are methods that enable students to showcase their learning achievements, progress, and goals, as well as to evaluate their own strengths, weaknesses, and areas for improvement. Portfolios and reflections can help identify at-risk students in digital learning by providing evidence of their learning outcomes, competencies, and skills, as well as their metacognitive and self-regulatory strategies. For example, portfolios and reflections can demonstrate students' mastery of digital literacy, critical thinking, problem-solving, and collaboration skills, as well as their ability to plan, monitor, and evaluate their own learning.

5 Social network analysis

Social network analysis is the method of mapping and measuring the relationships and interactions among individuals, groups, and organizations. Social network analysis can help identify at-risk students in digital learning by examining their social presence, participation, and influence in online communities and networks. For example, social network analysis can reveal students' patterns of communication, collaboration, and knowledge sharing with their peers, instructors, and mentors, as well as their centrality, density, and diversity in online social networks.

6 Data dashboards

Data dashboards are tools that display and organize data in a graphical and interactive way, to facilitate data-driven decision making and action. Data dashboards can help identify at-risk students in digital learning by integrating and synthesizing data from multiple sources and methods, and presenting them in a clear and concise way. For example, data dashboards can provide real-time and comprehensive information on students' engagement, performance, and progress in digital learning, as well as their risk level, needs, and interventions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?