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Question paper generation through progressive model and difficulty calculation on the Promexa Mobile Application.

Rishabh Singh1,2, Devansh Timbadia1,2, Vidhi Kapoor2,3

  • 1Computer Engineering Department, Mukesh Patel School of Technology Management & Engineering, NMIMS University, Mumbai, India.

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Summary

This study developed a progressive model for mobile-based online tests, calibrating difficulty to student capacity. It found a strong correlation (0.9833) between initial question difficulty and calculated difficulty based on student responses, enabling accurate grading.

Keywords:
DifficultyMobile based testOnline testProgressive modelQuestionQuestion paper

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Area of Science:

  • Educational Technology
  • Computer Science

Background:

  • Mobile learning adoption is increasing, particularly in higher education.
  • The COVID-19 pandemic accelerated the need for effective online assessment tools.
  • Current online testing methods face challenges in personalized assessment and grading.

Purpose of the Study:

  • To develop a progressive model for mobile-based online tests.
  • To calibrate test difficulty levels according to individual student capacities.
  • To establish a grading system based on student performance and question difficulty.

Main Methods:

  • A test with 20 Python questions, difficulty levels assigned by 8 experts, was administered to 120 students.
  • Data on student responses and initial question difficulty were analyzed using correlation tests.
  • A progressive model was simulated across five different performance scenarios.

Main Results:

  • A high correlation (0.9833) was found between initial question difficulty and difficulty calculated from student responses (incorrect answers).
  • The progressive model demonstrated effective calibration of difficulty levels across various student performance cases.
  • The study confirmed that question difficulty is highly dependent on student response patterns.

Conclusions:

  • A novel progressive model can accurately assess student capabilities through mobile-based online tests.
  • The model facilitates personalized grading and a smoother transition from traditional paper-based assessments.
  • Universities can leverage this model to overcome challenges in online test implementation and improve student assessment strategies.