Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction
Abstract
:1. Introduction
- For the first time, we used the multi-label attribute selection method to transform the pre-class student performance prediction problem into a multi-label learning model, and then applied the attribute reduction method to scientifically streamline the characteristic information of the courses taken, along with mining the characteristics of the previous courses for the upcoming advanced or upper courses. The attributes of the curriculum were significant in studying academic early warning from a new perspective, from pre-class student performance prediction to subsequent courses;
- We perceived the task as a multi-label learning problem, which can fully uncover the correlation between the students’ previous course information and multiple target courses, so as to detect and screen out high-risk students in each course prior to the start of the course;
- We collected a new set of student performance prediction data, and proposed a novel multi-label attribute selection method, which improved the ability to express feature information of the previously completed courses.
2. Related Work
3. Methods
3.1. Multi-Label Learning
3.2. Multi-Label Attribute Selection
4. Results
4.1. Data Preparation
4.2. Evaluation Indicators
4.3. Experimental Results
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors | Year | Features |
---|---|---|
Macfadyen and Dawson [29] | 2010 | Predictive modeling of students’ final grades using factors such as student discussion information, number of emails sent, and test completion. |
Zafra et al. [30] | 2011 | Use of information such as quizzes, assignments, forums, etc., to predict whether a student will pass or fail the course. |
Sweeny et al. [4] | 2015 | Predicting grades for the next semester based on information about students’ grades in completed courses. |
Ren et al. [25] | 2016 | Applying data from MOOC server logs to predict learning outcomes. |
Conijn et al. [36] | 2018 | Predicting student performance and discovering the potential for MOOC improvements. |
Oswaldo et al. [37] | 2019 | Comparing different educational data mining (EDM) algorithms to discover research trends and patterns in graduation rate indicators. |
Ma et al. [22] | 2020 | Multi-instance multi-label learning for pre-course student performance prediction. |
Ma et al. [36] | 2020 | Multi-instance multi-label learning with multi-task learning for pre-course student performance prediction. |
Data Sets | Instances | Features | Labels | Train | Test |
---|---|---|---|---|---|
CEE01 | 102 | 164 | 4 | 87 | 15 |
CEE02 | 58 | 153 | 3 | 49 | 9 |
CEE03 | 64 | 153 | 3 | 54 | 10 |
CAE01 | 83 | 175 | 5 | 71 | 12 |
CAE02 | 61 | 164 | 4 | 52 | 9 |
CFE01 | 205 | 142 | 4 | 174 | 31 |
CFE02 | 137 | 153 | 5 | 116 | 21 |
CBC01 | 92 | 186 | 7 | 78 | 14 |
CBC02 | 86 | 175 | 6 | 73 | 13 |
CAL01 | 317 | 231 | 10 | 269 | 48 |
Datasets | AMI [56] | RF-ML [55] | MFNMI [54] | MDDMproj [52] | MLFRS [53] | MLNB [51] | AMuL |
---|---|---|---|---|---|---|---|
CEE01 | 0.81 | 0.81 | 0.81 | 0.80 | 0.81 | 0.81 | 0.81 |
CEE02 | 0.84 | 0.78 | 0.83 | 0.81 | 0.80 | 0.83 | 0.84 |
CEE03 | 0.78 | 0.78 | 0.79 | 0.77 | 0.80 | 0.74 | 0.80 |
CAE01 | 0.75 | 0.75 | 0.74 | 0.51 | 0.74 | 0.75 | 0.75 |
CAE02 | 0.75 | 0.76 | 0.74 | 0.75 | 0.75 | 0.77 | 0.77 |
CFE01 | 0.61 | 0.99 | 0.83 | 0.61 | 0.85 | 0.69 | 0.89 |
CFE02 | 0.80 | 0.80 | 0.80 | 0.80 | 0.81 | 0.81 | 0.81 |
CBC01 | 0.88 | 0.89 | 0.88 | 0.85 | 0.89 | 0.89 | 0.89 |
CBC02 | 0.85 | 0.86 | 0.87 | 0.86 | 0.86 | 0.82 | 0.88 |
CAL01 | 0.76 | 0.73 | 0.75 | 0.78 | 0.81 | 0.80 | 0.80 |
Win/Draw/Loss | 10/0/0 | 10/0/0 | 10/0/0 | 10/0/0 | 9/0/1 | 9/1/0 | - |
