scholarly journals Predicting Student’s Academic Performance using Data Mining Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 215-219
Keyword(s):  
Data Mining ◽  
Family Background ◽  
Positive Direction ◽  
Performance Accuracy ◽  
Education Data ◽  
Academic Records ◽  
Using Data ◽  
Field Database ◽  
The One ◽  
Accuracy Performance

To meet the change in world in terms of digitalization and progress, the need and importance of education is known to everyone. The increasing awareness towards and digitization has given rise to increase in size of education field’s database. Such database contains information about students. The information includes students behavior, their family background, the facility they have, the society environment which surrounds them, their academic records etc. The increasing technology in data sciences can help utilize this huge education field database in a productive way by applying data mining on it. When the techniques of Data mining are applied on the database relating education records, then this process is called as education data mining. This process helps us understand the area and the students on whom the attention and the amendments are required. This increases the level of education system and also affects the success rate and understanding of the students in academics in positive direction. In this paper four different classification algorithms are used to predict grades of the students, by referring student’s previous academic records. Out of the four algorithms, the one which gave the most accurate prediction is considered as the final prediction. The performance accuracy of different algorithm is compared through accuracy performance percentage.

scholarly journals Using Data Mining In Learning Management Systems Amidst Covid-19

2020 ◽  
Vol 6 (3) ◽  
pp. 213
Author(s):  
Froilan D Mobo
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Learning Management Systems ◽  
The Philippines ◽  
Management Systems ◽  
Official Gazette ◽  
Learning Management ◽  
The Republic ◽  
Using Data ◽  
The One

<p>The Second Semester of Academic Year 2019-2020 was temporarily suspended due to the widespread COVID-19 last March 16, 2020, forcing the President of the Republic of the Philippines, Hon. Rodrigo Roa Duterte imposed an Enhanced Community Quarantine in Luzon which is known as a lockdown closing all the border points of each town and provinces. One of the major problem encountered during the lockdown is the suspension of classes because as per IATF guidelines you need to stay home, the said Memorandum Order was posted in the official gazette, (Medialdea, 2020)</p><p>The dataset on the features of the Learning Management Systems using Moodle is that Professors will be the one who will set the topics, quizzes, and exercises for his class even the assessment methods on the system. To prevent from slowing down the network,  the Team of Seaversity the developer of the learning management systems headed by C/E Ephrem Dela Cernan conducts a ZOOM Training to all Faculty to be familiarized more on the Learning Management Systems of the Philippine Merchant Marine Academy. </p><p>The Moodle Learning Management Systems is a user-friendly environment because of its features and users can easily adjust from the traditional face to face teaching going to e-Learning approach because of it’s all capabilities as a data mining methods such as statistics, association rule mining, pattern mining visualization, categorization, clustering, and text mining., (AlAjmi &amp; Shakir, 2013)</p>

scholarly journals Profiling Oman education data using data mining approach

2017 ◽  
Author(s):  
Sultan Juma Sultan Alawi ◽  
Izwan Nizal Mohd Shaharanee ◽  
Jastini Mohd Jamil
Keyword(s):  
Data Mining ◽  
Data Mining Approach ◽  
Education Data ◽  
Using Data

Using Data Mining Techniques to Predict Obstetric Fistula in Tanzania

2021 ◽  
pp. 169-191
Author(s):  
Titus Fihavango ◽  
Mustafa Habibu Mohsini ◽  
Leonard J. Mselle
Keyword(s):  
Data Mining ◽  
Prediction Model ◽  
Obstetric Fistula ◽  
Evidence Based ◽  
Patient Records ◽  
Medical Sciences ◽  
Data Mining Techniques ◽  
Improved Performance ◽  
Using Data ◽  
Accuracy Performance

DM practices in medical sciences have brought about improved performance in analysis of large and complex datasets. DM facilitates evidence-based medical hypotheses. Nowadays, health diseases, especially obstetric fistula, are increasing. CCBRT reports, approximately 3,000 women suffer from obstetric fistula annually. Since efforts to eradicate obstetric fistula have been inadequate, the researcher was motivated to employ MLA in BIO informatics to detect obstetric fistula. The purpose of this chapter was to use DM techniques to predict obstetric fistula. The datasets involving 367 patient records from January 2015 to February 2019 were collected from CCBRT. The environment was used to describe the accurate of predictive model was CV, ROC, and CM. The research was performed using six different MLA. The accuracy performance between algorithms shows that LR has better accuracies of 87.678%, precision measures of 91%, recall measures of 82%, f1-score measures of 86%, and support measures of 74%. Thus, the researcher chose to use LR as the proposed obstetric fistula prediction model.

Assessing the Relation between Family Background and Juvenile Delinquency using Data Mining

2019 ◽  
Author(s):  
Suresh Babu Changalasetty ◽  
Bouallegue Belgacem ◽  
Ahmed Said Badawy ◽  
Wade Ghribi ◽  
Abdelmoty M. Ahmed ◽  
...  
Keyword(s):  
Data Mining ◽  
Juvenile Delinquency ◽  
Family Background ◽  
Using Data

Using Data Mining Techniques with Open Source Software to Evaluate the Various Factors Affecting Academic Performance

2016 ◽  
Vol 7 (2) ◽  
pp. 72-92
Author(s):  
Feras Hanandeh ◽  
Majdi Y. Al-Shannag ◽  
Maha Mahdi Alkhaffaf
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Open Source Software ◽  
Tree Model ◽  
Factors Affecting ◽  
Technology Students ◽  
Academic Records ◽  
Information Technology Students ◽  
Using Data ◽  
Tools And Techniques

This research paper studies the different factors that could affect the Faculty of Information Technology students' accumulative averages at Jordanian Universities, by verifying the students' information, background and academic records. It also has the objective to reveal how this information will affect the students to obtain high grades in their courses. The information of the students is extracted from the students' records and its attributes are formulated as a huge database. Then, a free open source software (WEKA) which supports data mining tools and techniques are used to decide which attribute(s) will affect the students' accumulative averages. It was found that the most important factor affects the students' accumulative averages, is the student acceptance type. A decision tree model and rules are also built to determine how the students can get high grades in their courses. The overall accuracy of the model was 46.8% which is an accepted rate.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

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