What can learning analytics actually tell an educator?

For: Educators and students approaching educational data critically.

A platform can record a video opening. It cannot infer, from that event alone, whether the viewer understood the explanation, left the room or already knew the material. Learning analytics becomes useful when the recorded event is connected to a defensible educational question. This guide starts with that connection and puts the choice of software later.

SoLAR’s 2026 definition places interpretation, educational relevance and action alongside collecting and analysing data. The same page discusses privacy, opaque models and biased data. Read this framing before the tool demonstrations: the intended use affects which data are appropriate, which errors matter and who bears their consequences.

The introductory lecture defines the field. Data collection then asks what the variables actually represent. A count of clicks is a count of recorded clicks; calling it “engagement” adds an interpretation. Missing data deserve equal attention. A missing event can reflect an absent learner, an alternative route through the material, a technical problem or a choice not to use the platform. Those possibilities need investigation, not a default score of zero.

After the ethics and missingness selections, training and testing provide a route into checking predictions. A model’s success on observations used to build it is a different claim from success on new observations. Even a useful prediction does not demonstrate that the suggested intervention will improve learning. The performance-metrics lecture is included so that a headline accuracy can be unpacked into the kinds of error the educational setting can tolerate.

For an educator, a productive first project may be very small: identify a confusing concept with a short assessment, offer a relevant practice activity and check what learners can subsequently do. The data should serve that question. Gathering more traces is not an educational objective in itself.

The exercise therefore uses a fictional dataset. It asks you to document the measurement and its gaps before fitting a model. This guide is source navigation and methodological preparation; it supplies neither a validated early-warning system nor permission to collect personal learner data.

Next step

Continue to the course’s performance metrics and compare predictive success with evidence of educational benefit.

Limits of this route

SoLAR’s definition is dated 2026. The source check does not validate any predictive model, implementation or educational intervention. NPTEL selections use named topics and their official sequence; no real learner data are required. Source checks establish scope and selection usefulness; they do not claim full-video viewing, human review or scientific validation.