M Nunez

R Rosenbaum

Shooka Karimpour

Anne-Marie M Boulay

Michael J Lathuillière

Manuele Margni

Laura Scherer

Francesca Verones

Stephan Pfister

Montserrat Núñez

Ralph K Rosenbaum #

A multimedia hydrological

  L'archive ouverte pluridisciplinaire

TOC 1. Introduction

The impact category describing impacts of (fresh-)water use and consumption in life cycle assessment (LCA) has been subject to large advancements in the last decade. The current water footprint principles, requirements and guidelines described in ISO 14046 1 build on the framework described by Bayart et al., [START_REF] Bayart | A Framework for Assessing off-Stream Freshwater Use in LCA[END_REF] and define freshwater consumption as any use of freshwater that changes water availability in a watershed through evapo(transpi)ration, product integration, direct release to the sea, and inter-basin transfers.

Available freshwater consumption-related indicators are based on either a volumetric approach of the water consumed [START_REF] Hoekstra | The Water Footprint Assessment Manual. Setting the Global Standard[END_REF] (what we call first generation of indicators) or on scarcity indices [START_REF] Pfister | Assessing the Environmental Impacts of Freshwater Consumption in LCA[END_REF][START_REF] Boulay | The WULCA Consensus Characterization Model for Water Scarcity Footprints: Assessing Impacts of Water Consumption Based on Available Water Remaining (AWARE)[END_REF] (what we call second generation of indicators, see further details on both generations of indicators in section S1 of the Supporting Information, SI). The first generation of indicators 4 only performs an inventory and thus not a footprint assessment according to ISO 14046 [START_REF]ISO 14046 Environmental Management Water Footprint -Principles, Requirements and Guidelines[END_REF] , while the second generation of indicators shows limitations in describing the consequences of a lack of water on areas of protection (AoP), such as ecosystem quality. They assume that there are smaller or no impacts in areas of low water scarcity and greater impacts in areas of high water scarcity. However, it is possible that water rich areas may contain ecosystems that are accustomed to abundant water availability and may thus be more vulnerable than those in areas that are water stressed. To date, no LCA method comprehensively distinguishes water masses (e.g. surface water and ground water) and their transport flows (e.g. from river to atmosphere) within the boundaries of a watershed and beyond (e.g. air advection), thus overlooking details in hydrological processes that affect the environmental relevance of the assessment. In addition, a recent analysis [START_REF] Núñez | Critical Analysis of Life Cycle Impact Assessment Methods Addressing Consequences of Freshwater Use on Ecosystems and Recommendations for Future Method Development[END_REF] concluded that a structured life cycle impact assessment (LCIA) framework for assessing impacts of water consumption on ecosystems is currently lacking, as can be observed by the scattered and often incompatible developments of water impact assessment models published in recent years. [START_REF] Verones | Modeling the Local Biodiversity Impacts of Agricultural Water Use: Case Study of a Wetland in the Coastal Arid Area of Peru[END_REF][START_REF] Van Zelm | Implementing Groundwater Extraction in Life Cycle Impact Assessment: Characterization Factors Based on Plant Species Richness for The Netherlands[END_REF][START_REF] Tendall | Impacts of River Water Consumption on Aquatic Biodiversity in Life Cycle Assessment -A Proposed Method, and a Case Study for Europe[END_REF][START_REF] Lathuillière | Land Use in LCA: Including Regionally Altered Precipitation to Quantify Ecosystem Damage[END_REF] These models are all valuable contributions in themselves, but impossible to combine to an integrated, global characterisation model that makes such developments consistently operational in LCA.

Building on the recommendations by Núñez et al. [START_REF] Núñez | Critical Analysis of Life Cycle Impact Assessment Methods Addressing Consequences of Freshwater Use on Ecosystems and Recommendations for Future Method Development[END_REF] this paper aims to set a novel framework and methodological guidelines to support the consistent development of impact indicators for water consumption in LCA (third generation of indicators), with a focus on ecosystem quality, including comprehensive illustration of impact pathways that should be covered. The framework is applied to a hypothetical but realistic illustrative case study that represents water consumption in a generic coastal zone with a non-exhaustive number of phenomena represented.

The study contributes to the current WULCA (Water Use in LCA, a global expert task force of the UN Environment Life Cycle Initiative) activity, whose overall objective is to develop consensus-based indicators for water use impact assessment as part of the global guidance LCIA project. [START_REF] Jolliet | Global Guidance on Environmental Life Cycle Impact Assessment Page 28[END_REF] Consensus-based indicators for human health and for a generic scarcity-based indicator have already been developed and recommended. 12 This paper focuses on the third set of target indicators, namely ecosystem quality (or biodiversity), and does not consider aspects of water quality and consequences on ecosystems due to water quality degradation (see section S2 in SI).

Materials and methods

Methodological guidelines for water consumption impact assessment

Prior to introducing the guidelines, all the relevant environmental interventions and pathways, which may be associated with water consumption, are described (see section S2 in SI for further details). This step is necessary to identify to which causality chains the methodological guidelines of the article apply. We identified three types of environmental interventions that may lead to changes in water availability in one or more water compartments: consumptive use of water; land use and land use change; and water stream use and management. In addition, direct and indirect emissions to water, including degradative water use, are considered outside the scope of these guidelines since emissions do not lead to changes in purely physical water availability or water consumption. In the analysis, we introduced the consideration of hydrological stocks, which is going to redefine the environmental interventions and impact pathways that up to now have been associated with water consumption. Bayart et al. [START_REF] Bayart | A Framework for Assessing off-Stream Freshwater Use in LCA[END_REF] describe the watershed as being the most relevant hydrological unit to consider for water consumption in LCA, as a first step on integrating regionalisation in LCIA. However, each watershed hosts a series of hydrological stocks (e.g. soil, aquifers) and flows (e.g. evaporation, percolation) that vary in space and time due to meteorological, environmental and biophysical conditions. We introduce the "water compartment" term defined as any water body where water is temporarily stored and between which water flows may take place, thereby affecting its water availability.

A mechanistic characterisation factor structure

Assessing the environmental impacts of water consumption on ecosystem quality implies the modelling of cause-effect pathways linking water consumption (i.e. life cycle inventory (LCI) data) to potential habitat modifications and biodiversity damages through characterisation models, as shown in Figure S1 to Figure S3. According to ISO 14040, 13 characterisation models "should be based upon an identifiable environmental cause-effect mechanism and/or reproducible empirical observation". Mechanistic characterisation factors (CFs) capture this recommendation well, since they translate the environmental intervention to its impact in the selected category indicator stepwise, based on a sequence of interconnected sub-factors, further described in Equation 1. Mechanistic models for use in LCA were first formally proposed by Udo de Haes et al. 14 and are today widely used to characterise the impact of emissions 15,16 in LCA. Mechanistic models for emissions link the pollutant emission (LCI) to the mass in different environmental compartments (fate factor, FF), to the intake by ecosystems or humans due to direct and indirect exposure through e.g. ingestion (exposure factor, XF), and to the potential effects on target organisms at the midpoint (effect factor, EF) or endpoint level (damage factor or severity factor, DF). 17,18 The CF is the product of these sub-factors. Some emission models (e.g. terrestrial acidification) use a sensitivity factor (SF) to distinguish between the buffering capacity of different regional receiving environments instead of the XF. [START_REF] Roy | Life Cycle Impact Assessment of Terrestrial Acidification: Modeling Spatially Explicit Soil Sensitivity at the Global Scale[END_REF] This terminology accommodates well to the nature of ecosystems impacts due to change in water availability. Therefore, the term SF is used in the article from here on. Equation 1 shows the generic formulation of a mechanistic CF.

