David Wible
email: wible45@yahoo.com

Chin-Hwa Kuo

Meng-Chang Chen

Nai-Lung Tsao

Tsung-Fu Hung

A Computational Approach to the Discovery and Representation of Lexical Chunks

Keywords: « chunks » (tronçons) lexicaux, lexicographie computationnelle, association de mots, apprentissage de langues étrangères lexical chunks, computational lexicography, word association, foreign language learning

La connaissance des « chunks » (tronçons) lexicaux est maintenant reconnue comme une compétence essentielle pour l'apprentissage d'une seconde langue. Nous étudions deux des principaux problèmes que les « chunks » posent en lexicographie et nous présentons des méthodes de résolution informatiques. Le premier problème est celui de l'apprentissage de connaissances lexicales, c'est-à dire la nécessité de définir quelles suites de mots constituent des « chunks » utiles à l'apprenant. Le deuxième problème est celui de la représentation, c'est-à-dire comment mettre cette connaissance à la disposition de l'apprenant. Pour résoudre le premier problème, nous proposons un algorithme glouton exécuté sur un corpus de 20 millions de mots du BNC qui reproduit des mesures d'associations de mot sur des n-grams de plus en plus longs. Cette approche donne la priorité à un rappel élevé et tente d'isoler les faux positifs à l'aide de mécanismes de tri. Pour résoudre le problème de la représentation, nous nous proposons d'associer cet algorithme à un navigateur en tant qu'extension de notre outil de détection de collocations.

Introduction

There has been a recent growing trend in foreign language education research to recognize words as inextricably entwined with a range of syntagmatic contexts and contextual patterns as opposed to viewing them as discrete units that can be mastered in isolation. With this has come the recognition that multiword expressions are a central rather than peripheral aspect of lexical knowledge (See [START_REF] Wray | Formulaic Language in the Lexicon[END_REF] for an extensive review). Among the range of multiword phenomena, certain types, such as phrasal verbs and collocations, have enjoyed a long tradition of attention in lexicography and language pedagogy. Other sorts of multiword expressions such as lexical chunks and formulaic sequences, however, have only recently begun to attract this sort of attention. Knowledge resources concerning these expressions are thus correspondingly scarce. The work reported here is part of a larger project aimed at filling this gap in resources for the learning of lexical chunks.

Lexical Chunks and the Two Challenges to Lexicography

Lexicography traditionally has faced two kinds of challenges: lexical knowledge discovery and lexical knowledge representation. We want to suggest that lexical chunks introduce novel requirements to both sorts of challenge. In what follows we consider these two in turn and present our approach to each as we go. The literature on chunks includes a variety of criteria for defining what constitutes a chunk (see Weinert 1995 for an overview) from semantic and syntactic criteria such as (non-)compositionality and (non-)productivity to processing criteria, the most commonly cited of the latter being that users store and retrieve chunks as single multiword units rather than by rule-governed composition in real time. Implementing any of these criteria computationally for real world applications involves serious problems of scalability. The most promising trait of chunks in this respect is frequency of occurrence, yet the implementation of frequency must be appropriately nuanced. Taking inspiration from the achievements of statistical approaches to collocation extraction [START_REF] Church K | Using statistics in lexical analysis[END_REF]Smajda 1993, inter alia), we choose as a practical starting point to operationalize the notion of lexical chunk statistically. Our specific implementation of a statistical approach is elucidated in what follows as part of our approach to lexical chunk discovery.

Lexical Chunks and Knowledge Discovery

A fundamental task of the lexicographer is to uncover facts concerning the behavior and meaning of words. These facts constitute the substance of any dictionary. One of the most important developments in this work over the past few decades has been the rise of machine-readable corpora and computational tools that aid in their analysis. Learner dictionaries in particular have benefited from the lexicographers' extended capacity to distill patterns and nuances governing the use of specific words made possible by access to massive collections of texts and tools for mining them (Sinclair 1987, inter alia). Certain multiword expressions have proven particularly susceptible to discovery with such tools. Inspired by the work of Church and his colleagues in the late 1980's [START_REF] Church K | Using statistics in lexical analysis[END_REF], researchers have found, for example, that collocations are detectable by well-understood statistical measures of word association strength such as mutual information (MI). There are certain characteristics of collocations and, say, phrasal verbs as well, that render them especially vulnerable to detection by these statistical tools. Word association measures can detect the association strength between two event types. Construing a pair of lexemes occurring in close proximity to each other to be a pair of events in running text, lexicographers can use these word association measures to determine the strength of association between these two events and thus detect which word pairs have sufficiently strong association to be collocations.

Lexical chunks as a class of multiword expression, however, are much less homogeneous in this respect. A chunk could consist of two words (e.g., in fact) or five words (as a matter of fact). In fact, there would seem to be no principled limit to the size of a chunk. What we want to suggest is, first, that this property of chunks creates a non-trivial challenge to computational lexicography and, second, that it is tractable.

We have developed a computational approach to the extraction of lexical chunks from very large corpora. Our algorithm takes as its kernel a simple word association measure (any such measure can serve as this kernel, for example, mutual information or simple conditional probability, inter alia) and iterates its application. Unlike traditional word association measures, then, our algorithm is greedy. As a consequence, our approach is insensitive to chunk size. The same algorithm that detects in fact also detects as a matter of fact.

