Research on Clustering Mining and Feature Analysis of Online Learning Behavioral Data Based on SPOC

Zhang, Guiyun; Ran, Juan

doi:10.1109/iccse.2018.8468816

Cited by 5 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study adopts the K-means clustering algorithm in partitioning clustering, which was proposed by J.B. Macqueen in 1966 [6]. It has also been widely used in the field of college student performance analysis [1,9,10]. Its flowchart is shown in Fig.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Exploring the Evaluation of College Students’ Learning Effect Under SPOC Teaching Mode Based on Cluster Analysis

Shi¹

2022

Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022)

View full text Add to dashboard Cite

SPOC (Small Private Online Course) is an online and offline blended teaching mode. Many colleges and universities have adopted this teaching mode to improve teaching quality. However, due to the particularity of teaching mode, the way of evaluating students' learning effect under this mode needs to be further explored. In this study, a course with SPOC teaching mode was selected as an example and K-means algorithm was applied to conduct cluster analysis on students' learning behaviours and grades, so as to form an evaluation of students' learning effects by classification. The study also predicted the importance of various factors that might influence learning outcomes. The research results verify the effectiveness of K-means clustering method in evaluating students' learning effect. The application of this evaluation method can enable students to improve themselves in a targeted way, and also help teachers find potential problems in SPOC teaching mode, so as to further improve the teaching quality.

show abstract

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Exploring the Evaluation of College Students’ Learning Effect Under SPOC Teaching Mode Based on Cluster Analysis

Shi¹

2022

Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022)

View full text Add to dashboard Cite

show abstract

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

Lü

et al. 2023

Comp Applic In Engineering

View full text Add to dashboard Cite

With the widespread of Small Private Online Courses (SPOC) in colleges and universities, course organizers who provide high-quality personalized course activities need to understand learners' learning status and characteristics, and then optimize the course teaching. However, little research has been done on different learners' group behavior characteristics, such as which indicators can reflect learner groups' behavior, and what are the typical behavior characteristics of different learner groups. In this work, we established a Python Language Foundation self-built SPOC course, and 109 undergraduates' learning behavior data were collected and analyzed. From 74-dimensional behavior log data consisting of 72 video-viewing, course video score, and comprehensive score, Principal Component Analysis was performed to reduce dimensionality. Agglomerative hierarchical clustering was used to divide learners into different categories, and the results showed that 15 groups are clustered. According to the analysis of the four indicators for group characteristics, which are the completion and viewing-stability of task-point videos, unit test excellence, and comprehensive score, it is identified into five primary types, including positive type, regular type, negative type, seminegative type, and a fluctuation type. Experiments conducted on a real data set across different academic years and courses show that the proposed approach has better clustering accuracy and practicability. It is expected that this work may be used to strengthen the personalized learning support services system in educational institutions and develop a tool that integrates exploration and analysis work onto the web platform.

show abstract

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Xiong,

Li,

Liu

et al. 2024

Front. Digit. Educ.

View full text Add to dashboard Cite

Research on Clustering Mining and Feature Analysis of Online Learning Behavioral Data Based on SPOC

Cited by 5 publications

References 1 publication

Exploring the Evaluation of College Students’ Learning Effect Under SPOC Teaching Mode Based on Cluster Analysis

Exploring the Evaluation of College Students’ Learning Effect Under SPOC Teaching Mode Based on Cluster Analysis

SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Contact Info

Product

Resources

About