CF = FF × SF × EF × DF Equation 1

In LCA, the use of matrix algebra to assess environmental loads in different media (freshwater, air, etc.) is common practice in the emission-related impact categories (e.g. by the toxicity models like USES-LCA, [START_REF] Huijbregts | Priority Assessment of Toxic Substances in the Frame of LCA -Development and Application of the Multi-Media Fate, Exposure and Effect Model USES-LCAIVAM[END_REF]

ሺCFሻ = ሺDFሻ × ሺEFሻ × ሺSFሻ × ሺFFሻ Equation 2

Although Equation 1 has already been applied in a few water consumption LCIA models, [START_REF] Van Zelm | Implementing Groundwater Extraction in Life Cycle Impact Assessment: Characterization Factors Based on Plant Species Richness for The Netherlands[END_REF][START_REF] Lathuillière | Land Use in LCA: Including Regionally Altered Precipitation to Quantify Ecosystem Damage[END_REF][START_REF] Verones | Effects of Consumptive Water Use on Biodiversity in Wetlands of International Importance[END_REF] there is no general recommendation for adopting such a modelling principle for water consumption impact assessment. We suggest here extending the recommendation of using mechanistic CFs, which so far has only been applied to emissions, 14 to model the impacts of resource consumption as well. Specifically, we propose adopting the mechanistic CF structure for assessing the impacts of freshwater consumption on ecosystems, based on the reasoning explained in section S4 in the SI.

We propose the below definitions of fate, sensitivity, effect and damage factors in a mechanistic CF representing freshwater consumption impacts on ecosystem quality.

Fate factor (FF).

The FF models the propagation of water consumption between water compartments of the hydrological cycle, which results in a duration and magnitude of the effect in every affected compartment. It represents the mass balance in the system, which depends on momentum and energy, which accelerate mass transfer, as well as on environmental resistance, which are processes decelerating mass transfer between compartments. compartment can also be a withdrawal/release compartment (see (FF) in the illustrative example).

Sensitivity factor (SF). SF models any ecosystem response as a result of a change in water

quantity in a compartment in order to prevent the dependent ecosystem to be affected by the change in water availability. In other words, it reflects whether the change of available water in an affected compartment will be compensated for or will create a water deficit, similarly as defined in the adaptation capacity of the exposure factor by Boulay et al. [START_REF] Boulay | Regional Characterization of Freshwater Use in LCA : Modeling Direct Impacts on Human Health[END_REF] to model the human population affected by the change in water quantity. The result after applying the SF (i.e. FF×SF) can be interpreted as the time fraction of water availability change in a compartment affecting ecosystems. SFs can be defined at any ecosystem quality indicator level (e.g. species, guilds) and for any attribute of the ecosystem level selected (e.g. composition, function). [START_REF] Curran | Toward Meaningful End Points of Biodiversity in Life Cycle Assessment[END_REF] For instance, a SF based on functional properties of habitats can measure the physical stability of a habitat (e.g. river's resilience) against hydrological changes. [START_REF] Damiani | Habitat Suitability: Water Use Impact Assessment for Ecosystems beyond Counting Species[END_REF] A SF based on species responses to habitat structure can assess the capacity of a species to access alternative freshwater resources. [START_REF] Verones | Effects of Consumptive Water Use on Biodiversity in Wetlands of International Importance[END_REF] In this latter case, the longer the distance to alternative freshwater resources, the lower the capacity of the species to compensate for the reduced water availability. The sensitivity of an ecosystem can also be considered as resilience and included in the effect factor, instead of in the sensitivity factor. To calculate SFs, the distinction between terrestrial, aerial and purely aquatic biodiversity can be important, since the capacity of each biodiversity type to withstand to changes in water availability varies. On the one hand, sessile organisms (e.g. terrestrial plants) and generally also aquatic species (e.g. fish) may often not be able to offset reduced water availability (except if water is added in the affected compartment, whose physical effects on the water balance are already accounted for in the FF). On the other hand, aerial and mobile terrestrial organisms may 10 travel a certain distance in search of an alternative source of water to satisfy their vital needs.

However, this can lead to additional competition between the native and the new species for the alternative resource, ultimately increasing water scarcity and leading to new impacts through a rebound phenomenon. To facilitate applicability, SFs calculated at detailed ecosystem levels (e.g. species) can be aggregated across taxonomic groups or ecosystem types (e.g. biodiversity of river ecosystems, biodiversity of lake ecosystems). A possible dimension for the SF is the distance that a species or community is able to travel to reach another waterbody in the region, which can be expressed with units of e.g. km or it can be dimensionless as the percentage in relation to a full compensation. Dispersal distances of taxa and species can be used for this purpose and are available in the literature. [START_REF] Kharouba | Do Ecological Differences between Taxonomic Groups Influence the Relationship between Species' Distributions and Climate? A Global Meta-Analysis Using Species Distribution Models[END_REF][START_REF] Santini | Ecological Correlates of Dispersal Distance in Terrestrial Mammals[END_REF] The sensitivity factor matrix (SF) contains SF with a column denoting an affected compartment and a row denoting an ecosystem or one of its components. The size of (SF) is determined by the number of environmental compartments n i considered and the number of ecosystems or ecosystem components considered and is thus (n ecs × n i ) (see (SF) of the illustrative example).

Effect factor (EF).

The EF accounts for any kind of ecological changes in habitats and biodiversity that cannot offset changes in water availability in the compartment. For instance, an EF can measure a change in ecosystem productivity (e.g. in terms of net primary productivity) due to changes in water availability in the soil profile, [START_REF] Pfister | Assessing the Environmental Impacts of Freshwater Consumption in LCA[END_REF] a change in a river habitat as a result of the habitat sensitivity to a change in river discharge, [START_REF] Damiani | Habitat Suitability: Water Use Impact Assessment for Ecosystems beyond Counting Species[END_REF] and the species affected due to a change in habitat. The EF can also cover effects a species suffers when it cannot fully compensate for insufficient water availability and it needs to access other water resources. EFs should differentiate between aquatic, aerial and terrestrial ecosystems and between taxonomic groups, since water volume-to-effect response curves are very specific to each life form. [START_REF] Tendall | Impacts of River Water Consumption on Aquatic Biodiversity in Life Cycle Assessment -A Proposed Method, and a Case Study for Europe[END_REF] In nature, an environmental shift from baseline conditions can result in a community turnover determined by species-specific adaptation strategies (e.g. generalist, opportunistic, specialist taxa). Spatiallyexplicit curves and vulnerability statuses of the affected species can provide relevant information to capture regional biodiversity specificities and biodiversity damages. [START_REF] Verones | Effects of Consumptive Water Use on Biodiversity in Wetlands of International Importance[END_REF] For example, different species in terms of taxon, endemism, life traits and Red List status, dwelling in different habitats, will likely be affected differently by a change in water availability. The EF may be expressed in loss of habitat (e.g. in m 2 or m 3 ) or, when closer to the endpoint or damage, it can provide information on the effective influence of physical habitat change on target species, which results in an indicator expressed in terms of species affected (e.g. potentially affected fraction, PAF) per reduced water availability in each time. The effect factor matrix (EF) contains EF in its diagonal elements with each column and row denoting an ecosystem or ecosystem component and all offdiagonal elements being zero. The size of (EF) is determined by the number of ecosystem components n ecs considered in all spatial units and thus it is always square (n ecs × n ecs ) (see (EF) of the illustrative example).

Damage factor (DF).

The DF distinguishes the severity of effects. It converts the midpoint metric into an endpoint metric expressing an ecosystem quality loss, usually in terms of species disappearance (e.g. potentially disappeared fraction, PDF) per reduced water availability in each time, following the recommendation of the UN Environment Life Cycle Initiative. [START_REF] Verones | LCIA Framework and Cross-Cutting Issues Guidance within the UNEP-SETAC Life Cycle Initiative[END_REF] As for the EF, it is important to distinguish between impacts taking place on a regional level (e.g. watershed level, representing a local loss of ecosystem functionality) or impacts happening at a global scale (global extinction of a species). This means specially that the vulnerability of species or ecosystems towards human interventions need to be taken into account. Although there are examples in the literature for aquatic ecosystems, [START_REF] Tendall | Impacts of River Water Consumption on Aquatic Biodiversity in Life Cycle Assessment -A Proposed Method, and a Case Study for Europe[END_REF][START_REF] Verones | Effects of Consumptive Water Use on Biodiversity in Wetlands of International Importance[END_REF] there is so far no consensus on the approach and no harmonisation across impact categories. This representation of vulnerability is currently investigated as part of phase 3 of the Global Guidance on LCIA Indicators project by the Life Cycle Initiative for ensuring a compatible approach among ecosystems (terrestrial, aquatic, and marine) and impact categories. Similar to the EF matrix (EF), the damage factor matrix (DF) contains DF in its diagonal elements with each column and row denoting an ecosystem or ecosystem component and all off-diagonal elements being zero. The size of (DF) is determined by the number of ecosystem components n ecs considered in all spatial units and thus it is always square (n ecs × n ecs ).