The current interface is designed for lexicographers, not for learners (the design of a representation for learners is the topic of the next section). The algorithm takes as its input a single word/POS pairing. A sample input would be fact/N (i.e., the noun fact). The current version of the algorithm runs both a conditional probability measure and MI for the target word paired with each possible candidate collocate occurring within a five-word window of the target word in a 20-million-word portion of BNC (See Wible et al., 2004a for details). The user selects a minimum association score to set as a threshold for each of the two measures; a default threshold is used if the user makes no selection of score threshold. Word association scores are then tabulated for every word that has tokens which occur within the five-word span of any token of the target word (e.g., fact). These pairings are sensitive to linear order; for example, tokens of the pair in…fact and fact…in are scored separately. Those pairings with association scores that satisfy the threshold are considered hits and are highlighted in the results display.1 To this point, this process resembles closely the extraction of collocations. Since chunks are not limited to word pairs, however, we iterate this process, and all (ordered) pairings with the target word (e.g., X fact or fact X) serve as the input to the next iteration. We therefore refer to these inputs as bigrams. Word association measures look at the strength of association between two events x and y. In traditional collocation detection, as in our first iteration, x and y are individual words. At the second iteration, however, we use the same measures but change the definition of the events x and y. Here x is a word pairing (or bigram) from the first iteration (rather than just a word) and y is a word. This iteration then is measuring the association between each bigram (or a word pair) on the one hand and each word appearing within the five-word span of that bigram on the other. The output of this next iteration would consist of a set of ordered trigrams (potentially discontinuous due to our five-word window). Those trigrams that achieve the threshold word association score at this iteration are considered hits and highlighted in the results representation. All trigrams from this iteration in turn serve as input to the next iteration, and so on, in greedy fashion until no candidate strings meet the threshold association score. A hit for this algorithm, then, is a string of any number of words that results from satisfying the threshold score at the last iteration of any number of iterations. The system then creates a link from each such string to a display of all of the BNC sentences that instantiate that string.

Our searches provide two levels of results, which we will refer to as string types and string tokens. An example of a string type would be the string point of view whereas string tokens would be specific attested instances in the corpus that instantiate this string type. One reason this distinction becomes important is that our algorithm allows a five-word span for co-occurrence at any iteration. Thus, the string type point of view includes not only tokens where the three words are contiguous (e.g., According to her point of view…), but also tokens where the same three words are non-contiguous (e.g.

, The point of mentioning this view is to…).

There is no way of us predicting a priori which of these patterns within the tokens of one string type constitutes a true positive, so we cast our net wide in this way for the sake of recall at the cost of introducing noise. In the case of point of view, for example, it is the completely contiguous string tokens that are the true positives while the non-contiguous example (the point of mentioning this view…) illustrates a false positive. There are cases, however, where the reverse holds, that is, where discontinuity is a true property of a particular chunk. For example, our algorithm detects the longer string type from point of view as well. Notice, however, that for this case, in the true positives, from and point are non-adjacent (from a logical point of view; from their point of view, etc). Thus, while our use of a five-word window at each iteration is motivated, it also introduces substantial noise in the results. To address the noise, we apply to these results some sorting mechanisms intended to help distinguish noise from the true positives. While this sorting falls short of actually affecting the precision of the results, it is aimed at organizing the results into groups of patterns for the hand inspection of the lexicographers at the user end.

We illustrate our approach to the sorting with a specific string type: tell the time. This string token is extracted by taking the noun time as the query target. At the second iteration, the trigram tell the time is detected as satisfying both the conditional probability and the MI thresholds. Moreover, 54 tokens of this type are found in the 20-million-word portion of BNC that we use. While the string type tell the time would appear at first glance to be a true positive, a look at the 54 tokens shows the noise. Our original version of the system left these 54 tokens unsorted and displayed them in the order they were detected in BNC. A sample of these 54 tokens is provided here:

She told herself sternly that the time had passed when sympathy You tell me lies all the time and one half could not tell the time or correctly select a medicine bottle He found messages telling him that the time was not ripe observation of the sun was a useful way of telling the time I'm telling you for the last time, Harvey I was told at the time that this system had been adopted because… No mechanical indicator can tell you the right time to strike …activities of daily living (such as counting money, telling the time, reading…) observation of the sun was a useful way of telling the time

Our current version of the algorithm adds two stages of sorting to these 54 examples. The first step sorts the examples according to the patterns of contiguity of the string members. Thus, at this stage, all tokens where the string members (tell, the, time) are contiguous (tell the time) are grouped together, then all tokens where the first and second word (tell and the) are separated by one word are grouped together (tell him the time; tell of the time), and those with two words intervening there (tell him that the time), and so on. This stage of sorting, thus, is sensitive to the existence and location of any gaps separating the words in the string type and to how many words appear in those intervening gaps. These patterns are displayed in order of frequency. Thus, in the case of tell the time, the most frequent pattern is the one with no intervening words, such as So what if you can't tell the time? These comprise 16 of the 54 tokens. The second most common is the pattern where three words separate tell from the time, as in She told herself sternly that the time has passed. There are 8 tokens of this pattern. A look at these 8 tokens, however, will illustrate the motivation for adding a second stage to this sorting. Specifically, there is no coherent pattern shared by these 8 tokens. The fact that tell and the time are separated by three words in all eight examples follows from nothing interesting.

1.

