Education Assessment System (EAS) utilizes a machine-learning algorithm to evaluate prior learning and work experiences, generating a digital report that facilitates the Prior Learning Assessment (PLA) process. This approach reduces evaluation time and costs for institutions while enabling students to receive credit for unrecognized experiences, accelerating their path to degree completion.
Funding
Funding not disclosed
Founders
Product
Problem
Many adult learners possess valuable knowledge and skills gained through work and life experiences, but lack formal credentials, hindering their career advancement and access to higher education. Educational institutions often struggle with time-consuming and costly processes to evaluate prior learning for potential course credit. This can result in under-recognized competencies and delayed degree completion for adult learners.
Solution
Education Assessment System (EAS) offers a machine-learning-driven platform that streamlines the Prior Learning Assessment (PLA) process, enabling institutions to efficiently evaluate and recognize students' prior learning experiences. The platform provides a user-friendly interface for learners to submit their experiences and supporting documentation. EAS's algorithm analyzes this information, identifies links to college-level learning, and generates a digital report with course recommendations for faculty review. This accelerates degree completion for students while increasing enrollment and graduation rates for institutions.
Target Audience
EAS primarily targets higher education institutions seeking to improve enrollment and graduation rates among adult learners, as well as continuing professional studies programs, training providers, and employers aiming to upskill their workforce.
Features
- AI-powered analysis of prior learning and work experiences, mapping them to relevant college courses based on institutional policy guidelines.
- User-friendly interface for learners to upload documentation, including videos and artifacts, demonstrating competencies.
- Streamlined faculty review process with AI-generated course recommendations, enabling efficient evaluation and credit awarding.
- Customizable equivalency tables to differentiate institutionally equated experiences, such as exams, certifications, and training programs.
- Data-driven insights into PLA processes, including student progress, faculty evaluations, and credit awarding trends.
- Integration with existing institutional systems, facilitating seamless data transfer and reporting.
- Secure data storage on AWS with state-of-the-art security and protection.