Predicting Students Drop Out a Case assignment help sydney Psychology essay help

Predicting Students Drop Out: A Case Study Gerben W. Dekker 1, Mykola Pechenizkiy2 and Jan M. Vleeshouwersl g. w. dekker@student. tue. nl, {m. pechenizkiy,J. m. vleeshouwers}@tue. nl 1 Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands 2 Department of Computer Science, Eindhoven University of Technology, the Netherlands Abstract. The monitoring and support of university freshmen is considered very important at many educational institutions.

In this paper we describe the results of the educational data mining case study aimed at predicting he Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success- factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%.

Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students. Introduction The monitoring and support of the first year students is a topic that is considered very important at many educational institutions.

At some of the faculties yearly student enrollment for a bachelor program can be lower than desired, and when coupled with a high drop out rate of freshmen the need in effective approaches for predicting student drop out as well as identifying the factors affecting it speaks for itself. At the Electrical Engineering (EE) department of Eindhoven University of Technology (TU/e), the drop out rate of freshmen is about 40%. Apart from the epartment’s aim to enforce an upper bound to the drop-out rate, there are other reasons to want to identify successful and unsuccessful students in an early stage.

In the Netherlands, there is the legal obligation that universities have to provide students with the necessary support to evaluate their study choice. In general, students who choose to pursue their study career at another institution, should do this at an early stage. For EE students there is a very concrete reason to evaluate before the end of the first semester: the EE program of the nearby Fontys University f Applied Science accepts TU/e drop outs in their curriculum until the beginning of January, without any time losses involved.

Besides, there is always a subset of students which the department considers a “risk group”, i. e. students who may be successful but who need extra attention or specific individual care in order to succeed. Detecting this risk group in an early stage is essential for keeping these students from dropping out. It enables the department to direct its resources to the students who need it most. Current approach at EE department. To support students n making this decision, every enrolled student receives a study advice in December. tudy career at the faculty. It is based upon the grades and other results of the student so far and upon information obtained from 1st-semester-teachers and student-mentors, examined and interpreted by 41 Educational Data Mining 2009 the department’s student counselor. The final semester examinations are not taken into account, because they are in January; postponing the advice until after the results are known would preclude students from switching to Fontys.

The advices seem to be quite accurate in practice: students who are assessed as potentially uccessful are in general the same students that are successful after a year. Moreover, the students who are not encouraged to proceed their current study program, generally do not continue into the second year. The objectives. Despite the success, the assessment remains unsatisfactory because of its rather subjective character.

Therefore, a more robust and objective founding of the process may lead to advices which are more consistently followed up by students. Besides, a closer analysis is likely to lead to an improved selection process. First of all, the department s interested in which of the currently available student data are the strongest predictors of success, and in the performance of this predictor. Obviously, the lower the predictor’s quality, the more the department is curious to know what information makes the current assessment work.

If the predictor quality is high, the department’s interests are directed towards: (1) using the predictor as a back-up of the current assessment process; (2) identifying success-factors specific to the EE program; (3) identifying what data might result in a further increase of the predictor quality, and s a consequence, collect these data; (4) considering a more differentiated view on the risk group; (5) modifying the assessment process time-line, resulting in an earlier prediction, ideally even before entering the study.

Furthermore, if strong predictors for academic success can be found, these will also be used to gain understanding of success and risk factors regarding the curriculum. Awareness of these factors by teachers, education personnel and management will help to select appropriate measures to support the risk group, eventually resulting in a decrease of the drop- out rate. In this paper we present the results of the educational data mining case study aimed to address these identified issues. First, we discuss related work on addressing the problem of student dropout (Section 2).

Then, we consider the settings of our EDM case study and present the analysis of classification results (Section 3). In Section 4 we present the further evaluation of one of the models. We conclude this paper with a summary of the results and discussions of further work in Section 5. 2 Background and Related Work researched. In the earlier studies, the model of Tinto [12] was the predominant heoretical framework for considering factors in academic success. Tinto considers the process of student attrition as a socio-psychological interplay between the characteristics of the student entering university and the experience at the institute.

This interaction between the student’s past and the academic environment leads to a degree of integration of the student into this new environment. According to this model, a higher degree of integration is directly related to a higher commitment to the educational institute and to the goal of study completion. Later studies tried to perationalize this model identifying the factors like peer group interactions, interactions with faculty, faculty concern for 42 student development and teaching, academic and intellectual development, and institutional and goal commitments that affect the student’s integration