The court was told that teenagers were made to suck dummies and wear nappies , were bathed like babies and told to regress to the time they were last happy.

2.

But , I told myself , by the time you are standing at the airport terminal (not the train or the bus station) , you have burned off the top ones and , come on , lad , you can afford to relax a little.

3.

She told herself sternly that the time had passed when sympathy , hope , tender care , even love could have anything to do with the figure on the bed.

4.

You tell me lies all the time ! 5.

Once the commotion had died down , he told them to break the time pencils and get to work.

6.

I told her I thought The Times would probably have a man on the spot and it was late , and I prised my Toshiba away from her grasping hands.

7.

Junior accountants at Price Waterhouse , one of the big six firms , have been told that now is the time to take their once-in-a-lifetime world tour or perhaps a summer stint as a yacht deckhand and that applications for extended unpaid leave bquo will be looked on favourably.

8.

Carter had told the police at the time they had a row over money and his wife had walked out , never to return.

While this group is the second most common pattern for tell the time string type, it is a false pattern, one that represents no interesting regularity. In this case, then, the first stage of sorting does not contribute directly to chunk detection. Part of the motivation of the second stage is to bring us closer to detecting such results as noise automatically, eventually enabling us to not only sort examples but also filter out noise. To help achieve this, the second stage examines the POS of the intervening words in the string tokens within a pattern. The POS tags are from the CLAWS tagset used to tag BNC2 . Listed here are the eight different POS trigrams representing the three words separating tell from the time in this group.

1. TO0_VVI_PRP 3. PNX_AV0_CJT 5. PNP_TO0_VVI 7. CJT-DT0_AV0_VBZ 2.

PNX_PUN_PRP 4. PNP_VVZ_DT0 6. PNP_PNP_VVD 8. AT0_NN2_PRP

The fact that the eight sentences, while all sharing a 3-word gap between tell and the time, each has a different POS trigram in that gap can serve as readily detectable indication that this group of eight tokens does not reflect an interesting regularity concerning tell the time.

Eventually our goal is to represent results to learners and teachers, not only to lexicographers. For this reason, such strategies for automatically detecting false positives and increasing precision will be important to ensure such examples are not presented to learners as instances of the chunk tell the time. In what follows, we describe our approach to representing our lexical chunk knowledge for learners and teachers.

Lexical Chunks and Knowledge Representation

A second challenge for lexicography concerning chunks is how to represent them to learners. We propose embedding the representation of chunk knowledge within the contexts where learners encounter chunks. As learners encounter the target language in contexts of authentic use, they are exposed to chunks (whether they recognize them as such or not). Our aim is to identify these chunks in real time directly within digital contexts. We illustrate how we have implemented this approach with collocations and describe the challenges in extending this approach to chunks. Our collocation tool (called Collocator) is a browser-based tool that can detect collocations in real time within the web pages that the user browses (Wible et al., 2004a).

The core component of Collocator that detects collocations in web pages in real time works much like the first iteration of our chunk detecting algorithm. The collocation-extracting scheme is part-of-speech sensitive, which means we have to detect the part-of-speech information of each word in browsed web pages in real time. We train a Markov Model-based POS tagger [START_REF] Brants | TnT-A statistical part-of-speech tagger[END_REF] and use British National Corpus (BNC)3 as our training data. The internal evaluation shows this tagger has 93 % precision including identifying unknown words. After part-of-speech tagging, the agent uses the following equation from (Wible et al., 2004b) to measure the word association score for all candidate word pairs:

( ) ( ) ( ) ( ) ( ) ( )       ⋅       = y sn y P x sn x P y x P y x normMI
, log , 2 where x, y mean the word with specific part-of-speech and sn means the number of distinct senses for that word listed in WordNet. This adaptation of traditional MI takes into account the polysemy of the words x and y by normalizing for the number of senses of x and y, helping overcome traditional MI's under-extraction of collocations that contain high frequency words. For example, traditional MI does not detect take as a collocate of the noun temperature (The nurse took the patient's temperature), but our normalized MI does detect take in this case.

Similar to the first iteration in our chunk detector, Collocator takes as collocation candidates all possible pairings of POS-specific words (x and y above) in which the two words appear within a five-word window of each other in our 20 million-words of running text of the BNC. Using the above measure, each x,y ordered pair yields a word association score. Collocations are word pairs that show a sufficiently strong word association between the two words in the pair. Thus, a minimum score threshold is used to select which of the candidate word pairs constitute collocations. This threshold can be lowered or raised to adjust the agent's precision and recall. The collocation knowledge thus extracted from our POS-tagged BNC feeds our browser-based Collocator, enabling it to detect and highlight collocations that appear in the web pages that the user browses (See figure 1).

Figure 1. Toolbar's dropdown menu with detected collocations and link to examples

Using Collocator as our reference point, we can now describe how our lexical chunk extraction is to be applied to learners. In brief, our purpose is to enrich Collocator so that it detects and represents not only collocations (typically, word pairs) but longer strings, that is, chunks. This tool will then detect and highlight chunks in real time within the web pages that the user browses. The approach to lexical chunk discovery described above is intended, then, as the knowledge source supporting this tool. The fundamental challenge this application poses is that of precision.

