Updating the Input Values and Parameterisation

Every program has an input as well as output data. These are used mainly to achieve the specific objectives of verifying the processing operation being performed. The lists of input and ouput forms that are available for the users to use in this new system.

120 Figure 4.12 shows contain the login module for Admin, Lecturer and Student which includes the username and the password. Clicking on the login button will validate the data before launching the user on system and each user will be directed to their module.

Figure 4.13 shows Account Creation for Admin, Student or Lecturer. The admin is solely responsible for creating this account and specifying the category/ access level of the user. The user can thereafter change password on logging in.

File Login Account Registration Course Registration

STUDENT PERFORMANCE PREDICTION USING MULTI AGENT DATA MINING

Home Page View Structured Data Model Training Training/Test Set Hybrid Model Performance Prediction/E-Advisor About

ACCOUNT CREATION User ID

Username Password

Category Admin

Exit Reset

Submit

Figure 4.13: User Account Creation Figure 4.12: Login Module

Login Forgot Password?

Username Password Login Panel

121 Fig 4.14 shows the User Registration platform. This module is used to create account for newly admitted student and it is done by admin. Admin has to create account for each student before he/she can gain access to their semester course form. Information required in this the module include student ID, student registration number, session, and semester

File Login Account Registration Course Registration

STUDENT PERFORMANCE PREDICTION USING MULTI AGENT DATA MINING

Home Page View Structured Data Model Training Training/Test Set Hybrid Model Performance Prediction/E-Advisor About

STUDENT ACCOUNT REGISTRATION User ID

Reg No Session

Semester Select Semester

Exit Reset

Submit

Figure 4.14: Student Registration Form Select Year

0001 2016406085f 2017/2018 First Semester

0245 2017406005P 2017/2018 Second Semester

User ID Reg No Year Semester

Figure 4.15 displays the Student Course Registration Module. The module displays the courses available for the selected semester in addition to their credit hour. The student selects fills his name and registration number and thereafter selects the session and semester. The courses for the selected semester are displayed. The student goes ahead to select his courses based on his area of specialty

122 3

REGISTER YOUR FIRST SEMESTER COURSES

Name Reg No

Semester Session

1 ACC 811 Financial Accounting Theory 3

S/N Select Course Course Title Credit

2 ACC 831 Management Accounting Theory 3

3 Select Course Choose one Elective

Select Session

Select Semester

Save Total Credit

Figure 4.15 Student Course Registration Form

3

Figure 4.16 Displays the Home page. This module provides link to other module. It contains all the various task for user to use in accessingthe system. It contians both main menu and submenus.

Figure 4.14 Student Course Registration Form

File Login Student/Admin Account Registration Course Registration

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STUDENT PERFORMANCE PREDICTION USING MULTI AGENT DATA MINING

Figure 4.16: Home Page

123 Figure 4.17 displays the Structured Data Set Module. This module contains structured data set for predicting student performance. It is segmented into demographic factors, academic/work related factors social factors, personal factors and student academic result which forms the basis of the analysis.

File Login Students/Admin Account Registration Course registration STUDENT PERFORMANCE PREDICTION USING MULTI AGENT DATA MINING

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Structured Dataset DEMOGRAPHIC FACTORS ACADEMIC/WORK RELATED FACTORS SOCIAL FACTORS Reg No Mode Gender Marital Program City Age Emp/Stat Family Size Sponsor NAAC Job Co Attnd_Lect Sup_Unav

2014406008P 2 1 1 2 2 1 1 3 1 3 3 5 1

2017406085P 2 1 1 2 2 1 1 3 2 2 3 1

2015406058F 1 2 2 2 1 2 2 2 2 5 5 5 5

Figure 4.17: Data Set

124 s, t

The entire system is again split into two sets; the training and test data using percentage split, in this case 90% training data and 10% test data was used. The system is trained using the two algorithms/classifiers. Evaluation of the model is done with the processed test data set. The dataset is used for the student performance prediction and evaluation is done using Confusion Matrix. The result of the analysis, the graphical representation of the analysis (pie chart and bar chart) of the algorithm is displayed; including the various performance evaluation metrics for each model is shown in figure 4.19.

.

File Login Student/Admin Account Registration Course Registration

STUDENT PERFORMANCE PREDICTION USING MULTI AGENT DATA MINING

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K-NN EVALUATION

Pie Chart For KNN

Bar Chart for KNN

Refresh

Model Training Set

Kappa Statistics Mean Absolute Error Root Mean Squared Error Total number of instances 499

TP Rate FP Rate Precision Recall F-Measure MCC Class 0.800 0.004 0.927 0.857 0.851 0.955 High 0.968 0.002 0.825 0.789 0.781 0.954 Medium 0.965 0.003 0.758 0.829 0.821 0.949 High

Figure 4.18 Model Building and Evaluation Interface

125 The system evalu

ates the model by using the prediction model with the processed training data and evaluates themodel by the Confusion Matrix and performance accuracy.

The Prediction Model is loaded, and thenthe student is prompted to predict the Students’

Performance The system then goes ahead to fetch all required students’ data from the excel sheet,which is then arranged as a list and then converted to arff format to be suitable for tracing these data on the prediction model. The students’ performance is predicted and the performance result stored, ready for student/teacher request.

Figure 4.19 Training/Test Set

Figure 4.20 show the details performance prediction by student ID, the system gets the predicted performance from the stored students’ data set. The system views the student’s predicted performance with the suitable messages according to the predicted student‘s performance grade.