A Recommender for Improving the Student Academic Performance
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TLDR
In this paper, a framework of intelligent recommender system, based on background factors, which can predict students' first year academic performance and recommend necessary actions for improvement is designed, which could be the springboard for improving prediction of students' academic performance.About:
This article is published in Procedia - Social and Behavioral Sciences.The article was published on 2015-05-05 and is currently open access. It has received 58 citations till now. The article focuses on the topics: Recommender system.read more
Citations
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Journal ArticleDOI
Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions
Yuri Vanessa Nieto,Vicente Gacia-Diaz,Carlos Montenegro,Claudio Camilo Gonzalez,Rubén González Crespo +4 more
TL;DR: Three supervised classification algorithms are deployed to predict graduation rates from real data about undergraduate engineering students in South America and their effectiveness in supporting the institutions’ governance is depicted.
Journal ArticleDOI
New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests
Julio Herce-Zelaya,Carlos Porcel,Juan Bernabé-Moreno,Álvaro Tejeda-Lorente,Enrique Herrera-Viedma +4 more
TL;DR: This paper presents an approach on which social media data will be used to create a behavioural profile to classify the users and based on this classification will create predictions making use of machine learning techniques such as classification trees and random forests.
Journal ArticleDOI
Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques
TL;DR: The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction.
Proceedings ArticleDOI
A Course Recommender System based on Graduating Attributes
TL;DR: Experimental results using correlation thresholding and the nearest neighbors approach show that a course recommendation system for students based on the assessment of their "graduate attributes" can be effective when an active neighborhood of 10-15 students is used and that the numbers of users used can be decreased effectively to one fourth of the whole population for improving the performance of the algorithm.
Proceedings ArticleDOI
Student Performance Prediction using Multi-Layers Artificial Neural Networks: A Case Study on Educational Data Mining
TL;DR: The proposed neural network architecture works well with the selecting the feature data sets and accuracy in student performance prediction in feature vector has been achieved and satisfactory through appropriate classification to take better decision for efficient prediction of student performance.
References
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Journal ArticleDOI
Limits of Theory and Practice in Student Attrition
TL;DR: In this paper, the limits of theory and practice in student attrition are discussed, and a discussion of the relationship between attrition and the theory of student attrition is presented, with a focus on student attrition.
Journal Article
Predictors of Academic Achievement and Retention among College Freshmen: A Longitudinal Study.
TL;DR: This paper examined potential psychosocial predictors of freshman academic achievement and retention, including demographics, prior academic record, smoking, drinking, health-related quality of life, social support, coping, and maladaptive coping strategies.
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Who Succeeds at University? Factors Predicting Academic Performance in First Year Australian University Students
TL;DR: In this article, a prospective investigation of the academic, psychosocial, cognitive, and demographic predictors of academic performance of first year Australian university students was carried out to identify the factors that influence academic performance can improve the targeting of interventions and support services for students at risk of academic problems.
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A Longitudinal Approach to Assessing Attrition Behavior Among First-Generation Students: Time-Varying Effects of Pre-College Characteristics
TL;DR: This article found that first-generation students were more likely to leave college than their counterparts over time, after controlling for factors such as race, gender, high school grade point average (GPA), and family income.
Using data mining to predict secondary school student performance
TL;DR: The present work intends to approach student achievement in secondary education using BI/DM techniques, and shows that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available.