The Use of Schoology as Learning Management System in the College of Computing Education: A Response Assessment using Data Mining Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 3619-3623
Author(s):  
Allemar Jhone P
Keyword(s):  
Data Mining ◽  
Management System ◽  
Learning Management System ◽  
Response Assessment ◽  
Computing Education ◽  
Data Mining Techniques ◽  
Learning Management ◽  
Using Data

scholarly journals Data Mining Approach for Learning management system

2021 ◽  
Vol 1088 (1) ◽  
pp. 012013
Author(s):  
Harry Dhika ◽  
Fitriana Destiawati ◽  
Surajiyo ◽  
Musa Jaya
Keyword(s):  
Data Mining ◽  
Management System ◽  
Learning Management System ◽  
Data Mining Approach ◽  
Learning Management

scholarly journals Analisis pelatihan online teknis big data menggunakan data logger Moodle

2020 ◽  
Vol 13 (2) ◽  
pp. 137-146
Author(s):  
Rony Kasmanto
Keyword(s):  
Big Data ◽  
Management System ◽  
Learning Management System ◽  
Research Approach ◽  
Significant Activity ◽  
Data Logger ◽  
Test Analysis ◽  
Post Test ◽  
Learning Management ◽  
Using Data

AbstrakHasil pengamatan perilaku yang dilakukan pada peserta pelatihan daring dapat dijadikan bahan evaluasi untuk penyelenggaraan pelatihan pada masa yang akan datang menjadi lebih optimal. Penelitian ini bertujuan untuk mengetahui perilaku peserta, pengajar dan panita dalam proses pembelajaran dengan keseluruhan daring. Pendekatan penelitian adalah statistik deskriptif dengan menggunakan data logs report pada Learning Management System (LMS) Pusdiklat BMKG. Objek penelitian adalah 50 peserta pelatihan teknis big data yang berasal dari unit pelaksana teknis di BMKG, 13 pengajar dan 2 panitia. Pelatihan diselenggarakan di Pusat Pendidikan dan Pelatihan BMKG pada 28 Oktober sampai dengan 9 Desember 2019. Hasil penelitian menunjukkan bahwa perilaku pelaku pelatihan memiliki aktivitas yang signifikan hanya pada momen-momen tertentu. Sistem LMS mencatat beberapa kejadian: 1) terjadi peningkatan aktivitas peserta, ketika mendekati tenggang waktu pengumpulan semua tugas yaitu pada hari senin dan pada saat pelaksanaan post-test; 2) terjadi peningkatan aktivitas pengajar ketika mendapat peringatan dari panitia untuk pemberian grading terhadap semua tugas-tugas yang dikumpulkan peserta; 3) terjadi peningkatan aktivitas panitia pada saat agenda pembelajaran dimulai dan ketika pelaksanakan post-test. Analysis of big data technical online training using Moodle logger dataThe results of behavioral observations conducted on online trainees can be used as evaluation material for the implementation of future training to be more optimal. This research aims to find out the behavior of participants, teachers, and organizers in the process of full online learning. The research approach is statistically descriptive by using data logs report on Learning Management System (LMS) Pusdiklat BMKG. The research object was 50 big data technical trainees from the technical implementation unit at BMKG, 13 teachers and 2 committees. The training was held at BMKG Education and Training Center from October 28 to December 9, 2019. The results showed that the behavior of trainees had significant activity only at certain moments. The LMS system records several events: 1) there is an increase in participant activity, when approaching the grace period of all tasks that is on Monday and during post-test implementation; 2) there is an increase in teacher activity when it is alerted by the committee to grading all the tasks collected by participants; 3) there is an increase in committee activity at the time the learning agenda begins and when the implementation of post-test.

Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study

2011 ◽  
pp. 243-266 ◽  
Author(s):  
Rosaria Lombardo
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Management System ◽  
Multivariate Data Analysis ◽  
Learning Management System ◽  
Multivariate Data ◽  
Data Sets ◽  
System Data ◽  
Learning Management

By the early 1990s, the term “data mining” had come to mean the process of finding information in large data sets. In the framework of the Total Quality Management, earlier studies have suggested that enterprises could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk customers/consumers and allow for more timely interventions (Macfadyen & Dawson, 2009). The Learning Management System data and the subsequent Customer Interaction System data can help to provide “early warning system data” for risk detection in enterprises. This chapter confirms and extends this proposition by providing data from an international research project investigating on customer satisfaction in services to persons of public utility, like education, training services and health care services, by means of explorative multivariate data analysis tools as Ordered Multiple Correspondence Analysis, Boosting regression, Partial Least Squares regression and its generalizations.

