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Mining Educational Data to Enhance Intelligent Tutoring Systems Using Students Performance: a Case Study AE.Madurapperumage, JB

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Mining Educational Data to Enhance Intelligent Tutoring Systems Using Students Performance: a Case Study AE.Madurapperumage, JB International Journal of Scientific & Engineering Research Volume 10, Issue 5, May-2019 230 ISSN 2229-5518 Mining Educational Data to Enhance Intelligent Tutoring Systems Using Students Performance: A case Study AE.Madurapperumage, JB. Ekanayake Abstract—Effective course designing is a key challenge, which requires expert knowledge and a clear understanding of students’ behaviors. Intelligent tutoring systems can serve as a human tutor with excellent knowledge and less response time while understanding the knowledge level of the current cohort, which use to navigate them with most appropriate paths. In this study, we show how students’ data can use to improve course design by analyzing them in determining the comprehensive level of assignment questions. The outcome of this research can be used to enhance the capabilities of the intelligent tutoring systems, which will be able to create clusters in a given question set by considering the past experienc- es of student responses. Teachers and course designers are beneficiaries who would be used the outcomes to improve the quality of the course. Index Terms— Classification Techniques, Clustering, Educational Data Mining, Intelligent Tutoring System, Prediction, Multilayer Perceptron, Students Performance. —————————— —————————— 1 INTRODUCTION any educational institutes are engaging with providing The process of modeling a student’s ability as they interact M high-quality education to their students. As results, cur- with a learning application is known as knowledge tracing. riculum revision, upgrading the existing course materi- Here the information from previous students’ interactions als, redesigning courses, practicing novel teaching strategies was used to build a model, which can predict the interactions has taken place. Further, the popularity of Massive Online of future students. Intelligent Teaching Assistant for Pro- Open Courses (MOOCs) has created a recent trend on how to gramming (ITAP) is a data-driven tutoring system intro- teach effectively, while delivering expected knowledge to a duced by Kelly Rivers, Kenneth R.Koedinger [9]. It combines cohort in the distance. Various strategies have been using over algorithms for state abstraction, path construction, and state the past few decades with IJSERthe purpose of fulfilling these re- reification to fully automate the process of hint generation. quirements. However, one of the fundamentals of these strat- ITAP makes it possible to generate a full chain of hints from egies is to structure the program in a way, which is most ap- any new code state to the closest goal state [9]. propriate for the engaged cohort. Intelligent Tutoring systems (ITS), Intelligent Teaching Assistant Systems (ITAS), personal- In this research, we have analyzed a dataset, which collected ized education systems, etc have emerged from this idea. ITSs from the students who used ITAP ITS for their non- are computer-based instructional systems with models of in- compulsory Python programming course. The data used be- structional content that specify what to teach, and teaching longs to 89 total students who worked on the first 19 prob- strategies that specify how to teach [1]. The fundamental of lems over time. Course duration is seven weeks. No re- ITS is applying different types of data mining techniques on strictions applied to the number of attempts where students educational data repositories. This is known as Educational can work on each problem in any order. Feedback has given Data Mining (EDM), which is an emerging discipline, con- at each test case. Moreover, the students can request hints cerned with developing methods for exploring the unique from ITAP. The dataset itself contains a record for each at- types of data that come from educational settings and using tempt and hint request that students made while working [7]. those methods to understand students and the environments The dataset for this research was collected form DataShop [7]. which they learn in[2]. A better understanding of students’ The extracted list of attributes from the dataset is as follows, learning behavior towards different tasks of a course is vital in SubjectId, ProblemId, StartOrder, FirstCorrect, EverCorrect, many situations such as exam preparation, provide early UsedHints, Attempts. warning, course redesigning, maintain retention rate and etc. In this research, our primary goal is to analyze student be- Although, student learning behavior analyzing is popular on havior on a computer programming course. Since we real- many other subject-streams the analyzing of student behavior ized, the first attempt and the number of attempts can reveal in computer programming is comparatively rare [4]. Since the vital information of the question set, those two factors are code writing is a core competency for every computer scien- considered to answer the research questions b) and c). tists, it deserves more attention. Following are the research questions. IJSER © 2019 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 10, Issue 5, May-2019 231 ISSN 2229-5518 a) How to determine the comprehensiveness of the How to sequence the knowledge effectively to deliver the re- questions by considering student behaviors? quired skills, what would be the correct size of tasks in b) Will the students be able to code a given knowledge delivery, is fine-grained tasks are better than question in their first attempt? course- grained tasks are some problems that the researchers c) What would be the number of attempts that have been trying to resolve in this context. Considering the one may try on a given question? learning goal and the current state of students' knowledge reflected by the student model, various sequencing ap- proaches are able to determine which task is currently the ELATED WORKS 2 R most appropriate [3]. The field of educational data mining has emerged into multi- disciplinary areas where the researchers are capable of re- It is vital to determine the precise time to teach new pro- searching various aspects of education. Enhancement of gramming concepts to the cohort. Learning curve analysis is teaching and learning, course designing, curriculum revision, an approach from broader EDM research that focuses on es- evaluation of teaching and learning methods are a few exam- timating learners’ performance over time [4]. The probability ples of them. Further, the advancement of accessibility of the of making mistakes in assignments should lessen with the in- Internet has leveraged education among the community. creasing time where the student can master with skills. Tradi- Massive Online Open Courses (MOOC), Learning Manage- tionally, skills in this context are formalized as Knowledge ment Systems (LMS), Open Social Student Modeling (OSSM), Components (KCs), with a given task exercising one or more ITS, Learning and Teaching Assistant Systems, and many KCs. Students who possess a mastery of those KCs are more more ideas have appeared with this trending usage of the In- likely to perform the task Correctly [4]. ternet. The fundamental of implementing these systems is knowledge extraction from educational information through With the rapid growth of Web-based education, educational EDM. The methods of EDM are drawn from a variety of liter- data repositories overflowed with educational data. Re- ature, including data mining and machine learning, psycho- searches began to investigate how to automate human tutors metrics and other areas of statistics, information visualiza- who can help students in-distance by mining collected data tion, and computational modeling [5]. Discovery with models from various online educational systems such as MOOCs, has become an increasingly popular method in EDM re- LMS, and OSSMs. In the study of the online learning com- search. It has become the second most common category of munity, many investigators have turned attention to auto- EDM research, representing 19% of papers [5] in 2008-2009. matically logged web data [13]. Studies showed that students This includes creating intelligent tutoring systems, providing who receive help while programming does better in their course navigation, giving recommendations on related con- courses [9]. But due to practical issues, the assistance they get cepts, studying students’ behavior to extract the learning pat- during the programming is considerably less than required. terns, and many other prolific research areas. Easy-To access, automated help source would be a great solu- tion. Intelligent tutoring has been a prolific area of research In recent years there has beenIJSER growing interest in using ex- due to these reasons. ELM Adaptive Remote Tutor (ELM- tensive collections of logged programming data to under- ART), Logic ITA and ITAP are some examples for ITSs. stand how students learn, what they struggle with, and what we can do to improve computer science education [4]. The ELM-ART is a web-based ITS to support learning Lisp pro- ability to write code accurately and fluently is a core compe- gramming [10]. This supports example-based programming, tency for every computer scientist. However, learning to pro- intelligent analysis of problem solutions, and advanced test- gram is prohibitively difficult for many students, and this ing and debugging facilities. Logic- ITA is also a web-based limits diversity in students that pursue computer science or tool used in University of Sydney since 2001. This helps to gain programming skills to enhance their other careers [9]. extract
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