Transforming classrooms with learning analytics

Learning analytics is revolutionizing modern education by transforming data into actionable insights. It's the key to understanding how students learn, enabling educators to tailor educational experiences to individual needs and enhance overall student performance. As higher education becomes increasingly data-driven, the role of learning analytics has never been more critical.

In this blog, we will explore the core aspects of learning analytics, its profound impact on students' success, the role of educators in this data-centric approach, and the ethical considerations that come with it. Plus, for professionals looking to lead the charge in data-driven education, we’ll introduce how an M.S. in Learning Sciences can empower their career.

What is learning analytics?

In today's technology-centered world, information is constantly being gathered about the actions of individuals online and off, leading us into the age of Big Data. All this information is collected with such fervor because data has tremendous value. However, data is worthless without the skills to properly analyze it and put it to use. Data analytics converts raw information into actionable insights using a range of tools, technologies, and processes.¹ Using analytics research you can identify patterns, find trends and solve problems to improve decision-making, shape businesses, and, in the case of learning analytics: optimize education.

Learning analytics uses educational data mining to analyze students’ learning processes and extract valuable information that helps inform teaching strategies and improve student achievement.² Just as businesses use data analytics to learn more about human behavior and customer preferences, so too do higher education institutions and other educational organizations use learning analytics to uncover data about learners that the students themselves can't or don't articulate. This educational research can be used to alter the design of a learning management system, support students with additional learning tools, help an instructor better connect learning objectives to lessons, and more.

Benefits of learning analytics for student success

Learning analytics helps to bring a sense of human-centered design to the field of education and educational technology, offering a suite of benefits that can significantly monitor student progress, personalize learning, and optimize educational and instructional experiences.

Tracking student progress

Learning analytics tools can be used to track student progress via data like the amount of time the student spends watching lessons, using study tools, or participating in course discussion forums and assignment grades or exam performance. These data sets can be fed into dashboards, weekly emailed reports or the like, to help students pay attention to their own learning habits and warn them when they may be at risk for falling behind or getting a worse grade.³

Personalized learning experiences

Given all the time and resources in the world, plenty of educators would create individual learning plans for each of their students if it meant helping them succeed. When it comes to most learning experiences, especially in higher education, that's not a practical dream. However, learning analytics and learning management systems have evolved to offer varying levels of personalization for learners so that different questions are posed, more or less frequent reviews are deployed, and certain lessons are recommended to be revisited when necessary.³

Improving teaching practices

While learning analytics can't guarantee student success, they can help instructors intervene at key moments to avoid poor performance. Predictive analytics can help call attention to a student who might be at risk of a failed exam, poor grades, or even dropping out. One study created a model that combined demographics, academic records from historical data, prior scores and learning management system usage to predict whether students were likely to earn a grade of C or higher in a course.³ With this data collected, educational institutions can step in before it's too late, to help a student make a plan for improvement.

Additionally, data analytics can help improve teaching methods for any instructors who know how to properly employ learning analytics research. As Alyssa Friend Wise notes in her 2023 report on learning analytics:³

"Instructors can document their pedagogical intentions, describe activity patterns that indicate fulfillment of these intentions and then use learning analytics to evaluate the degree to which the patterns occurred."

Data collection and analysis process for learning analytics research

The backbone of learning analytics lies in its systematic approach to data collection, often called 'educational data mining' and analysis. There are a variety of methodologies used to gather and interpret educational data that enable a richer understanding and enhancement of the learning process.

Learning analytics data types

Researchers and educators today can collect large volumes of data with the proliferation of online learning and learning management systems. Through these platforms granular data can be collected to inform all aspects of instruction and lesson planning. Along with typical big data collected like demographics, learning analytics specific data might include:³

• Amount of time spent on lessons/in a learning management system
• Student performance/grades
• Surveys and self-reflections
• Quiz/exam answers and answers series
• Discussion board posts and engagement
• Use of play/fast-forward/rewind controls on videos
• Student gaze/gestures/posture

Data analysis methods

After collecting data, educators or researchers can apply typical data science methods to their information points in order to perform their educational research.

Predictive modeling or predictive analytics are likely the implementation of learning analytics that first comes to mind for most people. Using historical data and machine learning to forecast student performance, predictive analytics provide a proactive approach to instruction helping to identify at-risk students early, informing interventions to support and improve educational outcomes, and tailoring the learning path to individual needs.³

Structure discovery involves uncovering patterns within complex educational data without predefined models or hypotheses. This approach typically leverages unsupervised machine learning techniques such as clustering or principal component analysis to reveal inherent groupings, relationships, and structures within the dataset. By identifying these intrinsic patterns among variables such as student engagement, resource usage, or assessment results, educators can better understand the learning environment and devise strategies to enhance instructional design and student support systems.³

Natural text mining or natural language processing can be used to analyze data like discussion posts from learning management systems for professors to quickly get summaries of topics covered or common questions. This might help an instructor determine what topics they need to dedicate more time to in their lesson plan.³

A temporal approach to analysis intends to uncover previously undefined patterns in data as relating to the sequence and flow of events over time. This approach could help an educator pinpoint the correct amount of time to allow for lesson completion or help them in analyzing data to inform the order of lessons based on which sequence better aides in student comprehension.³

Ethical Considerations for learning analytics in the classroom

Because learning analytics deal with sensitive student data, the same ethics concerns that accompany the use of any Big Data come into play here as well. Researchers and educators must prioritize protecting individual privacy, ensuring that all student data is collected and analyzed with informed consent and under strict governance protocols. Additionally, transparency is key and instructors should be clear with their students about their specific intentions with learning analytics including: what data will be gathered, how it's used and for what purposes. Maintaining strong data security measures and anonymizing datasets can help prevent security breaches and misuse.⁴

Become a data-driven educator with an online M.S. in the Learning Sciences

Learning analytics is more than just a set of tools; it's a pathway to unlocking every student's potential and streamlining educational efficiency. If you're ready to harness data science for educational benefit and lead at the forefront of this dynamic field, the online Master of Science in the Learning Sciences offers a concentrated Learning Analytics specialization to gain the knowledge and skills necessary. Learn to navigate and innovate in a data-driven educational environment with courses like Introduction to Learning Analytics, Data Modeling and the Learning Sciences, Data, Education and Society, and Advanced Methods in Learning Analytics.

By choosing to advance your expertise at SMU, you have the opportunity to not only develop your own career but to improve the learning experiences of countless students. Explore the online program and join a community committed to excellence in education. For admissions or program inquiries, don't hesitate to schedule a call with an admissions outreach advisor and take the first step towards becoming a catalyst for meaningful change in education.