A list and detailed analysis of the FF, SF, EF, and DF that have been used in the literature to model water use impacts on ecosystem quality up to the year 2014 is provided by Núñez et al. 6

Fate factor

Spatial modelling aspects: multimedia water consumption setup. As shown in Figure 1, the FF multimedia model has two types of spatial components in a given spatial unit:

• Water compartments: individual building blocks of the hydrological cycle where water is temporally stored, for example the atmosphere

• Water flows: fluxes (displayed as arrows) into and out of every water compartment that together constitute the hydrological cycle, such as evaporation and precipitation

The water compartments and spatial units need to have variable sizes to account for local and global aspects to model the flows. Therefore, the model setup needs to apply techniques that simulate the flows across water compartments with optimal computational costs. Adaptive mesh refinement is a technique in high-performance scientific computing, which is applied in different contexts, such as hydrodynamic modelling and climate modelling, [START_REF] Rantakokko | Parallel Structured Adaptive Mesh Refinement[END_REF] with the purpose to adapt the mesh size to the resolution and detail required to represent the important features of the simulation in different scales and domains. We propose the same hierarchical structure used by many adaptive mesh refinement methods [START_REF] Edelsohn | Hierarchical Tree-Structures as Adaptive Meshes[END_REF] to be used in the formulation of FFs, which can be stored both in raster and vector formats (see further information in section S6 in the SI).

The model setup extends over different nested spatial units (local, regional, and rest of the world, see below). There are different structural solutions in terms of number of spatial units and its resolution (see section S6 in SI). The three nested spatial scales of the model are the following:

• Local scale: any open water system nested in a region and in the world that can be considered a basic unit of the landscape and of the water cycle in the context of LCA. It is commonly a sub-watershed or a watershed, but depending on the modelling option applied, it may also be any other smaller (e.g. a farm or a grid cell with multiple layers)

or bigger (e.g. a country) system. The local scale is the most refined spatial scale of the FF model. Within its boundaries, all existing (or at least relevant) water compartments and flows are differentiated (Figure 1) to the best possible extent.

• Regional scale: open water system nested in the globe, consisting of the connection of the local entity with its surrounding. The regional scale allows for the accounting of the effects that water consumption in the local scale has beyond its boundaries. For instance, it allows assessment of the cascade effect downstream the point of consumption in a watershed when associating sub-watersheds, and the assessment of the effects on a country's water availability when connecting evaporative recycling flows between watersheds. Some spatial configurations (e.g. Figure S7) do not need a regional scale. In terms of water compartments, the regional scale may be represented with different levels of detail from fully detailed to a simplified version depending on data availability, software capabilities, and the specific objectives of the LCIA model.

• Rest of the world (ROW) scale: closed water system within which the local and regional entities are nested. The sum of the water in the local, regional and ROW scales amount to the water in the globe. Since the objective of ROW is the conservation of a constant water mass balance on Earth, rather than the detail of its water stocks and flows, it can be represented by a single generic water compartment.

The proposed FF multimedia model follows the principle of parsimony (i.e. as simple as possible, as complex as necessary) successfully applied e.g. in USEtox. [START_REF] Rosenbaum | USEtox-the UNEP-SETAC Toxicity Model: Recommended Characterisation Factors for Human Toxicity and Freshwater Ecotoxicity in Life Cycle Impact Assessment[END_REF] Furthermore, it is based on the general architecture (compartments and flows) of global hydrological models and literature on land-surface-subsurface and climate interactions (see references in Table S1). The conceptual model is depicted in Figure 1, with what we consider to be the simplest, yet relevant, water cycle to model impacts of water consumption in a LCA context. This model is only a guideline for future method developers, who may add or remove components (i.e. compartments and flows) and adapt the configuration of the spatial scales depending on their model requirements (see further details in section S6 in SI). Definitions of the water compartments and water flows entering and leaving each compartment in Figure 1 are presented in Table S1. S1. Since the aim of most LCA studies is the comparative assessment of marginal changes in the environment, the reference state of our FF model represents a contemporary water balance under anthropogenic influence. This representation explains why artificial water bodies such as reservoirs are included in the surface water compartment, and modelled as part of the environment. Further FF models representing alternative hydrological cycles for different LCA purposes can also be developed. For instance, a pristine water balance under pristine land use (without e.g. agriculture) or a contemporary water balance without human withdrawals may be modelled. These two options may be used as possible reference states (baseline scenarios) to assess current water consumption-related impacts against historical changes in the water cycle for non-marginal uses of LCA. For example, to assess non-marginal deforestation impacts on the water cycle in the Amazon, one could compare current versus past, less human-modified landuse scenarios. Such baselines are also needed for deriving average CFs or normalisation factors in LCIA. [START_REF] Huijbregts | Do We Need a Paradigm Shift in Life Cycle Impact Assessment ?[END_REF][START_REF] Benini | Cross-Scale Consistency in Life-Cycle Impact Assessment[END_REF] Temporal modelling aspects: steady-state and dynamic system solutions. For a FF, the magnitude of the effect that a volume of water withdrawal (or release) has on a water compartment depends on the time frame of the assessment, as shown in Equation 3:

FF ୰,ୱ =  MPS ୱ dt ఏ ୲ୀ ∆ Environmental Intervention ୰ Equation 3
where r denotes the compartment where the environmental intervention (i.e. water withdrawal or release) occurs and s denotes the compartment where the midpoint stressor (MPS, i.e. change in mass, or volume, of water after the environmental intervention) is modelled. [START_REF] Núñez | Critical Analysis of Life Cycle Impact Assessment Methods Addressing Consequences of Freshwater Use on Ecosystems and Recommendations for Future Method Development[END_REF] Hence, FFs can be estimated following two different temporal approaches: 1) Steady-state (i.e.

time-independent solution predicting change in mass (or volume) of water per water compartment with the system at steady-state, θ = ∞); and 2) dynamic (synonymous with transient, where t is set to specific time frames, θ = a). An in-depth discussion of both options and their implications can be found in section S8 of SI.

Results

Illustrative example -system's description. In order to demonstrate the principles described in the article, we employed a hypothetical but realistic illustrative example that represents water consumption in a generic coastal zone (Figure S9) with a non-exhaustive number of phenomena represented. The zone consists of a local spatial unit with six compartments (atmospheric moisture or air (a), soil (s), ground water (gw), fossil ground water (fw), surface water (sw), and vegetation (v)), nested in two regional units (region 1 and region 2) with respectively five (air, soil, ground water, fossil ground water, and surface water) and four (air, soil, surface water, ground water) compartments, and the rest of the world unit aggregated into one single water mass (ROW) exchanging with the local and regional units. In the example, region 1 and region 2 are located downstream and upstream the local unit. The type of spatial setup represented in the example is a nested configuration of high spatial local detail, but other structural solutions could also have been applied, as explained in section S6. Every spatial unit has five ecosystem components: river (r-ecs), lake (l-ecs), aerial (a-ecs), sessile terrestrial (ster-ecs) and mobile terrestrial (mter-ecs) ecosystems. The environmental intervention assessed is the withdrawal of 2 m 3 /functional unit (FU) of freshwater from ground water in the local unit. The release is of The k and the FF matrices. Environmental compartments in the illustrative example are exchanging water flows within a single spatial unit, but also beyond it (e.g. surface water of the local unit receives water from surface water from region 2 via river flow). All these exchange flows are quantified in the k values (see section S5). The off-diagonal elements of (k) represent transport of water between compartments within a spatial unit or beyond, going from compartment i (column) to compartment j (row). The diagonal elements reflect the difference between water gain (i.e. water generation) and water loss (i.e. sum of the off-diagonal elements in column i) in a specific compartment. Water generation refers to water that has been confined in the distant past in a compartment which today is disconnected to the rest of the water cycle (i.e. fossil water).

