Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners' Engagement Rate

Lu, Owen H.T.; Huang, Anna Y.Q.; Huang, Jeff; Huang, Chester S. J.; Yang, Stephen J. H.

doi:10.1109/eitt.2016.28

Cited by 9 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The learning and educational analytics were investigated in a study conducted at Northern Taiwan University. The aim of using big data methods was to make an early prediction of the final academic result and the students’ ability to pass a calculation course in which the principal component analysis was used and the students’ final academic output was predicted by using regression analysis ( Elfeky, Alharbi & Ahmed, 2022 ; Lu et al, 2018 ). Table 1 shows a comparison between some previous studies that focus on algorithms and their impact on student performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Students’ performance in interactive environments: an intelligent model

elbourhamy

Najmi

Elfeky

2023

PeerJ Computer Science

View full text Add to dashboard Cite

Modern approaches in education technology, which make use of advanced resources such as electronic books, infographics, and mobile applications, are progressing to improve education quality and learning levels, especially during the spread of the coronavirus, which resulted in the closure of schools, universities, and all educational facilities. To adapt to new developments, students’ performance must be tracked in order to closely monitor all unfavorable barriers that may affect their academic progress. Educational data mining (EDM) is one of the most popular methods for predicting a student’s performance. It helps monitoring and improving students’ results. Therefore, in the current study, a model has been developed so that students can be informed about the results of the computer networks course in the middle of the second semester and 11 machine algorithms (out of five classes). A questionnaire was used to determine the effectiveness of using infographics for teaching a computer networks course, as the results proved the effectiveness of infographics as a technique for teaching computer networks. The Moodle (Modular Object-Oriented Dynamic Learning Environment) educational platform was used to present the course because of its distinctive characteristics that allow interaction between the student and the teacher, especially during the COVID-19 pandemic. In addition, the different methods of classification in data mining were used to determine the best practices used to predict students’ performance using the weka program, where the results proved the effectiveness of the true positive direction of functions, multilayer perceptron, random forest trees, random tree and supplied test set, f-measure algorithms are the best ways to categories.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Students’ performance in interactive environments: an intelligent model

elbourhamy

Najmi

Elfeky

2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Collaborative learning can be subject to analysis with Big Data techniques, as was the case with the work of Lu et al [70], in which this analysis was used to measure the rate of student participation in a collaborative learning environment. For this purpose, they developed a collaborative programming tool called the Software Project Development and Integrated Learning Environment (SPDI Learning Environment), which allowed computer science students at the National Central University and the University of Technology, both in Taiwan, to develop their coursework collaboratively.…”

Section: Data Science and Its Use For Educational Purposesmentioning

confidence: 99%

Sustainable Social Development through the Use of Artificial Intelligence and Data Science in Education during the COVID Emergency: A Systematic Review Using PRISMA

Aguilar-Esteva¹,

Acosta-Banda²,

Aguilera³

et al. 2023

Sustainability

View full text Add to dashboard Cite

In this paper, we aimed to investigate how sustainable development can be involved in educational contexts that use new trends in technology such as Artificial Intelligence (AI) and Data Science (DS). To achieve this goal, we conducted a documentary Systematic Review using PRISMA research to find and analyze applications of sustainable development in these aforementioned contexts. In the results, we explain how some applications of both AI and DS, including Big Data and Learning Analytics among others, offer alternatives to substantially improve the educational process by allowing either education to be personalized, learning to be predicted, or even possible school dropouts to be predicted. We found that the COVID emergency sped up the introduction of these technologies in educational environments. Nevertheless, the inclusion of new technologies to develop better processes in education is still in progress and will continue to grow. In conclusion, we identified and analyzed some of the main applications found in the literature regarding new computing technologies. AI and DS have introduced new learning and teaching methods to solve different context problems that promote sustainable development while making educational environments more dynamic. On the other hand, we observed that the divide in many countries will continue due to their economic and technological situations. We describe some of the challenges that the incorporation of these technologies will bring in the near future.

show abstract

“…LA is described as a major space of Technology Enabled Learning (TEL) that has emerged throughout the last decade (Lu et al, 2018). In the early stages of LA, a lot of focus was on creating predictive models based on massive amounts of educational data.…”

Section: Literature Surveymentioning

confidence: 99%

“…However, considering the latest trends in TEL, Blended Learning (BL) can be understood in a very broader sense covering a mix of spaces, activity varieties, and technologies (Conijn et al, 2016). Thus, LA applied to the BL environment requires gathering traditional as well as computer-mediated interactions of a learner to get a consolidated view of the learning situation (Lu et al, 2018). The author (Yi et al, 2017) for the development of evaluation mode has integrated LA in BL, which constitutes evaluation by self, peer, and tutor.…”

Section: Literature Surveymentioning

confidence: 99%

A Comprehensive Survey on Usage of Learning Analytics for Enhancing Learner's Performance in Learning Portals

Shabnam Ara,

Tanuja,

Manjula

et al. 2023

Journal of Educational Technology Systems

View full text Add to dashboard Cite

Learning analytics (LA) is considered a promising field of study as it's helping to improve learning and the context in which it occurs. A learner's performance can be defined as how well students are learning in terms of knowledge and skills development and can be analyzed based on students’ outcomes and engagement in the course. We have consolidated the work carried out from 2011 to 2022 to improve learners’ performance using LA, describe criteria that define learners’ performance, discuss parameters that impact learners’ performance, and how predictive models can be created to forecast learners’ performance using these parameters. Results showed that the data collected from log files of the Learning Management System (LMS) had been used to get insights into the learner's performance in online platforms and LA could bring incredible benefits in the field of the education sector, such as improvement of learners’ involvement with learning activities as well as learning outcomes, identification of students at risk, providing real-time feedback, and personalization of learning. Hence, we can say usage of LA significantly helps learners’ performance improvement in learning portals. But we can get better results if we augment data from log files of LMS with the learner's personal data from his birth to the current moment, which is a bit challenging with respect to data collection i.e., huge and from multiple sources.

show abstract

Early-Stage Engagement: Applying Big Data Analytics on Collaborative Learning Environment for Measuring Learners' Engagement Rate

Cited by 9 publications

References 17 publications

Students’ performance in interactive environments: an intelligent model

Students’ performance in interactive environments: an intelligent model

Sustainable Social Development through the Use of Artificial Intelligence and Data Science in Education during the COVID Emergency: A Systematic Review Using PRISMA

A Comprehensive Survey on Usage of Learning Analytics for Enhancing Learner's Performance in Learning Portals

Contact Info

Product

Resources

About