Determination of Current Competency Levels and Gaps through a Pre-Assessment to Provided Targeted Training Modules

About The Client

An organization that trained learners on best practices, rules, and regulations for heavy vehicle driving before the license test. The driver took this program to improve their knowledge of various driving conditions before attempting the license test.

Business Need

The organization required an innovative training program that helps to ensure the safety of heavy vehicle drivers, minimize risks, and reduce overall liability.

Harbinger’s Solution

The customer had developed video learning content for each competency. Instead of the standard methodology of opening all learning modules for every learner, the video-based learning modules were planned to be opened based on competency gaps. These learning modules of the program were stored on the client’s LMS as SCORM packages.

The program had 2 main components:

a) A pre-assessment that tested the learner’s current knowledge and proficiency on 12 driving competencies, and
b) Focused learning modules that provided training for each of the 12 competencies.

The assessment had 2 types of questions:

a) Interactive video-based questions mapped to the competencies which tested aspects such as the learner reaction time and recognition, which are critical for determining competency.

b) A set of multiple-choice questions that tested the learner knowledge level around that competency.
Once all the questions were attempted, based on the learner responses and the reaction time, the competency level (early, intermediate, advanced) was calculated and shared as a percentage. The assessment score is calculated as a breakdown of the questions along with details such as reaction time, number of incorrect attempts, correct answer expected, and so on.

Along with the individual competency score breakdown, the assessment also calculated an overall combined % score of various competencies which is then sent to the LMS. Since the LMS was a traditional one, Harbinger used its competency-based learning framework which stored the assessment questions and content tagged with their competencies in a JSON file.

The individual competency scores were then used to open training modules for the learner. The competencies with the lowest percentage scores were noted, and respective learning modules opened for the learner on priority. Modules for competencies with higher scores were opened later.

As the learners completed the modules at their own pace, the subsequent modules would open for the learner after a set time lag. When the learner completed all the modules, the course was marked as complete on the LMS.

Watch this Power Hour session to learn about the case study and the best practices to shift to a competency-based learning approach

Solution Snapshots

Impact On Learners And Organization

The use of a competency-based learning approach and Harbinger’s framework allowed the company to develop a holistic and complete training that improved the drivers’ skills and knowledge, related to safe driving. The drivers went through the learning modules according to their personal competency levels and thus improve learning outcomes and test results.



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