The fate matrix (FF) is a function of the inverse of the k matrix (k) for systems in steady-state conditions, which is the case in the example. (FF) for the local unit and the complete (FF) representing exchanges between compartments and units are collated in section S9. Matrix algebra determines that even though direct exchanges between two compartments are 0 (i.e. kvalue = 0), FFs are positive, since they account for all direct and indirect (i.e. via third compartments) water exchanges.

The SF matrix. The SF can be quantified either for each spatial unit specifically (e.g. if the capacity of ecosystems to offset a change in water availability varies spatially, which is the case in the illustrative example, see Equation S3) or it can be one value per ecosystem component and affected compartment, which is the same for all spatial units. If the SF varies spatially, its matrix (SF) is populated similarly to (FF) in Equation S2, with the respective matrices for each spatial unit on the diagonal positions and in the same order as in (FF). The elements in the off-diagonal matrices in (SF) are then all zero. The size of (SF) in the example is (20 × 18).

The EF and the DF matrices. As in the SF, the ecosystem component may also be spatially variable and respond differently to a change in water availability. In the illustrative example, however, we assume that every ecosystem component reacts always similarly, hence (EF) has the same value per ecosystem component for all spatial units. The damage matrix is similar to (EF) but contains DF instead. In the illustrative example both matrices ((EF) and (DF)) are of (20 × 20) (see the matrices in S9 and in the SI excel file).

The CF matrix. The CF matrix contains the characterisation factors for each spatial unit (see section S9). Due to the propagation of water movement between compartments and spatial units, CFs vary spatially. Units of the CF in the illustrative example are in PDFm 3 day/m 3 water consumption.

The LCI vector and the LCA score. The LCI vector of water consumption ܥܹ( ሬሬሬሬሬሬሬԦ ) contains the information on the water elementary flows per FU of the processes under study. Water withdrawals are identified with a positive sign [+m 3 ] and water releases are identified with a negative sign [-m 3 ]. The size of the LCI vector is determined by the total amount of water compartments in all spatial units (18 in the example, see the excel file).

The LCI vector times the regionalised CF matrix results in the LCA matrix with units of PDFm 3 day, which estimates the environmental consequences on ecosystems due to the water consumption of the LCI. Since the LCI vector contains negative and positive flows, LCA scores are also negative (associated with potential environmental benefits) and positive (associated with potential environmental impacts). Intermediate results can also be calculated by multiplying the LCI vector to the FF matrix (meaning absence of water in each compartment due to the consumption of the LCI), and by multiplying the LCI vector to the FF×SF matrix (meaning the time effectively affecting each component of the ecosystem).

Comparison with the former generations of indicators.

Discussion

Environmental relevance. The above set of guidelines serves to support the development of a third generation of impact indicators for water consumption in LCA. Its operationalisation will bring multiple benefits. First, it will allow for harmonisation and, to the possible extent, combination of existing water consumption impact assessment methods under a unique framework, including soil moisture consumption and effects of land use and land use change on the hydrological cycle. Second, it will ensure consistency of future methodological developments. Third, it will improve the relevance of the impact characterisation with respect to the local/regional and global hydrological cycles. All in all, the operationalisation of the guidelines will bring the characterisation of water consumption impacts to the same level of environmental relevance as the characterisation of emissions in LCA, thus increasing overall consistency and coherence between LCA impact categories. The latter effect is particularly important when indicator results are aggregated or compared to each other across impact categories, which is common and frequent practice.

The regionalised FF multimedia model allows for differentiation of impacts from consuming water from different sources in different environments. Moreover, since it captures the interrelation between water compartments, the distinction between impacts due to soil moisture consumption (also called green water in the literature [START_REF] Hoekstra | The Water Footprint Assessment Manual. Setting the Global Standard[END_REF] ) and consumption from surface and ground water bodies (called blue water [START_REF] Hoekstra | The Water Footprint Assessment Manual. Setting the Global Standard[END_REF] ) is no longer needed. This solves the debate about the consideration of green water in LCA. [START_REF] Hoekstra | A Critique on the Water-Scarcity Weighted Water Footprint in LCA[END_REF][START_REF] Pfister | Letter to the Editor: Understanding the LCA and ISO Water Footprint: A Response to Hoekstra (2016) "A Critique on the Water-Scarcity Weighted Water Footprint in LCA[END_REF] Fossil water is also regarded in the FF model and, although its consumption does not lead to ecosystem quality damage, stress inflicted on available resources can already be measured in terms of renewal time. From this point in the causality chain, an indicator that links to the resources AoP could be developed. Overall, since fate factors only model the physical movement of water on Earth, they can also be used in the human health causality chain, which would then bring harmonisation of whole water consumption impact indicators. The type of midpoint indicator that we would obtain by applying the guidelines of this article is on the impact pathway for ecosystem quality, which the UN Environment Life Cycle

Initiative encourages above the use of proxy midpoint indicators (e.g. water scarcity indicators) which are not defined along the cause-effect chain. [START_REF] Verones | LCIA Framework and Cross-Cutting Issues Guidance within the UNEP-SETAC Life Cycle Initiative[END_REF] Furthermore, the guidelines could also be useful in other impact categories, both emission-based (e.g. toxicity models would benefit from modelling regionalised interactions of water flows) and resource-based (e.g. applying similar principles to the fate of soil loss due to erosion).

LCA scores obtained are both positive (associated with potential environmental impacts due to water withdrawal) and negative (associated with potential benefits due to water release) (see the illustrative example in SI). Negative scores are debatable, since they assume that when an ecosystem benefits from additional water, it responds by comparable proportion as it responds when facing a water drop. While this may be true for the FF since it is no more than a water balance, it is less certain for the sub-factors that reflect ecosystems' behaviour to water availability change. For the sake of transparency of LCA results and following ISO 14040, [START_REF]ISO 14046 Environmental Management Water Footprint -Principles, Requirements and Guidelines[END_REF] we recommend reporting and interpretation of positive and negative scores separately instead of aggregating them assuming compensation.

LCI data requirements.

To carry out an LCA study following the guidelines, the LCI shall record the following information: (a) freshwater balance of inflows (water withdrawal from the ecosphere) and outflows (water release to the ecosphere) expressed in mass or volume of water per functional unit (i.e. m 3 /FU or kg/FU), and not as a continuous flow (i.e. m 3 /y or kg/y), since the inventory flows must be scaled to the FU, (b) the geographical location of each exchange (c) the water compartment of each exchange, (d) temporal specification of each exchange if the FF (or any other CF sub-factor) reflects time dependency. For unit processes where the source compartment is unknown, the water supply mix [START_REF] Leão | A Worldwide-Regionalised Water Supply Mix (WSmix) for Life Cycle Inventory of Water Use[END_REF] with regionalized statistics about the use of water sources per sector of activity can be used. Return flows to the technosphere may be considered sector-dependent, with for instance agricultural uses contributing to replenishing soil and groundwater, whereas domestic and industry users may most likely release to surface water. 37

Uncertainty. The main objective of having a detailed spatial model is to (a) reduce uncertainty of the model and due to spatial variability and to (b) better reflect mechanistic relations in the cause-effect chain/impact pathway. Recent research resulted in more detailed datasets useful to improve the characterisation modelling at high spatial detail. Remote sensing products are increasingly allowing models to work with distributed data as inputs and not just interpolated parameters. Thus, parameter uncertainty can be considered and eventually reduced, which was highly limiting detailed modelling in the past. This means that the optima between parameter and model uncertainty 38 can be shifted towards more complex models. However, in order to find such optima for specific applications, uncertainty information of underlying models (e.g. Furthermore, all uncertainty results should be documented (including a description of the uncertainty method) and made publicly available for further research. Provision and analysis of uncertainty information is a key element of developing the models and allows prioritizing future research needs. Finally, these results facilitate proper uncertainty assessment in LCA, including impact assessment.

