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Mining LMS data to develop an "early warning system" for educators: A proof of concept
2010
Computers & Education
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Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and ...
Analysis of LMS tracking data from a Blackboard Vista-supported course identified 15 variables demonstrating a significant simple correlation with student final grade. ...
The views expressed in this publication do not necessarily reflect the views of the Australian Learning and Teaching Council. ...
doi:10.1016/j.compedu.2009.09.008
fatcat:b46ushwdkzdqzdgawkljhtupfy
A Learning Analytics Approach For Student Performance Assessment
2018
Zenodo
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Due to the increasing interest in big data especially in the educational field and online education has led to a conflict in terms of performance indicators of the student. ...
achievement. ...
In this context, the present study aims to identify significant success indicators, including to predict course achievement. ...
doi:10.5281/zenodo.1421585
fatcat:epgqfuoa2vclpapxxwcrp35moy
Identifying Critical LMS Features for Predicting At-risk Students
[article]
2022
arXiv
pre-print
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In this paper, we present an additional use of LMS by using its data logs to perform data-analytics and identify academically at-risk students. ...
Supervised machine learning algorithms were used to predict the final course performance of students, and several algorithms were found to perform well with accuracy above 90%. ...
An examination was conducted on the significant behavioral indicators of learning using LMS data that may predict course achievement [31] . ...
arXiv:2204.13700v1
fatcat:eoz3lxudhnhcbnzlxhofvtxdjm
An Empirical Investigation of Learners' Decision to Resume an Online Degree Program
2017
Arts and Social Scienses Journal
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The decision to resume an online degree program found to be effected by the gained knowledge, the course content quality, and the use of suitable and proper course delivery medium (interactive material ...
However, e-learning is more than just posting static or interactive material, or recording live sessions through the Learning Management Systems (LMS). ...
For further confirmation in identifying the effect of course delivery medium on the perceived ease of use of the LMS, the predicted value of the perceived ease of use of the LMS was verified using linear ...
doi:10.4172/2151-6200.1000323
fatcat:5m3ge2t6j5bcxdhimxmtjpp4zq
USING K-MEANS CLUSTERING TO MODEL STUDENTS LMS PARTICIPATION IN TRADITIONAL COURSES
2015
Issues in Information Systems
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The focus of this research is on the relationship between student participation in a learning management system(LMS) in traditional courses and course grades using Blackboard Learn tracking data from two ...
In addition, detailed LMS participation profiles were obtained from using k-means clustering, an unsupervised data mining method. ...
Whitmer [23] , however, suggests that the frequency of student LMS use "is more predictive of students success in the fully online environment than in a hybrid environment, in which some learning activities ...
doi:10.48009/4_iis_2015_102-110
fatcat:j3u7ztpntrfnxejv7q7nfp57vy
Using Learning Analytics to Predict Students Performance in Moodle LMS
2020
International Journal of Emerging Technologies in Learning (iJET)
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The findings are proposed to be used in higher education institutions for early detection of stu-dents experiencing difficulties in a course. ...
Education institutions often use learning management systems (LMS), such as Moodle, Edmodo, Canvas, Schoology, Blackboard Learn, and others. ...
Data mining can be useful to explore, visualize, and analyze data with the aim of identifying useful patterns in order to understand students' learning behavior and feedback [26] . ...
doi:10.3991/ijet.v15i20.15915
fatcat:nd73tqz4cbbs3nr4mx7qmc577u
The Impact of Motivation and Personality on Academic Performance in Online and Blended Learning Environments
2018
Educational Technology & Society
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In the online learning environment, the results showed that the conscientiousness trait was significantly related to LMS use whereas in blended learning, there were no significant relations between personality ...
Conscientiousness and LMS use were significantly related to course grades in both learning environments. ...
Hypothesis 3: Self-efficacy predicts course grades and LMS use in both online and blended learning environments. ...
dblp:journals/ets/AlkisT18
fatcat:cp46btx73nbqvduxtogtkahgru
On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining
2022
Big Data and Cognitive Computing
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In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. ...
This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. ...
