Full length articleEarly prediction of undergraduate Student's academic performance in completely online learning: A five-year study
Section snippets
Author contribution
Javier Bravo-Agapito, Conceptualization, Software, Investigation, Writing - original draft. Sonia Janeth. Romero, Methodology, Formal analysis, Data curation, Visualization, Writing - original draft. Sonia Pamplona, Writing - original draft, Validation, Resources.
Theory
Several works have been done recently to predict academic performance based on LMS data. One challenge that it is noted is the difficulty of finding a set of variables that can consistently predict student performance across multiple courses (Conijn, Snijders, Kleingeld, & Matzat, 2017). One of the reasons for this difficulty is that instructional conditions could influence the predictions of academic success based on log files of LMS (Gašević, Dawson, Rogers, & Gasevic, 2016). The students may
Sample
The sample was composed by 802 students: 377 females and 425 males. They were all students of UDIMA in Spain. Data of students' interaction with the LMS were collected from four courses in the academic year 2012–2013. In addition, to perform the early prediction, longitudinal data of academic achievement was gathered during the years 2013–2014, 2014–2015, 2015–2016, and 2016–2017. The courses selected were: Knowledge Management (N = 151, which is 18.8% of the sample), General Sociology
Distributions
As can be seen in Table 2, the independent variables exhibit great dispersion, positive skewness and they are leptokurtic (except total of assignments).
Correlations
Table 3 shows almost consistently significant correlations. For that reason, and also due to the skewed and leptokurtic form of the distributions presented in Table 2 we decide to perform an EFA. This EFA checks in advance whether some of the variables extracted from the log files could be better represented in a series of combined factors, more
Discussion
The results obtained provide information to meet the objectives outlined in the introduction of the present paper. On the one hand, we have found a group of variables that allows predicting the academic performance of a sample of undergraduate students using data collected from an LMS during an academic semester (G1 and G2). These variables may be considered as EWI in order to carry out preventive support measures. On the other hand, we analyzed the relationship between variables and developed
Conclusions
This work proposed a collection of models that could be useful to consistently predict the academic performance of students at the end of a degree. These models utilized variables of two data sources: LMS interaction data of students and institutional data that included information of student enrollment, age and sex of students, and GPA of each academic year from 2012 to 2017. The models presented in this work make an early prediction using LMS students’ interaction data of the first semester
References (29)
- et al.
Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning
Computers in Human Behavior
(2014) - et al.
Self-regulated learning strategies academic & achievement in online higher education learning environments: A systematic review
The Internet and Higher Education
(2015) - et al.
Students' LMS interaction patterns and their relationship with achievement: A case study in higher education
Compututers & Education
(2016) - et al.
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success
The Internet and Higher Education
(2016) - et al.
Tap into visual analysis of customization of grouping of activities in eLearning
Computers in Human Behavior
(2015) - et al.
Contrasting prediction methods for early warning systems at undergraduate level
The Internet and Higher Education
(2018) - et al.
Predicting student achievement in learning management systems by log data analysis
Computers in Human Behavior
(2018) - et al.
From participation to dropout: Quantitative participation patterns in online university courses
Computers & Education
(2010) Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications
(2014)- et al.
Predicting students' final performance from participation in on-line discussion forums
Computers & Education
(2013)
Centralized student performance prediction in large courses based on low-cost variables in an institutional context
The Internet and Higher Education
Identifying significant indicators using LMS data to predict course achievement in online learning
The Internet and Higher Education
Using handheld devices for tests in classes. CMU-CS-00-152
Predicting student performance from LMS data: A comparison of 17 blended courses using Moodle LMS
IEEE Transactions on Learning Technologies
Cited by (40)
Untangling the Great Online Transition: A network model of teachers’ experiences with online practices
2023, Computers and EducationPredictive Analysis of Computer Science Student Performance: An ACM2013 Knowledge Area Approach
2024, Ingenierie des Systemes d'InformationPatterns of participation and performance at the class level in English online education: A longitudinal cluster analysis of online K-12 after-school education in China
2024, Education and Information TechnologiesDesigning and evaluating a big data analytics approach for predicting students’ success factors
2023, Journal of Big DataIntegration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course
2023, International Journal of Educational Technology in Higher Education