Towards practical implementation. The guidelines set the basis of a more accurate, novel way of modelling water consumption impacts in LCA. Operationalisation will therefore need much research, both in LCA and also for the development of hydrological models and datasets that LCA relies on. To start with, efforts may be directed towards:

• Performing simple, but real case studies similar to the illustrative example to serve as proofs of concept, like Verones et al., [START_REF] Verones | Modeling the Local Biodiversity Impacts of Agricultural Water Use: Case Study of a Wetland in the Coastal Arid Area of Peru[END_REF] who modelled effects on wetland biodiversity in a coastal area in Peru prior to developing global CFs. [START_REF] Verones | Effects of Consumptive Water Use on Biodiversity in Wetlands of International Importance[END_REF][START_REF] Verones | Quantifying Area Changes of Internationally Important Wetlands Due to Water Consumption in LCA[END_REF] The case studies should ideally be located in contrasted local climatic conditions. A sensitivity analysis to figure out the appropriate temporal detail of the FFs should be conducted. The simulation may be performed in a system comprised of a few water compartments, where the comparison should focus on detecting the details of the system's dynamic that steady-state solutions or large time steps would miss. Usually, steady-state solutions and large time steps bring along larger computational error.

• Adapting water flow exchanges between compartments and regions modelled in existing methods [START_REF] Verones | Quantifying Area Changes of Internationally Important Wetlands Due to Water Consumption in LCA[END_REF][START_REF] Hanafiah | Including the Introduction of Exotic Species in Life Cycle Impact Assessment: The Case of Inland Shipping[END_REF][START_REF] Berger | Water Accounting and Vulnerability Evaluation (WAVE): Considering Atmospheric Evaporation Recycling and the Risk of Freshwater Depletion in Water Footprinting[END_REF] to the harmonised framework.

• Developing new, coarse FFs at the global scale following both the spatial and temporal recommendations of the paper and considering lessons learned from the two points above, with the aim of identifying major modelling challenges and data gaps to overcome for further refinement. This base of FFs may also serve as starting point for further development, enhancing their quality and robustness. To help with operationalisation,

Figure 1 .

 1 Figure 1. Multimedia hydrological fate model: a) the three nested spatial scales. Note that the

  Author-produced version of the article published in Environmental Science & Technology, 2018, N°52(8), p. 4658-4667The original publication is available at https://pubs.acs.org/doi/10.1021/acs.est.7b05207 Doi: 10.1021/acs.est.7b05207

1m 3 /

 3 FU to the air in the local unit and of 1 m 3 /FU to surface water in region 1. Temporal dynamics of the hydrological flows are not considered (i.e. steady-state solution), which translates in only one set of FFs, instead of having different sets of FFs adapted to the granularity of every time step. The definition and interpretation of each sub-factor of the CF are provided above. The matrices described below are collated in the excel file of the SI, and a hypothetical CF solution and LCA score have been calculated for the purpose of showing the type of results obtained and their interpretation.

Figure 2 Figure 2 .

 22 Figure 2. Assessment of the illustrative example with the A) first generation of indicators; B)

  hydrological models) needs to be assessed and propagated through the whole impact assessment model. The following uncertainty aspects should be differentiated and quantified:12 (1) variability induced uncertainty; (2) Parameter uncertainty; (3) Model uncertainty; (4) Value choices; and (5) Scenario uncertainty.

  Author-produced version of the article published in Environmental Science & Technology, 2018, N°52(8), p. 4658-4667 The original publication is available at https://pubs.acs.org/doi/10.1021/acs.est.7b05207 Doi: 10.1021/acs.est.7b05207

	Environmental Science & Technology
	ACS Paragon Plus Environment

Table S1

 S1 in the SI collects guidance on specific attributes to consider and bibliographic Author-produced version of the article published in Environmental Science & Technology, 2018, N°52(8), p. 4658-4667 The original publication is available at https://pubs.acs.org/doi/10.1021/acs.est.7b05207 Doi: 10.1021/acs.est.7b05207 Indicators: Findings of the Scoping Phase. Int. J. Life Cycle Assess. 2014, 19 (4), 962-967. (12) Frischknecht, R.; Jolliet, O.; Eds. Global Guidance for Life Cycle Impact Assessment Udo de Haes, H.; Finnveden, G.; Goedkoop, M.; Hauschild, M.; Hertwich, E.; Hofstetter, P.; Jolliet, O.; Klöpffer, W.; Krewitt, W.; Lindeijer, E.; Müller-Wenk, R.; Olsen, S.; Pennington, D.; Potting, J.; Steen, B. Life-Cycle Impact Assessment: Striving Towards Best Practice; Society of Environmental Toxicology and Chemistry (SETAC), 2002. Toxicological Effect and Damage Factors of Carcinogenic and Noncarcinogenic Chemicals for Life Cycle Impact Assessment Human-Toxicological Effect and Damage Factors of Carcinogenic and Noncarcinogenic Chemicals for Life Cycle Impact Assessment. Integr. Environ. Assess. Manag. 2005, 1 (3), 181-244. (18) Rosenbaum, R. K.; Margni, M.; Jolliet, O. A Flexible Matrix Algebra Framework for the Multimedia Multipathway Modeling of Emission to Impacts. Environ. Int. 2007, 33 (5), 624-634.

	Page 25 of	Environmental Science & Technology
		Indicators : Volume 1; 2016.
	(13) ISO. ISO 14040. Environmental Management -Life Cycle Assessment -Principles and
		Framework; 2006; Vol. 2006.
	(14)
		ACS Paragon Plus Environment

(15) Margni, M.; Gloria, T.; Bare, J.; Seppäla, J.; Steen, B.; Struijs, J.; Toffoletto, L.; Jolliet, O.

Guidance on How to Move from Current Practice to Recommended Practice in Life Cycle

Impact Assessment; 2008. (16) Hauschild, M.; Huijbregts, M. A. J.; Eds. LCA Compendium -The Complete World of Life Cycle Assessment. Life Cycle Impact Assessment; Springer, 2015.

(17) Huijbregts, M. A. J.; Rombouts, L. J. A.; Ragas, A. M. J.; van de Meent, D. Human-

Author-produced version of the article published in Environmental Science & Technology, 2018, N°52[START_REF] Van Zelm | Implementing Groundwater Extraction in Life Cycle Impact Assessment: Characterization Factors Based on Plant Species Richness for The Netherlands[END_REF], p. 4658-4667 The original publication is available at https://pubs.acs.org/doi/10.1021/acs.est.7b05207 Doi: 10.1021/acs.est.7b05207

ACS Paragon Plus Environment Environmental Science & Technology ACS Paragon Plus Environment Environmental Science & Technology Acknowledgements Page 26 of 33 ACS Paragon Plus Environment Environmental Science & Technology The authors are grateful for the contributions of other members of the Ecosystem Quality subgroup of WULCA (Jane Bare, Cécile Bulle, Mattia Damiani, Paula Quinteiro and Philippe Roux) and for the individual contributions of Christian Bouchard, Peter Fantke and Inga Klemmayer. M. Núñez and R.K. Rosenbaum acknowledge ANR, the Occitanie Region, ONEMA and the industrial partners (BRL, SCP, SUEZ, VINADEIS, Compagnie Fruitière) for the financial support of the Industrial Chair for Environmental and Social Sustainability Assessment "ELSA-PACT" (grant no. 13-CHIN-0005-01). The Sponsors of WULCA for the 2016-2018 period are also acknowledged for their financial contribution: Hydro-Quebec, Cotton inc, Veolia environment, L'Oréal, Danone, Engie. ACS Paragon Plus Environment Environmental Science & Technology

Author-produced version of the article published in Environmental Science & Technology, 2018, N°52 (8) references per water compartment of the FF model. Some of the tools and strategies that are listed in Table S1 include hydrological models, [START_REF] Hughes | Documentation for the MODFLOW 6 Framework[END_REF][START_REF] Arnold | Large Area Hydrologic Modeling and Assessment Part I: Model Development[END_REF] flow equations, [START_REF] Trapp | Chemodynamics and Environmental Modelling[END_REF] numerical and modelling techniques, [START_REF] Holzbecher | Environmental Modeling Using MATLAB[END_REF] geographic information systems data, [START_REF] Esa | Land Cover CCI: Product User Guide[END_REF]47 and sensitivity analysis platforms. 48 Depending on the complexity and numbers of compartments and spatial units, few or all of these tools might be used.

• Analysing temporal dynamics of the sensitivity of ecosystems to water consumption and availability, since environmental water requirements vary a lot throughout the year. For that, and as done similarly with the FF, a sensitivity analysis comparing monthly or seasonal SFs to annual average SFs can be performed.

