Learning analytics — what you measure, what you don't, and why
Learning analytics gives teachers insight into where students stand, what they understand and where they struggle. But more data isn't automatically better — vanity metrics, privacy risks and reduction to numbers are real pitfalls. This article describes which data is useful, what the pitfalls are, and how LectaMe offers analytics at learning-objective level without identifying students.
Four dimensions of useful data
Participation
Who takes part in a session, when they drop off, how many participants at each moment. For the teacher: you know whether your group is still engaged and at which moments attention dips.
Answers
Which quiz questions are often answered wrong, which open questions get similar answers, which concepts are difficult. You see the pass rate per learning objective.
Time progression
How long students spend on a slide, what gets reviewed, which video sections are replayed. For self-paced learning paths, a valuable hint about where difficulty arises.
Sentiment
How students feel about the topic — via word clouds, ranking questions or sentiment polls. Makes visible what stays invisible in an ordinary lecture: the silent majority.
The pitfalls
Vanity metrics. A nice dashboard with participant counts, completion percentages and average scores is fun, but often says little. Ask yourself: what do I do with this data? If the answer is unclear, you are measuring too much.
Reduction to numbers. Learning is more than pass rates. A student who scores 60% may have gained greater insight than a student who scores 95% on fill-in exercises. Data is a signal, not a story.
Privacy erosion. Measuring more sounds better but quickly creates ethical problems. For underage students the GDPR applies strictly — only measure what you need for your didactic goal, and no more.
Frequently asked questions
What are learning analytics?+
Learning analytics is the measuring, collecting, analysing and reporting of data about students or participants and their context, with the goal of understanding and optimising learning processes. Practically for teachers: seeing who is participating, which questions are difficult, and where students struggle — input to adjust your instruction.
Which data is useful for teachers?+
Four dimensions. Participation (who takes part, when they drop off), Answers (which quiz questions are often wrong, which concepts are difficult), Time progression (how long per slide, what gets reviewed again), and Sentiment (how students feel about the topic). You don't measure everything at once — choose what gives you insight for your next lesson.
What are the pitfalls of learning analytics?+
Three major pitfalls. (1) Vanity metrics — fancy dashboards without actionable insight. (2) Reduction to numbers — learning is more than pass rates. (3) Privacy erosion — measuring more sounds better but quickly creates ethical problems. Always ask yourself: "what do I do with this data?" If the answer is unclear, you are measuring too much.
How does LectaMe handle student data?+
Students don't log in and don't provide any personal data. Analytics is aggregated: you see "8 of 15 wrong answers on question 3" — not "Tom Jansen answered wrong". For schools that is a GDPR advantage: no identifiable student data in the teacher cockpit. For formal assessment (grades) you use other systems.
What is the difference between formative analytics and summative analytics?+
Formative analytics measure during the learning process — to adjust course. "Which concepts are difficult right now?" Example: live quiz data during a lesson. Summative analytics measure after the learning process — to determine. "Has the student achieved the learning objectives?" Example: final exam data. LectaMe focuses on formative analytics; for summative measurements you connect to an LMS or grading system.
How do I interpret analytics data without drawing too many conclusions?+
Three rules. (1) Trends over incidents — a question that went badly once says less than a question that consistently goes badly. (2) Combine quantitative with qualitative — data tells you what, not why. Ask students for context. (3) Hypothesis-driven — formulate a question in advance ("do students think X is more important than Y?") and interpret data against it, not "fishing for insights".