Automated Reports On FaceCode

Writing a balanced and well-detailed interview summary is not only time consuming but also demands significant cognitive overload to take notes and record feedback during the interview. FaceCode’s latest update does all the heavy lifting for you and automatically generates a summary on the candidate’s interview performance.

The platform takes inputs from multiple sources and compiles that to an interview summary.

The key sources for summary generation are:

1. Scores given by the interviewer: Each interviewer gets to rate the candidate on a few criteria like quality of code, ability to understand the question (for the default list of criteria read this) and so on. The platform comes with default criteria which can be changed when setting up the interview.

2. Code written by the candidate: The system tracks the amount of time spent coding, code compilations, syntactical correctness of the code, etc.

3. Candidate engagement during the interview: The system tracks candidate engagement metrics like speak to listen ratio and the time candidate is actively engaged and uses that to include candidate engagement as part of the summary.

4. Structure of the interview: The system tracks the number of questions asked, time spent on each question and whether code was written for each question or not and uses that to add points to the summary.

The system aggregates all the data collected from these sources, matches some of the data points like speak to listen ratio and time spent coding with overall averages and generates a summary based on that. If needed, the summary can then be edited by the interviewer.

Kumari Trishya Author 51 Blogs