Associated content

Supporting information. The following files are available free of charge: i) further details on the scope of the guidelines, spatial and temporal FF modelling aspects, and a representation of matrices of the illustrative example (pdf file); ii) detailed matrices of the illustrative example (excel file).

Author information (*) Corresponding author e-mail: montserrat.nunez@tu-berlin.de.

Present address: Sustainable Engineering, Technische Universität Berlin, Berlin, Germany; phone: +49 (0)30/ 314 -26713.

Author contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.


Simone Torsani
email: s.torsani@gmail.com

THE "BLAKKAT" SOFTWARE FOR TAGGING ONLINE LANGUAGE LEARNER CORPORA: ISSUES IN SLA ASSESSMENT AND RESEARCH

Keywords: Computer Learner Corpus, Network Based Language Teaching, Computer Assisted Second Language Research, Instructional Design

This paper aims at showing, through a case study, one possible application of Computer Learner Corpus (CLC) to Network Based Language Teaching (NBLT). Research has shown how CLC can be used both for Second Language Acquisiton (SLA) research and Foreign Language Teaching (FLT), especially if they are tagged, that is, if interpretative annotations are added to the corpus (e.g. error annotations). Online learning generally takes place inside virtual environments where learners exchange mainly written asynchronous productions, which can be easily collected into a CLC. Although different tools and systems are used for coding texts, we have developed a flexible web based interface where researchers can define and apply their own set of tags to a given corpus and work at distance. As with many web-based tools, it is not the tool only that is interesting, rather the possibility to transfer the software core, that is its main algorithms, inside a Virtual Learning Environment (VLE), in order to allow teachers and researchers to code and analyse their learners productions for those uses shown in literature.

INTRODUCTION: CORPUS LINGUISTICS AND LEARNER CORPORA

Corpus linguistics1 has nowadays a long tradition as a discipline related to Computer-Assisted Language Learning (CALL), the first experiments of concordancer used as a software in language instruction dating back to the 1970s (Leech, 1997, p.2). Although now a major issue in Computer Applications in Second Language Acquisition (CASLA2, Chapelle, 2001, 36-38), Corpus Linguistics in language learning and teaching is still perceived by many mainly in relation to the well known paradigm of "data-driven learning", developed by Johns, who introduced research methods in language learning as explained by his well-known maxim "research is too serious to be left to researchers" (Johns, 1991, p. 2). According to this perspective corpora are to be perused by the learner who acts "as 'linguistic researcher', testing and revising hypotheses, or as 'language detective', learning to recognize and interpret clues from context ('Every student is a Sherlock Holmes')" (Johns, 1997, p. 101). But if we were to limit the extent of language corpora to teaching by exploiting a concordance, we might be missing an important share of their potential 3 . Computer Learner Corpora (CLC) 4 are, as it can be easily argued, collections of learners language productions and are defined as "electronic collections of authentic FL/SL [foreign language / second language] textual data assembled according to explicit design criteria for a particular SLA/FLT [second language acquisition / foreign language teaching] purpose" (Granger, 2002, p. 7). Granger, whose work is considered seminal in this respect, edited in 1998 a collection of studies dealing with learner corpora, mainly oriented at comparing the learners' language with that of native-speakers: although this seems to be the main perspective (Hunston, 2002, p. 206), learner corpora can be used for a wide range of activities which also encompass syllabus and materials design (Mukherjee, 2005, p. 17). Most of the papers in Granger's volume make use of the International Corpus of Learner English (ICLE), a database collected under the direction of Granger herself, containing corpora (200,000 words each, that is medium size corpora 5 ) collecting language written productions of advanced learners from different countries, while the native-speakers corpus is the LOCNESS Corpus (300,000 words). Besides this corpus Granger also directed the collection of the Louvain International Database of Spoken English Interlanguage (LINDSEI, de Cock, 2003), comprising oral productions of advanced learners form different countries as well. Another important corpus is the Longman Learner Corpus (of intermediate and advanced learners) used by Biber and Reppen in the same volume. Mukherjee mentions also the Giessen Long Beach Chaplin Corpus. These corpora show evidences of having been collected for SLA research: data, for instance, are organised so as to reflect an average learner, whom Granger refers to as the "archetypal learner" (1998, p. 7), and not, therefore, for any assessment practice. This is a most important point, since it is necessary to distinguish between SLA Research, Language Teaching Research and Instructional Design: this distinction, apart from academic matters, is fundamental in defining the objectives and tasks for which a CLC is investigated. SLA Research was born in the 1950/60s as a sub field of Language Teaching and gradually evolved into an autonomous discipline -that is without necessarily having practical applications such as improving teaching practices -since the 1970s (Chini, 2005, p. [START_REF] Mukherjee | Corpus linguistics and language pedagogy: The state of the artand beyond, in Corpus Technology and Language Pedagogy: New Resources, New Tools, New Methods[END_REF]) and has now become a major field of Applied Linguistics 6 . Language Teaching Research, on the contrary, is a more practical discipline, mainly interested in developing effective methodologies, that is "the study of the system or range of methods that are used in teaching" (Adamson, 2005, p. 604). This said, however, researchers advise not to "draw strict dividing lines" (Littlewood, 2005, p. 502) between these two fields, since findings in the first can have consistent influence over the second, since, as we noticed, SLA research originally was a sub discipline of language teaching. Instructional design, finally, is a most practical activity consisting in an inquiry field whose object is defining "the rules governing the choice of the 3 Another documented use of corpora in SLA is the creation of CALL exercises using them (Wilson,1997). 4 A full list of coprora types is provided by Hunston (2002, pp. 14-16). 5 See Chiari (2007, p. 45) for a reference table of corpus sizes. 6 For an introduction to SLA research issues see Adamson (2005) and Chini (2005). most suited instruction means according to the conditions of learning" [START_REF] Ranieri | Instructional Design: modelli e teorie[END_REF], my translation).

LEARNER CORPORA IN DEPTH

Having introduced its historical and theoretical foundations, and having defined the related disciplines, it is now possible to have a closer look at CLC at work. Although quite a recent field of inquiry (Granger, 2004, p. 128), learner corpus analysis has given rise to a number of expectations and experiments SLA researchers and teachers should not neglect. As it was noted by Hunston, "the essence of learner corpora is comparison" (2002, p. 206): they are useful, in other words, as they can be compared, for instance, with native speakers corpora in order to gather evidence on SLA issues and make "interesting, qualitative generalisations" (ibid.). Although certainly true as far as research is concerned, this perspective can be enlarged and it is possible to make such collections available to more practical uses, such as improving assessment, instructional design and teaching materials (Chapelle, 2001, p. 37). [START_REF] Mukherjee | Rethinking Applied Corpus Linguistics from a Language-pedagogical Perspective: New Departures in Learner Corpus Research[END_REF] provide a recent and in-depth overview, as well as practical examples, on how to work with such materials. Their research, focused on discourse markers, shows how conclusions can be drawn from their corpus of German learners: the results suggest that corpora "provide a good starting point for an improvement of ELT textbooks and materials, given that -apart from well -all other discourse markers are notoriously underrepresented even in modern materials." (p. 216). They concentrate, then, on two further perspectives: individualisation and localisation. Learner corpora, in fact, are often viewed as "reference learner corpora" (Mukherjee, 2006, p. 18), since they collect evidence as regards an "abstract learner" (Granger, 1998, p. 7), that is the whole body of learners, careless of individual and local differences. This kind of CLC is useful for SLA research: for instance, it could be used to test the Language Acquisition Device theory, although Mukherjee & Rohrbach, on the other hand, suggest that corpus analysis could be deepened to a more individual and contestualised level.

Individual analysis "is intended to mean that for the purpose of individual assessment and analysis of the variation between learners, it would be useful to complement the learnerlanguage-as-a-whole perspective by also taking into account the differences between learners." (Mukherjee & Rohrbach, 2006, p. 217). The use of the term assessment is important here, since it hints at the fact that learner corpora could be viewed not only as research materials, but also as an instrument through which teachers can assess learners, i.e. individual learners' works and productions. On the other hand, localised analysis is focused more on the classroom at a specific stage (p., 219-220), and is even more focused on issues of language assessment and instructional design, since it aims at evaluating the learning process at a given time. As shown by Mukherjee & Rohrbach in their example of a localised learner corpus, "one of the key advantages of a computerised local learner corpus for the teacher: he/she is now able to immediately identify patterns of use in learner language across all students in his/her class. The identification of 'typical mistakes' can now be put on an empirical footing and be based on the analysis of all learners' actual output" (p. 225).