Figure 2. Example sentences

As shown above, each iteration of the chunking algorithm introduces noise and leads to more false positives with each iteration. A ubiquitous browser-based version of the chunker would detect strings in the web page that match any string types that have achieved a threshold word association measure at our 'discovery' stage. Recalling the example of tell the time, this means that a browser-based chunk detector would treat as a match not only the true positive He hasn't learned to tell the time, but also false positives like Tell him that unfortunately the time has not arrived. These failures in precision, while forgivable for a tool intended for lexicographers, are unacceptable for a tool intended directly for learners as they browse the Web. We choose to retain the higher recall created by our 5-word window and focus on increasing precision by adding a second stage that sorts these results into patterns, as described above. While our current sorting method does make it much easier to filter out false positives by hand, this is not improvement enough for a browser-based version for learners. The sorting strategy that we mentioned early of exploiting POS patterns, though requiring refinements, does hold promise. For example, it would enable the chunker to discard false positives like Tell him that unfortunately the time has not arrived since, as we have seen, it can discover that cases like this with a 3-word gap between tell and the time exhibit no regularities in the POS sequences appearing in that gap. However, the implementation of this strategy must be much more fully articulated and tested. Recently, we have added entropy measures to detect how stable the POS patterns are that occur within the strings. Preliminary results suggest high entropy as a promising indicator of false positives, allowing for automatic filtering for increased precision of our results.

The results display distinguishes three conditions: word pairs that meet the conditional probability threshold only are highlighted in red, those meeting the MI threshold only are displayed in blue, and those meeting both in purple. This makes it easy to compare the performance of the two measures or the value of using both.

http://www.natcorp.ox.ac.uk/what/garside_allc.html

http://www.natcorp.ox.ac.uk/


BSc George Fessakis
email: gfesakis@rhodes.aegean.gr

Angelique Dimitracopoulou

EXPLOITATION OF DATA MODELLING FOR DATABASE DESIGN IN SECONDARY EDUCATION LEARNING ACTIVITIES: A CASE STUDY CONCERNING REAL STORIES ANALYSIS

Keywords: Data modelling, database design, story analyses

Data models are representation systems for structural modelling of physical systems. Data models application results in databases that are amenable to computation enabling the modeller to interact with them for information retrieval, hypotheses testing, comparisons etc. In this paper we initially approach the didactic and learning value of data modelling for database design in terms of the features related to students' involvement in the related learning activities. In order to clarify the proposed analysis a case study is presented concerning the design of a database for the analysis of real stories about vehicular accidents. The case study results support the fundamental consideration that database design based learning activities offer opportunities for rich learning situations that activate high order thinking.

Introduction

Database educational interest has its own sources in supporting arguments from two main directions. The first direction concerns utilitarian arguments based on the economic significance of database technology, while the second concerns arguments about the value of databases as learning tools according to modern pedagogical approaches and learning theories. In this paper we initially formulate arguments from both directions about the educational interest of databases' design in secondary education and then we present a case study of real stories analysis by students using database design for the study of vehicular accidents. The case study supports the identification of the qualitative features of students' involvement in database design learning activities as they proposed in the first part of the paper. In addition the case study permits the examination of students' chances to improve their database use comprehension during the exploitation of databases in a general problem solving activity.

The educational interest of database design

Utilitarian interest for database education

Data processing systems are still of the most widely required application of information technology in organizations of any size. Databases are the cornerstone for most of the modern data processing systems, as well as, for the decision support systems that constitute the "nervous system" of the organizations. Databases enable dynamic information access and retrieval requiring very small physical volume and cost in contrast with their manual predecessors. The economic significance of databases and the spreading of their applications make the ability of their designing and development comparable to that of programming [START_REF] Batini | Conceptual Data Base design[END_REF]. International organizations predict that database administration related jobs will face among the greatest demand increasing in the interval 1996-2006 mainly because of the internet applications that are database based, the Enterprise Resource Management systems that organisations are installing and the availability of desktop dbms's [START_REF] Antony | CODASYS: A consulting Tool for Novice Database Designers[END_REF]. Non trivial database design by untrained and/or non expert people is not always successful [START_REF] Batra | Comparing Representations with Relational and EER Models[END_REF]. The above facts generate utilitarian interest and social requirement for large scale education in database use and design.

Didactic interest for database education

Databases are models representing the structural attributes of corresponding physical systems, [START_REF] Hancock | Computerized tools and the process of data modeling in[END_REF][START_REF] Minsky | Matter, Mind, and Models[END_REF][START_REF] Knight | Artificial Intelligence, 2 nd Ed[END_REF]]. Dbms's are general purpose modelling environments using data models as representation systems [START_REF] Batini | Conceptual Data Base design[END_REF][START_REF]On Conceptual Modelling[END_REF]. Data modelling process during database design has a clear constructivist character with final product the implementation of the database. In contrast to other structural modelling methods, (e.g. concept maps) data models' application produces databases that are amenable to computation enabling the modeller to interact with them. Interaction includes information retrieval, hypotheses testing, comparisons etc. The interactivity of databases facilitates the feedback "circuitry" construction to the designers. In addition database design requires often social collaboration with other designer and/or with the problem provider.

Working on dbms users exploit computational resources in problem solving using conceptual frameworks closer to the solver and/or the problem than to the architecture of the machine. One of the most obvious differences of databases to other modelling environments is that problems' exploiting databases can concern a large amount of entities which attributes' relationships are not simple algebraic ones or they are under investigation.

