Educational data mining - a case study

2016 ◽  
Vol 8 (2) ◽  
pp. 187 ◽  
Author(s):  
Saurabh Manek ◽  
Saurav Vijay ◽  
Deepali Kamthania
Keyword(s):  
Data Mining ◽  
Educational Data Mining

An Association Rule on eDisiplin Case Study: An Educational Data Mining Approach

2018 ◽  
Vol 24 (3) ◽  
pp. 1872-1875 ◽  
Author(s):  
Mustafa Man ◽  
Wan Aezwani Wan Abu Bakar ◽  
Ily Amalina Ahmad Sabri
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Educational Data Mining ◽  
Data Mining Approach

scholarly journals Mining Educational Data to Analyze Students’ Performance: A Case Study of Mawuli School, Ho

2020 ◽  
Vol 5 (5) ◽  
pp. 1920-1948
Author(s):  
Cephas Lokpo
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Predictive Models ◽  
Quantitative Research ◽  
Educational Data Mining ◽  
Simple Linear Regression ◽  
Huge Data ◽  
Hidden Patterns ◽  
Insight Into

Data mining is the extraction of prospective valuable information from large chunk of data through the employment of many different data mining techniques. The usefulness of data mining coupled with the huge data generated in scholastic settings has made it an interesting field of research known as Educational Data Mining (EDM). The intent of EDM is to derive understanding from hidden patterns in data collected from institutions of learning to aid in identifying issues that influence students’ scholastic accomplishment, to solution of which will lead to improvement in accomplishment. Because scholastic achievement is dependent on several issues, it is essential to develop predictive models on students’ academic performance. This study’s objective, therefore, is to acquire an insight into performance through knowledge discovery by the use of simple linear regression in order to build a predictive model capable of predicting students’ grades to give a general overview of students’ performance in the WASSCE, and help improve students’ performance. In accomplishing the set objective for which the study was carried out, that is to predict the possible outcome of students in WASSCE, a widely sampled study was applied in undertaking the study (Quantitative research).

scholarly journals Performing Learning Analytics via Generalised Mixed-Effects Trees

Data ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 74
Author(s):  
Luca Fontana ◽  
Chiara Masci ◽  
Francesca Ieva ◽  
Anna Maria Paganoni
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Exponential Family ◽  
Educational Data Mining ◽  
Mixed Effects ◽  
Dropping Out ◽  
Response Variable ◽  
Student Dropout ◽  
Classical Models

Nowadays, the importance of educational data mining and learning analytics in higher education institutions is being recognised. The analysis of university careers and of student dropout prediction is one of the most studied topics in the area of learning analytics. From the perspective of estimating the likelihood of a student dropping out, we propose an innovative statistical method that is a generalisation of mixed-effects trees for a response variable in the exponential family: generalised mixed-effects trees (GMET). We performed a simulation study in order to validate the performance of our proposed method and to compare GMET to classical models. In the case study, we applied GMET to model undergraduate student dropout in different courses at Politecnico di Milano. The model was able to identify discriminating student characteristics and estimate the effect of each degree-based course on the probability of student dropout.

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