The Research on Teaching Method of Basics Course of Computer based on Cluster Analysis

Tie, Zhixin; Jin, Rong; Zhuang, Hong; Wang, Zhaoqing

doi:10.1109/cit.2010.338

Cited by 4 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Association rules, clustering, classification, sequential pattern analysis, dependency modeling, and prediction have been used to improve webbased learning environments to subsequently enhance the degree to which the educator can evaluate the learning process [26]. Analysis of user access log in Moodle to improve e-learning and to support the analysis of trends is presented in [27] Comparison of different DM algorithms are made to classify learners (predict final marks) based on Moodle usage data [28]. Prediction of student's performance (final grade) based on features extracted from logged data is presented in [29] and university academic student performance is presented in [30].…”

Section: Using Clusteringin Edmmentioning

confidence: 99%

Clustering Algorithms Applied in Educational Data Mining

M.A

2015

IJIEE

View full text Add to dashboard Cite

Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.

show abstract

Section: Using Clusteringin Edmmentioning

confidence: 99%

Clustering Algorithms Applied in Educational Data Mining

M.A

2015

IJIEE

View full text Add to dashboard Cite

show abstract

“…According to Dutt et al (2017), it enables educators and other interested stakeholders to analyse student motivation, attitude and behaviour, understand their students' learning styles, customise e-learning and promote collaborative learning. Research conducted so far has proved that students' performance can be predicted using a dataset consisting of students' gender, parental education, their financial background (Tie et al, 2010), attendance, performance in class tests and assignments in their studies (Chi et al, 2008). Nasiri et al (2012) used regression analysis and classification (CS5.0 algorithm, which is a type of decision tree) to predict the academic dismissal of students and the GPA of graduated students in an e-learning centre.…”

Section: Introductionmentioning

confidence: 99%

Using online student interactions to predict performance in a first-year computing science course

Goundar

Deb

Lal

et al. 2022

Technology, Pedagogy and Education

View full text Add to dashboard Cite

Student performance is a critical factor in determining a university's reputation because it has a negative effect on student retention. Students who do not perform well in a course are more likely to drop out from their programmes before graduating. Many students who enrol in Computing Science programmes struggle to find success because it is considered a difficult discipline. In this study, a sample of 918 observations were selected containing demographic and academic information about students enrolled in a first-year undergraduate Computing Science course at a university. Classification algorithms such as Decision Tree, Random Forest, Naïve Bayes and Support Vector Machine were used to build predictive models to determine whether a student will pass or fail the course. The results showed the Random Forest algorithms are capable of producing better predictive performance compared with traditional Decision Tree algorithms.

show abstract