Using Learning Management System Activity Data to Predict Student Performance in Face-to-Face Courses

2020 ◽  
Vol 12 (3) ◽  
pp. 20-31 ◽  
Author(s):  
Najib Ali Mozahem
Keyword(s):  
Student Performance ◽  
Event History Analysis ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Private University ◽  
Management Systems ◽  
Activity Data ◽  
Face To Face ◽  
History Analysis ◽  
Learning Management

Higher education institutes are increasingly turning their attention to web-based learning management systems. The purpose of this study is to investigate whether data collected from LMS can be used to predict student performance in classrooms that use LMS to supplement face-to-face teaching. Data was collected from eight courses spread across two semesters at a private university in Lebanon. Event history analysis was used to investigate whether the probability of logging in was related to the gender and grade of the students. Results indicate that students with higher grades login more frequently to the LMS, that females login more frequently than males, and that student login activity increases as the semester progresses. As a result, this study shows that login activity can be used to predict the academic performance of students. These findings suggest that educators in traditional face-to-face classes can benefit from educational data mining techniques that are applied to the data collected by learning management systems in order to monitor student performance.

scholarly journals The Effects of Using a Fully Integrated Mobile Application to Access Learning Management Systems in Higher Education

2020 ◽  
Vol 26 (9) ◽  
pp. 1213-1229
Author(s):  
José Martín-Núñez ◽  
Susana Sastre ◽  
José Peiró ◽  
José Hilera
Keyword(s):  
Student Satisfaction ◽  
Student Performance ◽  
Mobile Application ◽  
Learning Experience ◽  
Learning Management Systems ◽  
Academic Major ◽  
Management Systems ◽  
Fully Integrated ◽  
Learning Management ◽  
The Web

The use of mobile devices in the classroom is increasingly frequent. However, the LMS are still not completely adapted to this format, preventing students from using all the LMS web-functionalities in their mobiles. Hence, we present and evaluate the use of a new mobile application fully integrated with Learning Management Systems (LMS). We examined access to LMS by 95 postgraduate university students, differentiating between the services accessed and the means used. Students belonged to four consecutive promotions. In the first two, access to the system was through the web, while in the third and fourth, an app fully integrated with the LMS was available. The results showed an overall increase in access to LMS, with a considerable reduction in access via the web in favor of access via the application. Significant differences were found in the access patterns to communication and assessment services depending on the students' age, gender, academic major and previous m-learning experience. Satisfaction with the LMS rose when the app was available, with greater growth within the academic major on IT and previous m-learning experience group. Finally, students with high performance accessed the system significantly more than those with low performance. In conclusion, the integration of the app with the system showed useful and efficient results. The app eased the use of the system, increased student satisfaction with LMS, and student performance improved with increased access.

Role of Indian Print and Digital Media in Learning Analytics

2022 ◽  
pp. 199-218
Author(s):  
Chandana Aditya
Keyword(s):  
Data Mining ◽  
Digital Media ◽  
News Media ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Learning Management Systems ◽  
Educational Programs ◽  
Mass Scale ◽  
Management Systems ◽  
Learning Management

There is a pressing need for data management and learning management systems. Educational data mining and learning analytics are two related aspects of educational technology that promote an overall effective teaching-learning system. The news media has the potential to act as a tool of learning analytics since they can easily access information at a mass scale. There are instances of leading newspapers organizing different educational programs where students from all the social layers have an opportunity to participate. A review of the programs reveals that all the programs collect and analyze educational data, which can form a research base of learning analytics. This chapter presents the description of three such educational programs organized by the leading media houses of India. This chapter also reflects on the contribution to learning management systems and educational data mining for the improvement of the overall educational system.

scholarly journals Challenges Facing Faculty Members When Using a Learning Management System

2020 ◽  
Vol 16 (4) ◽  
pp. 35-47
Author(s):  
Emad A. Abu-Shanab ◽  
Jumana Samara ◽  
Mohamed Arselene Ayari
Keyword(s):  
Educational System ◽  
Management System ◽  
Teaching And Learning ◽  
Learning Management Systems ◽  
Faculty Members ◽  
Management Systems ◽  
Gulf Region ◽  
Face To Face ◽  
System A ◽  
Learning Management

Universities use learning management systems (LMS) to support teaching practices and add value to the educational system. A leading university in the gulf region (XYZ) provides support for faculty members (FMs) through its Center of Excellence in Teaching and Learning (CETL), where experts respond to their enquiries on how to use the LMS features. This study analyzed data available from such interactions and concluded that FMs preferred office (face-to-face) contacting method, assessment is the major generator of FMs enquiries, and also the majority of enquiries were clustered into five major dimensions. Full details and analyses are available in this study.

scholarly journals Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2078
Author(s):  
Farrukh Saleem ◽  
Zahid Ullah ◽  
Bahjat Fakieh ◽  
Faris Kateb
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Learning Management Systems ◽  
Machine Learning Algorithms ◽  
Learning Performance ◽  
Management Systems ◽  
Electronic Learning ◽  
Ensemble Techniques ◽  
Proposed Model ◽  
Learning Management

Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

scholarly journals Techniques for Fostering Self-Regulated Learning Via Learning Management Systems in On-Campus and Online Courses

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Sarah E. Heath ◽  
Beau Shine
Keyword(s):  
Learning Management Systems ◽  
Management Systems ◽  
Delivery Mode ◽  
Full Potential ◽  
Course Content ◽  
Face To Face ◽  
Self Regulated Learning ◽  
Online Coursework ◽  
Regulated Learning ◽  
Learning Management

While self-regulated learning is a standard model for online coursework, this approach emphasizes the applicability of Learning Management System (LMS) usage in face-to-face and hybrid course formats. Self-regulated learning has become an important component of education, both as a primary tool in online coursework and as a supplemental resource in face-to-face courses. (Boekaerts, 1999).  Yet despite its importance, research suggests that rather than utilizing the full potential of learning management and course management systems, instructors primarily use LMS and CMS as a delivery mode for course content (Boekarts, 1997; Vovides et al., 2007).  Such underutilization not only minimizes the capacities of such systems, but limits the opportunities for students to engage in multimodal self-regulated learning. This paper offers three specific techniques to improve self-regulated learning via LMS: flipped learning, chunking, and micro-learning.  Research findings have led to support for each of the above-mentioned techniques (Nwosisi et al., 2016; Miller, 1956; Major & Calandrino, 2018). The authors provide examples of techniques used in their own courses, how each facilitates self-regulated learning, and how utilizing the full capabilities of learning management systems engages students in multimodal self-regulated learning.  Common findings and recommendations will also be noted, with the goal of providing a framework for instructors to apply each technique via learning management systems in their own courses.

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