Current Research

Cognitive Learning

With our cognitive learning R&D, we introduce a transformative approach to education by integrating Personality and Emotional Intelligence (EI) profiles of a student into career discovery and the development of individualized lesson plans. The RIASEC profile has traditionally been used to suggest career options, but our novel approach extends this by optimizing personalized educational pathways to create personalized lesson planning. This will help students, particularly those with creative, artistic, or otherwise non-traditional aptitudes, to follow more relevant and inspiring academic and career trajectories.

Current K-12 learning tools focus primarily on academic assessments derived from state-level tests and ongoing quizzes. Our solution proposes combining RIASEC personality profiles (Realistic, Investigative, Artistic, Social, Enterprising, Conventional) and EI dimensions (self-awareness, self-management, motivation, team awareness, team management) to provide a more holistic understanding of each student. This enables the development of personalized learning plans, catering to both regular and special services students. Our latest R&D area for cognitive learning is to employ Virtual Reality (VR) to enhance student engagement and assessment.

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Design Structure Matrix (DSM)

DSM is a powerful tool for representing and analyzing complex systems, particularly in engineering, project management, and organizational design. It helps understand dependencies between system elements and can be used for sequencing tasks in various processes.

Steps to Create the DSM

1.
First, identify your system's or process’ elements or components that need to be sequenced. These could be tasks, modules, components, or any relevant units.
2.
Determine the relationships between these elements. A relationship exists when one element depends on another for some aspect of its completion. These dependencies could be technical, logical, or temporal.
3.
Construct a square matrix where both rows and columns represent the elements identified above. The intersection of each row and column will be filled based on the relationship between the corresponding elements. Common representations include using '1' to indicate a dependency and '0' to indicate no dependency.

Sequencing Tasks

Sequencing the DSM helps identify patterns and dependencies. It helps visualize clusters of interdependent elements and any loops or cycles.

Implementation

Continuously monitor the sequencing plan in practice and be prepared to adjust it as needed based on feedback, changes in requirements, or new information.

The problem of sequencing interrelated activities in the DSM is a well-known combinatorial optimization problem and is known to be NP-complete. We have implemented a novel graph-based DSM sequencing algorithm using a mixed integer quadratic model for sequencing the Design Structure Matrix to sequence tasks or elements in a complex system or process effectively.

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