Datasets | AMI [56] | RF-ML [55] | MFNMI [54] | MDDMproj [52] | MLFRS [53] | MLNB [51] | AMuL |
---|---|---|---|---|---|---|---|
CEE01 | 0.17 | 0.17 | 0.17 | 0.17 | 0.17 | 0.17 | 0.17 |
CEE02 | 0.16 | 0.21 | 0.17 | 0.19 | 0.19 | 0.20 | 0.16 |
CEE03 | 0.23 | 0.24 | 0.23 | 0.25 | 0.23 | 0.23 | 0.23 |
CAE01 | 0.16 | 0.14 | 0.12 | 0.12 | 0.10 | 0.10 | 0.10 |
CAE02 | 0.17 | 0.16 | 0.16 | 0.16 | 0.17 | 0.16 | 0.16 |
CFE01 | 0.08 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 |
CFE02 | 0.07 | 0.07 | 0.07 | 0.08 | 0.07 | 0.07 | 0.07 |
CBC01 | 0.13 | 0.13 | 0.14 | 0.14 | 0.13 | 0.13 | 0.10 |
CBC02 | 0.11 | 0.10 | 0.10 | 0.10 | 0.11 | 0.12 | 0.10 |
CAL01 | 0.17 | 0.18 | 0.17 | 0.16 | 0.17 | 0.17 | 0.16 |
Win/Draw/Loss | 10/0/0 | 10/0/0 | 10/0/0 | 10/0/0 | 9/0/1 | 9/1/0 | - |
Datasets | AMI [56] | RF-ML [55] | MFNMI [54] | MDDMproj [52] | MLFRS [53] | MLNB [51] | AMuL |
---|---|---|---|---|---|---|---|
CEE01 | 0.072 | 0.069 | 0.065 | 0.064 | 0.061 | 0.057 | 0.057 |
CEE02 | 0.060 | 0.064 | 0.066 | 0.067 | 0.059 | 0.063 | 0.057 |
CEE03 | 0.055 | 0.060 | 0.052 | 0.062 | 0.064 | 0.058 | 0.052 |
CAE01 | 0.075 | 0.072 | 0.078 | 0.064 | 0.076 | 0.074 | 0.066 |
CAE02 | 0.083 | 0.083 | 0.087 | 0.086 | 0.078 | 0.079 | 0.078 |
CFE01 | 0.044 | 0.045 | 0.048 | 0.048 | 0.056 | 0.043 | 0.050 |
CFE02 | 0.049 | 0.054 | 0.052 | 0.047 | 0.058 | 0.049 | 0.046 |
CBC01 | 0.036 | 0.026 | 0.031 | 0.035 | 0.033 | 0.029 | 0.028 |
CBC02 | 0.041 | 0.045 | 0.035 | 0.047 | 0.035 | 0.040 | 0.034 |
CAL01 | 0.088 | 0.074 | 0.081 | 0.070 | 0.076 | 0.082 | 0.070 |
Win/Draw/Loss | 10/0/0 | 9/0/1 | 10/0/0 | 9/0/1 | 10/0/0 | 08/1/1 | - |
Datasets | AMI [56] | RF-ML [55] | MFNMI [54] | MDDMproj [52] | MLFRS [53] | MLNB [51] | AMuL |
---|---|---|---|---|---|---|---|
CEE01 | 3.86 | 3.78 | 3.84 | 3.83 | 3.83 | 3.82 | 3.75 |
CEE02 | 4.61 | 4.51 | 3.83 | 4.10 | 4.11 | 4.33 | 3.55 |
CEE03 | 5.08 | 5.26 | 4.95 | 5.41 | 5.25 | 5.05 | 4.95 |
CAE01 | 3.65 | 3.18 | 3.55 | 3.71 | 3.46 | 3.06 | 2.78 |
CAE02 | 3.50 | 3.56 | 3.52 | 3.64 | 3.74 | 3.52 | 3.50 |
CFE01 | 3.09 | 3.04 | 3.11 | 3.10 | 3.08 | 2.93 | 2.93 |
CFE02 | 2.53 | 2.47 | 2.46 | 2.50 | 2.51 | 2.70 | 2.42 |
CBC01 | 1.85 | 1.82 | 1.88 | 1.95 | 1.84 | 1.82 | 1.81 |
CBC02 | 1.88 | 1.86 | 1.87 | 1.84 | 1.82 | 1.85 | 1.79 |
CAL01 | 3.79 | 3.94 | 3.80 | 3.56 | 3.76 | 3.58 | 3.25 |
Win/Draw/Loss | 10/0/0 | 10/0/0 | 9/0/1 | 10/0/0 | 10/0/0 | 9/1/0 | - |
Datasets | AMI [56] | RF-ML [55] | MFNMI [54] | MDDMproj [52] | MLFRS [53] | MLNB [51] | AMuL |
---|---|---|---|---|---|---|---|
CEE01 | 0.36 | 0.34 | 0.33 | 0.32 | 0.30 | 0.29 | 0.28 |
CEE02 | 0.30 | 0.32 | 0.33 | 0.33 | 0.30 | 0.32 | 0.29 |
CEE03 | 0.27 | 0.30 | 0.26 | 0.31 | 0.32 | 0.29 | 0.26 |
CAE01 | 0.38 | 0.36 | 0.39 | 0.32 | 0.38 | 0.37 | 0.33 |
CAE02 | 0.41 | 0.42 | 0.44 | 0.43 | 0.39 | 0.38 | 0.39 |
CFE01 | 0.22 | 0.23 | 0.24 | 0.24 | 0.28 | 0.22 | 0.25 |
CFE02 | 0.25 | 0.27 | 0.26 | 0.24 | 0.29 | 0.25 | 0.23 |
CBC01 | 0.18 | 0.13 | 0.15 | 0.17 | 0.17 | 0.15 | 0.19 |
CBC02 | 0.20 | 0.22 | 0.18 | 0.24 | 0.18 | 0.20 | 0.17 |
CAL01 | 0.44 | 0.27 | 0.41 | 0.35 | 0.38 | 0.41 | 0.35 |
Win/Draw/Loss | 10/0/0 | 9/0/1 | 9/1/0 | 10/0/0 | 10/0/0 | 9/0/1 | - |
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Yang, J.; Hu, S.; Wang, Q.; Fong, S. Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction. Entropy 2021, 23, 1252. https://doi.org/10.3390/e23101252
Yang J, Hu S, Wang Q, Fong S. Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction. Entropy. 2021; 23(10):1252. https://doi.org/10.3390/e23101252
Chicago/Turabian StyleYang, Jie, Shimin Hu, Qichao Wang, and Simon Fong. 2021. "Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction" Entropy 23, no. 10: 1252. https://doi.org/10.3390/e23101252