Assessing the impact of students’ activities in e-courses on learning outcomes: a data mining approach (original) (raw)
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International Association for Development of the Information Society, 2018
In the paper, we present the results of a case study conducted at Faculty of Administration, University of Ljubljana among 1st year undergraduate students. We investigated the correlations between students' activities in the e-classroom and grades at the final exam. The sample included 92 participants who took part at the final exam in the course Basic Statistics. In the e-classroom, students learn new content for individual self-study is prepared and their knowledge is checked with quizzes. In the empirical study, we used data mining software Orange for two tasks of predictive modelling: The research question was: based on the student's performance on quizzes is it possible to predict if (1) a student will pass an exam, and (2) a student's grade at the exam will be good. The empirical results indicate very strong connection between student's performance on quizzes and their grade at final exam in the course. Moreover, the results pointed out which quizzes, in other words topics, are most important for passing an exam or obtaining better grade.
Students behavioural analysis in an online learning environment using data mining
7th International Conference on Information and Automation for Sustainability, 2014
The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.
INTED Proceedings, 2020
E-learning platforms used in Higher Education Institutions store valuable information that can be analysed. Data mining is a multidisciplinary technique that integrates computer science, education, and statistics, and that could serve to interpret results and predict academic performance through the virtualized subjects. Objective: To identify the use of the interactive platform based on Moodle LMS (Learning Management System) in the Occupational Health compulsory subject in the curriculum of the podiatry degree and its relationship with academic success from 2017 to 2019 courses. Materials and methods: Logs (files of the interactions between a system and the students in the virtual system) for analyzing a variety of information from different activities carried out through the virtual campus on a Moodle platform were extracted, depurated and prepared for analysis. Finally, 33,776 (13,818 in 2017 and 19,958 in 2018) logs were used to perform the statistical analysis using the RStudio program and the SPSS v.22 software. A descriptive analysis, Pearson correlations and continuous variable decision trees diagrams were performed to determine the use of the Moodle classroom activities and resources and their relationship with academic results obtained in this subject. Results: 62 students enrolled in the academic course 2017-18 and 59 in the 2018-19 were studied. In the academic course of 2017-18, 62.9% were women, and mean and SD of academic results was 7.5±1.09. In 2018-19 academic course, 76.3% were women, and the mean results was 7.2±1.06. The highest peak of activity registered on the virtual subject was 200 visits in each academic course, with differences by months in relation to the distribution of tasks. The highest activity recorded was on Tuesdays and Sundays, in both years, but with more activity in the 2018-19 academic year. Lessons were the most used tools in both courses (40.3% in 2017 vs 46.2% in 2018) followed by the participation in forums (32.7% in 2017 vs 12.8% in 2018). Participation in the forums was 100% vs 93.1% and URLs entries 71.43% vs 87.93%; comparing both academic courses. In 2018, 3 new tools were introduced with high participation: Self-assessment Test 96.55%, glossary 87.93% and a wiki activity with a 48.28% of participation. Tools that significantly correlated with better test scores in year 2017 was the participation in the forums (p=0.016), while in 2018 test scores were significant correlated with the participation in the tasks (p=0.041) and self-assessment tests (p=0.044) carried out on the virtual classroom. Tree-like graphs identified two clusters of students related with forums and URLs entries, in the virtual classroom. Conclusion: This study reveals the importance of identifying and selecting tools with the capacity to improve and stimulate active and significant learning.
Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning
2010
In the past few years, Iranian universities have embarked to use e-learning tools and technologies to extend and improve their educational services. After a few years of conducting e-learning programs a debate took place within the executives and managers of the e-learning institutes concerning which activities are of the most influence on the learning progress of online students. This research is aimed to investigate the impact of a number of e-learning activities on the students' learning development. The results show that participation in virtual classroom sessions has the most substantial impact on the students' final grades. This paper presents the process of applying data mining methods to the web usage records of students' activities in a virtual learning environment. The main idea is to rank the learning activities based on their importance in order to improve students' performance by focusing on the most important ones.
