What’s the difference between Assess.ai and FastTest?

FastTest has been ASC’s flagship platform for the past decade, securely delivering millions of exams, loaded with best practices like computerized adaptive testing and item response theory. FastTest is based off decades of experience in computerized test delivery and item banking, from MICROCAT in the 1980s to FastTest PC/LAN in the 1990s. And now the time has come for FastTest to be replaced with its own nextgen successor: Assess.ai.

With Assess.ai, we started by redesigning everything from the ground up, rather than just giving a facelift to FastTest. This leads to some differences in capability. Moreover, FastTest has seen more than 10 years of continuous development, so there is a massive amount of powerful functionality that has not yet been built into Assess.ai. So we’ve provided this guide to help you understand the advancements in Assess.ai and effectively select the right solution for your organization?

Will FastTest be riding into the proverbial sunset? Yes, but not anytime soon. For current users of FastTest, we’ll be working with you to ensure a smooth transition.

Important differences between FastTest and Assess.ai

AspectFastTestAssess.ai
AvailabilityCloud or On-PremiseCloud only
UI/UX2010 design with right-click menusModern Angular with completely new UX
Item types1250+
Automated item generationNoYes
Test delivery methodsLinear, LOFT, CATLinear (LOFT and CAT in development)
ExamineesNot reusable (must upload for each test)Reusable (can take more than one test)
Examinee testcode emailsNot customizableCustomizable
AccessibilityTimeTime, zoom, color
WidgetsCalculator, protractorProtractor, calculator, scientific calculator
Content managementFoldersOrthogonal tags
Delivery languagesEnglish, Spanish, ArabicEnglish, Arabic, Chinese, French, German, Italian, Russian, Spanish, Tagalog

There are of course many more differences. Want to hear more? Email solutions@assess.com to set up a demo. You might also be interested in this outline.

The following two tabs change content below.
My Twitter profileMy LinkedIn profileMy YouTube channel

nthompson

Nathan Thompson earned his PhD in Psychometrics from the University of Minnesota, with a focus on computerized adaptive testing. His undergraduate degree was from Luther College with a triple major of Mathematics, Psychology, and Latin. He is primarily interested in the use of AI and software automation to augment and replace the work done by psychometricians, which has provided extensive experience in software design and programming. Dr. Thompson has published over 100 journal articles and conference presentations, but his favorite remains https://pareonline.net/getvn.asp?v=16&n=1.
/by

What is online proctoring? item review SME Holland K Holland K Index and K Variants for Psychometric Forensics