International audience

Exploratory Data Analysis (EDA) is the interactive process of gaining insights from a dataset. Comparisons are popular insights that can be specified with comparison queries, i.e., specifications of the comparison of subsets of data. In this work, we consider the problem of automatically computing sequences of comparison queries that are coherent, significant and whose overall cost is bounded. Such an automation is usually done by either generating all insights and solving a multi-criteria optimization problem, or using reinforcement learning. In the first case, a large search space has to be explored using exponential algorithms or dedicated heuristics. In the second case, a dataset-specific, time and energy-consuming training, is necessary. We contribute with a novel approach, consisting of decomposing the optimization problem in two: the original problem, that is solved over a smaller search space, and a new problem of generating comparison queries, aiming at generating only queries improving existing solutions of the first problem. This allows to explore only a portion of the search space, without resorting to reinforcement learning. We show that this approach is effective, in that it finds good solutions to the original multi-criteria optimization problem, and efficient, allowing to generate sequences of comparisons in reasonable time.

International audience

The objective of any tutoring system is to provide resources to learners that are adapted to their current state of knowledge. With the availability of a large variety of online content and the disjunctive nature of results provided by traditional search engines, it becomes crucial to provide learners with adapted learning paths that propose a sequence of resources that match their learning objectives. In an ideal case, the sequence of documents provided to the learner should be such that each new document relies on concepts that have been already defined in previous documents. Thus, the problem of determining an effective learning path from a corpus of web documents depends on the accurate identification of outcome and prerequisite concepts in these documents and on their ordering according to this information. Until now, only a few works have been proposed to distinguish between prerequisite and outcome concepts, and to the best of our knowledge, no method has been introduced so far to benefit from this information to produce a meaningful learning path. To this aim, this article first describes a concept annotation method that relies on machine-learning techniques to predict the class of each concept—prerequisite or outcome—on the basis of contextual and local features. Then, this categorization is exploited to produce an automatic resource sequencing on the basis of different representations and scoring functions that transcribe the precedence relation between learning resources. Experiments conducted on a real dataset built from online resources show that our concept annotation approach outperforms the baseline method and that the learning paths automatically generated are consistent with the ground truth provided by the author of the online content.

The objective of any tutoring system is to provide meaningful learning to the learner, thence it is important to know whether a concept mentioned in a document is a prerequisite for studying that document, or it can be learned from it. In this paper, we study the problem of identifying defined concepts and prerequisite concepts from learning resources available on the web. Statistics and machine learning tools are exploited in order to predict the class of each concept. Two groups of features are constructed to categorize the concepts: contextual features and local features. The contextual features enclose linguistic information and the local features contain the concept properties such as font size and font weigh. An aggregation method is proposed as a solution to the problem of the multiple occurrences of a defined concept in a document. This paper shows that best results are obtained with the SVM classifier than with other classifiers.