The OER Knowledge Cloud makes use of cookies. By continuing, you consent to this use. More information.
Mining Humanistic Data with Machine Learning to Improve Open Educational Resources (OER) Engagement
Vonitsanos, Gerasimos · Moustaka, Ioanna · Doukakis, Spyridon · Vlamos, Panagiotis

Published6 August 2025
JournalInternational Journal on Artificial Intelligence Tools
CountryGreece, Europe


LanguageEnglish
ISSN0218-2130
RefereedYes
DOI10.1142/S0218213025400056
URLhttps://www.worldscientific.com/doi/10.1142/S0218213025400056
Export optionsBibTex · EndNote · Tagged XML · Google Scholar


Viewed by 28 distinct readers




CLOUD COMMUNITY REVIEWS

The evaluations below represent the judgements of our readers and do not necessarily reflect the opinions of the Cloud editors.

Click a star to be the first to rate this document


POST A COMMENT
SIMILAR RECORDS

Understanding student engagement in large-scale open online courses: A machine learning facilitated analysis of student’s reflections in 18 highly rated MOOCs
Hew, Khe Foon; Qiao, Chen; Tang, Ying
Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study ...
Match: machine learning; engagement

Clustering patterns of engagement in Massive Open Online Courses (MOOCs): the use of learning analytics to reveal student categories
Khalil, Mohammad; Ebner, Martin
Massive Open Online Courses (MOOCs) are remote courses that excel in their students’ heterogeneity and quantity. Due to the peculiarity of being massiveness, the large datasets generated by MOOC platforms require ...
Match: engagement

Early prediction and variable importance of certificate accomplishment in a MOOC
Ruipérez-Valiente, José A.; Cobos, Ruth; Muñoz-Merino, Pedro J.; Andujar, Álvaro; et al.
The emergence of MOOCs (Massive Open Online Courses) makes available big amounts of data about students' interaction with online educational platforms. This allows for the possibility of making predictions about future ...
Match: machine learning; europe

Spotting at-risk droppers in MOOCs
Itani, Alya; Brisson, Laurent; Garlatti, Serge
In this paper, we propose a machine learning based drop-out prediction process for spotting and preventing drop-out among MOOC learners early upon their interaction with the course. Two main goals are perused in this ...
Match: machine learning; europe

Towards full engagement for open online education. A practical experience for a MicroMaster
Hernández, Rocael; Amado-Salvatierra, Hector R.; Kloos, Carlos Delgado; Jermann, Patrick; et al.
This work explores on the different phases of the student's participation in a MOOC. For this particular study three phases of a MOOC are defined: pre-MOOC, MOOC and post-MOOC. This work presents an innovative framework ...
Match: engagement; europe

Assessing the savings from open educational resources on student academic goals
Ikahihifo, Tarah K.; Spring, Kristian J.; Rosecrans, Jane; Watson, Josh
Our study found that most students considered OER to be as good or better in terms of quality and engagement as traditional texts, while also allowing them to put saved funds toward their educational pursuits. As rising ...
Match: engagement

Squaring the open circle: resolving the iron triangle and the interaction equivalence theorem
Lane, Andy
A number of visual models have been proposed to help explain the interplay and interactions between specified components of higher education systems at different levels and to take account of emerging trends towards ...
Match: engagement

Towards a model of engaging online students: Lessons from MOOCs and four policy documents
Hew, Khe Foon
The paper describes a model of engaging students in fully online or blended learning environments. To do this, I first discuss the notion of student engagement and how it relates to the Self-Determination Theory of ...
Match: engagement

Dimensions of openness: Beyond the course as an open format in online education
Dalsgaard, Christian; Thestrup, Klaus; McGreal, Rory; Conrad, Dianne
The objective of the paper is to provide a framework for understanding the pedagogical opportunities of openness in education. The paper will argue that openness in education should not only be viewed as opening ...
Match: engagement; europe

Examining the relations among student motivation, engagement, and retention in a MOOC: A structural equation modeling approach
Xiong, Yao; Li, Hongli; Kornhaber, Mindy L.; Suen, Hoi K.; et al.
Students who are enrolled in MOOCs tend to have different motivational patterns than fee-paying college students. A majority of MOOC students demonstrate characteristics akin more to "tourists" than formal learners. As ...
Match: engagement