Motivation
CodeTrial already collects a substantial amount of information from each mock interview. In addition to the final assessment, coding interviews are evaluated through the REACTO framework, behavioral interviews through the STAR framework, and previous reports are stored in the interview history.
However, most of this information is currently presented as individual text-based reports. As the number of completed interviews increases, it becomes difficult for users to quickly identify recurring weaknesses or understand how their performance changes over time.
Presenting aggregated interview history visually could make the existing assessment data much easier to understand and turn the history page into a more useful self-improvement tool.
Proposed solution
Add a performance analytics section to the browser interface that aggregates compatible historical interview reports and presents the results using charts.
Some possible visualizations include:
1. REACTO performance radar chart
Display the user's average performance across the six REACTO phases using a six-axis radar chart:
This would provide a quick overview of which parts of the coding interview process are relatively strong or weak.
The chart could also display the number of assessed interviews used to calculate each value, since some phases may be unassessed in individual reports.
2. Coding-topic performance
Associate interview problems with topic tags such as : Array, Hash Table, Two Pointers, Dynamic Programming etc.
Aggregate the user's results for each topic and display them using a bar chart or similar visualization.
For example:
Array 82
Hash Table 76
Two Pointers 71
Dynamic Programming 58
Graph 54
This would help users identify which categories of interview questions require more practice.
3. STAR performance
For interviews containing behavioral questions, aggregate the four STAR components:
These could be displayed using a bar chart or four-axis radar chart.
4. Performance history
A simple line chart could show how the user's overall or framework-specific performance changes across interviews over time.
Filters could optionally allow users to view results by:
- coding topic
- problem difficulty
- programming language
- interview type
Follow-up actions
-
Identify available report fields
- Determine which REACTO/STAR scores are already stored in historical reports.
- Check whether problem topic metadata is currently available in reports or can be obtained from the problem bank.
-
Define aggregation rules
- Decide how phase scores should be averaged.
- Exclude unassessed values.
- Respect
interviewContract / rubric compatibility when combining reports.
- Keep sample counts for each aggregated metric.
-
Implement an analytics layer
- Convert historical reports into aggregated REACTO, STAR, topic, and time-series statistics.
- Keep the aggregation logic independent from the visualization layer so it can be tested separately.
-
Add visualization to the browser UI
- Add an analytics/dashboard section near the existing interview history.
- Use lightweight SVG/Canvas or a suitable charting solution consistent with CodeTrial's current static browser architecture.
- Provide accessible textual values in addition to charts.
Possible extension
Once the basic analytics dashboard is available, several additional features could be built on top of it:
- Practice recommendations: suggest problem categories based on the user's weaker areas.
- Topic drill-down: clicking a topic such as
Dynamic Programming could show the individual interview reports contributing to that statistic.
- Language breakdown: compare interview performance when using different programming languages.
Motivation
CodeTrial already collects a substantial amount of information from each mock interview. In addition to the final assessment, coding interviews are evaluated through the REACTO framework, behavioral interviews through the STAR framework, and previous reports are stored in the interview history.
However, most of this information is currently presented as individual text-based reports. As the number of completed interviews increases, it becomes difficult for users to quickly identify recurring weaknesses or understand how their performance changes over time.
Presenting aggregated interview history visually could make the existing assessment data much easier to understand and turn the history page into a more useful self-improvement tool.
Proposed solution
Add a performance analytics section to the browser interface that aggregates compatible historical interview reports and presents the results using charts.
Some possible visualizations include:
1. REACTO performance radar chart
Display the user's average performance across the six REACTO phases using a six-axis radar chart:
This would provide a quick overview of which parts of the coding interview process are relatively strong or weak.
The chart could also display the number of assessed interviews used to calculate each value, since some phases may be unassessed in individual reports.
2. Coding-topic performance
Associate interview problems with topic tags such as : Array, Hash Table, Two Pointers, Dynamic Programming etc.
Aggregate the user's results for each topic and display them using a bar chart or similar visualization.
For example:
This would help users identify which categories of interview questions require more practice.
3. STAR performance
For interviews containing behavioral questions, aggregate the four STAR components:
These could be displayed using a bar chart or four-axis radar chart.
4. Performance history
A simple line chart could show how the user's overall or framework-specific performance changes across interviews over time.
Filters could optionally allow users to view results by:
Follow-up actions
Identify available report fields
Define aggregation rules
interviewContract/ rubric compatibility when combining reports.Implement an analytics layer
Add visualization to the browser UI
Possible extension
Once the basic analytics dashboard is available, several additional features could be built on top of it:
Dynamic Programmingcould show the individual interview reports contributing to that statistic.