Deep Knowledge Tracing To Help Students Achieve

‘Achieve’ creates ‘Personalised Achievement Journeys’ tailored to every goal powered by student’s data across ‘Learn’, ‘Practice’ and ‘Test’ journeys. The foundation of ‘Achieve’ is built on Embibe’s deep knowledge tracing algorithms for ‘Concept Mastery’. The following are the salient features of ‘Achieve’:

1. ‘Achieve’ discovers the strengths and weaknesses of a student through dynamically generated diagnostic assessments created through Embibe’s AI-driven Automated Test Generator.
2. It chooses one or multiple achievement objectives, including mastery of pre-requisites, mastering the current exam/chapter/subject, strengthening the foundation for future life goals and skills.
3. It breaks the achievement goal into multiple steps and generates a journey for each step based on Embibe’s proprietary Achievement Engine. 
4. Each step is a set of learning content, like videos or a set of practice content, like questions. 
5. Each of these sets is personalised and dynamically generated based on students’ strengths and weaknesses on concepts. Dynamic re-calibration of steps is based on performance in prior steps of a journey.
6. It re-assesses the student’s strengths and weaknesses at the end of the journey.

Machine Learning methods are, as a rule, seriously applied in instructive settings. They are utilised to anticipate capabilities and abilities, grade tests, perceive social scholarly examples, assess available answers, recommend proper instructive assets, and gather or partner students with comparable learning attributes or academic interests.

‘Achieve’ is based on Deep Knowledge Tracing technique. AI-based education promises open access to world-class teaching and instruction and reduces the growing cost of learning. Knowledge tracing is modelling student knowledge over time to predict how students will perform in future interactions accurately. Improvement on this task means that resources can be suggested to students based on their individual needs and content which is predicted to be too easy or too hard can be skipped or delayed.

One-on-one human tutoring can produce learning gains for the average student on the order of two standard deviations. Machine learning solutions could provide these benefits of high-quality personalised teaching to anyone in the world for free. 

Stepping through ‘Achieve’ on Embibe assists students with investigating their strengths and weaknesses. It helps students to improve on their weak subjects by investing more energy in these themes and giving more tests to know the amount they improved. Embibe learns the student’s feeble themes and gives them the material and expected direction to defeat their shortcomings. Likewise, there is a truthfulness score to realise how true students are in their endeavours to defeat their shortcomings.

Embibe provides various types of analysis on the test students take:

Overall Analysis: Based on how a student attempts a test, their behaviour may vary from Careless, Jumping Around, Getting There, etc. 

Question-wise Analysis: It provides an analysis of each question a student has attempted under six categories, namely, Too Fast Incorrect, Perfect Attempt, Overtime Incorrect, Overtime Correct, Wasted Attempt, Incorrect and Unattempted.

Skill-wise Analysis: Questions are categorised under various bloom levels, like Application, Comprehension, Rote Learning and Analysis. Based on the student’s attempt effectiveness, their skill-level analysis is provided.