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1. Predicting Students' Academic Performance: Comparing Artificial Neural Network,
Decision Tree and Linear Regression
Methods:
a. Artificial neural network
b. Decision tree
c. Linear regression
Tools:
a. SAS Enterprise Miner
Predictors (Inputs):
a. Student’s demographic profile
b. CGPA for the first semester of the undergraduate studies
Predicting results:
Outputs:
a. More than 80% accuracy of the three models
b. Artificial neural network outperforms the other two
2. Predicting student academic performance in an engineering dynamics course: A
comparison of four types of predictive mathematical models
Methods:
a. Multiple linear regression
b. Multilayer perception network model
c. Radial basis function network model
d. Support vector machine model
Predictors:
a. CGPA
b. Grades earned in four pre-requisite courses (statics, calculus I, calculus II, physics)
c. Scores on three dynamics mid-term exams
Outputs:
a. Student’s scores on the dynamics final comprehensive exam
b. Multiple linear regression prevails when predicting the average academic
performance of his/her dynamics class as a whole
c. The support vector machine is better when predicting individual’s academic
performance
3. The Third Information Systems International Conference: A Review on Predicting
Student's Performance using Data Mining Techniques
This paper is a literature review on attributes and data mining methods applied to student
performance predicting.
4. Predicting general academic performance and identifying the differential contribution
of participating variables using artificial neural networks
Methods (algorithm):
a. Artificial neural network (ANN, applied 3 models, two of the three models for the two
extreme performance levels, the other model for all of the three performance levels
simultaneously)
Evaluation methods:
a. Precision
b. Sensitivity
c. Specificity
d. Accuracy
Inputs:
a. Working memory capacity
b. Attentional networks
c. Learning strategies
d. Background variables
Outputs:
a. Classify the students to the top 33%, the lowest 33%(top, middle and low performance
levels, GPA levels)
5. Predicting Academic Performance in Engineering Using High School Exam Scores
Methods:
a. Regression analysis (linear regression and logistic regression)
Inputs:
a. High school exam scores
Outputs:
a. First-year GPA in Dutch university
b. Completion of B.Sc.
Results:
a. The natural science and Mathematics factor (physics, chemistry and mathematics)
was strongest predictor
b. Liberal Arts factor and gender were weak predictors
Methods:
a. Principal component analysis
Inputs:
a. First-year engineering students’ academic records
Outputs:
a. Identify the predictors for whether the students drop out or fail the program
7. Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic
Algorithms Inspired by Cuckoo Birds for Training Neural Networks
Methods:
a. Artificial neural network (ANN with cuckoo search and cuckoo optimization algorithm)
Inputs:
a. University entrance exam results
b. Overall average score of high school graduation examination
c. Location of student’s high school
d. Gender
e. Time from between high school and university admission
f. Type of school (private, public)
Outputs:
a. Average score of first academic year
8. Predicting Student Academic Performance at Degree Level: A Case Study
Methods:
a. Classifications (decision tree, KNN, naïve Bayesian, neural network)
Inputs:
a. Pre-university marks
b. Marks of 1st and 2nd year courses
Outputs:
a. Degree completion
9. Predicting Students' Academic Performance using Artificial Neural Network: A Case
Study of an Engineering Course
Methods:
a. Artificial neural network (ANN)
Objectives:
a. Determine the factors that suitable for the student performance
b. Transform the factors into forms suitable for the adaptive system coding
c. Model the neural network
Inputs:
a. Subjects’ scores
b. Matriculation examination scores
c. Age, gender, parental background
d. Type and location of secondary school
Outputs:
a. Academic performance (CGPA): identify the performance levels (good, average, poor)
10. Predictive Modeling and Analysis of Student Academic Performance in an Engineering
Dynamics Course (dissertation, too long to read and leave it read later)
11. Data Mining: A prediction for performance improvement using classification
Methods:
a. Naïve Bayesian classification
Inputs:
a. Sex, Cat, Med, SFH, SOH, LLoc, Hos, FSize, FStat, FAIn, GSS, TColl, FQual, MQual, FOcc,
MOcc, GObt
Outputs:
a. Student’s division (first, second, third, fail)
12. Data Mining: A Prediction for Performance Improvement of Engineering Students using
Classification
Methods:
a. C4.5, ID3, CART decision tree
Inputs:
a. The same as the previous paper’s inputs
Outputs:
a. Student’s performance: pass, fail, promoted to next year
b. The steps to improve the performance of the students who were predicted to fail or
promoted
13. Predicting student performance: An application of data mining methods with an
educational web-based system
Methods:
b. 1-nearest neighbor
c. k-nearest neighbor
d. Parzen-window
e. Multi-layer perceptron
f. Decision tree
g. Combination of multiple classifiers (CMC): 1) with least error rate on the given dataset
(offline CMC), and 2) another combination with all the classifiers followed by a vote
(online CMC)
h. Genetic algorithm to optimize the results
Inputs:
a. Success rate
