Maria Satratzemi

A system for program visualization and problem-solving path assessment of novice programmers

This paper describes an educational programming environment, called AnimPascal. AnimPascal is a program animator that incorporates the ability to record problem-solving paths followed by students. The aim of AnimPascal is to help students understand the phases of developing, verifying, debugging, and executing a program. Also, by recording the different versions of student programs, it can help teachers discover student conceptions about programming. In this paper we describe how our system works and present some empirical results concerning student conceptions when trying to solve a problem of algorithmic or programming nature. Finally, we present our plans for further extensions to our software.

Modeling information sources as integrals for effective and efficient source selection

In this paper, a new source selection algorithm for uncooperative distributed information retrieval environments is presented. The algorithm functions by modeling each information source as an integral, using the relevance score and the intra-collection position of its sampled documents in reference to a centralized sample index and selects the collections that cover the largest area in the rank-relevance space. Based on the above novel metric, the algorithm explicitly focuses on addressing the two goals of source selection; high-recall, which is important for source recommendation applications and high-precision which is important for distributed information retrieval, aiming to produce a high-precision final merged list. For the latter goal in particular, the new approach steps away from the usual practice of DIR systems of explicitly declaring the number of collections that must be queried and instead focuses solely on the number of retrieved documents in the final merged list, dynamically calculating the number of collections that are selected and the number of documents requested from each. The algorithm is tested in a wide range of testbeds in both recall and precision-oriented settings and its effectiveness is found to be equal or better than other state-of-the-art algorithms.

Collection-integral source selection for uncooperative distributed information retrieval environments

We propose a new integral-based source selection algorithm for uncooperative distributed information retrieval environments. The algorithm functions by modeling each source as a plot, using the relevance score and the intra-collection position of its sampled documents in reference to a centralized sample index. Based on the above modeling, the algorithm locates the collections that contain the most relevant documents. A number of transformations are applied to the original plot, in order to reward collections that have higher scoring documents and dampen the effect of collections returning an excessive number of documents. The family of linear interpolant functions that pass through the points of the modified plot is computed for each available source and the area that they cover in the rank-relevance space is calculated. Information sources are ranked based on the area that they cover. Based on this novel metric for collection relevance, the algorithm is tested in a variety of testbeds in both recall and precision oriented settings and its performance is found to be better or at least equal to previous state-of-the-art approaches, overall constituting a very effective and robust solution.

Integral based source selection for uncooperative distributed information retrieval environments

In this paper, a new source selection algorithm for uncooperative distributed information retrieval environments is presented. The algorithm functions by modeling each information source as an integral, using the relevance score and the intra-collection position of its sampled documents in reference to a centralized sample index and selects the collections that cover the largest area in the rank-relevance space. Based on the above novel metric, the algorithm explicitly focuses on addressing the two goals of source selection; high recall which is important for source recommendation applications and high precision aiming to produce a high precision final merged list. For the latter goal in particular, the new approach steps away from the usual practice of DIR systems of explicitly declaring the number of collections that must be queried and instead receives as input only the number of retrieved documents in the final merged list, dynamically calculating the number of collections that are selected and the number of documents requested from each. The algorithm is tested in a wide range of testbeds in both recall and precision oriented settings and its effectiveness is found to be equal or better than other state-of-the-art algorithms.

X-compiler: yet another integrated novice programming environment

The paper presents a simple programming language, called X, and an educational programming environment, called X-Compiler, designed to introduce students to programming. X-Compiler can be used to edit, compile, debug and run programs written in X, a subset of Pascal. X-Compiler could be didactically interesting because of the following features: (a) users can watch the intermediate steps of the execution of a program: source code compilation, correspondence of source and pseudo-assembly code during execution, register content, and intermediate values of user and temporary system variables; also, they can edit the produced pseudo-assembly code and re-execute it, (b) there are many detailed and explanatory messages that can guide novice programmers when debugging their programs and, in general, help them write better programs.

A results merging algorithm for distributed information retrieval environments that combines regression methodologies with a selective download phase

The problem of results merging in distributed information retrieval environments has gained significant attention the last years. Two generic approaches have been introduced in research. The first approach aims at estimating the relevance of the documents returned from the remote collections through ad hoc methodologies (such as weighted score merging, regression etc.) while the other is based on downloading all the documents locally, completely or partially, in order to calculate their relevance. Both approaches have advantages and disadvantages. Download methodologies are more effective but they pose a significant overhead on the process in terms of time and bandwidth. Approaches that rely solely on estimation on the other hand, usually depend on document relevance scores being reported by the remote collections in order to achieve maximum performance. In addition to that, regression algorithms, which have proved to be more effective than weighted scores merging algorithms, need a significant number of overlap documents in order to function effectively, practically requiring multiple interactions with the remote collections. The new algorithm that is introduced is based on adaptively downloading a limited, selected number of documents from the remote collections and estimating the relevance of the rest through regression methodologies. Thus it reconciles the above two approaches, combining their strengths, while minimizing their drawbacks, achieving the limited time and bandwidth overhead of the estimation approaches and the increased effectiveness of the download. The proposed algorithm is tested in a variety of settings and its performance is found to be significantly better than the former, while approximating that of the latter.

Results merging algorithm using multiple regression models

This paper describes a new algorithm for merging the results of remote collections in a distributed information retrieval environment. The algorithm makes use only of the ranks of the returned documents, thus making it very efficient in environments where the remote collections provide the minimum of cooperation. Assuming that the correlation between the ranks and the relevancy scores can be expressed through a logistic function and using sampled documents from the remote collections the algorithm assigns local scores to the returned ranked documents. Subsequently, using a centralized sample collection and through linear regression, it assigns global scores, thus producing a final merged document list for the user. The algorithms effectiveness is measured against two state-of-the-art results merging algorithms and its performance is found to be superior to them in environments where the remote collections do not provide relevancy scores.

Simple Adaptations of Data Fusion Algorithms for Source Selection

Source selection deals with the problem of selecting the most appropriate information sources from the set of, usually non-intersecting, available document collections. On the other hand, data fusion techniques (also known as metasearch techniques) deal with the problem of aggregating the results from multiple, usually completely or partly intersecting, document sources in order to provide a wider coverage and a more effective retrieval result. In this paper we study some simple adaptations to traditional data fusion algorithms for the task of source selection in uncooperative distributed information retrieval environments. The experiments demonstrate that the performance of data fusion techniques at source selection tasks is comparable with that of state-of-the-art source selection algorithms and they are often able to surpass them.

DIDAGRAPH: software for teaching graph theory algorithms

Graph theory and in particular its algorithmic aspect is known as being a difficult topic in Computer Science. In this paper we propose the software DIDAGRAPH, which we are in the process of developing, as a support for teaching graph algorithms. The environment of DIDAGRAPH offers the possibility of visualisation and experimentation so as to overcome didactic problems, i.e. the intermediate stages of an algorithm, their implementation in a programming language etc. In DIDAGRAPH we are developing two different frameworks to explore an algorithm: one to explore in detail predetermined algorithms and a second to develop arbitrary algorithms expressed with command language in a visual environment.

A Comparison of Centralized and Distributed Information Retrieval Approaches

Distributed Information Retrieval (DIR) has been suggested to offer a prospective solution to a number of issues concerning information retrieval in the WWW. On the other hand, previous studies have indicated that centralized approaches offer the best solution for optimal quality of result (i.e. effectiveness). In this paper, we revisit those claims and investigate if and under which conditions can DIR offer a new paradigm for both efficient and effective information retrieval.