Using data base design, it is possible to propose interesting learning activities for a large variety of subjects where students construct interactive artifacts (databases) collaboratively fulfilling both the social constructivists learning theories issues and the modern student centred pedagogies. Databases are in this manner executable structural models that have general didactic and learning value.

In the following sections we support the above considerations/assumptions, presenting the general categpries of learning activities using databases design as well as the features of students' involvement in them.

General categories of learning activities using database design

According to Jonassen [START_REF] Jonassen | Computers as mindtools for schools,2 nd Ed[END_REF] databases are considered as a kind of mindtool that not only amplifies human cognitive power but transforms it requiring new ways of thinking [START_REF] Pea | Beyond amplification: Using the computer to reorganize mental functioning[END_REF]. Furthermore, Jonassen considers concept maps as another mindtool of the same category, namely "Semantic organization tools". We consider database design as a combination of informal and typical semantic analysis [START_REF] Fessakis | Improving database design teaching in secondary education. Using action research for documentation of didactic requirements and strategies[END_REF] that requires the construction of a concept map in order to produce a database schema. Combining the kinds of learning activities proposed by Jonassen for databases and concept maps, we can distinguish at least the following five categories of database design learning activities:

I. Ontological analysis of study field

In activities of this category students are supplied with a set of information sources (e.g. books, papers, www sites, authentic documents etc) in order to design a database that stores significant data in the framework of a study or the solution of a problem. During this kind of activities students could be committed to:

• Recognize and define concepts and/or object types from the problem domain as well as their relationships of interest. Furthermore, students must decide which attributes of the entities concerning the problem should be stored in the database in order to be useful for the activity.

• Collect data systematically to store in the database.

• Process data in order to discover or investigate relationships among the entities' attributes, to compare, to reason, to confirm, to contradict etc. The active involvement in ontological and conceptual analysis could be helpful in the improvement of the understanding of a study field or problem domain as well as to the development of metacognitive ability. Databases can be used for the discovery of knowledge and relationships using constructive activities in cases, where usual teaching means and strategies result in memorization exercises due to the large amount of data.

II. Real stories analysis

A large part of human knowledge is organized as a network of cases that humans recall, when they confront similar situations. Human experts often explain or decide and plan their actions mentioning similar cases. The systematic analysis of case studies a basic technique in the education of sciences like medicine or law. Case based reasoning is also a known technique for the development of Artificial Intelligence (AI) systems that present common sense. AI case based reasoning systems have a knowledge base of cases that enables them to trait similar problems [START_REF] Knight | Artificial Intelligence, 2 nd Ed[END_REF].

In 'real stories analysis' learning activities, students study a large amount of a problem family cases in order to design a database that they could exploit in a case of an analogous problem. The stories could be found in bibliographic references or can be a product of interview and/or observation, etc.

III. Using databases as study guides

Study guides are databases that a basic design has been implemented by the teacher and students store data as they study a subject. As an example, a student could store chemical characteristics of materials that he/she studies in a predesigned database. The resulted database could be used for information retrieval and general data analysis. In addition, the combination of the study guides of a large number of students could lead to common database containing a very large amount of data, which could enable interesting questions' formulation.

IV. Using database design for understanding assessment

The database design ability indicates at least structural knowledge for a problem. Furthermore, design errors often externalize misconceptions. These observations are the base of the last kind of learning activities in which students design databases in order to assess their understanding of a subject. In these activities it is purposeful to facilitate feedback mechanisms.

Characteristics of the involvement in learning activities using databases

Students involved in learning activities of the general categories described above are expected to have increased opportunities to experience many interesting characteristics that constitute meaningful learning [START_REF] Jonassen | Computers as mindtools for schools,2 nd Ed[END_REF]. More specifically students:

Activate their own cognitive models in order to plan their actions to the database through the interface of the dbms as well as to interpret the results of such manipulations.

Design and construct an interactive artifact in the framework of authentic problems that preserve their motivation. In addition, students often self manage their learning process, formulating the learning goals as well as the process to obtain them.

Reflect during the conceptual database design for the determination of the entities and their relationships. Conceptual modeling is a highly metacognitive and reflective process in the sense that externalizes the mental processing of students experiences. Students reflecting on a problem improve their structural knowledge about it and increase the possibility to advance their problem solving ability. Moreover the database design reflection can be supported during data analysis phase where students are able to check hypotheses, interpret information, compare data etc. For example student could reflect using a geographical database to relate general economic with social indices such as infants' death rate etc.

Collaborate in groups in the problem analysis as well as in the data collection and analysis phases. The collaboration advances their socialization and helps them to formulate common meaning using social negotiation.

Overcome the limitations of human memory and to decrease significantly the data processing time. As a result students can save time for critical analysis of data and understanding of interrelations between entities of the problem domain.

Reorganize students thinking combining the basic processing structures of dbms's for the solution of complex problems. Especially in relational dbms's students can solve complex problems exploiting relational algebra through the declarative language SQL.

The above characteristics and their learning facilitation value can be estimated through the following analysis of a case study where students were involved in a real story's analysis activity for the study of vehicular accidents.

