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Group ID: 19ZHU Wenya & LIN Dandan
Predicting student performance from book-borrowing records
• Background• Research Gap• Observations• Methodology• Experimental Results• Conclusion & Future Work
Outline
Background
Predicting student’s academic performance (PSP)
Student
s Books
Ranking
What is our work ?
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
Research Gap
Existing workLimitation
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)
Inferring private traits and attributes from digital records of human behavior
input
predict
Observations
Predicting student’s academic performance (PSP)
Our taskInput： book borrowing records
Student ID 2011221050019
Book name Digital Signal Processing A Computer-Based Approach (Fourth Edition)
Book category The automation and computer technology
Borrowed time 2013-05-10
Output： ranking comparison between two students
Predicting student’s academic performance (PSP)
A B C D E0
10
20
30
40
50
60
70
80
90
100
the academic performance level
the a
vera
ge n
um
ber
of
books b
orr
ow
ed
Students with good performance intend to borrow more books
MotivationThe book-borrowing records have predictive power of academic performance
Predicting student’s academic performance (PSP)
Faculty Book category
Telecommunication Engineering
TP I H O TN
Electrical Engineering
TP I TN H O
Economic Management
F I H TP O
Different faculties may emphasize various books
MotivationThe faculty differences also should be considered (The predicting function should vary among faculties)
Methodology
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
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
Modelling student book preference
Matrix factorization
Student book preferenceBook characteristics
Book-borrowing records
Drawback1) the book preference vector cannot capture the preference difference among students at various
performance levels2) The book characteristic vector cannot reflect contribution extent to achieve good performance of
various booksOur Solutionsimultaneously optimizing matrix factorization and predicting model
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
Joint optimization framework: simultaneously does matrix factorization and prediction model learning
Predicting modelMatrix factorization
DrawbackDon’t emphasize the faculty difference in predicting model
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
Student performance prediction framework for multiple facultiesTo balance the trade-off between the different and the common, we incorporate multi-task learning into the novel framework proposed in this paper.
The similarity parameter: control the similarity of
predicting functions of all faculties
the trade-off coefficient between the factorization
loss and prediction loss
Experimental Results
DatasetUESTC10 -11All students from grade 2010 are used as training data and testing data comprises students from grade 2011Notice: For both two dataset, we predict student performance from 14 faculties and books having been borrowed can be divided into 33 categories.
UESTC10-11Duration Sep 1, 2011- Jan 1, 2014No. of students Train data 4048
Test data 4257No. of books 33216Average book-borrowings per student
Book-borrowing density Train data 11e-4Test data 9.1494e-04
Evaluation
Precision
For evaluation, since most of students would like to know whether they can outperform others, we compare the rank between two students in the same faculty. The comparison result between two students ( and ) is 0 or 1. means that student outperform , and otherwise.
UESTC10-11
Average Precision
LG+MTL 56.26%
MFMTL 50.90%
SMF 60.46%
SMFMTL 62.21%
Experiment Result
Experiment Result
1 2 3 4 5 6 7 8 9 10 11 12 13 1454
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70
72
74
the p
red
itin
g p
recis
ion
(%)
SMF
SMFMTL
Conclusion & Future Work
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