Educational Analytics

• What is educational analytics
• What is learning analytics
• How is learning analytics used
• Why is learning analytics important
• Empowering schools and institutions with data
• Benefits of data analytics tools in education
• Predicting and ensuring student success

What is educational analytics?

Educational analytics leverages data in education contexts to find meaningful correlations and mine intelligence. This type of analytics is based on intelligence that can inform instructional decisions and improve student performance. An example is correlating student classroom attendance with test scores so accurately that the staff can forecast student success.

What is learning analytics?

Educational analytics can encompass all aspects of an institution’s operations, while learning analytics is specifically about learners in their context (which today could be classroom learning, distance learning, or other types). There are numerous branches of learning analytics. Generally, it provides the basis for evaluating the effectiveness of teaching methodologies, student engagement, and performance monitoring.

While educational analytics aims to offer intelligence around the entire learning management system, learning analytics can be more directly beneficial to students and teachers: Instructors can detect ineffective learning behaviors as well as counterproductive issues that interfere with student achievement. Learning analytics attempts to directly inform schools about students’ learning patterns and help them choose the most effective incentives and improvement strategies.

How is learning analytics used?

To deepen our understanding, let’s look at examples of how institutions can harness the power of their school data to track metrics that matter and optimize outcomes and scores. A school attendance dashboard can give school principals and other officials an insight into student attendance and detentions, which can be an indicator of dropout rates and student success. A dashboard that tracks college acceptance rates will help colleges and universities balance their need to increase enrollment income along with maintaining high academic standards, which raises the institution’s reputation and could allow access to more research funding.

Why is learning analytics important?

Not only can learning analytics give schools and institutions the ability to make smarter, data-driven decisions, it can empower students as well. Insights learned from education data can give students a real-time visualization of their performance and help them choose the study concentration or major that makes sense for them. In more detail, learning-specific targets can help both students and schools by:

• Measuring the effectiveness of course components and resources
• Identifying patterns to improve student knowledge retention
• Emphasizing resources that increase test performance over time
• Identifying discussions and teaching components that give rise to richer essay composition

The last item on this list is an innovative new natural language processing (NLP) analytics feature, which is available through BI platforms like Sisense. The text analytics now available are capable of recognizing and finding patterns in verbal information. In other words, analytics can now be applied to grading student essays and other compositions. Teachers and institutions are no longer limited to numerical evaluation.

To further delineate between learning analytics and the broader educational analytics, consider some of the organizational intelligence derived from AI-based analytics in the wider scope of guaranteeing positive student success outcomes:

• Comparing student performance with national averages to drive decisions about faculty acquisition and placement
• Identify academically at-risk student performance and suggest interventions such as targeted tutoring or counseling
• Compare current and previous applicant data to drive decisions about admissions and improve overall institutional performance
• Discover patterns in differences between expected student behavior and outcomes
• Recognize patterns leading to drop-out and intervene accordingly
• Course success rates
• Year-over-year enrollment tracking

Administrative staff, even those who are nontechnical users, can now leverage these insights to suggest interventions and creative adaptations tailored to increase students’ academic awareness and involvement. Educational analytics features this and many more inspiring use cases to improve the performance of educational institutions overall. AI-based big data analytics brings myriad key performance indicators within reach of a wide and diverse user group and reduces dependence on IT.

Empowering schools and institutions with data

Institutions are not just looking at student data isolated in a petri dish; they can now correlate columns of data in student records across professor and student characteristics, courses, and resources to improve student and faculty educational experience, university budget compliance, and student retention forecasting.

Educators can use data analytics to record and analyze different datasets, such as student data, course data, instructor data, and facility data.

Benefits of data analytics tools in education

Demonstrating the benefits of higher education analytics is best achieved with real-world examples: for example, setting performance thresholds in mathematics and comparing education data stores so that a low test score triggers a cascade of suggested improvement interventions. This correlates with, for example, supplemental online coursework and tutoring in resource and infrastructure data. Results of participation then feed forward to a neural network that determines the probability of success in each scenario and refines its own suggestion model in future outcomes. This behavior is similar to recommender systems (like those used by Amazon), but its model can be trained on diverse input parameters from big data across all institution departments.

• Pinpointing academic subject problem areas for individual student success is an important use case in big data insights embedded into learning analytics today. An intelligent system could suggest proven methods to overcome hurdles and (using student contact info in the system) to email suggestions such as online labs that address specific difficulties.
• AI and machine learning can help balance accreditation requirements and college acceptance rates with proven performance benefits. Machine learning reveals correlations that contradict assumptions. A traditional requirement may not be ideal for preparing a student to jump from differential equations to function analysis, or an entrenched tenured instructor may be skewing the assessment. AI-based analytics can reveal which is occurring. Testing experimental alternatives and extracting the truth from resulting data are pinnacle applications of AI-based analytics tailored to learning analytics.
• Optimizing campus physical plant resources can reduce transportation costs in a large university, improving student attendance by intelligently planning resource locations to reduce student and faculty travel time.
• For budget planning and allocation purposes, correlate facility usage to improve utilization of at-risk sites or decide which buildings to shutter or rent out.

Nowadays, even nontechnical staff can easily plug analytics into a learning management system web app. Enterprise training with a gamification-based learning management system (LMS) benefits from learning analytics because users see their success in using the system as being linked to learning outcomes. The efficacy of training must be constantly adjusted to achieve performance in branching scenario games for executive and management training. AI-based analytics can immediately display performance data from student outcomes so that administrators can decide which courses and methods are most effective.

Predicting and ensuring student success

Positive student outcomes benefit higher educational analytics in all institutions, including LMS enterprises that feature innovative gamification. Cross-departmental sharing and correlation of big data stores lead to exciting new intelligence. Learning analytics systems leverage existing data to benefit operations and quality assurance, as well as campus life and student support systems. Developing predictive models for student success can identify and flag students who may be in need of counseling. Both qualitative and quantitative analyses now improve learning performance. Studies reveal that improving student behavior is often simply a matter of awareness of their learning engagement patterns. Fortunately, analytics is now very accessible, even to nontechnical staff. Data visualizations and dashboards from third-party providers are now easy to interpret for all areas of the institution — administration, faculty, and students alike.