A case study of real stories of vehicular accidents analysis using databases

In the case study presented in the following sections, 11 th grade students participated from two classes of a public vocational school in Greece, named 2 nd TEE of Rhodes. The 41 students were assigned an obligatory subject named "databases" in which they where familiarized with the desktop dbms that was available in the school lab while they had a typical introduction to the relational and Entity-Relationship data models for logical and conceptual database design respectively. For the familiarization of students with the relational databases design, and use, a series of lectures and hands on lab activities has been designed and implemented. The instruction of database design to students is an educational research questions generation point that it interests the authors but [START_REF] Fessakis | Students' cognitive models about databases and difficulties in manual database design. Third Pan-Hellenic conference with international participation[END_REF][START_REF] Fessakis | Improving database design teaching in secondary education. Using action research for documentation of didactic requirements and strategies[END_REF] the analysis of which is out the scope of current paper.

The activity scenario required students to get interviews from people that have been involved to vehicular accident in any way in order to analyze the collected data to produce knowledge and formulate conclusions of any kind about the phenomenon. The scenario is organized in four discrete phases. In the 1 st phase, students constructed a common concept map collaboratively, merging the maps produced by students groups, in order to understand the problem and determine the abstraction level that they will adopt. In the 2 nd phase the produced concept map, as a "typical" problem definition was used as input for the database design process. Students designed the database in a collaborative manner for each class. The design of the first class was merged with that of the second class in order to use a common data base. In the 3 rd phase students used the database design for the formulation of the record that should be constructed for each interview. Interview records were paper forms that reminded the interviewer what data should be recorded in a systematic manner. In the same phase also, the data collected by the interviews were stored in a common database. In the 4 th phase students engaged in information retrieval and data analysis activities for knowledge production, hypotheses test etc. In the following sections the students activities are briefly described for each phase.

The 1 st phase: Conceptual map conctruction

During the first phase of the activity, students constructed a conceptual map for the problem of vehicular accidents. Students decided about the concepts their attributes and characteristics concerning the problem clearing out ambiguous points. Students worked for two sessions of 45 minutes each. During the first session students worked in small groups while in the second as a whole class where they combined their concept maps in that of fig. 1 using the whiteboard. Each student took a hard copy of this concept map.

The concept map was constructed using brainstorming, social negotiation and limited teacher support. Students had to decide the abstraction level for the description of the concepts as well as to define their meaning. Disagreements are unavoidable in an operation like this, but students solved these problems using social negotiation in which they formulated arguments using terms from the problem domain and asking for teacher support. During conceptual map construction students' reflect on and become conscious about the conceptual structure of the problem domain. Students used the conceptual map in order to design the database as well as an interview record as it is described in the following sections.

Vehicular

The 2 nd phase: Database design

The database design complexity was significantly decreased for students since they had already decided about the information that should be stored and faced most of the understanding problems during the conceptual map construction. Fig. 2 presents the ER schema for the database produced by students in common. Database design was implemented during two sessions, 45 minutes each. As in the case of the conceptual map students worked in small groups in the first session and then as a class they produced combined their designs in a common one. Database should be common not only among the groups of each class but also among the two classes in order to store data in one database of a more interesting size. In fig. 2 we can see that the ER conceptual schema it is about three entities full of attributes and related with two binary relationships. Students face problems with database design but there is a percentage of students that can accomplish successfully the design process for many interesting cases. Distributing these students in groups in companion with the class function phase can eliminate the motivation elimination cases.

Students reviewed the database design in this phase was by test interviewing each other and considering questions that should be answered by the proposed database.

The 3 rd phase: Data collection and entry

Students designed interview records using conceptual map and database design. In order to get familiarized with the interview records students interviewed each other. After the familiarization and the final corrections of the interview records, students had a period of a week in order to collect the real data. The people that students asked for interview where students from other classes, or other schools, teachers, parents, family friends etc. The data collection week was a pleasant disturb for the normal school life since it become the main issue at school and was accompanied by several incidents that equipped students with rich social experiences. Finally, students collected 45 interview records concerning 69 accident participants. A small group of students was assigned to implement the data entry using a special data entry form designed by them (fig. 3).

The 4 th phase: Data analysis end exploitation

Data analysis was the last phase of the learning activity. Students' groups supplied with a, full of data, copy of the database in order to create answers to their own and/or teacher made questions. The teacher's action in this activity includes:

• Proposition of questions to groups that could not decide what to study and advanced questions in groups that completed some basic ones.

• Checking of the queries' correctness and interpretations reasoning.

• Supporting the dbms' use and the syntax of queries.

• Encouraging students' groups to produce interpretations of their results and to compare their findings with those of other groups for the same question. Students worked in small groups for the first session and then presented their findings to the class. Students develop a competitive climate among the groups in terms of conclusion production and information retrieval that helped to advance their dbms use ability as well as the understanding of the subject. Situations with special learning interest appeared when different groups disagreed on the results for the same questions. Students tried to find which group was right triggering social negotiation and powerful feedback mechanisms.

Information discovered using database

Queries produced by students are a good indication about the quality of the learning situation that they have experienced, as well as of the related concepts understanding. Three of the most interesting and representative questions and underline concepts are presented in the following sections. Students TA02 and TA20 formulated the question "is the participant vehicle motorcycle or car;" meaning (as they explained) that they would like to see whether more of the accidents concern motorcycles than cars. Students hypothesized that it is more likely to have an accident using a motorcycle. In order to approach the question students produced a query that computes the frequency distribution of the participants to the vehicle kind as it is presented in fig. 4. The teacher asked students to produce the frequencies' distribution of participants in vehicle kinds, for students and non students separately. Students produced the frequency distributions of figures 5 and 6 respectively. Comparing the results, students were confused initially and then said that students' accidents concerned motorcycles more frequently than non students and that the total number of students' accidents in the database is larger than those of non students. After a short discussion students seemed to understand that in order to see if motorcycles participate more frequently in accidents they should compare the ratio of accidents concerning a motorcycle per number of motorcycles in use with corresponding ratios of other vehicle kinds, thus working on "simple statistical concepts".

