scholarly journals Predicting Learning Behavior Using Log Data in Blended Teaching

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shu-Tong Xie ◽  
Zong-Bao He ◽  
Qiong Chen ◽  
Rong-Xin Chen ◽  
Qing-Zhao Kong ◽  
...  
Keyword(s):  
Student Performance ◽  
Multiclass Classification ◽  
Future Trend ◽  
Learning Behavior ◽  
Factors Affecting ◽  
Grade Levels ◽  
Optimal Subset ◽  
Classifier Selection ◽  
Classification Framework ◽  
Blended Teaching

Online and offline blended teaching mode, the future trend of higher education, has recently been widely used in colleges around the globe. In the article, we conducted a study on students’ learning behavior analysis and student performance prediction based on the data about students’ behavior logs in three consecutive years of blended teaching in a college’s “Java Language Programming” course. Firstly, the data from diverse platforms such as MOOC, Rain Classroom, PTA, and cnBlog are integrated and preprocessed. Secondly, a novel multiclass classification framework, combining the genetic algorithm (GA) and the error correcting output codes (ECOC) method, is developed to predict the grade levels of students. In the framework, GA is designed to realize both the feature selection and binary classifier selection to fit the ECOC models. Finally, key factors affecting grades are identified in line with the optimal subset of features selected by GA, which can be analyzed for teaching significance. The results show that the multiclass classification algorithm designed in this article can effectively predict grades compared with other algorithms. In addition, the selected subset of features corresponding to learning behaviors is pedagogically instructive.

scholarly journals An examination of the factors affecting the performance of Management Accounting students at the Durban University of Technology

2020 ◽  
Author(s):  
◽  
Stephanie Caroline Singh
Keyword(s):  
South Africa ◽  
Student Performance ◽  
Positive Impact ◽  
Management Accounting ◽  
Cost Accounting ◽  
Accounting Students ◽  
Factors Affecting ◽  
Five Factors ◽  
University Of Technology ◽  
The Impact

The success of a module at a university of technology is measured by student performance. At the Durban University of Technology in the Department of Management Accounting, students in their second year of study struggle with conceptualising content in Cost Accounting two which affects their performance. The purpose of this study was to identify the factors which may impact on the performance of Cost Accounting two students and to determine if these factors have a significant association with a student’s performance in Cost Accounting two. Many studies have identified various factors which may influence students’ academic performance. For the purpose of this study, five factors that may affect student performance were identified and examined. The independent variables or factors identified were attendance, age, gender, grade 12 results and language. The dependent variable for this study was performance (in Cost Accounting two). In order to measure the performance of students included in the study, the percentage achieved in Cost Accounting two for the semester was used. Although studies have been conducted on student performance at universities across South Africa and around the world, limited studies were conducted on the performance of Cost Accounting two students within South Africa. The study aimed to identify the factors that affect the performance of Cost and Management Accounting students at a university of technology and the impact of those factors on performance. The study found that only student attendance has a positive impact on student performance in Cost Accounting two. The findings of this study may be useful to the Department of Management Accounting at the DUT and other universities of technology. It is hoped that the current study will be useful to other teachers of cost and management accounting at universities on which factors influence the academic achievement of students.

Ecological Assessment: Implications for Teachers of Learning Disabled Students

1982 ◽  
Vol 5 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Timothy E. Heron ◽  
William L. Heward
Keyword(s):  
Student Performance ◽  
Global Assessment ◽  
Learning Disabled ◽  
Ecological Assessment ◽  
Assessment Data ◽  
Disabled Students ◽  
Factors Affecting ◽  
Behavior Difficulties ◽  
Starting Point ◽  
Criterion Referenced

Results obtained from normative or criterion-referenced assessments are sufficient to determine the starting point for most students' academic or social instruction. However, some students' learning/behavior difficulties are subtle and complex and, thus, necessitate a more global assessment to ensure the most appropriate instructional approach. This paper discusses the rationale for conducting an ecological assessment, a model for conceptualizing ecological assessment data, factors affecting student performance, sources of ecological assessment data, and implications of such data for the teacher of learning disabled students.

