1. Group ID: 19 ZHU Wenya & LIN Dandan Predicting student performance from book-borrowing records

2. Background Research Gap Observations Methodology Experimental Results Conclusion & Future Work Outline

3. Background

4. Predicting student’s academic performance (PSP) Student s Books Ranking What is our work ?

5. Predicting student’s academic performance (PSP) Why do we do this work ? Students can early realize whether they have fallen behind other students allow school to offer help to possible low-achieving students in time

6. Research Gap

7. Existing work Limitation 1 ） mainly aims to predict students’ scores on some specific problems 2) try to model students’ mastery on the skills needed to solve corresponding problems. Predicting student’s academic performance (PSP)

8. Inferring private traits and attributes from digital records of human behavior input predict

9. Observations

10. Predicting student’s academic performance (PSP) Our task Input ： book borrowing records Student ID2011221050019 Book nameDigital Signal Processing A Computer-Based Approach (Fourth Edition) Book categoryThe automation and computer technology Borrowed time2013-05-10 Output ： ranking comparison between two students

11. Predicting student’s academic performance (PSP) Students with good performance intend to borrow more books Motivation The book-borrowing records have predictive power of academic performance

12. Predicting student’s academic performance (PSP) FacultyBook category Telecommunication Engineering TPIHOTN Electrical Engineering TPITNHO Economic Management FIHTPO Different faculties may emphasize various books Motivation The faculty differences also should be considered (The predicting function should vary among faculties)

13. Methodology

14. Modelling student book preference The evolution process of our model Matrix factorization Inferring student performance from student book preference Joint optimization framework Student performance prediction framework for multiple faculties Multi-task learning

15. Modelling student book preference The evolution process of our model Matrix factorization Inferring student performance from student book preference Joint optimization framework Student performance prediction framework for multiple faculties Multi-task learning

16. Student book preference Book characteristics Book-borrowing records Drawback 1)the book preference vector cannot capture the preference difference among students at various performance levels 2)The book characteristic vector cannot reflect contribution extent to achieve good performance of various books Our Solution simultaneously optimizing matrix factorization and predicting model

17. Modelling student book preference The evolution process of our model Matrix factorization Inferring student performance from student book preference Joint optimization framework Student performance prediction framework for multiple faculties Multi-task learning

18. Predicting model Matrix factorization Drawback Don’t emphasize the faculty difference in predicting model

19. Modelling student book preference The evolution process of our model Matrix factorization Inferring student performance from student book preference Joint optimization framework Student performance prediction framework for multiple faculties Multi-task learning

20. The similarity parameter: control the similarity of predicting functions of all faculties the trade-off coefficient between the factorization loss and prediction loss

21. Experimental Results

22. Dataset UESTC10 -11 All students from grade 2010 are used as training data and testing data comprises students from grade 2011 Notice: For both two dataset, we predict student performance from 14 faculties and books having been borrowed can be divided into 33 categories. UESTC10-11 DurationSep 1, 2011- Jan 1, 2014 No. of studentsTrain data 4048 Test data 4257 No. of books 33216 Average book-borrowings per student Book-borrowing densityTrain data11e-4 Test data9.1494e-04

23. Evaluation

24. UESTC10-11 Average Precision LG+MTL56.26% MFMTL50.90% SMF60.46% SMFMTL62.21% Experiment Result

26. Conclusion & Future Work

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