scholarly journals Using Data Acquired from Learning Management Systems to Predict Examination Results

2019 ◽  
Author(s):  
Francis B. Lavoie ◽  
Pierre Proulx ◽  
Ryan Gosselin
Keyword(s):  
Management System ◽  
Learning Management System ◽  
Learning Management Systems ◽  
Management Systems ◽  
Learning Management ◽  
Using Data ◽  
Dynamic Relationships

This work presents a novel learning management system (LMS), named Catalyseur, which allows the instructors to easily visualize which lessons and exercises allowed the students to better perform at an examination. This LMS feature is based on a regression methodology calculating easy-to-analyze models and being able to fit dynamic relationships. These models are calculated automatically and only require as human input to upload the student results at an examination.

Exploring students digital activities and performances through their activities logged in learning management system using educational data mining approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adel Bessadok ◽  
Ehab Abouzinadah ◽  
Osama Rabie
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Management System ◽  
Learning Management System ◽  
Statistical Significance ◽  
Educational Data Mining ◽  
Data Set ◽  
Content Type ◽  
Learning Management ◽  
The Relationship

Purpose This paper aims to investigate the relationship between the students’ digital activities and their academic performance through two stages. In the first stage, students’ digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the significance of the relationship between these profiles and the associated academic performance was tested statistically. Design/methodology/approach The LMS delivers E-learning courses and keeps track of the students’ activities. Investigating these students’ digital activities became a real challenge. The diversity of students’ involvement in the learning process was proven through the LMS which characterize students’ specific profiles. The Educational Data Mining (EDM) approach was used to discover students’ learning profiles and associated academic performances, where the activity log file exemplified their activities hosted in the LMS. The sample study data is from an undergraduate e-course hosted on the platform of Blackboard LMS offered at a Saudi University during the first semester of the 2019–2020 academic year. The chosen undergraduate course had 25 sections, and the students attending came from science, technology, engineering and math background. Findings Results show three clusters based on the digital activities of the students. The correlation test shows the statistical significance and proves the effect of the student’s profile on his academic performance. The data analysis shows that students with different profiles can still get similar academic performance using LMS. Originality/value This empirical study emphasizes the importance of the EDM approach using clustering techniques which can help the instructor understand how students use the provided LMS content to learn and then can deliver them the best educational experience.

Path Mining in Web Processes Using Profiles

2011 ◽  
pp. 896-901 ◽  
Author(s):  
Jorge Cardoso
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Business Process ◽  
Management System ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Data Mining Techniques ◽  
Using Data

Business process management systems (BPMSs) (Smith & Fingar, 2003) provide a fundamental infrastructure to define and manage business processes, Web processes, and workflows. When Web processes and workflows are installed and executed, the management system generates data describing the activities being carried out and is stored in a log. This log of data can be used to discover and extract knowledge about the execution of processes. One piece of important and useful information that can be discovered is related to the prediction of the path that will be followed during the execution of a process. I call this type of discovery path mining. Path mining is vital to algorithms that estimate the quality of service of a process, because they require the prediction of paths. In this work, I present and describe how process path mining can be achieved by using data-mining techniques.

scholarly journals ANALYSIS OF THE HONEYPOT SYSTEM DATA USING DATA MINING TECHNIQUES

2014 ◽  
Vol 6 (2) ◽  
pp. 325-340
Author(s):  
Krzysztof CABAJ ◽  
Michał BUDA
Keyword(s):  
Data Mining ◽  
Management System ◽  
Data Mining Techniques ◽  
System Data ◽  
Using Data

Od kilku lat systemy HoneyPot są coraz szerzej wykorzystywane w celu szybkiego zdobywania informacji dotyczących nowych ataków pojawiających się w Internecie. Mimo dużej liczby badań dotyczących nowych systemów HoneyPot, brakuje oprogramowania umożliwiającego analiza danych przez nie uzyskanych. W artykule znajduje się opis systemu WebHP/HPMS (ang. HoneyPot Management System) umożliwiającego analizę z wykorzystaniem metod eksploracji danych, zastosowanych technik oraz rezultaty pierwszych eksperymentów. Uzyskane wyniki są obiecujące, ponieważ w natłoku uzyskanych danych wykryte wzorce umożliwiły szybką identyfikację nowych zagrożeń.

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