Query 2. Safety means use frequencies.

Many students groups produced the query of figure 7 for the question "How many of the participants did use the belt or the helmet". Students commented on the infrequent use of the safety means but they observed that the total number of participants is 68 instead of 69. This observation uncovered an orphan participant that was not assigned to any accident and triggered a discussion about the "foreign key" and the "referential integrity constraint" concepts.

Discussion on the Learning activity general characteristics

In this section some essential factual observations are collected in order to support the assumptions about the didactical interest of database design based activities as they presented in paragraph 2.2.2 above. The previously described learning activity permits at least the following, didactical interesting, observations:

• It concerns the collection of a large amount of authentic data.

• Problem domain entities and their relationships are not given, but they are defined during problem analysis' phase using apart from references and teacher support, the brainstorming, and social negotiation processes. • Entities attributes' relations are not known and/or simple algebraic, but they are a discovery object. • Students worked in a group manner even in after school hours in social related activities.

• Students managed to retrieve interesting information querying the collected data in a relatively short time interval producing mainly frequencies' distributions. The time requirements for the solution of the same problems without database technology tools are not comparable. Furthermore, students could compare, try and improve several queries before the adoption of a solution. • The comparison of the different solutions among student groups for the same questions provided feedback to students and triggered discussion about the subject of the study as well as the databases technology. • Students are able to formulate interpretations for a study domain and to try to support those using queries for the database. During this process students can transfer knowledge from the database to the problem domain and vice versa. This transfer function is called conceptual reframework and is a main learning mechanism of modeling environments. • Group work is possible to decrease the possibility of student failure engagement in database design based learning activities if there are a variety of ability levels among the group members. The above observations of the learning activity implementation constitute evidence that students engaged in Real Story Analysis learning activities using database design present the characteristics of activation, design and construction, reflection, collaboration, limitations overcoming, and thinking reorganization as described in section 2.2.2. In other words, students have increased possibilities to experience high quality meaningful learning situations and activate high order thinking in a manner consisted to the modern social constructivist learning theories and pedagogical approaches.

Conclusions

The educational interest about instruction for the use and design of databases is based on utilitarian as well as pedagogical reasons. Utilitarian interest sources from the economic significance of databases technology while pedagogical interest from the possibility of implementation of general learning activities using databases' design and application. Students' systematic familiarization with database design and use is expected to increase the likelihood of successful involvement in such learning activities. Database design based activities can be at least of the following general categories: Ontological analysis of study field, Real stories analysis, Study guides, and Understanding assessment.

In this paper, it was presented a database design based learning activity, of the category of Real Stories Analysis. As the case study presented supports, database design based learning activities characteristics are consistent to modern learning theories and pedagogical approaches. Databases are considered as executable structural models and their design as a modelling activity. During data modelling, students have the opportunity to experience situations in which they activate high order thinking and reflection obtaining meaningful learning. Students designing databases improve the structural knowledge about the subject under study and increase their problem solving ability. In addition, due to the collaborative character of the design activities, they exercise their social and communication skills in the framework of authentic and meaningful problems.

Figure 1 .

 1 Figure 1. The students' concept map for the vehicular accidents study (translated form the Greek manuscript).

Figure 2 .

 2 Figure 2. The conceptual schema of the database for the vehicular accidents study as translated from the Greek manuscript.

Figure 3 .

 3 Figure 3. The data entry form. It contains a subform for the participants of each accident.

Query 1 .

 1 Frequency distribution of participants to the vehicle kind.

Figure 4 .

 4 Figure 4. Query for the frequency distribution of participants to vehicle kind, translated from the Greek version of the database.

Figure 5 .

 5 Figure 5. Frequency distribution of students per vehicle kind.

Figure 6 .

 6 Figure 6. Frequency distribution of non students per vehicle kind.

Figure 7 .

 7 Figure 7. Query safety means use.

Figure 8 .Query 3 .

 83 Figure 8. Frequencies of safety mean use.

Figure 9 .

 9 Figure 9. Accident causes frequencies query by TA05 and TA14.Figure 10. Accident causes frequencies by TA05 and TA14.

Figure 10 .

 10 Figure 9. Accident causes frequencies query by TA05 and TA14.Figure 10. Accident causes frequencies by TA05 and TA14.

Figure 11 .

 11 Figure 11. Accident causes frequencies query by TA16 and TA19.

Figure 12 .

 12 Figure 12. Accident causes frequencies by TA16 and TA19.


Polina Lemenkova

ILWIS GIS for monitoring landscapes in tundra ecosystems : Yamal peninsula, Russia

la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Brief Summary Brief Summary

Research aim:

• distribution of various land cover types in Yamal Peninsula

• monitoring changes in tundra landscapes

• analysis of the landscape dynamics during the past two decades .

Data: Landsat TM scenes for 1988 and 2011 years.