b. Success at the first try
c. Number of attempts before correct answer is derived
d. The time at which the student got the problem correct relative to the due date
e. Total time spent on the problem
f. Number of online interactions of the student both with other students and with the
instructor
Outputs:
14. Data mining approach for predicting student performance
Methods:
a. Naïve Bayesian
b. Multilayer perceptron
c. J48
Inputs:
a. Gender, family, distance, high school, GPA, entrance exam, scholarship, time,
materials, the Internet, earnings
Outputs:
Evaluation:
a. Cross validation
15. Prediction of 4-Year College Student Performance Using Cognitive and Non-Cognitive
Predictors and the Impact on Demographic Status of Admitted Students
Methods:
a. Regression
b. Hierarchical regression
Inputs (cognitive and non-cognitive predictors):
a. Knowledge
b. Continuous learning
c. Artistic appreciation
d. Multicultural appreciation
f. Responsibility
g. Health
h. Careen orientation
j. Perseverance
k. Ethics
l. SJT (situational judgment test; scholastic assessment Test/American College Testing
Assessment)
m. SAT/ACT score (scholastic assessment Test/American College Testing Assessment)
n. HSGPA (high school grade point average)
Outputs:
a. GPA
c. Class absenteeism
d. BARS (behaviorally anchored rating scale)
e. OCB (organizational citizenship behavior)
16. Predicting Student Performance: A Statistical and Data Mining Approach
Tools:
a. Weka
Methods:
a. Naïve Bayes
b. Multilayer perceptron
c. SMO
d. J48
e. REPTree
Inputs:
a. Sex, student’s community, parental status, student’s food habit, student’s living area,
student’s family size, mode of transportation to school, student had primary
education or not, school area at elementary level, institution at elementary level,
school area at secondary level, institution at secondary level, secondary syllabus,
medium of instruction at secondary level, type of school, private tuition at secondary
level, grade obtained at secondary level
b. Group of study, school area at higher secondary level, student having mobile or not,
interest in sports, computer at home or not, internet access, care at home, parent’s
education, father’s occupation, mother’s occupation, parent’s salary
Outputs:
a. Mark obtained
17. Student Performance Prediction using Machine Learning
Methods:
a. Neural networks
b. Naïve Bayesian classification
Inputs:
a. Previous course performance (student’s grade in secondary education)
b. Living location
c. Medium of teaching
Outputs:
a. Identify student performance level: poor, average, good, excellent
18. Predicting Student Performance in Higher Education
Tools:
a. weka
Methods:
a. naïve bayes
b. support vector machine
c. instance based learning
d.
e.
f.
g.
h.
i.
classification rules (PART)
one rule (oneR)
decision tree (J48)
Vote
Bagging
Inputs:
a. Student-related attributes:
1) Gender
2) Year of birth
4) Capacity-to-study test score
b. Attributes related to a study and the time when the student attended the course:
1) Number of semester completed
2) Field of study
3) Program of study
4) Type of study (bachelor or master)
5) Number of parallel studies at the faculty
6) Number of parallel studies at the university
7) Number of all studies at the faculty
8) Number of all studies at the university
c. Attributes related to a student and the time when the student attended the course:
1) Credits to gain
2) Gained credits
3) The ratio of the number of gained credits to the number of the credits to gain
4) Courses not completed
5) Second resits done
6) Excused days
Outputs:
a. Course grades in different level from A (best) to F (fail)
19. Data Mining: A Prediction for Student's Performance Using Classification Method
Methods:
a. ID3
Inputs:
a. department of student
b. high school degree of student
c. midterm marks
e. seminar performance
f. assignment
g. measure of student participate
h. attendance
i. homework
Outputs:
a. student’s final grade (excellent, very good, good, acceptable, fail)
20. Using Data Mining to predict secondary school student performance
Methods:
a. Decision tree
b. Random forest
c. Neural networks
d. Support vector machines
Inputs:
a. Mark repots
b. Questionnaires
c. Demographic, social and school related attributes
Outputs (two core courses: mathematics and Portuguese):
a. Student achievement (pass or fail)
b. Grade level in five levels from I: very good to V: insufficient
c. Regression, with a numeric output that ranges between zero (0%) and twenty
(100%)
21. Students' Performance Prediction based on their Academic Record
Tools:
a. RapidMiner
Methods:
a. Decision tree
Evaluation:
a. Confusion matrix
Inputs:
b. Specific courses
c. Sex
d. Academic status in 1st and 2nd year of the students
Outputs:
a. Student performance: course grade levels
22. Students' Performance Prediction System Using Multi-Agent Data Mining Technique
Tools:
Methods:
b. logitBoost ensemble classifiers
c. C4.5
d. Multi Agent data mining
e. SAMME
Evaluation:
a. Confusion matrix
Inputs:
a. Student’s name
b. Student’s ID
c. Number of solved quizzes
d. Number of the correct answers of all quizzes
e. Number of submitted assignments
f. Number of the correct answers of all assignments
g. Total time of login hours the students spent on e-learning system
h. Number of pages the student read
Outputs:
a. Course final grade class: high, medium, low
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