Learner corpora, then, can be examined through the most common concordance software and they become even more interesting and revealing if they are tagged. Although a learner corpus can be accessed "as is", that is, even if it is a raw collection of (learners) text, applying a set of tags to this data increases its potential and allows quantitative data to provide qualitative ones (Granger, 2004, p. 129). Different experiments have been conducted: an instance of corpus tagging can be glimpsed in [START_REF] Nicholls | The Cambridge Learner Corpus -error coding and analysis for lexicography and ELT[END_REF], where an extremely accurate set of tags is shown along with the advantages of a coded corpus over a raw one. In Nicholls errors are generally tagged with a two-letter code, where the first letter defines the "type of error" and the second the "word class": this results in a very exact error-tagging operation, where, for instance, erroneous forms can be separated from correct ones, and the different kinds of errors extracted and considered separately. Researchers use different tools in order to tag they corpora, especially POS taggers 7 , software coding the text automatically (Granger, 2004, p. 128). The analysis of one such collection is that common corpus software, Wordsmith 8 for instance, allows users to define a set of tags to be used along with their tagged corpus.

If, as we noticed, learner corpora can be used either for research or for pedagogical purposes, it is also evident, however, that research is normally allowed much more time and resources than assessment or instructional design are. If teachers were to perform such complex operations as those described in the literature in order to assess their students' errors, it goes without saying that it would be very hard to convince them to adopt this practice. There is evidence, nevertheless, that such practice is of great value as regards pedagogical matters. In the following paragraphs I will show the case of online language learning; inside Virtual Learning Environments, in fact, language exchanges generally take place inside web forums, that is ready-to-use and organised electronic text. In this case a longitudinal perspective 9 could easily be applied, provided the adequate tools are available, to small learner corpora in order to assess the whole learning process, either at individual or general level [START_REF] Mukherjee | Rethinking Applied Corpus Linguistics from a Language-pedagogical Perspective: New Departures in Learner Corpus Research[END_REF].

THE BLAKKAT SOFTWARE

The Blakkat software was originally conceived and developed for the analysis of a corpus of forum messages in order to allow different researchers to cooperatively assess the social presence in a post-degree course and not, therefore, for a strictly linguistic investigation 10 ; it was soon found, however, that it could be effectively used also for assessing issues of language learning. The software allows one or more researchers to define and apply an arbitrary set of tags to a given corpus and then extract and compare data through different filters.

Every project has a coordinator who defines the set of tags and the documents to be coded. Tags are identified through name, colour (tag colour is very important since it is through colour that tags are printed on the screen while a text is coded) and a short description, and can be exported/imported for recycling within other projects. Text, on the other hand, can be written/pasted inside a form or imported from an XML file that can be easily produced by many database management systems: most Virtual Learning Environments, say Moodle, are based on PHP technology that relies on a MySQL database. 7 POS (Part Of Speech) taggers apply a part-of-speech label to words in a corpus, such words as light might appear in different contexts as a noun, an adjective, a verb or an adverb. See [START_REF] Hunston | Corpora in Applied Linguistics[END_REF] pp. 79-92 for an overview of corpus annotation and parsing. Difficult as this may seem, it is a short and easy operation, provided one has access to the database and a basic knowledge of this software.

Every text of the corpus, is associated with a title, a timestamp 11 date, so that timelines can be produced with the data, and an extra field where an arbitrary value, the text's author for instance, can be inserted: in this way comparative timelines can be produced. The tagging mask is very easy to use: the text to be coded is in a form field allowing formatting and, for each tag defined for the project, a button is available inside the form through which users can tag a test portion in a visual way, i.e. they highlight the interested portion with the colour corresponding to a given tag.

When the texts are tagged, a set of filters allows the user to extract the collected data in different ways, in order to:

• generate statistics on one or more tags • generate timelines on the arbitrary value (e.g. the user, so that a timeline of different tags is produced) • export the tagged corpus and the corresponding set of tags so that it is possible to analyse it through a specific corpus software (e.g. Wordsmith).

CASE STUDY OF CLC APPLICATION TO NETWORK-BASED LANGUAGE TEACHING: TAGGING A FRENCH BEGINNERS CORPUS

The impact of the Internet on language teaching in general and on CASLA in particular has been, as it can be easily imagined, a great one and soon became the subject of a vast literature. A most important point in this history is the volume, edited in 2000 by Warschauer and Kern, "Network-based Language Teaching: Concepts and Practices". Although the NBLT scenario has somehow changed since the volume publication, some of the ideas underlying its whole content are still valid and constitute a basic reference for all those interested in web based language teaching: in particular, the concept that "NLBT represents a new and different side of CALL, where human-to-human communication is the focus" (Warschauer & Kern, 2000, p. 1) is an essential one. To what extent this principle is central is well explained by Gruba (2004, p. 624), who points out how pedagogical approaches have changed along with the introduction of the Internet as a CASLA tool, and by the various works focusing on such issues such as Computer Supported Collaborative Learning (CSCL) in SLA (see. Chapelle, 2001, pp. 31-32) or Computer-Mediated Communication (CMC), one of the main topic areas of Language Learning & Technology, a professional online journal devoted to CASLA 12 .

If, by the time Warschuer and Kern edited their volume, online learning environments were only a perspective or still at a prototypical stage (Chun & Plass, 2000), today literature illustrates how this field has vigorously developed, and how instruction increasingly takes place online inside various learning environments. Since socio-collaborative approaches are becoming dominant in SLA practice (Gruba, 2004, p. 624), a number of collaborative tools have been developed, planned or whished-for to foster collaboration and exchange among learners [START_REF] Calvani | Teorie CSCL e piattaforme Open Source per l'e-learning: verso un'integrazione[END_REF]; the web forum, however, remains the most used tools for such tasks and different works focus on web forums as the pivot location where learners interaction takes place 13 .

Tagging case study

In this case study I have collected a small sample corpus (2500 words approximately) of 11 second year French learners and analysed it as regards simple errors (spelling, missing words and words to be replaced), that is through a set of tags defining such errors. Small as this corpus may seem, however, it must be borne in mind that CLC tagging and analysis for assessment does not happen on large collections. The corpus consists of different forum messages written in a time span of four months (November 2006 -March 2007) in the Moodle VLE; students were required to interact with French students attending a Master FLE course, that is an university course in teaching French as a Foreign Language. I have easily extracted the messages from the database, since the University server administrators have chosen (like many) the phpMyAdmin administration system to access the MySQL database which allows users to export data in the XML format. I have defined a set of tags, in order to extract relevant data according to this perspective: since the interface is flexible, however, I could have also defined a set of "L1 (that is Italian) transfer tags" aiming at coding influence of the learners L1 on their L2 productions 14 , which would be more suited for SLA research. Then I have imported the XML file containing the messages into the Blakkat interface, through a form allowing users to associate a given XML tag to a given field: the "message" tag was associated with the "text" field, etc. so that the system recognises this text as the text to be tagged.

FIGURE 1 Forum messages as XML data

I have also added a user id as extra arbitrary field, so that tagging data can be associated with a user and, therefore, an ad personam analysis is also possible. Now this small corpus is ready along with its own set of tags and can be coded. 13 An interesting example of web forum analysis, especially as regards socio-constructivist theories, is in Ligorio (2005). 14 Influence of L1 on SLA is one of the main topics of SLA research and has been differently interpreted according to the various approaches (see Chini, 2005 pp.54-59). It is not my intention, however, to draw any conclusion form the data collected in this case study-the analysis being inaccurate and unstructured: the only aim of this experiment is to show the possibility of integrating the activities described inside the teaching practice.

The set of tags for the "error analysis" is shown in table 1 and is a subset of the system defined by [START_REF] Nicholls | The Cambridge Learner Corpus -error coding and analysis for lexicography and ELT[END_REF]:

<#R>

Since the text is coded inside a form field it can be freely edited, in the case of error tagging, then, it is also possible for the research to follow the convention of adding a correction to the learner's error in the form <#CODE>wrong word/phrase | corrected word/phrase</#CODE> defined by Nicholls.

