> For the complete documentation index, see [llms.txt](https://docs.lamsfoundation.org/lams/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lamsfoundation.org/lams/students-at-risk.md). # Students at Risk ## Student Performance Risk Analytics **Student Performance Risk** is a learning analytics feature that helps you identify students who may benefit from **additional academic support** before academic difficulties become more serious. Rather than providing a single snapshot of student performance, Student Performance Risk analyses assessment results over time to help you answer **three important questions** about every learner: * **How well are they performing?** * **Which direction is their performance moving?** * **How consistent is that performance over time?** By combining these perspectives into a single visualisation, you can quickly identify students whose learning patterns suggest they may require additional attention. This allows you to **prioritise conversations**, **investigate potential concerns**, and **provide timely support** while there is still an opportunity to improve student outcomes. Student Performance Risk is designed as an **academic triage tool**, **not a predictive system**. It does **not** determine whether a student will succeed or fail, nor should it be used to make judgements about a student's ability. Instead, it **surfaces meaningful patterns in assessment data** to support your **professional judgement** and help you decide which students may benefit from further investigation. **Early identification** is an important component of effective student support. By recognising changes in performance before they become entrenched, you can **intervene earlier**, **provide appropriate guidance**, and help students overcome challenges before they lead to course failure or withdrawal. At an institutional level, this contributes to **improved student success**, **stronger retention**, and **reduced attrition**. As with any learning analytics, Student Performance Risk should always be considered alongside **your knowledge of the student and their circumstances**. The visualisations are intended to **support informed decision-making**, providing evidence to guide conversations and interventions **rather than replacing academic judgement**. Together, these elements allow you to **focus the analysis on a meaningful teaching period**, review the overall pattern of the cohort, and identify students whose performance may warrant closer investigation. ### Understanding the Student Performance Risk view The Student Performance Risk view brings together the **controls you use to define the analysis** and a **visual overview of student performance** across the selected period.
The page is divided into two main areas: * On the **left**, you can define the period and criteria used in the analysis. This includes the **date range**, **risk threshold**, **minimum number of weeks required**, and whether you want to display only students who have been flagged. * On the **right**, the **Cohort Scatter** provides a visual overview of the students included in the analysis. Each student is represented by a point whose position reflects their attainment and trajectory, while its size reflects the consistency of their performance over time. The values shown in the chart are **recalculated using only the assessment data within the selected date range**. This means that changing the period can change a student's position in the chart, because their recent performance may differ from their longer-term pattern. The summary indicators on the left show how many students are currently included in the analysis, how many have been **flagged**, and how many have been **excluded because there is not enough data** within the selected period. Below the chart, the **Interpretation** section provides guidance on how to read the visualisation, what the different areas of the chart suggest, and how to use the information when considering whether a student may benefit from further support. ### Understanding the Cohort Scatter The **Cohort Scatter** is the main visualisation in Student Performance Risk. It places each student on the chart according to their performance across the selected date range.