Using Data Mining in E-Learning Environment for Student Modelling
2019
E-Learning systems are just traditional based web portals or Intelligent Tutoring Systems (ITS) or Learning Management Systems (LMS). During the usage of such systems, various kind of data is generated, this includes navigation sequence, kind of contents accessed, participation in various activity and performance of them. This data can be mined to find some hidden patterns and result of mining process is used to redesign the course contents according to student’s need. There is need to focus use of data mining to understand how student learns in such environment. This article describes various methods of student modelling and use of data mining for it.
Students Behavioural Analysis in an Online Learning Environment Using Data Mining (ICIAfS)
The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.
Exploring Online Activities to Predict the Final Grade of Student
Mathematics
Student success rate is a significant indicator of the quality of the educational services offered at higher education institutions (HEIs). It allows students to make their plans to achieve the set goals and helps teachers to identify the at-risk students and make timely interventions. University decision-makers need reliable data on student success rates to formulate specific and coherent decisions to improve students’ academic performance. In recent years, EDM has become an effective tool for exploring data from student activities to predict their final grades. This study presents a case study for predicting the students’ final grades based on their activities in Moodle Learning Management System (LMS) and attendance in online lectures conducted via Zoom by applying statistical and machine learning techniques. The data set consists of the final grades for 105 students who study Object-Oriented Programming at the University of Plovdiv during the 2021–2022 year, data for their activ...
Using Instructive Data Mining Methods to Revise the Impact of Virtual Classroom in E-Learning
NADIA, 2012
In the past few years, Saudi universities have boarded to utilize e-learning tools and technologies to expand and look up their educational services. After a few years of carrying out e-learning programs, a discussion took place within the directors, supervisors, executives and managers or decision makers of the e-learning associations regarding which activities are the most impact on the learning development for online students. This research is expected to inspect the impact of a number of e-learning actions on the students’ learning development. The results show that involvement in virtual classroom sessions has the most considerable impact on the students’ final scoring or grade. This paper presents the procedure of applying data mining methods to the web observing records of students’ behaviors in a practical learning environment. The main idea is to rank the learning activities supported on their importance in order to develop students’ performance by focusing on the most important ones.
Web usage mining for predicting final marks of students that use Moodle courses
Computer Applications in Engineering Education, 2013
This paper shows how web usage mining can be applied in e-learning systems in order to predict the marks that university students will obtain in the final exam of a course. We have also developed a specific Moodle mining tool oriented for the use of not only experts in data mining but also of newcomers like instructors and courseware authors. The performance of different data mining techniques for classifying students are compared, starting with the student's usage data in several Cordoba University Moodle courses in engineering. Several wellknown classification methods have been used, such as statistical methods, decision trees, rule and fuzzy rule induction methods, and neural networks. We have carried out several experiments using all available and filtered data to try to obtain more accuracy. Discretization and rebalance pre-processing techniques have also been used on the original numerical data to test again if better classifier models can be obtained. Finally, we show examples of some of the models discovered and explain that a classifier model appropriate for an educational environment has to be both accurate and comprehensible in order for instructors and course administrators to be able to use it for decision making.
Predict Students’ Academic Performance based on their Assessment Grades and Online Activity Data
International Journal of Advanced Computer Science and Applications, 2020
The ability to predict students' academic performance is critical for any educational institution that aims to improve their students' learning process and achievement. Although students' performance prediction problem is studied widely, it still represents a challenge and complex issue for educational institutions due to the different features that affect students learning process and achievement in courses. Moreover, the utilization of web-based learning systems in education provides opportunities to study how students learning and what learning behavior leading them to success. The main objective of this research was to investigate the impact of assessment grades and online activity data in the Learning Management System (LMS) on students' academic performance. Based on one of the commonly used data mining techniques for prediction, called classification. Five classification algorithms were applied that decision tree, random forest, sequential minimal optimization, multilayer perceptron, and logistic regression. Experimental results revealed that assessment grades are the most important features affecting students' academic performance. Moreover, prediction models that included assessment grades alone or in combination with activity data perform better than models based on activity data alone. Also, random forest algorithm performs well for predicting student a cademic performance, followed by decision tree.