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Others’ work notes 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: a. Final CGPA on graduating 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 6. Predicting Student Academic Performance 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: a. Quadratic Bayesian classifier 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: a. Grade of PHY 183 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 b. Grade importance Outputs: a. Students’ assessment (grade level) 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 e. Leadership f. Responsibility g. Health h. Careen orientation i. Adaptability 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 b. Graduation status 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 AdaBoost Bagging Inputs: a. Student-related attributes: 1) Gender 2) Year of birth 3) Year of admission 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 7) Average grades 8) Weighted average grades 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 d. lab test grade 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: a. Academic records 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: a. Jade Agent Platform Methods: a. AdaBoost.M1 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