Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms

Data Mining for Studying the Impact of Reflection on Learning

Title: Data Mining for Studying the Impact of Reflection on Learning Keywords: educational data mining, Reflect, learning behaviour, impact Abstract On-line Web-based education learning systems generate a large amount of students' log data and profiles that could be useful for educators and students. Hence, data mining techniques that enable the extraction of hidden and potentially useful information in educational databases have been employed to explore educational data. A new promising area of research called educational data mining (EDM) has emerged. Reflect is a Web-based learning system that supports learning by reflection. Reflection is a process in which individuals explore their experiences in order to gain new understanding and appreciation, and research suggests that reflection improves learning. The Reflect system has been used at the University of Sydney’s School of Information Technology for several years as a source of learning and practice in addition to the classroom teaching. Using the data from a system that promotes reflection for learning (such as the Reflect system), this thesis focuses on the investigation of how reflection helps students in their learning. The main objective is to study students' learning behaviour associated with positive and negative outcomes (in exams) by utilising data mining techniques to search for previously unknown, potentially useful hidden information in the database. The approach in this study was, first, to explore the data by means of statistical analyses. Then, popular data mining algorithms such as the K-means and J48 algorithms were utilised to cluster and classify students according to their learning behaviours in using Reflect. The Apriori algorithm was also employed to find associations among the data attributes that lead to success. We were able to group and classify students according to their activities in the Reflect system, and identified some activities associated with student performance and learning outcomes (high, moderate or low exam marks). We concluded that the approach resulted in the identification of some learning behaviours that have important impacts on student performance

Comparison of some artificial neural networks for graduate students

Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, University of Baghdad for the period from 2014-2015 to The academic year 2017-2018. The variables are use in the research is (student’s success, age, gender, job, type of study (higher diploma, master’s, doctorate), specialization (statistics, economics, accounting, industry management, administrative management, and public administration) and channel acceptance). It became clear that the best variables that affect the success of graduate students are the type of study, age and job

Predictive Modelling of Student Academic Performance – the Case of Higher Education in Middle East

One of the main issues in higher education is student retention. Predicting students' performance is an important task for higher education institutions in reducing students' dropout rate and increasing students' success. Educational Data mining is an emerging field that focuses on dealing with data related to educational settings. It includes reading the data, extracting the information and acquiring hidden knowledge. This research used data from one of the Gulf Cooperation Council (GCC) universities, as a case study of Higher Education in the Middle East. The concerned University has an enrolment of about 20,000 students of many different nationalities. The primary goal of this research is to investigate the ability of building predictive models to predict students' academic performance and identify the main factors that influence their performance and grade point average. The development of a generalized model (a model that could be applied on any institution that adopt the same grading system either on the Foundation level (that use binary response variable (Pass/ Fail) or count response variable which is the Grade Average Point for students enrol in the undergraduate academic programs) to identify students in jeopardy of dismissal will help to reduce the dropout rate by early identification of needed academic advising, and ultimately improve students' success. This research showed that data science algorithms could play a significant role in predicting students' Grade Point Average by adopting different regression algorithms. Different algorithms were carried out to investigate the ability of building predictive models to predict students' Grade Point Average after either 2, 4 or 6 terms. These methods are Linear/ Logistic Regression, Regression Trees and Random Forest. These predictive models are used to predict specific students' Grade Point Average based on other values in the dataset. In this type of model, explicit instruction is given about what the model needs to learn. An optimization function (the model) is formed to find the target output based on specific input values. This research opens the door for future comprehensive studies that apply a data science approach to higher-education systems and identifying the main factors that influence student performance

University-company collaboration: A platform for open data innovations in the circular economy

The utilization of open data has many hidden business possibilities. Services created with open operating models may be the next success story of Finland. The utilization of open data in company-level operations is, however, still in its beginning phase. With the help of universities’ innovation potential, companies can reach the next level of open data use. At the same time, students from all fields of study can gain valuable knowledge and skills related to open data by working in open data projects. Open data does not only change how future business is done but also how universities need to prepare their students for working life.Avoimen datan hyötykäytössä piilee paljon liiketoimintamahdollisuuksia. Avoimilla toimintamalleilla tuotetut palvelut voivat olla Suomen seuraava menestystarina. Avoimen datan hyödyntäminen yritysten toiminnassa on kuitenkin vielä alkutekijöissään. Tällä hetkellä suurin osa tiedosta on piilossa eri virastoissa ja yrityksissä, eikä pk-yrityksillä ole mahdollisuutta hyödyntää uusia, haastavia teemoja. Korkeakoulujen ja yritysten yhteistyö tukee yrityksiä avoimen datan potentiaalin hyödyntämisessä. Samalla eri alojen opiskelijat saavat tilaisuuden kehittää omia innovaatiokompetenssejaan sekä oppia ymmärtämään avoimen datan merkityksen tulevaisuuden työelämän kannalta. Onnistuakseen korkeakoulu-yritysyhteistyön tulee rakentua dynaamisen yhteistyön ja yhteiskehittämisen menetelmien pohjalta. Tällaisia yhteistyöverkostoja rakennetaan kaksivuotisessa Open DaaS -hankkeessa, jossa neljä suomalaista korkeakoulua valjastavat innovaatiopotentiaalinsa paikallisten yritysten käyttöön monialaisten hackathonien muodossa

A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

Hidden in Plain Sight: Homeless Students In America's Public Schools

Student homelessness is on the rise, with more than 1.3 million homeless students identified during the 2013-14 school year. This is a 7 percent increase from the previous year and more than double the number of homeless students in 2006-07. As high as these numbers seem, they are almost certainly undercounts.Despite increasing numbers, these students - as well as the school liaisons and state coordinators who support them - report that student homelessness remains an invisible and extremely disruptive problem.Students experiencing homelessness struggle to stay in school, to perform well, and to form meaningful connections with peers and adults. Ultimately, they are much more likely to fall off track and eventually drop out of school more often than their non-homeless peers.This study:provides an overview of existing research on homeless students,sheds light on the challenges homeless students face and the supports they say they need to succeed,reports on the challenges adults - local liaisons and state coordinators - face in trying to help homeless students, andrecommends changes in policy and practice at the school, community, state and national level to help homeless students get on a path to adult success.This is a critical and timely topic. The recent reauthorization of the Every Student Succeeds Act (ESSA) provides many new and stronger provisions for homeless students (effective Oct. 1, 2016); requires states, district and schools for the first time to report graduation rates for homeless students (effective beginning with the 2016-17 school year); and affirms the urgency and importance of dealing with homelessness so that all children can succeed