High-Performance Multiclass Classification Framework Using Cloud Computing Architecture

2015 ◽  
Vol 35 (6) ◽  
pp. 795-802 ◽  
Author(s):  
Feng-Sheng Lin ◽  
Chia-Ping Shen ◽  
Chia-Hung Liu ◽  
Han Lin ◽  
Chi-Ying F. Huang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Multiclass Classification ◽  
Computing Architecture ◽  
Classification Framework

Factors Affecting Students’ Performance in Distributed Pair Programming

2017 ◽  
Vol 57 (2) ◽  
pp. 513-544 ◽  
Author(s):  
Stelios Xinogalos ◽  
Maya Satratzemi ◽  
Alexander Chatzigeorgiou ◽  
Despina Tsompanoudi
Keyword(s):  
Student Performance ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Pair Programming ◽  
Individual Student ◽  
Broadband Internet ◽  
Factors Affecting ◽  
Final Exams ◽  
The Individual ◽  
Pair Compatibility

Pair Programming has been shown to increase productivity and code quality not only in professional software development but also in the context of programming education. The provision of broadband Internet access gave rise to Distributed Pair Programming (DPP) enabling two programmers to collaborate remotely. To gain insight into the benefits of DPP, we performed an empirical study on an object-oriented programming course where 62 students carried out assignments through a DPP platform. The goal of the study is to investigate, in the context of DPP, whether prior programming skills (assessed at the level of student, his or her partner and pair) and pair compatibility are related to student performance. To further examine the effect of DPP on learning outcomes, we have studied whether a pair’s performance on DPP assignments is related to the students’ grade. The findings indicate that the student’s actual skill and the pair’s actual skill affect his or her performance in an object-oriented programming course. The results also suggested that there is no association between pair compatibility and his or her own performance. Finally, pair performance on DPP assignments is related to the individual student performance in the final exams. Such evidence can be used to guide instructors when planning DPP assignments and especially when forming student pairs.

scholarly journals Educational Data Mining for Student Learning Pattern Analysis using Clustering Algorithms

2020 ◽  
Vol 9 (6) ◽  
pp. 481-488
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Data Mining Algorithm ◽  
Learning Behavior ◽  
Study Program ◽  
Data Mining Algorithms ◽  
Students First ◽  
And Performance

The exponential increase in universities’ electronic data creates the need to derive some useful information from these massive amounts of data. The progression in the data mining field causes it conceivable to educational data to improve the nature of educational processes. This study, thus, uses data mining methods to study the learning behavior and performance of university students. It focused on two aspects of the performance of the students. First, predicting students' learning behavior at the end of a complete year of the study program. Second, predict student performance with the help of the data model proposed by this study. Finally, provide course material recommendations using the data mining algorithm. Three data mining algorithms were considered which are K-Means, FCM, and KFCM., and maximum accuracy of 90.22% was achieved by KFCM. The study indicates that in terms of time and memory usages K-means algorithm give better results. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so early intercession might be possible.

scholarly journals Factors Affecting the Development Policies of Digital Deposit Accounts for the Bank in Taiwan

2021 ◽  
pp. 35-44
Author(s):  
Tsung-Chun Chen ◽  
Fu-Hsiang Kuo ◽  
Wei-Bin Lin
Keyword(s):  
Technology Policy ◽  
Negative Relationship ◽  
Credit Cards ◽  
Future Trend ◽  
Pearson Product Moment Correlation ◽  
Factors Affecting ◽  
Development Policies ◽  
Age Limit ◽  
The Relationship ◽  
Financial Technology

Banks' digitalization is a future trend and a national financial technology policy. This research aims to study which factors will affect digital accounts' promotion by digital banking. Specifically, we apply the Pearson product-moment correlation (PPMC) to analyze the relationship between variables. The empirical findings can briefly be summarized as follows: 1. In the PPMC model, the research shows that digital accounts have a significant positive relationship with the card in force and active cards. 2. The digital accounts' negative relationship with account opening age limit. On the whole, there are two ways for digital banking to promote digital accounts. First, digital banking effectively promotes digital accounts by targeting customers who hold the bank's credit cards. Second, digital banking best doesn't set the account opening age limit. The results of this research can also serve as a reference for bank authorities when formulating policies to promote digital accounts' promotion. Keywords: Fintech, Digital Banking, Digital Account, Pearson product-moment correlation, Pearson's r.

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