Each student is represented by a dot, and the chart combines three measures: * **Attainment** answers **How well are they performing?** * **Trajectory** answers **Which direction is their performance moving?** * **Volatility** answers **How consistent is that performance over time?** #### Attainment The **Y axis** shows the student's **mean weekly percentage**, from 0% to 100%, across the selected date range. A higher position on the chart indicates stronger overall attainment within that period, while a lower position indicates weaker overall performance. Because the value is calculated from the selected date range, it reflects performance in that specific window rather than a student's entire history. #### Trajectory The **X axis** shows the student's **trajectory**, expressed as the slope of their weekly performance in percentage points per week. * Students positioned further to the **left** have a declining performance trend. * Students positioned further to the **right** have an improving performance trend. * Students near the centre have relatively stable performance over the selected period. Trajectory adds important context to attainment. Two students may currently have similar average results, but one may be improving while the other is declining. #### Volatility The **size of each dot** represents **volatility**, calculated as the standard deviation of the student's weekly performance. A larger dot indicates greater week-to-week variation, while a smaller dot indicates more consistent performance. Volatility should be interpreted with context. A student whose results vary substantially from week to week may warrant closer investigation, but variability alone does not necessarily indicate academic difficulty. Together, these three measures provide a more complete picture than a single mark or average. They show **where a student is performing, where their performance is heading, and how stable that pattern is**. ### Interpreting the quadrants The Cohort Scatter can be divided into four broad areas based on **attainment** and **trajectory**. Each area suggests a different performance pattern and can help you decide where further investigation or support may be most useful. a.htmlHTML #### Bottom-left: low and declining Students in the **bottom-left** have lower attainment and a declining trajectory. This is generally the **highest-priority area for follow-up**, because the student is performing relatively poorly and their results are also moving in the wrong direction. Consider checking in promptly and looking for possible barriers such as attendance, workload, wellbeing, or misunderstanding of expectations. a.htmlHTML #### Bottom-right: low but improving Students in the **bottom-right** have lower attainment but an improving trajectory. These students may still benefit from support, but the direction of travel is positive. You may want to **monitor progress, reinforce what is working, and provide targeted support** to help sustain the improvement. a.htmlHTML #### Top-left: strong but slipping Students in the **top-left** have stronger attainment but a declining trajectory. Although their overall performance may still be good, the downward trend can be an **early warning sign**. A timely check-in may help you identify recent changes and support course correction before the decline becomes persistent. a.htmlHTML #### Top-right: strong and improving Students in the **top-right** have stronger attainment and an improving trajectory. This generally indicates **lower concern within the selected period**. These students are performing well and continuing to improve, so only light-touch monitoring or support may be needed. a.htmlHTML The quadrants are best used as **signals for professional judgement**, not as fixed categories. A student's position can change when you select a different date range, and the chart should always be interpreted alongside the wider context of the student's learning experience. ### Using filters and defining risk The controls on the left of the Student Performance Risk view allow you to **change the period being analysed**, **define which students are flagged**, and **control how much data is required** before a student is included. a.htmlHTML These controls are important because Student Performance Risk is not a fixed score. The results depend on the **selected date range**, the **risk threshold**, and the **minimum amount of evidence** you require.
#### Choose the date range Use **Start date** and **End date** to choose the period you want to analyse. Only weeks within that range are included in the calculations. a.htmlHTML You can also use the preset options: * **Full range** to include all available data * **Last 6 weeks** to focus on recent performance * **This year** to restrict the analysis to the current year Changing the date range recalculates the student's **attainment, trajectory, volatility and flag status** using only the data within that period. a.htmlHTML This allows you to focus on a particular teaching block, semester period, or recent change in performance. #### Set the risk threshold The **Risk threshold** defines the mean weekly percentage below which a student is flagged for potential concern. a.htmlHTML You can enter a percentage manually, or use: * **Bottom 10%** to set the threshold based on the lowest-performing 10% of the cohort * **Bottom 25%** to set the threshold based on the lowest-performing 25% The threshold is a **triage mechanism**, not a diagnosis. Being flagged simply indicates that a student's attainment falls below the selected threshold within the current date range and may warrant further investigation. a.htmlHTML #### Set the minimum weeks required Use **Minimum weeks required** to specify how many weekly data points a student must have within the selected range before they are included in the analysis. a.htmlHTML This helps avoid drawing conclusions from too little evidence. Students with fewer weekly points than the minimum you set are **excluded from the chart** and counted separately under **Excluded due to insufficient weeks**. a.htmlHTML #### Show flagged students only Enable **Show flagged students only** when you want to focus the chart on students whose attainment is below the current risk threshold. a.htmlHTML This can be useful when you have a large cohort and want to concentrate first on students who may need closer attention. #### Review the summary counts The summary indicators show: * **Students shown**: the number of students currently included in the chart * **Flagged**: the number of students below the current risk threshold * **Excluded due to insufficient weeks**: students who do not meet the minimum data requirement a.htmlHTML Use these counts to understand how your current filter settings are affecting the cohort being analysed. ### Reviewing individual students When a student requires closer attention, you can select the dot in the chart to open the **Student Detail View** to examine their performance over time in more context.
