Learning Analytics and Assessment Validity

Learning analytics collects and analyses data about learners and learning contexts to understand and improve learning and its environments. Assessment uses evidence to support an interpretation about knowledge, skill, participation, or another educational construct. The two overlap when dashboard indicators, predictions, or activity traces influence feedback and decisions. A number is not automatically valid evidence. Validity concerns whether the interpretation and intended use are supported by an argument and appropriate evidence.

The construct is the quality an educator wants to understand. A quiz may sample conceptual knowledge; a discussion trace may show visible participation; a model may estimate risk. Each indicator represents only part of the construct and can also reflect irrelevant factors. Low platform activity may mean disengagement, but it may also reflect offline study, shared devices, inaccessible design, employment, illness, or a learner who downloaded materials earlier. Treating the trace as motivation would require evidence that rules out important alternatives.

Validity is use-specific. The same indicator might support a low-stakes conversation but be inadequate for grading, discipline, or restricting opportunity. Evidence can include content alignment, response processes, internal structure, relationships with other measures, and consequences. Reliability matters because unstable measurements cannot support fine distinctions, but a consistently measured quantity can still be the wrong one. Predictive accuracy likewise does not establish that a decision based on the prediction is fair or beneficial.

Analytics systems introduce feedback loops. When a dashboard labels a student at risk, a teacher may provide support, lower expectations, or change interaction. The label can then influence the outcome it was meant to predict. Missing data, group differences, model drift, and interface design also shape interpretation. Students should know what information is used, have a route to correct it, and be able to offer contextual evidence. High-stakes decisions need multiple sources and accountable human judgment.

In education, learners can audit a fictional engagement dashboard. They name the claim behind each indicator, identify the observable data, list alternative explanations, and decide which uses are defensible. They compare the dashboard with interviews, work samples, and an unaided assessment, then write a validity argument that includes uncertainty and possible consequences. If the evidence cannot support the intended decision, they redesign the measure or narrow the claim.

Good analytics makes interpretation more disciplined, not more automatic. Institutions should predefine purposes, minimize data, test technical quality and subgroup performance, study how users understand the display, monitor consequences, and retire measures that no longer fit. Assessment validity keeps the question educational: what conclusion is being drawn about which learner, from what evidence, for what purpose, and with what effects? A defensible answer remains open to new evidence and should become more demanding as the consequences for a learner become more serious or difficult to reverse.