What Are Learning Analytics Dashboards?

Alongside AI tutors and automated feedback, some schools and learning platforms are rolling out “analytics dashboards” which promise parents and students real‑time insight into learning. These dashboards pull together data such as homework completion, quiz scores, time spent on platform, reading progress, attendance, and sometimes behaviour notes, then present them as charts, traffic‑light indicators, or weekly reports. Some systems also send automated nudges: emails or app notifications when work is missing, performance dips, or a student appears “off track” relative to class benchmarks.​

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Technically, this is often the same data schools have always collected, but made visible and digestible at a glance. In more advanced setups, dashboards incorporate predictive analytics – risk scores like those used in early‑warning systems (which we discussed a few weeks ago on the blog here)– or compare current performance with past cohorts. Commercial edtech platforms aimed at home learning now can offer similar features, giving parents detailed logs of what their child did in the app: which questions they missed, which skills they “mastered”, even how their usage compares with anonymous peers. The hope is that this transparency will make it easier to spot problems early and to have evidence‑informed conversations about support.​

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When dashboards help – When they overwhelm & When they lead to serious questions about privacy and cybersecurity risks

Used carefully, there can be benefits here. For parents who have previously felt “in the dark” about what happens at school, seeing concrete indicators of progress can be reassuring. Dashboards can highlight patterns – a student consistently struggling with one topic, or a gradual drop‑off in homework completion – that might otherwise only surface during report season, when there is less time to respond. For older students, especially those in senior secondary or university, self‑analytics can support self‑regulation: tracking study time, noticing when a course is being neglected, or celebrating steady improvement.​

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However, there is a tipping point where visibility becomes surveillance. Constant notifications about minor dips can create a background hum of anxiety: for parents who are already worried, and for students who may feel that every misstep is recorded and shared. Metrics like “time spent on platform” or “number of logins” can be particularly misleading – a student might stare at a screen without engaging, or prefer to study offline with printouts, yet still appear “inactive”. Dashboards also tend to emphasise what is easy to count (clicks, scores, streaks), not slower‑developing qualities like curiosity, resilience, or conceptual understanding. As Einstein said ‘not everything that counts can be counted, and not everything that can be counted counts’.​ 

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There are also concerns here about the level of student privacy being protected and if that data is being properly stored and protected from cybersecurity risks. Referring to The Age of Surveillance Capitalism by Shoshanna Zuboff can be useful here for example when she says ‘“surveillance capitalism unilaterally claims human experience as free raw material for translation into behavioral data.” The incentives behind the tracking of this student data needs to be kept top and front of mind.

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Practically, families can make dashboards more useful and less stressful by:

• Setting expectations about how often to check. For many, a weekly or monthly review may be healthier than multiple daily log‑ins.
• Focusing on trends, not individual blips. Look for patterns over several weeks rather than reacting to every red icon.​
• Using data as a conversation starter, not a verdict. Ask, “What do you think was happening here?” rather than “Why did you get this wrong?”
• Ensuring you can opt out of the system, or metrics and indicators you feel uncomfortable or unnecessary to track.

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For teachers and tutors, it can help to translate dashboard language into human terms for parents, and to be honest about the limits of what the numbers can and cannot say. The goal is to treat analytics as one lens among many – alongside student work, direct conversations, and professional judgement – rather than as a new, blackbox truth.

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Note: the first draft of this article was done by AI Chatbot Claude with the support of Max Capacity. The text was then edited and adapted by Jaye Sergeant of Turtle & elephant, who is responsible for the published version.

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