Descriptive Statistical Analysis in the Process of Educational Data Mining

Dimić, Gabrijela; Rančić, Dejan; Rancic, Olivera Pronic; Spalević, Petar

doi:10.1109/telsiks46999.2019.9002177

Cited by 6 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These reports use techniques of frequency analysis and descriptive statistics (mean and standard deviation of observations), and the most advanced ones include an analysis of minimum and maximum ranges and distribution asymmetry and kurtosis. All this information is offered from visualisation tools and can provide the teacher with information about how the learning process is developing in their students [21]. However, these reports do not allow a visual and fast detection of the student at risk [22] and require the teacher to have knowledge of data processing and analysis from the use of EDM techniques.…”

Section: Learning Analytics Proceduresmentioning

confidence: 99%

“…As already indicated, the analysis and interpretation of the logs generated in LMSs has certain difficulties and requires the teacher to have skills in the use of data extraction and interpretation techniques [32]. If LMSs included EDM and data visualisation techniques [21,33], this would make it easier to detect the student at risk. EDM procedures include different techniques, one of the simplest being frequency analysis with heat maps [34].…”

Section: Educational Data Mining (Edm) Proceduresmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

et al. 2021

View full text Add to dashboard Cite

In this study, we used a module for monitoring and detecting students at risk of dropping out. We worked with a sample of 49 third-year students in a Health Science degree during a lockdown caused by COVID-19. Three follow-ups were carried out over a semester: an initial one, an intermediate one and a final one with the UBUMonitor tool. This tool is a desktop application executed on the client, implemented with Java, and with a graphic interface developed in JavaFX. The application connects to the selected Moodle server, through the web services and the REST API provided by the server. UBUMonitor includes, among others, modules for log visualisation, risk of dropping out, and clustering. The visualisation techniques of boxplots and heat maps and the cluster analysis module (k-means ++, fuzzy k-means and Density-based spatial clustering of applications with noise (DBSCAN) were used to monitor the students. A teaching methodology based on project-based learning (PBL), self-regulated learning (SRL) and continuous assessment was also used. The results indicate that the use of this methodology together with early detection and personalised intervention in the initial follow-up of students achieved a drop-out rate of less than 7% and an overall level of student satisfaction with the teaching and learning process of 4.56 out of 5.

show abstract

Section: Learning Analytics Proceduresmentioning

confidence: 99%

Section: Educational Data Mining (Edm) Proceduresmentioning

confidence: 99%

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The collected data were subjected to both quantitative and qualitative analyses. For quantitative analysis, the closed questions in the questionnaire were analyzed using descriptive statistical methods, which involved organizing, summarizing, and representing the data (Dimić, Rančić, Rančić, & Spalević, 2019). Microsoft Office Excel was used for this purpose.…”

Section: Description Of Case Studymentioning

confidence: 99%

Development of 21st-Century Skills and Competencies in High School Students Through the Interactive E-Books Creation

Silva,

Corrêa Sobrinho,

Valentim

2023

RBIE

View full text Add to dashboard Cite

Currently, the need to prepare students for the challenges of the 21st-Century is being recognized, referred to as Education 4.0. This paradigm aims to redefine the education model by fostering the development of 21st-Century skills and competencies, such as problem-solving, creativity, collaboration, communication, and learning to learn. Therefore, this article explores the uses of the Teacher Assistance Educational Process (TAEP4.0) in a real educational scenario. TAEP4.0 is a student-centered process inspired by Education 4.0, which assists teachers in creating projects in the Informatics Lab. The TAEP4.0 process was implemented through a case study focused on fostering 21st-Century skills and competencies among High School students, using the Book Creator platform for interactive e-book creation projects. TAEP4.0 consists of a series of steps that include suggested activities, tools, and support materials. However, teachers have the flexibility to adapt the TAEP4.0 process, exercising their creativity and making decisions regarding project elements, such as the choice of Information and Communication Technology (ICTs) for project creation and monitoring, with the aim of promoting skills and competencies. In this case study, five classes of two High School teachers participated, involving a total of 189 students in the projects. The data collected were analyzed both quantitatively and qualitatively. During the projects, students engaged in activities and reported difficulties, primarily related to communication and teamwork. This feedback enables teachers to gain insight into students' challenges in applying the necessary skills to produce a project. Such evidence can assist teachers in addressing these issues in the future, allowing for project reuse and the creation of new projects that focus on improving specific skills and competencies.

show abstract

Mutual information-based filter hybrid feature selection method for medical datasets using feature clustering

Asghari

Nematzadeh

Akbari

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Descriptive Statistical Analysis in the Process of Educational Data Mining

Cited by 6 publications

References 5 publications

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

Development of 21st-Century Skills and Competencies in High School Students Through the Interactive E-Books Creation

Mutual information-based filter hybrid feature selection method for medical datasets using feature clustering

Contact Info

Product

Resources

About