Analysis of Student Performance using EDM Methods

Meghji, Areej Fatemah; Mahoto, Naeem Ahmed; Unar, Mukhtiar Ali; Shaikh, Mohammad Abuzar

doi:10.1109/imtic.2018.8467226

Cited by 12 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data has been used to make smarter choices, such as organizations using it for productive business processes and efficiency. Several other corporations are now using DM to make better strategic and operational decisions [33]. The data describes details about the educational procedure offers better insight into learning and enhancing its outputs.…”

Section: Methodsmentioning

confidence: 99%

User Behavioral Data Enhancement and Vectorization in E-Learning Models

Krupa¹

2021

cibg

View full text Add to dashboard Cite

The data acquiring mechanism on the criteria of users-interaction to the users-interface has been developed in the initial stage of this applied science research. The input system gets multiple inhomogeneous texts about the interface actions of a specific user, whereas the vector has been demonstrated by the results that describe the user in a consolidated manner. Moreover, the vector collection for various users has been employed as an input of clustering algorithm (i.e. K-means), the outcomes of behaviors of users are among the one of these (k-clusters) that differentiates the users by their behavioral types. Almost, 67.8% of the Interactional data of the interface of the user is accessible to GlobalLab platform users. However, for the electronic diary (i.e.Diary.ru) there is no corresponding data. Although not every user of the GlobalLab system takes the initiative to create the projects, ideas, questionnaire task, and educational resources, the proportion of learners have been 9.7 thousand whose value with all 4 variables varied according to a neutral one.

show abstract

Section: Methodsmentioning

confidence: 99%

User Behavioral Data Enhancement and Vectorization in E-Learning Models

Krupa¹

2021

cibg

View full text Add to dashboard Cite

show abstract

“…These factors (which may include attendance, demographics, attentiveness in class, and completion of homework) could be quantified using a simple 1 to 5 ranking scale and analysed using the mt model to identify and rank the impact of these factors on pupil progress. Whilst this may be seen to be similar to the work in [17], this approach will add further value by quantifying the impact of each factor to overall progress, as opposed to only ranking their association.…”

Section: Practical Implicationsmentioning

confidence: 99%

“…Daniel, in [14], Merceron et al, in [15], and Weng, in [13], noted that ARM has been very useful in educational applications such as in finding mistakes that are commonly made together by students, making recommendations to students on e-learning course choices, and finding associations in behavioural patterns of students. Similarly in [17], ARM was used to find factors that influenced student performance in courses, with the study concluding that student performance directly correlated to attention in class (including attendance), completing assignments, and good note-taking.…”

Section: Educational Data Miningmentioning

confidence: 99%

Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

et al. 2020

View full text Add to dashboard Cite

Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools.

show abstract

“…Additionally, by the association rules, we can obtain frequent patterns of behaviors that have a significant impact on student performance and students' Failures cases can be identified. This may help educational institutions understand and improve students' behavior and also make the appropriate decisions, besides, the use of the association rule method that offers insight into improving admissions planning [14], [28], [31].…”

Section: Related Workmentioning

confidence: 99%