How can students benefit from learning analytics?

How Can Students Benefit from Learning Analytics? 4 Takeaways

What issues exist with online education?

Although it is well recognized that students learn differently and at varying rates, it is still challenging to pinpoint the best engagement and material delivery strategies, especially when looking at each individual student.

Teachers struggle to recognize students’ learning disabilities and lack the data they need to fine-tune their real-time responses.

How can students benefit from learning analytics?

Instead of a more thorough ongoing evaluation of learning, knowledge, and development throughout the student education experience, learning outcome assessment still relies on infrequent milestone events (such as examinations and assignment delivery).

1. The use of behavior analytics as a remedy

We think the solution to the issues lies in human behavior, particularly real-time engagement while learning. In our studies at the University of Technology, Sydney, we have seen students use the keyboard to look up keywords, the mouse to click text segments to help them read, and digital pens to take notes—all of which are common learning behaviors.

We may also communicate with students through their eye movements and facial expressions to discover more about their areas of interest and whether they have any difficulties in the classroom. In reality, if the signals produced by digital instruments like the mouse, keyboard, or camera could be recorded, processed, and analyzed using signal-processing and machine-learning approaches, certain common patterns may be detected in these behaviors.

We have created a multimodal learning analytics platform in collaboration with Acer Australia and neighborhood schools to solve these issues, and we have put it to the test in actual classroom settings. It can measure real-time engagement, capture data on natural learning behaviors, and evaluate each student’s learning progress.

These data may help with teaching and learning in a variety of ways, enabling users to evaluate how well educational materials are suited for certain students and monitor changes in engagement patterns. They may also serve as an input for subsequent downstream data science processing.

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2. A versatile, inexpensive, and non-intrusive solution

A pricey piece of hardware with specifically crafted cameras or eyewear, along with a challenging software package, make up the usual commercial eye-tracker suite. Before utilizing the eye tracker, the user often has to undergo calibration. These systems and practices are inadequate given the demands of rapid and widespread deployment in educational institutions, research facilities, and private residences.

Contrary to most commercial devices, our developed resolution may be used with just a laptop’s built-in camera, keyboard, and mouse. Once installed on a computer and started, the program may gather and synchronize user-interaction data from a variety of modalities based on the user’s preferences.

It can be tailored to fit any computer-based learning environment and is a platform-free, installation-free learning analytics solution. It stands out from other built-in learning analytics remedies because of this.

4. Research ethics, student admission

When behavioral data is gathered using eye-tracking technology, privacy issues are always taken into account. We have undergone the required ethics approval procedures for our work.

Strict guidelines have been used in the data collecting and management plan, including anonymizing all of the students’ learning records and capturing extracted data characteristics only, not re-identifiable data or any facial photos. The time frame for data collection is exclusively restricted to the instructional process.

When using a piece of software for the first time, questions like “What does it do?” and “How would it assist me?” are frequently asked. The majority of students and instructors are willing to try out our learning analytics software, despite the fact that there isn’t an instant effect that can be seen in the early stages of deployment. They are always excited to see their learning progress as time passes once enough data have been gathered and a learning curve has been generated.

4. Improved knowledge and experience

Our solution can provide teachers with real-time student engagement analytics, outlining where and when a student is enthralled by the subject matter and when they may be a little less interested. It does this by drawing on hundreds of hours of computer interaction data, utilizing cutting-edge machine-learning approaches, and leveraging behavioral theory.

The outcome helps teachers learn what is most effective both now and over the long run in their particular classes and for each individual student. Additionally, the data offer a fresh technique to comprehend the relationship between student behavior and comprehension when combined with conventional performance evaluation.


Data analytics software may be used to construct individualized development plans for students, find engagement opportunities for student assistance, and provide important information about how to make degree programs and curricula more effective.

User-generated data from learning activities may be collected by learning analytics, which can also provide trends in learning engagement. The learning habits and learning preferences of pupils can be discovered by analyzing such tendencies.

Program directors and other administrators can more quickly see how well their program is doing thanks to learning analytics. Learning analytics can enable meaningful comparisons across courses in addition to the possible requirement to drill down into particular faculty, students, and course-level data.

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