Learning Analytics Platform Features: A Comprehensive Guide

Learning analytics is a rapidly evolving field with the potential to transform education and training. While the concept of analyzing learning and business data has existed for decades, learning analytics platforms (LAPs) are relatively new, offering sophisticated tools to understand and improve learning outcomes. This article provides a detailed overview of LAP features, their benefits, and how they can be effectively implemented.

Evolution of Learning Analytics Platforms

Over the past decade, learning and development (L&D) reporting has evolved in two primary directions. Learning management systems (LMSs) offer transactional and specific reports but are often limited. Business intelligence (BI) tools, on the other hand, provide access to vast amounts of data for experienced analysts to mine and manipulate.

LAPs bridge this gap by combining the familiarity and off-the-shelf functionality of an LMS with the advanced analytics capabilities of a BI tool. An LAP aggregates data from all training and learning events within an organization, applying sophisticated reporting and analytics to provide a deep understanding of learning's impact on the business.

What is a Learning Analytics Platform?

A learning analytics platform (LAP) aggregates data about all of the training and learning events across your organization and applies sophisticated reporting and analytics capabilities so you can gain a deep understanding of the learning happening within your company as well as how that learning impacts the overall business.

Distinguishing LAPs from Other Tools

To understand what an LAP is, it's important to differentiate it from other related tools:

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It isn’t an LMS: The biggest difference between an LAP and an LMS is the lack of training delivery or any other attempt to “manage” learning. An LMS typically only records learning events that happen within that LMS. As a result, LMS reporting is often limited and requires additional cost or expertise to access effectively. Think of the learning analytics platform as the parts of the LMS that track learning activity data and provide reports on that data-only implemented far more robustly.
It isn’t really a BI tool: You can think of a learning analytics platform as a BI tool designed specifically for L&D. And it’s that specificity that provides a few advantages. Chief amongst them is the out-of-the box reporting functionality that comes with a specialized offering. Most L&D practitioners generally ask a lot of the same questions, and an LAP can readily answer those 100 or so common questions without the need to create custom reports. Because LAPs are designed to be used directly by L&D departments without the need for specialists or IT resources, they're often a lot easier to use than BI tools, especially for non-analysts.
And, it isn’t just an LRS: An LRS is a tool defined by xAPI (a.k.a. Experience API or Tin Can API) for the storage and exchange of data about learning activities. A LAP typically contains an LRS, but adds significant reporting and analytics capabilities not typically found in an LRS. Typically, an LRS is part of another system, but it may also exist on its own. A standalone LRS will usually provide basic reporting, but it's mostly technical in nature and deals with the number and types of learning activity statements it contains. LAPs and standalone LRSs are often used to consolidate data from many learning systems. They can act as systems of record for all learning data as a tool for feeding learning data into other systems, either in raw or aggregated form.

Why Use a Learning Analytics Platform?

An LAP enhances your LMS’s value by tracking what’s outside of it. How much of what you’ve learned has come from a formal eLearning course delivered via an LMS? Probably not very much. Learning is much broader than what organizations are usually able to track. LAPs capture data from any learning event, not just the tiny fraction of formal learning happening in LMSs.

LAPs typically take advantage of xAPI to seamlessly capture learning activity data from virtually any learning system. Remember, a key component of an LAP is an LRS. That means an LRS that implements xAPI protocols can capture and store data about any learning event, including:

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Informal learning
Social learning
Mobile learning
Game-based learning
Simulation-based learning
eLearning courses
Classroom sessions
Conference attendance
And much more.

A robust LAP also can capture data from a variety of other sources. Most important, an LAP aggregates data about employee behavior and organizational performance. These additional data points allow you to take a broad look at learning’s true impact.

By connecting training and performance, L&D's impact can be clearly demonstrated. Marketing teams have HubSpot, sales teams have Salesforce, logistics departments have SAP, and so on-they all have tools that provide specialized reporting for each respective function. All these systems were designed to deliver value within a specific department, while offering the flexibility to send data to a data warehouse where analysts can run numbers and dig around. And LAPs provide L&D practitioners with that same strategic, data-driven functionality to take a peek under the hood, to measure and evaluate training, and send data onto the mothership, if needed.

