Do beginners benefit more than experts from learning analytics?
The short answer is yes — beginners appear to gain more from learning analytics than experts do. The strongest evidence comes from a 2025 study of automotive engineering students using a learning analytics tool called Smart Repair Tracker [2]. In that study, 75% of students said the direct feedback feature helped them understand their own progress and weaknesses — exactly the kind of self-awareness that beginners typically lack [2]. Another 80% reported that the tool increased their engagement in learning [2]. These are large majorities, and they point to a clear pattern: when you're new to a subject, getting immediate, data-driven feedback on where you stand is a big help.
A broader 2024 systematic review of 27 studies on learning analytics in learning management systems found that analytics interventions generally improved teaching and learning practices [1]. While that review didn't single out beginners versus experts, the fact that the strongest effects came from tools that gave students direct feedback supports the idea that novices — who need that feedback most — are the primary beneficiaries.
The key mechanism is simple: beginners often don't know what they don't know. Learning analytics fills that gap by showing them, in concrete terms, where they are struggling and where they are improving. Experts, by contrast, already have well-developed mental models of their own performance, so the same feedback is less transformative.
How does learning analytics actually help a beginner?
Learning analytics works by collecting data on your learning activities — like how much time you spend on a module, which questions you get wrong, or how you interact with simulations — and then presenting that data back to you in a useful way. For a beginner, this can be a game-changer. In the Smart Repair Tracker study, students used virtual reality simulations alongside the analytics dashboard, and the combination scored 4.9 out of 5 for interest and effectiveness [2]. That means beginners found the experience not just helpful but genuinely engaging.
The same study also showed that the tool was rated 4.7 out of 5 by experts for feasibility, meaning it was well-designed for real classroom use [2]. So the tool itself is solid — but the biggest impact was on beginners' ability to see their own learning trajectory. When you're starting out, that visibility is empowering. It turns vague anxiety about 'am I doing okay?' into a clear picture of your strengths and weaknesses.
Other research supports this. A 2023 study on ethical learning analytics emphasized that tools should be designed to 'effectively support student learning and development,' not just protect privacy [4]. That principle is especially important for beginners, who need the tool to be transparent and actionable, not a black box.
Do experts get anything out of learning analytics?
Experts can benefit from learning analytics, but the gains are smaller and more about efficiency than discovery. A 2023 study on building organizational capabilities for learning analytics found that the main challenge for institutions is not whether analytics helps experts, but how to scale adoption across all users [3]. That suggests that for experts, the value is more about streamlining what they already do well, rather than revealing new insights.
Another 2023 study on trust in learning analytics found that both students and faculty have concerns about how data is used, and that trust is essential for any group to engage with the tools [5]. Experts may be more skeptical or more selective about which analytics they find useful, because they already have strong self-assessment skills. So while experts can benefit — for example, by spotting patterns in their own performance that they might miss — the evidence here suggests that the biggest wins are for beginners who lack that self-awareness.
In short, learning analytics is not useless for experts, but its most dramatic effects — the 75-80% engagement and self-understanding boosts — are seen in novices. If you're a beginner, these tools are likely to give you a clear, immediate picture of where you stand and what to do next. If you're an expert, you'll probably find them useful but less transformative.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2023 to 2025, 2 from 2024 or later, 3 in Q1 journals, collectively cited 58 times — selected as the most relevant from 7 studies that passed quality screening, drawn from 55 papers retrieved from a database of over 500 million.
Sources used in this answer
A Systematic Review of Learning Analytics
A 2024 systematic review of 27 studies found that learning analytics interventions within learning management systems generally improved teaching and learning practices, though it did not specifically compare beginner vs. expert outcomes.
SMART REPAIR TRACKER: EDUKIT LEARNING ANALYTICS UNTUK PENGUATAN 6C'S MAHASISWA KEJURUAN DI ERA CYBER
A 2025 study of automotive engineering students using the Smart Repair Tracker found that 75% said direct feedback helped them understand their progress and weaknesses, and 80% reported increased engagement; the tool scored 4.9/5 for interest and effectiveness in VR simulations.
Supporting Learning Analytics Adoption: Evaluating the Learning Analytics Capability Model in a Real-World Setting
A 2023 study evaluating a learning analytics capability model found that the model helped practitioners plan adoption at higher education institutions, but noted that uptake remains low and the focus is on organizational capability, not user expertise level.
Six Practical Recommendations Enabling Ethical Use of Predictive Learning Analytics in Distance Education
A 2023 case study on ethical predictive learning analytics at a distance learning university produced six practical recommendations for ethical use, emphasizing that tools should support student learning and development, not just protect privacy.
“Trust us,” they said. Mapping the contours of trustworthiness in learning analytics
A 2023 Delphi study with 31 learning analytics experts established a working definition of trust in learning analytics and identified factors that affect trust for students and faculty, including concerns about data use and transparency.