Combining Self-Training and Tri-Training for Course-Level Student Classification

2018 ◽  
Author(s):  
Vo Thi Ngoc Chau ◽  
Nguyen Hua Phung
Keyword(s):  
Student Classification ◽  
Level Student

scholarly journals Enhanced CNN Models for Binary and Multiclass Student Classification on Temporal Educational Data at the Program Level

2020 ◽  
pp. 1-25
Author(s):  
Vo Thi Ngoc Chau ◽  
Nguyen Hua Phung
Keyword(s):  
Neural Networks ◽  
Educational Data Mining ◽  
Learning Approaches ◽  
Imbalance Data ◽  
Final Study ◽  
Effective Manner ◽  
Temporal Aspects ◽  
Consistent Basis ◽  
Student Classification ◽  
Level Student

In educational data mining, student classification is an important and popular task by predicting final study status of each student. In the existing works, this task has been considered in many various contexts at both course and program levels with different learning approaches. However, its real-world characteristics such as temporal aspects, data imbalance, data overlapping, and data shortage with sparseness have not yet been fully investigated. Making the most of deep learning, our work is the first one addressing those challenges for the program-level student classification task. In a simple but effective manner, convolutional neural networks (CNNs) are proposed to exploit their well-known advantages on images for temporal educational data. As a result, the task is resolved by our enhanced CNN models with more effectiveness and practicability on real datasets. Our CNN models outperform other traditional models and their various variants on a consistent basis for program-level student classification.

Attitude of Higher Secondary Level student towards Games and Sports

2013 ◽  
Vol 2 (4) ◽  
pp. 80-85 ◽  
Author(s):  
R. K Chandrakumar Singh ◽  
Khuraijam Sanatombi Devi
Keyword(s):  
Secondary Level ◽  
Level Student

Are Teachers' Unions Really to Blame? Collective Bargaining Agreements and Their Relationships with District Resource Allocation and Student Performance in California

2011 ◽  
Vol 6 (3) ◽  
pp. 354-398 ◽  
Author(s):  
Katharine O. Strunk
Keyword(s):  
Resource Allocation ◽  
Collective Bargaining ◽  
Student Performance ◽  
Item Response ◽  
Response Model ◽  
Item Response Model ◽  
Teachers Unions ◽  
Achievement Growth ◽  
Unique Measure ◽  
Level Student

Increased spending and decreased student performance have been attributed in part to teachers' unions and to the collective bargaining agreements (CBAs) they negotiate with school boards. However, only recently have researchers begun to examine impacts of specific aspects of CBAs on student and district outcomes. This article uses a unique measure of contract restrictiveness generated through the use of a partial independence item response model to examine the relationships between CBA strength and district spending on multiple areas and district-level student performance in California. I find that districts with more restrictive contracts have higher spending overall, but that this spending appears not to be driven by greater compensation for teachers but by greater expenditures on administrators' compensation and instruction-related spending. Although districts with stronger CBAs spend more overall and on these categories, they spend less on books and supplies and on school board–related expenditures. In addition, I find that contract restrictiveness is associated with lower average student performance, although not with decreased achievement growth.

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