Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model

This paper presents a Support Vector Machine predictive model to determine if prior programming knowledge and completion of in-class and take home formative assessment tasks might be suitable predictors of examination performance. Student data from the academic years 2012 - 2016 for an introductory programming course was captured via ViLLE e-learning tool for analysis. The results revealed that student prior programming knowledge and assessment scores captured in a predictive model, is a good fit of the data. However, while overall success of the model is significant, predictions on identifying at-risk students is neither high nor low and that persuaded us to include two more research questions. However, our preliminary post analysis on these test results show that on average students who secured less than 70% in formative assessment scores with little or basic prior programming knowledge in programming may fail in the final programming exam and increase the prediction accuracy in identifying at-risk students from 46% to nearly 63%. Hence, these results provide immediate information for programming course instructors and students to enhance teaching and learning process.

Download Full-text

Using the Text Categorization Framework for Protein Classification

Handbook of Research on Text and Web Mining Technologies ◽

10.4018/978-1-59904-990-8.ch008 ◽

2010 ◽

pp. 128-140 ◽

Cited By ~ 1

Author(s):

Ricco Rakotomalala ◽

Faouzi Mhamdi

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Predictive Model ◽

Text Categorization ◽

Learning Algorithm ◽

Support Vector ◽

Protein Classification ◽

Fixed Length ◽

Selection Algorithms ◽

Proteins Classification

In this chapter, we are interested in proteins classification starting from their primary structures. The goal is to automatically affect proteins sequences to their families. The main originality of the approach is that we directly apply the text categorization framework for the protein classification with very minor modifications. The main steps of the task are clearly identified: we must extract features from the unstructured dataset, we use the fixed length n-grams descriptors; we select and combine the most relevant one for the learning phase; and then, we select the most promising learning algorithm in order to produce accurate predictive model. We obtain essentially two main results. First, the approach is credible, giving accurate results with only 2-grams descriptors length. Second, in our context where many irrelevant descriptors are automatically generated, we must combine aggressive feature selection algorithms and low variance classifiers such as SVM (Support Vector Machine).

Download Full-text

Developing a Predictive Model of Stroke using Support Vector Machine

2019 IEEE 13th International Conference on Telecommunication Systems, Services, and Applications (TSSA) ◽

10.1109/tssa48701.2019.8985498 ◽

2019 ◽

Author(s):

Jovel T. Rosado ◽

Alexander A. Hernandez

Keyword(s):

Support Vector Machine ◽

Predictive Model ◽

Support Vector

Download Full-text

Learning to Identify At-Risk Students in Distance Education Using Interaction Counts

Revista de Informática Teórica e Aplicada ◽

10.22456/2175-2745.62211 ◽

2016 ◽

Vol 23 (2) ◽

pp. 124 ◽

Cited By ~ 2

Author(s):

Douglas Detoni ◽

Cristian Cechinel ◽

Ricardo Araujo Matsumura ◽

Daniela Francisco Brauner

Keyword(s):

Machine Learning ◽

At Risk ◽

At Risk Students ◽

Drop Out ◽

Support Vector ◽

Learning Models ◽

Data Set ◽

Student Dropout ◽

Vector Machines ◽

Machine Learning Models

Student dropout is one of the main problems faced by distance learning courses. One of the major challenges for researchers is to develop methods to predict the behavior of students so that teachers and tutors are able to identify at-risk students as early as possible and provide assistance before they drop out or fail in their courses. Machine Learning models have been used to predict or classify students in these settings. However, while these models have shown promising results in several settings, they usually attain these results using attributes that are not immediately transferable to other courses or platforms. In this paper, we provide a methodology to classify students using only interaction counts from each student. We evaluate this methodology on a data set from two majors based on the Moodle platform. We run experiments consisting of training and evaluating three machine learning models (Support Vector Machines, Naive Bayes and Adaboost decision trees) under different scenarios. We provide evidences that patterns from interaction counts can provide useful information for classifying at-risk students. This classification allows the customization of the activities presented to at-risk students (automatically or through tutors) as an attempt to avoid students drop out.

Download Full-text

A predictive model of chemical flooding for enhanced oil recovery purposes: Application of least square support vector machine

Petroleum ◽

10.1016/j.petlm.2015.10.002 ◽

2016 ◽

Vol 2 (2) ◽

pp. 177-182 ◽

Cited By ~ 21

Author(s):

Mohammad Ali Ahmadi ◽

Maysam Pournik

Keyword(s):

Support Vector Machine ◽

Predictive Model ◽

Enhanced Oil Recovery ◽

Oil Recovery ◽

Least Square ◽

Support Vector ◽

Chemical Flooding

Download Full-text

Crude Oil Production Predictive Model Based on Support Vector Machine and Parameters Optimization Algorithm

Advances in Intelligent and Soft Computing - Software Engineering and Knowledge Engineering: Theory and Practice ◽

10.1007/978-3-642-29455-6_88 ◽

2012 ◽

pp. 637-643 ◽

Cited By ~ 1

Author(s):

Zhou Xiao-lin ◽

Wu Hai-wei

Keyword(s):

Support Vector Machine ◽

Crude Oil ◽

Predictive Model ◽

Optimization Algorithm ◽

Oil Production ◽

Parameters Optimization ◽

Support Vector ◽

Model Based ◽

Crude Oil Production

Download Full-text

An Early Warning System to Detect At-Risk Students in Online Higher Education

Applied Sciences ◽

10.3390/app10134427 ◽

2020 ◽

Vol 10 (13) ◽

pp. 4427 ◽

Cited By ~ 1

Author(s):

David Bañeres ◽

M. Elena Rodríguez ◽

Ana Elena Guerrero-Roldán ◽

Abdulkadir Karadeniz

Keyword(s):

Higher Education ◽

At Risk ◽

Predictive Model ◽

Early Warning ◽

Early Warning System ◽

Time Lag ◽

At Risk Students ◽

Warning System ◽

Online Higher Education ◽

Depth Analysis

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

Download Full-text