Originality: Application of ILWIS GIS spatial analysis tools and Landsat imagery for Bovanenkovo region in Yamal.

Brief Summary Brief Summary

Research aim:

• distribution of various land cover types in Yamal Peninsula

• monitoring changes in tundra landscapes

• analysis of the landscape dynamics during the past two decades .

Data: Landsat TM scenes for 1988 and 2011 years.

Originality: Application of ILWIS GIS spatial analysis tools and Landsat imagery for Bovanenkovo region in Yamal. Processes:

• seasonal flooding,

• active erosion processing,

• permafrost distribution and Human activities on Yamal: part 1

Yamal Peninsula: reindeer herding

The most typical anthropogenic activity on Yamal Peninsula is reindeer herding (Fig. 3). Yamal is a homeland for ca 5000 nomadic Nenets tribes migrating with herds up to 1200 km annually. Herding is natural process. However, it may have negative effects: pasture overgrazing and pressure on vegetation coverage.

Human activities on Yamal: part 2

Yamal Peninsula: gas exploration

Yamal Peninsula is a place where the gas field "Bovanenkovo" is being explored (Fig. 3). Geological gas exploration cause serious anthropogenic pressure on the environment. Indirectly it also includes additional construction of roads, settlements, human facilities, etc.

(n) Yamal: scheme of gas exploration.

(www.reindeerblog.org)

(o) View on the gas exploration station.

Research Methods: Step 2.

Image classification

• The key research method is supervised classification (Minimal Distance), which is based on the spatial analysis of spectral signatures of object variables, i.e. vegetation types.

• The classes sampling was performed using Sample Set tool in ILWIS GIS.

• The training pixels for each land cover type were selected as representative samples and stored as classification key.

• Requirement for training pixels: they have contrasting colors, visually visible and distinguishable on the image.

Research Methods: Step 3. Mapping (1988) Landsat TM scene

Results

Land cover classes: assessment of changes

Results show:

• overall increase of woody vegetation (willows and shrubs)

• decrease of peatlands, grass and heath areas. This illustrates environmental process of greening in Arctic, i.e. the unnatural increase of woody plants. The gradual changes in patterns and distribution of plant species affect landscape structure in Yamal. Triggering factors:

• complex environmental changes in Arctic

Figure: 4

 4 Figure: 4. Landscapes of Yamal. Source: http://pixtale.net/

Figure

  Figure: 5. Landscapes of Yamal.

  (l) Tundra landscape: reindeer herds. Source: environmentalresearchweb.org (m) Typical scene of reindeer grazing. Photo: Bryan Alexander

  Photo: http://barentsobserver.com

Figure

  Figure: 21. Thematic mapping

FigureGIS

  Figure: 22. Landsat TM, 1988

  One of the typical process in Yamal tundra: cryogenic landslides. Landslides affect local ecosystem structure, because they change vegetation types recovering after the disaster.

	Introduction	Study area	Methods	Results	Discussion	Résumé	Conclusion	Thanks	References
		Environment of Yamal, part 2
	Yamal Peninsula: environmental settings
	• intensive
		local landslides formation.

Table : 1

 : . Statistics on land cover classes, Bovanenkovo region, Yamal

	Introduction
	Land Cover Class	1988, # pixels	2011, # pixels	1988, ha	2011, ha
	Shrub tundra	220447	168226	1146.3244	874.7752
	Short shrub tundra	165079	270158	858.4108	1404.8216
	Willows	193645	457004	1006.954	2376.4208
	Tall willows	103954	71952	540.5608	374.1504
	Sparse short shrub tundra	176511	759380	917.8572	3948.776
	Dry grass heath	641420	231719	3335.384	1204.9388
	Sedge grass tundra	27545	57052	143.234	296.6704
	Dry short shrub tundra	8984	16993	46.7168	88.3636
	Wet peatland	761231	531809	3958.4012	2765.4068
	Peatland (sphagnum)	120328	93979	625.7056	488.6908
	Dry short shrub-sedge tundra	173693	92242	903.2036	479.6584

  local cryogenic processes (e.g. successive change in vegetation recovering after cryogenic landslides) GIS-based mapping (e.g. ILWIS GIS) is important tool for the landscape monitoring and management.

	Introduction Introduction Introduction Introduction	Study area Study area Study area Study area	Methods Methods Methods Methods	Results Results Results Results	Discussion Discussion Discussion Discussion	Résumé Résumé Résumé Résumé	Conclusion Conclusion Conclusion Conclusion	Thanks Thanks Thanks Thanks	References References References
				Résumé Conclusion
	Summary
	...to conclude:

•

Processing of remote sensing data (e.g. Landsat TM scenes) by means of GIS improves technical aspects of the landscape studies.

Application of RS data is especially important for studies of northern ecosystems, since it enables to perform spatial analysis of remotely located areas in Arctic regions. Spatial analysis of land cover types can help to detect local environmental changes.

Acknowledgement

Thanks The financial support of this research has been provided by the Fellowship of the Center for International Mobility (CIMO) of Finland. Contract No. TM-10-7124 (Decision 9.11.2010). This research was done at the Arctic Center, University of Lapland. I thank my scientific chief Prof. Dr. Bruce C. Forbes from the Arctic Center and colleague Dr. Timo Kumpula from the University of Eastern Finland for our collaboration.