Key Features and Functionalities

Learning analytics platforms offer a range of features designed to measure, collect, analyze, and report data for learners and their contexts. These features help organizations encourage a continuous learning culture and achieve their business goals.

Data Collection and Integration

LMS Integration: LAPs seamlessly integrate with existing learning management systems to gather data on course completion, assessment scores, and learner engagement.
xAPI Support: By leveraging xAPI, LAPs can capture data from a wide variety of learning experiences, including informal, social, and mobile learning activities.
Data Aggregation: LAPs consolidate data from multiple sources, such as LMS, SIS, attendance systems, and collaboration tools, providing a holistic view of learning activities.

Reporting and Analytics

Real-time Insights: LAPs offer real-time insights into training programs with data visualizations and detailed reports.
Customizable Reports: Users can customize reports by type, filter, and columns to monitor the performance of their learning strategy and achieve business impact.
Dashboards and Scorecards: LAPs provide dashboards and scorecards to set alerts and measure performance against business metrics.
Predictive Analytics: Some LAPs use AI-powered predictive analytics to forecast future events and performance, helping organizations better understand learner behavior.

Personalization and Adaptive Learning

Personalized Learning Paths: LAPs can analyze learner data to create personalized learning plans that focus on individual strengths and weaknesses.
Adaptive Content Delivery: Some platforms dynamically adapt content to individual student performance and learning pace.
Feedback Mechanisms: LAPs enable the design of custom evaluation processes to collect feedback on engagement and the impact of learning.

User Experience and Accessibility

User-Friendly Interface: LAPs are designed to be easy to use, even for non-analysts, with intuitive interfaces and out-of-the-box reporting functionality.
Mobile Accessibility: Many LAPs offer mobile access, allowing learners to engage with training materials and track their progress on the go.
Accessibility Features: LAPs should adhere to accessibility standards to ensure that all learners can access and benefit from the platform.

Implementing Learning Analytics Effectively

Implementing learning analytics tools effectively is paramount for educators to harness their full potential while maintaining the integrity and security of student learning data. Here’s a structured approach to ensure that the integration of these tools adds value to the educational process without compromising privacy.

Strategic Planning and Goal Setting

Before adopting any learning analytics tool, it’s crucial to outline clear objectives. What do you wish to achieve with analytics? Whether it’s improving student engagement, personalizing learning experiences, or boosting graduation rates, goals should guide tool selection and utilization. Establishing a roadmap for how analytics will support these outcomes is essential.

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Selecting the Right Tools

With a plethora of tools available, selecting the right one for your learning programs can be daunting. Institutions should prioritize platforms that align with their strategic goals and integrate seamlessly with existing systems. Consider factors such as ease of use, scalability, support services, and the ability to produce actionable insights.

Developing Policies and Protocols

To effectively leverage analytics tools, educational institutions need comprehensive policies governing data usage. This includes standardized processes for data collection, storage, analysis, and reporting. Establishing protocols ensures consistent and ethical handling of information across all analytics initiatives.

Investing in Training Programs

The success of learning data analysis and the use of these tools, depends on the proficiency of those who operate them. Educators, administrators, and IT staff require adequate training to interpret and apply data insights effectively. Professional development sessions and hands-on workshops can build this capacity within the institution.

Ensuring Data Privacy and Security

Protecting students' sensitive information is non-negotiable. Best practices for data privacy include encryption, access controls, regular security audits, and adherence to legal standards like the Family Educational Rights and Privacy Act (FERPA). Policies should also be transparent, informing students about data collection and its intended use.

Continuous Evaluation and Improvement

Once implemented, the effectiveness of learning analytics tools should be continuously assessed. Feedback from educators and learners can provide valuable insights into the utility and impact of these tools. Use this feedback loop for ongoing adjustments, ensuring the analytics serve their intended purpose and evolve alongside educational needs.

Fostering a Culture of Data-Informed Decision Making

Finally, for analytics to truly take root, there needs to be a culture shift within the institution. Decision-makers must prioritize evidence-based tactics and foster an environment where data-driven insights are welcomed and applied.

