Predicting academic performance: a systematic literature review

Cites (439)

Items 201–400 of 437

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118. Fuzzy predictive models to help teachers in e-learning courses
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121. A Study of Relationship between Performance, Temperament and Personality of a Software Programmer
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122. Predicting learning behaviour of students: Strategies for making the course journey interesting
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123. Automatic prediction of vocabulary knowledge for learners of Chinese as a foreign language
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124. Doing More with Less: Student Modeling and Performance Prediction with Reduced Content Models
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125. Predicting the Academic Performance of Construction Engineering Students by Teaching and Learning Approaches: Case Study
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126. Feature Extraction Model to Identify At -- Risk Level of Students in Academia
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127. Predicting individual performance in student project teams
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128. Study Habits, Exam Performance, and Confidence
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129. 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)
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130. Are Undergraduate GPA and General GRE Percentiles Valid Predictors of Student Performance in an Engineering Graduate Program
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131. Prediction of student academic progression: A case study on Vignan University
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132. Comparison analysis of data mining methodology and student performance improvement influence factors in small data set
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133. Uncertain measurement for student performance evaluation based on selection of boosted fuzzy rules
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134. The effect of personality and learning styles on individual and collaborative learning: Obtaining criteria for adaptation
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135. Predicting Student Performance in an ITS Using Task-Driven Features
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136. An Ensemble-Based Decision Support System for the Students’ Academic Performance Prediction
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137. Evaluation techniques of machine learning in task of reprovation prediction of technical high school students
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139. Factors that predict success in CS1
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140. Correlation between Course Tracking Variables and Academic Performance in Blended Online Courses
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141. Grit for engineering students
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142. Drops and Kinks
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160. The effects of imagination between psychological factors and academic performance: the differences between science and engineering majors
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161. Initial Investigation into Spatial Skills as Predictors of Success in First-year STEM Programmes
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162. Medical University Students' Personality and Learning Performance: Learning Burnout as a Mediator
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163. Factors that predict success in CS1
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165. Exploring The Impact Of First Year Engineering Student Perceptions On Student Efficacy
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166. Prediction of Students Performance of an Institute Using ClassificationViaClustering and ClassificationViaRegression
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167. Intellectual performance analysis of students by comparing various data mining techniques
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169. Academic Outcomes of Cooperative Education Participation
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170. Predicting Student Performance and Differences in Learning Styles Based on Textual Complexity Indices Applied on Blog and Microblog Posts: A Preliminary Study
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172. Grit and two-year engineering retention
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173. An SVM approach to predict student performance in manufacturing processes course
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174. Ebook Uses and Class Performance in a College Course
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175. Correlation of Grade Prediction Performance with Characteristics of Lesson Subject
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176. Mathematics: A powerful pre- and post-admission variable to predict success in Engineering programmes at a University of Technology
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177. Perceived Internet Usage Behaviours as Predictor to Outlier Detection in Students’ Communities in Academic Environments
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178. DEVELOPING CREATIVE PROBLEM SOLVING IN CIVIL ENGINEERING
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179. The Results of Certain Standard Mental Tests as Related to the Academic Records of College Seniors
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180. Opportunities for Moodle data and learning intelligence in Virtual Environments
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181. Characteristics of self-regulation of engineering students to predict and improve their academic performance
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182. Model student selection using fuzzy logic reasoning approach
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183. Forecasting student outcomes at university-wide scale using machine learning
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184. Using Educational Data Mining to Identify Correlations Between Homework Effort and Performance
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185. Factors Affecting Study Performance of Engineering Undergraduates: Case Studies of Malaysia and Australia
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186. Taxonomizing features and methods for identifying at-risk students in computing courses
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187. Predicting Academic Performance Using a Rough Set Theory-Based Knowledge Discovery Methodology
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188. How connections matter: factors affecting student performance in stem disciplines
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189. Looking beyond transfer models: finding other sources of power for student models
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190. Hybrid sampling for multiclass imbalanced problem: Case study of students' performance prediction
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191. Genetic fuzzy system for predictive and decision support modelling in e-learning
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192. Identifying At-Risk Students in LIS Distributed Learning Courses
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193. Modeling peer assessment as a personalized predictor of teacher's grades: The case of OpenAnswer
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194. Learning analytics to identify the influence of leadership on the academic performance of work teams
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196. Modeling Academic Performance using Subspace Clustering Algorithm
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197. Implementation of student SGPA Prediction System(SSPS) using optimal selection of classification algorithm
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198. Applying connectivism? Does the connectivity of concepts make a difference for learning and assessment?
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199. Fuzzy-mamdani inference system in predicting the corelation between learning method, discipline and motivation with student's achievement
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200. The Effect of Contextual Support in the First Year on Self-Efficacy in Undergraduate Engineering Programs
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