Designed a rule-based ‘recommended placements’ for an edtech customer


Teachers faced challenges in analyzing students’ performance and progress to recommend supplementary learning resources. Key factors, such as learning speed, time dedicated to reading and writing, and the duration spent on assessments and problem-solving, significantly influence a student’s performance and the teacher’s ability to suggest suitable learning materials. Manual assessment of these aspects is particularly challenging.

Given the substantial number of classes and students each teacher oversees, the manual analysis of student performance and the subsequent recommendation of appropriate learning resources becomes a cumbersome and challenging task.


We developed a personalized engine that analyzes combinations of performance parameters to recommend tailored learning objectives. Teachers can assign these resources based on the student’s assessment and ability.

The engine is configurable, allowing for direct recommendations to students or teachers. It can also analyze teacher performance, providing similar learning resource recommendations directly to the teacher or through the school administrator. Scaling this up at a district level offers additional benefits, providing a broader perspective on school performance and the nature of recommendations.

Our analytical engine is continuously enhanced to sift through various aspects of students’ performance data, deriving insights to personalize recommendations at the unit or paragraph level, thereby enhancing learning outcomes.


Improved learning resource suggestions​​

Enhanced student outcomes​

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Designed a rule-based ‘recommended placements’ for an edtech customer

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