Student Performance Analysis



Performance analysis of outcomes based on learning is a system that will strive for excellence at different levels and diverse dimensions in the field of student’s interests. This paper proposes a complete EDM framework in a form of a rule-based recommender system that is not developed to analyze and predict the student’s performance only, but also to exhibit the reasons behind it. Does the proposed framework analyze the students? demographic data, study-related and psychological characteristics to extract all possible knowledge from students, teachers and parents. Seeking the highest possible accuracy in academic performance prediction using a set of powerful data mining techniques. The framework succeeds to highlight the student’s weak points and provide appropriate recommendations. The realistic case study that has been conducted on 200 students proves the outstanding performance of the proposed framework in comparison with the existing ones.

Existing System:

The previous predictive models only focused on using the student’s demographic data like gender, age, family status, family income, and qualifications. In addition to the study-related attributes including the homework and study hours as well as the previous achievements and grades. These previous works were only limited to providing the prediction of academic success or failure, without illustrating the reasons of this prediction. Most of the previous researches has focused to gather more than 40 attributes in their data set to predict the student’s academic performance. These attributes were from the same type of data category whether demographic, study-related attributes or both, which lead to a lack of diversity of predicting rules.


  • As a result, these generated rules did not fully extract the knowledge for the reasons behind the student?s dropout.
  • Apart from the previously mentioned work, there were previous statistical analysis models from the perspective of educational psychology that conducted a couple of studies to examine the correlation between mental health and academic performance.
  • The type of the recommendations was too brief, they missed illustrating the methodologies to apply them.

Proposed System:

The proposed framework firstly focuses on merging the demographic and study-related attributes with the educational psychology fields, by adding the student?s psychological characteristics to the previously used data set (i.e., the students? demographic data and study-related ones). After surveying the previously used factors for predicting the student’s academic performance, we picked the most relevant attributes based on their rationale and correlation with the academic performance.


  • The proposal aims to analyze student?s demographic data, study related details and psychological characteristics in terms of final state to figure whether the student is on the right track or struggling or even failing. In addition to extensive comparison of our proposed model with the other previous related models.
  • These recommendations are based on experimented studies for enhancing the student?s academic performance.
  • In addition to the mentioned above functionalities, the System will also alert all parties with the possible upcoming mental illnesses that the student might suffer from.


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Hardware and Software Requirements:


  1. Windows 7,8,10 64 bit
  2. RAM 4GB


  1. Data Set
  2. Python 2.7
  3. Anaconda Navigator

Python?s standard library

  • Pandas
  • Numpy
  • Sklearn
  • tkMessageBox
  • matplotlib

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