Learning Analytics Methodologies

The Society for Learning Analytics Research cites four common learning analytics methodologies that are commonly used. These are descriptive, diagnostic, predictive and prescriptive.

Descriptive Analytics

Descriptive analytics is a type of data analytics that describes what happened in the past. This type of data can help organisations understand participation and engagement rates, and whether learners achieved the set goals. This type of analysis looks at the facts and figures to determine exactly what happened.

Diagnostic Analytics

To understand why something happened, diagnostic analytics is often applied. Diagnostic analytics can help learning analytics teams spot patterns or anomalies in order to better understand the plausible reasons why something happened.

Predictive Analytics

In order to make predictions about what is likely to occur in the future, learning analytics will typically employ predictive analytics. This type of learning analytics is helpful in predicting trends, and preparing for future outcomes.

Prescriptive Analytics

When learning analytics teams want to make data-driven recommendations, they often rely on prescriptive analytics. This is a type of data analytics that is often automated through algorithms. For example, if a student hasn’t logged into the LMS in a certain number of days, they may receive an automated message.

Learning Analytics Techniques

Learning analytics involves employing a number of tools and techniques to discover patterns and relationships in the data that can lead to actionable conclusions. Two techniques that are commonly used in learning analytics are data mining and machine learning.

Data Mining

In order to make use of large volumes of data, organisations go through a process of data mining. This involves collecting, organising and filtering data in order to gain useful information. Learning Analysts would typically look for patterns or trends in the data from which they could make conclusions.

Machine Learning

Machine learning is a branch of artificial intelligence (AI) that replicates the way humans learn from experience, and recognizes trends and patterns. Due to its ability to use historical data to accurately make future predictions, it has become a valuable tool within digital learning.

Examples of Learning Analytics in Action

InterContinental Hotels Group

One example of learning analytics being utilised in corporate L&D, is a project led by InterContinental Hotels Group (IHG). As a multinational organisation with thousands of employees, the objective of implementing the program was to improve internal communication in the workplace. With help from the learning platform Learning Pool, they delivered a Massive Open Online Course for thousands of employees. Following the course, IHG then used learning analytics to determine the impact of the course on the quality of online conversations. It was only through using learning analytics that IHG was able to understand how effective the course actually was in achieving the learning objective.

University of Maryland Baltimore County

The University of Maryland Baltimore County (UMBC) used a predictive learning analytics campaign to contribute to better student achievement outcomes. The project involved configur…

Challenges of Learning Analytics

Although there are numerous practical uses and benefits that come along with leveraging learning analytics, it is not without challenges. Below we address some of the main challenges associated with learning analytics and how organisations can overcome them.

Integrating Large Amounts of Data

Learning analytics often involves working with large amounts of data from varying sources. Some of the challenges that come along with working with large amounts of data include safely storing the data and effectively integrating it. These are two logistical challenges that organisations should keep in mind when planning their learning analytics strategy.

Lack of Analytical Skills

Although technology is able to collect large amounts of data, it is of little use without the skills required to understand and interpret the data in meaningful ways. Therefore, one challenge of learning analytics is attracting and retaining talent with the analytical skills required to effectively work with learning analytics.

Data Security

Due to the fact that learning analytics deals with user data, ensuring data is properly handled and stored can be a challenge. It’s crucial that students consent to their data being collected and that the data is anonymised when possible. It’s also a good idea to provide employees with training on data protection in order to minimise the risk of data breaches.

The Future of Learning Analytics

As technology continues to advance, the role of learning analytics in education and training will only grow. Future trends include:

Increased Use of AI and Machine Learning: AI and machine learning will play an increasingly important role in personalizing learning experiences and providing predictive insights.
Integration with Emerging Technologies: LAPs will integrate with new technologies such as virtual reality (VR) and augmented reality (AR) to capture data from immersive learning experiences.
Focus on Skills-Based Learning: Learning analytics will be used to track and assess the development of specific skills, helping learners demonstrate their competencies to employers.

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