The Technology behind Jungroo's Adaptive Engine

  1. Predict the student’s knowledge state with minimal interactions with the student.
  2. Predict his/her shortest learning path to mastery.
  1. Fixed Questions with Item Response Theory
  2. Adaptive testing with Item Response Theory
  1. Bayesian Knowledge Tracing
  2. Probabilistic Graph Models
  1. It will work only if all the questions can be calibrated on the same scale i.e. difficulty level. Assume two unrelated questions Q1 (Multiplication) and Q2 (Subtraction) with different difficulty levels D1 and D2 are present in the assessment. Also, Q1 is difficult than Q2 (D1 > D2) If the student is able to answer Q1 and some questions that are difficult than Q2 correctly. Then the system built on this data predicts that the student can answer the easier question Q2. This might be unlikely to happen in reality since the easier question (Subtraction) is not related to questions with higher difficulties (Multiplication). Student knowing Q1 (Multiplication) might still not know Q2 (Subtraction).
  2. It can’t diagnose why the student can’t answer questions beyond specific difficulty levels.
  1. Learning gaps across grades are not found. Example, a sixth-grade student learning algebra might have a learning gap in multiplication, which is taught in third grade.
  2. Learning gaps across domains are not found. Example, a student who can’t answer addition of unlike fractions in fractions domain might have a learning gap in multiplication domain
  3. The shortest path to mastery is not found using any of the approaches because Longitudinal data models that are used in recommendations require more student interactions to perform accurate predictions. Inaccurate recommendations can make the student less engaged.




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