A modelling process translates these indicators (extracted from training data) into predictive insights, which can be used on new data (or test data) to gauge student online behaviours. ...
doi:10.3390/bdcc6010006
fatcat:zr3lyr2mrfetfhhhfatxf6ad2u
A new ML-based approach to enhance student engagement in online environment
2021
PLoS ONE
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The instructor can identify the students' difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting. ...
However, evaluating and predicting the student's engagement level in an online environment remains a challenge. ...
The main purpose of the current study is to use LMS data to identify student engagement levels in terms of behavioral, cognitive and social dimensions. ...
doi:10.1371/journal.pone.0258788
pmid:34758022
pmcid:PMC8580220
fatcat:px4crqin3ref3m3p523vy3a6r4
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success
2016
The Internet and higher education
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The study illustrates the differences in predictive power and significant predictors between course-specific models and generalized predictive models. ...
This study examined the extent to which instructional conditions influence the prediction of academic success in nine undergraduate courses offered in a blended learning model (n = 4134). ...
course interaction data (LMS) would be collected for better understanding student online behavior in order to provide insights into the learning experience and improve course quality. ...
doi:10.1016/j.iheduc.2015.10.002
fatcat:ovjtbgjc7vadfgwj2g36nq2jam
Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths
2022
Scientific Programming
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The findings showed a significant increase in student success rates (98.86%). ...
One of the challenges in e-learning is the customization of the learning environment to avoid learners' failures. ...
authors used the root mean square error (RMSE) to evaluate the model performance. e prediction result of stacking compared to the three classifiers was better. ...
doi:10.1155/2022/3805235
fatcat:4n6qzhwetvgppetospajwsmdzi
Using access log data to predict failure-prone students in Moodle using a small dataset
2021
SHS Web of Conferences
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In this paper, the authors present a predictive model for failure-prone students using access log data from two small datasets in the Moodle learning system. ...
, especially in developing countries, to predict learners' future outcomes. ...
The authors would like to thank the JICA PEACE project for providing a chance to conduct this research. ...
doi:10.1051/shsconf/202110204001
doaj:1ecde97e1799440fbaed77742a2f7709
fatcat:c4jirqumgvhmjnmtlrpef256ii
An Artificial Neural Network Based Early Prediction of Failure-Prone Students in Blended Learning Course
2019
International Journal of Emerging Technologies in Learning (iJET)
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In this paper, we propose an early prediction scheme to identify students at risk of failing in a blended learning course. ...
We employ a neural network on the set of prediction variables extracted from the online learning activities of students in a learning management system. ...
However, a significant amount of work should be conducted to achieve revolutionary prediction results using academic and webbased learning environment data. ...
doi:10.3991/ijet.v14i19.10366
fatcat:bzhms37t3zbivchbir6tulmyfu
Using Learning Analytics to Predict Students' Performance in Moodle Learning Management System: A Case of Mbeya University of Science and Technology
2017
Electronic Journal of Information Systems in Developing Countries
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The study found that discussion posts, peer interaction, and exercises were determined to be significant factors for students' academic achievement in blended learning at MUST. ...
Data from LMS log of two courses delivered at Mbeya University of Science and Technology (MUST) were extracted using developed Learning Analytics tool and subjected into linear regression analysis with ...
Kotsiantis et al. (2013) used Learning Analytics tool called Moodle Parser to collect data from logs and to identify successful learners in blended learning course through students activities in Moodle ...
doi:10.1002/j.1681-4835.2017.tb00577.x
fatcat:tyi7v3tewrgv5dagar45j5qrwu
Analysis of Students Online Learning Behavior in a Pedagogical Model combining Blended Learning and Competency Based Approach
2019
International Journal of Advanced Trends in Computer Science and Engineering
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This work has shown that log data exploration and analysis can be used to generate visualizations about students' learning behavior. ...
The article investigates correlations between students' activities in online environment and competencies acquisition in classroom. ...
Various studies, using log files, have been conducted to analyze learners' online behaviors and predict their future achievements. ...
doi:10.30534/ijatcse/2019/113862019
fatcat:lvddfbqqljc23ifdu326vgwpsy
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