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Mining LMS data to develop an "early warning system" for educators: A proof of concept

Leah P. Macfadyen, Shane Dawson
2010 Computers & Education  
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

Mohamed H Haggag
2018 Zenodo  
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]

Ying Guo, Cengiz Gunay, Sairam Tangirala, David Kerven, Wei Jin, Jamye Curry Savage, Seungjin Lee
2022 arXiv   pre-print
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

Zakaria I Saleh
2017 Arts and Social Scienses Journal  
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


2015 Issues in Information Systems  
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

Yaqun Zhang, Ahmad Ghandour, Viktor Shestak
2020 International Journal of Emerging Technologies in Learning (iJET)  
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

Nurcan Alkis, Tugba Taskaya-Temizel
2018 Educational Technology & Society  
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

Gomathy Ramaswami, Teo Susnjak, Anuradha Mathrani
2022 Big Data and Cognitive Computing  
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

Sarra Ayouni, Fahima Hajjej, Mohamed Maddeh, Shaha Al-Otaibi, Jyotismita Chaki
2021 PLoS ONE  
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

Dragan Gašević, Shane Dawson, Tim Rogers, Danijela Gasevic
2016 The Internet and higher education  
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

Lassaad K. Smirani, Hanaa A. Yamani, Leila Jamel Menzli, Jihane A. Boulahia, Chenxi Huang
2022 Scientific Programming  
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

Sokout Hamidullah, Usagawa Tsuyoshi
2021 SHS Web of Conferences  
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

Otgontsetseg Sukhbaatar, Tsuyoshi Usagawa, Lodoiravsal Choimaa
2019 International Journal of Emerging Technologies in Learning (iJET)  
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

Imani Mwalumbwe, Joel S. Mtebe
2017 Electronic Journal of Information Systems in Developing Countries  
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

Sami Hachmoud, University of Hassan I, FST Settat, Morocco
2019 International Journal of Advanced Trends in Computer Science and Engineering  
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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