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Recurrent academic path recommendation model for engineering students using MBTI indicators and optimization enabled

Anupama V1, Sudheep Elayidom M2

  • 1Division of Computer Science and Engineering, Cochin University of Science and Technology, Kalamassery, Kochi, Kerala, 682022, India. anupamajims@cusat.ac.in.

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Summary

This study introduces an intelligent recommendation system using a deep recurrent neural network (DRNN) and Myers-Briggs Type Indicator (MBTI) to guide engineering students toward suitable academic paths. The model effectively recommends courses based on student background and personality, achieving high precision and recall.

Keywords:
Academic path recommendationData miningDeep recurrent neural networkEngineering studentsMyers-briggs type indicator

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Science

Background:

  • The proliferation of online learning content complicates educational pathway selection for students, especially in engineering.
  • Engineering students require structured guidance for choosing appropriate academic courses.
  • Personal background and personality traits are crucial factors in academic path selection.

Purpose of the Study:

  • To propose an intelligent recommendation model for engineering students to discover suitable academic paths.
  • To leverage a hybrid optimization-based deep recurrent neural network (DRNN) integrated with Myers-Briggs Type Indicator (MBTI) for personalized course recommendations.
  • To enhance the accuracy and effectiveness of academic path recommendation systems.

Main Methods:

  • Data transformation using a log kernel for improved data quality.
  • Feature selection using Sparse Fuzzy C-Means Clustering (Sparse FCM).
  • Adaptive recommendation using a DRNN trained with the Magnetic Invasive Weed Optimization (MIWO) algorithm.
  • MBTI personality type categorization and correlation with courses using MIWO-based DRNN.

Main Results:

  • The proposed MIWO-based DRNN achieved high performance metrics: 0.900 precision, 0.900 recall, and 0.899 F-measure.
  • The model demonstrated effectiveness in accurately recommending academic paths for engineering students.
  • Evaluation conducted on datasets from Kerala and Tamilnadu, including personality traits, academic performance, and MBTI scores.

Conclusions:

  • The developed hybrid DRNN model effectively addresses the challenge of academic path recommendation for engineering students.
  • Integrating personality traits (MBTI) with academic data significantly improves recommendation accuracy.
  • The proposed system offers a robust solution for personalized educational guidance in engineering disciplines.