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International audience; Trace Base Management System (TBMS) offers processing and querying functionalities for traces that may be of interest to users of tracked systems. Our goal is to ensure the importing of various external traces into kernel for Trace-Based System (kTBS), which is a TBMS developed in the LIRIS laboratory. To overcome the problem of traces heterogeneity, we propose to define a generic collector. To this end, a user with enough knowledge of the tracked system is prompted to define its kTBS trace model and correspondences between the elements of this model and the elements of the trace to import. The system generalises the mappings previously elicited by the user through interaction to create mapping rules. After this phase, the collector will generate modelled traces from the existing ones and the already defined mapping rules.

Several adaptive and intelligent tutoring systems (AITS) have been developed with different variables. These variables were the cognitive traits, cognitive styles, and learning behavior. However, these systems neglect the importance of learner's multiple intelligences, learner's skill level and learner's feedback when implementing personalized mechanisms. In this paper, the authors propose AITS based not only on the learner's multiple intelligences, but also the changing learning performance of the individual learner during the learning process. Therefore, considering learner's skill level and learner's multiple intelligences can promote personalized learning performance. Learner's skill level is obtained from pre-test result analysis, while learner's multiple intelligences are obtained from the analysis of questionnaire. After computing learning success rate of an activity, the system then modifies the difficulty level or the presentation of the corresponding activity to update courseware material sequencing. Learning process in this system is as follows. First, the system determines learning style and characteristics of the learner by an MI-Test and then makes the model. After that it plans a pre-evaluation and then calculates the score. If the learner gets the required score, the activities will be trained. Then the learner will be evaluated by a post-evaluation. Finally the system offers guidance in learning other activities. The proposed system covers all important properties such as hypertext component, adaptive sequencing, problem- solving support, intelligent solution analysis and adaptive presentation while available systems have only some of them. It can significantly improve the learning result. In other words, it helps learners to study in “the best way.”

work proposes a computer assisted assessment approach of algorithmic competencies. Aside from the fact that this approach brings a solution for delicate problem of e-assessment of algorithmic, in addition it is formative. Drawing one's inspiration from the basic principles of the algorithmic field itself, it recovers an interesting efficiency. It reclines on use of scalable solutions basis. Any learner's production, for a given problem, is automatically assessed if it is recognized, or assessed by a human expert if necessary. In the last case, it will enrich the basis if it is judged pedagogically interesting The purpose of this approach is to provide a formative and diagnostic assessment in order to empower the learner to acquire problem- solving skills of algorithmic.

In any learning systems and especially automated assessment tools, the most common task is to evaluate the students' performance using training exercise. The selection of the next exercise is generally performed as a static set of exercises or free by students. But, it would clearly be advantageous if this exercise selection process were to be automated based on their previous performances. Therefore, the focus of this paper is the development of a method capable of determining exercise progression and sequencing during a training session based on the students' past performance. A dynamic planning of algorithmic exercises was developed based on a semantic and pedagogical description to be used in training exercise.

Programming skills become ever more important and a core competency in 21st century and for its learning students need learning environments where they can practice and receive immediate assessment that help them to progress. In this paper we have developed eALGO, an automated assessment tool of flowchart programs that allows the students to practice algorithms problems. The assessment method used in eALGO is based on graph matching. To validate the method, an experimental study was conducted for investigating the effects of the automated flowchart algorithms scoring system compared to teachers scoring. About 35 flowchart algorithms were selected during a lab session and scored by eALGO system and after that scored by five teachers. Statistical analysis of the results reveals that eALGO tool seems to correlate well with teachers. Furthermore, a strong correlation was noticed between eALGO and the average scores of teachers. Hence, the findings of this study show that using automated assessment of flowcharts algorithms based on graph matching methods can help teachers to alleviate the scoring load, while allowing students learning designing algorithms.

An algorithm is a consequence of logical instructions that, once executed, reduce to a result and it is not always evident to good understand its functioning. The progression of the technologies of information and communication and its availability in the teaching and in the learning offer the possibility to put at the disposal of the learners the tools that facilitate their learning. The algorithmic is a very important field in computer science; it is currently studied using different intelligent environments for human learning dedicated to each discipline. Algobase, is an Environment for assessment algorithms and learning resolution with decomposition of tasks. This is an editor that offers the learner opportunities to create, modify, evaluate algorithms and view mistakes. The assessment in this Environment is done through a matching of solutions.

The majority of the students of Computer Science often start directly writing a program code when they have been asked to resolve a problem in algorithms. Although, what is way more important is the algorithm the code has to represent. Learning how to design an algorithm which resolves a given problem it is not easy. In this paper, we propose a formative-diagnostic assessment approach which helps students on acquiring problem-solving skills that enable them to cope with problems and to design more efficient and elegant solutions. Aside from the fact that this approach brings a solution for delicate problem of e-assessment of algorithmic skills, in addition it is formative aiming at strengthening algorithmic competencies. Drawing inspiration from the basic principles of the algorithms field itself, it is based on using known components called: Basic Operations which are organized in a library. For modeling, solutions of each problem are regrouped in a bunch and each solution is represented by a plan which is the canonical representation of a program in an approach of program understanding. The purpose of this approach is to provide a formative and diagnostic assessment in order to empower the learner to acquire algorithmic problem-solving skills. Even if it has been conceived for algorithmic competencies assessment, this approach can easily be adapted to any field to assess its know-how.

Many systems for learning programming have been developed in view of learning programming is difficult. Unfortunately, a few tools for learning algorithmic have been developed. In this paper, we present Algo+, a tool based on Information and Communications Technology (ICT), to assess algorithmic competencies. Its pedagogical aim is purchasing algorithmic competencies, analyzing ability and refinement steps strategy to resolve a problem. Algo+ gives a problem to learner and let him/her resolve it graphically in terms of basic and elementary operations (operation well known in algorithmic) using the refinement steps strategy. After constructing solution, the last one is submitted to, not only a formative and diagnostic assessment (a formative feedback) but also a summative assessment. The role of this tool is to assess algorithmic skills and to make an evolutionary base of pedagogical errors for reusing it to enhancing the student's problem solving abilities.

This paper attempts to explore a new method to improve the teaching of algorithmic for beginners. It is well known that algorithmic is a difficult field to teach for teacher and complex to assimilate for learner. These difficulties are due to intrinsic characteristics of this field and to the manner that teachers (the majority) apprehend its bases. However, in a Technology Enhanced Learning environment (TEL), assessment, which is important and indispensable, is the most delicate phase to implement, for all problems that generate (noise…). Our objective registers in the confluence of these two axes. For this purpose, we focused essentially to elaborate an assessment approach of algorithmic competences in a TEL environment. This approach consists in modeling an algorithmic solution according to basic and elementary operations which let learner draw his/her own step with all autonomy and independently to any programming language. This approach assures a trilateral assessment: summative, formative and diagnostic assessment.

For the majority of undergraduate students of computer science, inadequate designing and planning algorithm skills are among the most salient hurdles. Learning how to design an algorithm which resolves a given problem is not easy. We propose a formative-diagnostic assessment approach which helps students on acquiring problem-solving skills that enable them to cope with problems and to design more efficient and elegant solutions. This method is based on using known components called: basic operations, which are defined and regrouped in a library. A plan-based programme understanding is used for modelling and a similarity measure is chosen for matching between plans to assess.