Questionnaire Normalization and Auto-Population for Job Applications
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Solution Overview
Problem
Online job questionnaires often require lengthy and repetitive information from applicants, particularly on mobile devices, due to differences in question formats and incomplete social networking profiles, leading to increased application dropout rates.
Innovation Solution
A system that normalizes questions across various questionnaires, using machine learning to automatically prepopulate fields in job application questionnaires by extracting relevant information from social networking profiles and other sources, reducing the time and effort required for applicants to complete applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If questionnaires ask for detailed information in different formats, then data completeness is improved, but completion time increases significantly
Solution Approach 1:
The system pre-populates questionnaire fields by automatically extracting information from the user's social networking profile before the user begins filling out the form. This preliminary action reduces the time required during actual questionnaire completion while maintaining data completeness.
Solution Approach 2:
The system creates a copy of relevant information from the social networking profile and uses it to pre-fill questionnaire fields. This copying mechanism allows the same information to be reused across multiple questionnaires without requiring repeated user input.
2Measurement precision
If questionnaires require manual information entry, then data accuracy is maintained, but user effort increases
Solution Approach 1:
The system automatically extracts and populates questionnaire fields using information from the user's existing social networking profile without requiring manual user input. The system serves itself by leveraging already-collected user data.
Solution Approach 2:
The system acts as an intermediary between the social networking profile and the questionnaire, automatically mapping and transferring relevant information. This intermediary function reduces user effort while maintaining data accuracy through systematic field matching.
3Reliability
If questionnaires are hosted on separate sites requiring registration, then data security is improved, but accessibility decreases
Solution Approach 1:
The system provides multiple access points and methods for completing questionnaires, including direct links from social networking feeds and integrated forms. This multi-functionality maintains security while improving accessibility by allowing users to apply through different pathways.
4Measurement precision
If typing and navigation on mobile devices is required, then input precision is maintained, but time consumption increases
Solution Approach 1:
The system pre-populates mobile questionnaire fields with information from the user's profile before they begin filling out the form on their mobile device. This preliminary action significantly reduces the time required for mobile input while maintaining precision through automatic field mapping.
Data Source
AI summary
In an example embodiment, a solution that creates a questionnaire mapping record for questions in a computerized document is utilized to map questions in the computerized document to normalized questions. Where necessary, normalized questions can be automatically created and included in the questionnaire mapping record. Handing strategy rules may also be automatically created for the normalized question, with the handling strategy rules defining how data may be automatically retrieved and used to prepopulate answers to the questions in the computerized document.