Student performance prediction model for early-identification of at-risk students in traditional classroom settings

2018 ◽  
Author(s):  
Hutchatai Chanlekha ◽  
Jitti Niramitranon
Keyword(s):  
At Risk ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Early Identification ◽  
At Risk Students ◽  
Traditional Classroom

Development of Accurate and Timely Students' Performance Prediction Model Utilizing Heart Rate Data

2020 ◽  
pp. 123-144
Author(s):  
Mu Lin Wong ◽  
Senthil S.
Keyword(s):  
At Risk ◽  
Heart Rate ◽  
Prediction Model ◽  
Performance Prediction ◽  
Prediction Models ◽  
At Risk Students ◽  
Model Development ◽  
Rate Data ◽  
Heart Rate Data ◽  
The Difference

Academic Performance Prediction models mustn't be accurate only, but timely too, to identify at-risk students at the earliest to provide remedy. Heart rate data of 50 students in 3 main courses are collected, processed, and analyzed to distinguish the difference between excellent students and at-risk students. Three of the 12 heart rate attributes were chosen to calculate the threshold values, which are used to predict at-risk students. Half of the at-risk students were identified after week 5. Later, the datasets were rebalanced. Using four Data Mining classifiers, six attributes were identified to be the best attributes for prediction model development. The datasets were then dimensionally reduced. Applying classification, half of the at-risk students were identified earliest around week 5 of the 12-week semester. J48 is the most robust classifier, compared to JRip, Multi-Level-Perceptron, and RandomForest, making accurate prediction on at-risk students earlier most of the time.

scholarly journals Student Performance Prediction with Optimum Multilabel Ensemble Model

2021 ◽  
Vol 30 (1) ◽  
pp. 511-523
Author(s):  
Ephrem Admasu Yekun ◽  
Abrahaley Teklay Haile
Keyword(s):  
High School Students ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Transformation Method ◽  
Classification Task ◽  
Support Vector ◽  
School Students ◽  
K Nearest Neighbors ◽  
Classifier Chains

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

Identifying at-risk students based on the phased prediction model

2019 ◽  
Vol 62 (3) ◽  
pp. 987-1003 ◽  
Author(s):  
Yan Chen ◽  
Qinghua Zheng ◽  
Shuguang Ji ◽  
Feng Tian ◽  
Haiping Zhu ◽  
...  
Keyword(s):  
At Risk ◽  
Prediction Model ◽  
At Risk Students

scholarly journals Role of FCBF Feature Selection in Educational Data Mining

2020 ◽  
Vol 39 (4) ◽  
pp. 772-778
Author(s):  
Maryam Zaffar ◽  
Manzoor Ahmad Hashmani ◽  
K.S. Savita ◽  
Syed Sajjad Hussain Rizvi ◽  
Mubashar Rehman
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Prediction Models ◽  
Educational Data Mining ◽  
Action Plans ◽  
Factors Affecting ◽  
Academic Organization

The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.

Style Over Substance Reconsidered: Intrusive Intervention and At-Risk Students with Learning Disabilities

2002 ◽  
Vol 22 (2) ◽  
pp. 66-77 ◽  
Author(s):  
Robert Abelman ◽  
Anthony Molina
Keyword(s):  
At Risk ◽  
Learning Disabilities ◽  
Student Performance ◽  
At Risk Students ◽  
Retention Rates ◽  
Students With Learning Disabilities ◽  
Grade Point ◽  
Probationary Students ◽  
The Individual ◽  
The Impact

In two recent publications, we reported that the academic intervention process, not the specific intervention content, was responsible for a short-and long-term influx in at-risk student performance (grade-point average) and persistence (retention). All at-risk students who participated in the most intrusive of three interventions had higher cumulative grade-point averages and retention rates than those who received less intrusive interventions. In this post hoc analysis, we looked at probationary students with learning disabilities and found that they are only responsive to the individual attention and personalized accommodation provided under a highly intrusive model, and the impact is temporary.

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