This view moves from cohort-level triage to an **individual longitudinal view**, helping you understand whether a student's current position reflects a temporary fluctuation, a sustained decline, an improving pattern, or more variable performance. #### Review the weekly attainment trend The **Weekly Attainment Trend** chart shows the student's average assessment performance for each active week, expressed as a percentage from 0% to 100%. A horizontal **risk threshold** provides a visual reference point so you can quickly see when weekly attainment falls below the level you have set for potential concern. The summary indicators also show: * **Active weeks**, the number of weeks with assessment data * **Overall Mean**, the student's average weekly attainment across the selected view * **Latest weekly attainment**, the most recent weekly average and the corresponding week This helps you move beyond a single current score and review **how performance has changed over time**. #### Filter by course Use the **Course** dropdown to focus the trend on a particular course the student has been enrolled in during the selected period. You can choose: * **Average of all courses**, to review the student's overall weekly attainment across their courses * a **specific course**, to focus the weekly averages on that course only Importantly, this view does **not expose individual assessment or examination results from other courses**. Instead, it shows only the **weekly average of assessment performance** for the selected period, either across all courses or for the course you choose. This gives lecturers useful longitudinal context while avoiding unnecessary access to detailed assessment results from courses outside their own. #### Review peer evaluation feedback Where peer evaluation feedback is available, the **Peer Comments** section shows feedback that other students have provided about the selected student. Feedback is grouped by week and course, allowing you to review comments alongside the student's attainment trend. This can provide valuable additional context. For example, peer comments may highlight patterns in participation, teamwork, contribution, communication, or areas where the student could improve. Peer feedback should be treated as **supporting evidence**, not as a definitive judgement. Consider it alongside the student's attainment pattern and any other relevant academic context before deciding whether follow-up is appropriate. #### Use the detail view to investigate, not to conclude The Student Detail View is designed to help you **investigate the context behind a potential concern**. Use it to look for patterns, compare performance across time or courses, and review relevant peer feedback before deciding whether the student may benefit from additional support. The aim remains the same as in the cohort view: **identify, investigate, understand the context, and support**. ### Using Student Performance Risk responsibly Student Performance Risk is designed to **support academic judgement**, not replace it. The information shown in the cohort and student detail views should be used as **evidence for further investigation**, not as a definitive judgement about a student's ability, engagement, or likelihood of success. A student may appear at risk for many different reasons. Changes in assessment performance can reflect factors such as attendance, workload, wellbeing, unfamiliarity with expectations, competing commitments, or temporary circumstances that are not visible in the chart. #### Treat risk indicators as signals A flagged student, a declining trajectory, or high volatility should be treated as a **signal that further context may be useful**. These indicators do not diagnose a problem and should not be used in isolation to make high-stakes decisions. Instead, use them to help you decide **where to look more closely and where a timely conversation may be beneficial**. #### Confirm the context before acting Before escalating a concern or making assumptions, review the available information and, where appropriate, speak with the student or relevant teaching team. The most useful workflow is: **Identify → investigate → understand the context → support** This helps ensure that interventions are proportionate, informed, and focused on helping the student succeed. #### Use the feature for support, not punishment Student Performance Risk is intended for **early support and intervention**. Its purpose is to help you identify students who may benefit from guidance, academic assistance, or a check-in before difficulties become more serious. It should not be used as a punitive mechanism or as the sole basis for disciplinary, progression, or other consequential decisions. #### Interpret patterns over time Avoid placing too much weight on a single week or isolated result. The value of Student Performance Risk comes from reviewing **patterns across time**, including: * overall attainment * direction of performance * consistency of performance * recent changes * relevant peer feedback and contextual information This broader view helps reduce the risk of overreacting to temporary fluctuations. #### Keep professional judgement central The system can highlight patterns, but it cannot know the full circumstances behind them. Use the data to **prioritise attention, guide conversations, and inform support**, while keeping your professional knowledge of the student and their learning context at the centre of any decision. The aim is not to label students as "at risk". It is to **identify opportunities for timely support before academic difficulties become harder to address**. --- # Agent Instructions This documentation is published with GitBook. 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