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Discovering the Academic Situation of Students by Relationship Mining
Abstract
While the data mining in education field gained more and more popularity in recent years, there have many research endeavors to find association rules in students' academic situation. The current methods normally apply traditional association rules mining technique to identify those rules. However, traditional association rules mining technique can not identify difference between different types of students' academic situation. To solve this problems, we applied a novel contrast target rules mining method in this paper. Real world data set from Computer Science department of a university of China, the empirical results show the difference characteristics of different types of students in their academic situation.
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With the emergence of Web 2.0, electronic word-of-mouth (eWOM) shared through social networking sites has become the primary information source for many travelers. An enormous quantity of reviews has been posted by customers, and has become a valuable means by which hoteliers can better understand customer satisfaction and expectations. Efforts have been made to analyze these parameters in terms of customers’ backgrounds. Although customer expectations vary according to background, whether or not this is still the case across different trip modes remains unknown. In this study, an eWOM dataset was obtained from an online source and sentiment mining used to improve its quality by imputing missing values. A complete analysis of customer profiles and their contrast by trip mode was then conducted using association rule mining. The empirical results demonstrate differences in both customer expectation and satisfaction when the same traveler engages in different trip modes.
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an eecient algorithm that generates all signiicant association rules between items in the database. The algorithm incorporates buuer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the eeectiveness of the algorithm.
This work presents an experience on the use of KDD (Knowledge Discovery in Databases) to identify and understand whether curriculum revisions can affect students in a Brazilian university. Presently, there is no framework to define the notion of impact caused by curriculum revisions, and the use of KDD can bring significant contributions, given the amount of data involved. The paper describes the analysis framework defined so far for measuring the impact of curriculum revisions, and reports the results obtained after the analysis of students records related to five distinct degrees. The results obtained so far indicate that individual revisions quite often do not affect students, being sometimes even beneficial to them. However, considering the set of revisions they face during their academic lifetime, it is possible to generalize that many students are lightly harmed. This harm influences the number of extra-classes they have to take to fulfill the requirements for obtaining a given degree, but the time required to graduate is not affected by revisions.
Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.
Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.
The education domain offers a fertile ground for many interesting and challenging data mining applications. These applications can help both educators and students, and improve the quality of education. In this paper, we present a real-life application for the Gifted Education Programme (GEP) of the Ministry of Education (MOE) in Singapore. The application involves many data mining tasks. This paper focuses only on one task, namely, selecting students for remedial classes. Traditionally, a cut-off mark for each subject is used to select the weak students. That is, those students whose scores in a subject fall below the cut-off mark for the subject are advised to take further classes in the subject. In this paper, we show that this traditional method requires too many students to take part in the remedial classes. This not only increases the teaching load of the teachers, but also gives unnecessary burdens to students, which is particularly undesirable in our case because the GEP students are generally taking more subjects than non-GEP students, and the GEP students are encouraged to have more time to explore advanced topics. With the help of data mining, we are able to select the targeted students much more precisely.
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In this paper, we show how using data mining algorithms can help discovering pedagogically relevant knowledge contained in databases obtained from Web-based educational systems. These findings can be used both to help teachers with managing their class, understand their students' learning and reflect on their teaching and to support learner reflection and provide proactive feedback to learners.
This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬ B, ¬ A ⇒ B, and ¬ A ⇒ ¬ B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.
Data mining is used to extract the interesting patterns from databases or repositories. Frequent Pattern Tree is a technique for discovering association between the variables and finds the frequent patterns in the Students’ dataset. The student’s data set contains the demographic details, practical mark and theory mark. The chore is to determine the programming skill similarity between rural and urban students. This paper focuses on two mining approaches to discover the knowledge from the students’ dataset such as (a) Identifying Frequent Patterns (b) Finding similarity between urban and rural studentsprogramming skill.
We introduce a new kind of patterns, called emerging patterns (EPs), for knowledge discovery from databases. EPs are defined as itemsets whose supports increase significantly from one dataset to another. EPs can capture emerging trends in timestamped databases, or useful contrasts between data classes. EPs have been proven useful: we have used them to build very powerful classifiers, which are more accurate than C4.5 and CBA, for many datasets. We believe that EPs with low to medium support, such as 1%-- 20%, can give useful new insights and guidance to experts, in even "well understood" applications. The efficient mining of EPs is a challenging problem, since (i) the Apriori property no longer holds for EPs, and (ii) there are usually too many candidates for high dimensional databases or for small support thresholds such as 0.5%. Naive algorithms are too costly. To solve this problem, (a) we promote the description of large collections of itemsets using their concise borders (the pa...
a b s t r a c t Despite the numerous research endeavors aimed at investigating tourists' preferences and motivations, it remains very difficult for practitioners to utilize the results of traditional association rule mining methods in tourism management. This research presents a new approach that extends the capability of the association rules technique to contrast targeted association rules with the aim of capturing the changes and trends in outbound tourism. Using datasets collected from five large-scale domestic tourism surveys of Hong Kong residents on outbound pleasure travel, both positive and negative contrasts are identified, thus enabling practitioners and policymakers to make appropriate decisions and develop more appropriate tourism products.
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