Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System

In an intelligent tutoring system (ITS), the domain expert should provide\ud relevant domain knowledge to the tutor so that it will be able to guide the\ud learner during problem solving. However, in several domains, this knowledge is\ud not predetermined and should be captured or learned from expert users as well as\ud intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud techniques can help to build this domain intelligence in ITS. This paper proposes\ud a framework to capture problem-solving knowledge using a promising approach\ud of data and knowledge discovery based on a combination of sequential pattern\ud mining and association rules discovery techniques. The framework has been implemented\ud and is used to discover new meta knowledge and rules in a given domain\ud which then extend domain knowledge and serve as problem space allowing\ud the intelligent tutoring system to guide learners in problem-solving situations.\ud Preliminary experiments have been conducted using the framework as an alternative\ud to a path-planning problem solver in CanadarmTutor

Categorization of interestingness measures for knowledge extraction

Finding interesting association rules is an important and active research field in data mining. The algorithms of the Apriori family are based on two rule extraction measures, support and confidence. Although these two measures have the virtue of being algorithmically fast, they generate a prohibitive number of rules most of which are redundant and irrelevant. It is therefore necessary to use further measures which filter uninteresting rules. Many synthesis studies were then realized on the interestingness measures according to several points of view. Different reported studies have been carried out to identify "good" properties of rule extraction measures and these properties have been assessed on 61 measures. The purpose of this paper is twofold. First to extend the number of the measures and properties to be studied, in addition to the formalization of the properties proposed in the literature. Second, in the light of this formal study, to categorize the studied measures. This paper leads then to identify categories of measures in order to help the users to efficiently select an appropriate measure by choosing one or more measure(s) during the knowledge extraction process. The properties evaluation on the 61 measures has enabled us to identify 7 classes of measures, classes that we obtained using two different clustering techniques.Comment: 34 pages, 4 figure

Prospective elementary teachers\u2019 perceptions of the processes of modeling: A case study

In this paper we discuss a study on the approaches to modeling of students of the 4-year elementary school teacher program at the University of Palermo, Italy. The answers to a specially designed questionnaire are analyzed on the basis of an a priori analysis made using a general scheme of reference on the epistemology of mathematics and physics. The study is performed by using quantitative data analysis methods, i.e. factorial analysis of the correspondences and implicative analysis. A qualitative analysis of key words and terms used by students during interviews is also used to examine some aspects that emerged from the quantitative analysis. The students have been classified on the basis of their different epistemological approaches to knowledge construction, and implications between different conceptual strategies used to answer the questionnaire have been highlighted. The study\u2019s conclusions are consistent with previous research, but the use of quantitative data analysis allowed us to classify the students into three \u201cprofiles\u201d related to different epistemological approaches to knowledge construction, and to show the implications of the different conceptual strategies used to answer the questionnaire, giving an estimation of the classification or implication \u201cstrength.\u201d Some hints on how a course for elementary school physics and mathematics education can be planned to orient the future teachers to the construction of models of explanation are reported

Efficient Mining Support-Confidence Based Framework Generalized Association Rules

Mining association rules are one of the most critical data mining problems, intensively studied since their inception. Several approaches have been proposed in the literature to extend the basic association rule framework to extract more general rules, including the negation operator. Thereby, this extension is expected to bring valuable knowledge about an examined dataset to the user. However, the efficient extraction of such rules is challenging, especially for sparse datasets. This paper focuses on the extraction of literalsets, i.e., a set of present and absent items. By consequence, generalized association rules can be straightforwardly derived from these literalsets. To this end, we introduce and prove the soundness of a theorem that paves the way to speed up the costly computation of the support of a literalist. Furthermore, we introduce FasterIE, an efficient algorithm that puts the proved theorem at work to efficiently extract the whole set of frequent literalets. Thus, the FasterIE algorithm is shown to devise very efficient strategies, which minimize as far as possible the number of node visits in the explored search space. Finally, we have carried out experiments on benchmark datasets to back the effectiveness claim of the proposed algorithm versus its competitors