FIGURE 2 Blakkat: tagging window

Once the coding has been completed, results can be extracted through different filters, so that data relevant to a given issue can be analysed. For instance, it was found that:

• noun errors tend to occur more often than others (28.13%) • user 3 is the most tagged: she made more errors if compared to others (31.25%), but also wrote more (9 messages), the written/tagged ratio is 2.2 that is an average value • user 3 made more "adjective spelling errors" in the earlier stage of her activity • the most common error is the wrong spelling of bientôt, which is differently misspelled FIGURE 3 Blakkat: results

Exporting data to Wordsmith Tools

Learner Corpora are usually analysed through specific corpus software, such as Wordsmith Tools [START_REF] Granger | Computer Learner Corpus Research: current status and future prospects[END_REF]: this can handle larger amounts of data if compared to webbased applications, which suffer from severe limitations (see. [START_REF] Torsani | DDL en réseau: un exemple d'utilisation des technologies ajax pour améliorer les outils internet d'apprentissage des langues[END_REF], and offer a wide range of analysis tools. Another great advantage of Wordsmith is that it supports tagged corpora and, by consequence, allows user to upload tag lists to analyse their collections. This is especially the case with SLA research where, as noticed before, more time and resources are allocated to complete the task. We chose, therefore, to allow users to export the whole tagged corpus along with its own tag set in order be used inside such software as Wordsmith tools. Each corpus text is accompanied by a data description tag containing the text title, date and extra field, so that further complex extractions can take place, in the example an instance of error tagging can be seen: <data text_title="msg_21" date="24/11/06" extra="9" />Bonjour a tout le monde, je m'appelle F.. Je viens de Arenzano, un petit village à coté de la mer. Je suis italienne et j'ai 23 ans. Je suis <FN>etudiante | étudiante</FN> et j'aime beaucoup pratiquer le ski et lire des livres. J'adore aussi les chats, j'ai 8 chats dans mon jardin. Et j'aime l'espagnol! Au revoir.

As is shown in Figure 3, Wordsmith tools allows users to examine the corpus tags and produce a KWIC 16 concordance, in this case noun spelling errors tags:

FIGURE 4 Exported tagged corpus analysed in Wordsmith tools

CONCLUSIONS

As with most software, it is not the tool itself which is important, but rather any possible implications and uses it may happen to have; this is especially true as regards linking with other software (as with Wordsmith tools) and, in the case of web based applications, as regards the integration inside learning environments. As we have seen Blakkat allows users to perform a number of tasks, but other software could do more: it is important, therefore, to create links with other applications in order to further widen the possible range of applications and deepen the level of analysis. But issues of language assessment are very important in the field of language teaching, especially as regards CASLA and NBLT. If such practices as those described above can take place inside the common SLA research framework, as noticed before, they seem hardly feasible in a teacher's perspective. Even if Blakkat is an open source and freely downloadable software, I would not expect a teacher collecting data and manually tagging large corpora of his students works to find out, for instance, that French beginners tend to misspell the word français, or mix up the French verb endings -er, eg. parler (inf.) > *parlé. This is not a realistic perspective. But if we consider NBLT activities taking place inside a Virtual Learning Environment, where texts are produced as electronic text, and if we consider the possibility of transforming one such tool into a Moodle module, for instance, it could be effectively used as an assessment tool by teachers, especially as regards small individualised and localised text collections. As I have noticed elsewhere [START_REF] Torsani | DDL en réseau: un exemple d'utilisation des technologies ajax pour améliorer les outils internet d'apprentissage des langues[END_REF], it is now time to force traditional web programming in order to transfer desktop programmes algorithms to web based applications, especially inside VLEs: the development of more than two hundred VLEs during the last few years [START_REF] Devauchelle | Les outils, situation actuelle et prospective, in Comprendre les communautés virtuelles d'enseignants[END_REF] illustrates how the need and demand for suitable tools is an important issue in Educational Technology, but illustrates also how much easy it is today, thanks to web server technologies, to build one's own "ideal tool".

Even if this practice does not fall in the mainstream of Computer-assisted assessment (CAT), defined as "testing practices requiring a computer to assist in construction, delivery, response analysis and score reporting" (Chapelle, 2001, p. 38), it could prove extremely useful, especially as it could assess learners' output produced out of assessment-oriented practices, such as tests, and therefore more capable of viewing students "real" productions. Evidence collected in such a way, then, could influence syllabus design and provide evidence for global assessment and learning tracking. As it is shown in our case study, then, assessment issues of written language in VLEs, need not be conducted on large or even medium size corpora, thus making it a feasible task for teachers who chose online activities for their learners.

  8 http://www.lexically.net/wordsmith/9 In SLA research "longitudinal" refers to a diachronic analysis of the learners' development; for a recent overwiew of this issue see Ortega L. & Iberri-Shea (2005).10 Lupi V., Pozzi, F, Torsani S., Développer la dimension sociale dans un master post-universitaire à distance: outils, animation et analyse des interactions. The paper, presented at the EPAL conference in Grenoble (June 2007, in press) contains a brief overview of the software.

TABLE 1 The set of tags for the error coding

 1

	Error type	Error code/colour
	Wrong verb spelling, e.g. *je	<#FV> 15
	parl instead of je parle
	Wrong noun spelling, e.g.	<#FN>
	*francaise instead of français
	Wrong adjective spelling, e.g.	<#FJ>
	*dificil instead of difficile
	Wrong adverb spelling, e.g.	<#FY>
	*bientot instead of bientôt
	Something is missing, e.g.	<#M>
	*Sont des chansons…? instead
	of Est-ce que ce sont des
	chansons…?
	Word should be replaced, e.g.
	*difficile pour un principiant
	instead of difficile pour un
	débutant

A recent overview and reference of Corpus Linguistics is to be found in[START_REF] Stubbs | Language Corpora[END_REF], while an up to date reference on Corpus Linguistics and language teaching is found byMukherjee (2004).

Although the acronym CALL is the most used in specialised literature(Gruba, 2004, p. 623) and is usually referred to through Levy's definition -"the search for any study of applications of the computer in language teaching and learning" -I have preferred Chapelle's acronym CASLA, "an umbrella phrase" (ibid.) covering CALL activities, as well as computer-assisted language assessment and second language research.

A timestamp is a measure of time consisting in the number of seconds since January 1 st 1970: data transmitted in this format can be differently transformed into dates or easily used for computation.

http://llt.msu.edu/index.html

As the tags are defined they must not contain the tag signs "<" and ">", since these are automtically added by the software.

A KWIC (Key Word In Context) is the very comon concordance output in which the key word is displayed in its context.


Aliki Papakonstantinou
email: alikipap@csd.auth.gr

Stavros Demetriadis

Nick Bassiliades

Ontology development for computersupported collaborative learning scripts

Keywords: ontology, computer supported collaborative learning, collaboration script

This paper presents an ongoing effort to develop an ontology for Computer-Supported Collaborative Learning (CSCL) Scripts. Our work merges the field of collaborative learning with the field of semantic web and provides a framework for the formalization of collaboration scripts using the OWL language. Collaboration scripts are didactic scenarios that prescribe learners' interactions in collaborative settings. A script comprises a number of phases and each phase specifies the activity that learners have to perform, the composition of the group, the distribution of the activity, the mode of interaction and the phase duration. Scripts can also be positioned along various design dimensions, such as their granularity, coercion degree and locus of control. The presented ontology is being developed with the purpose of formalizing scripts in order to promote their reusability and portability between various computer-supported learning platforms. We discuss design decisions and illustrate how this ontology could be beneficial when embedded in a knowledge-based system that supports collaborative learning.

INTRODUCTION

Collaborative learning refers to methodologies and environments, in which learners take part in a common activity. During the activity, each individual depends on and is responsible to each other. Computer-Supported Collaborative Learning (CSCL) is a research field on supporting collaborative learning with the help of computers [START_REF] Dillenbourg | Introduction: What do you mean by "collaborative learning[END_REF]. CSCL supports and facilitates group processes and group dynamics in ways that are not possible by face-to-face interactions, however, without replacing this kind of communication. This type of learning is typically tailored for use by multiple learners, working either synchronously or asynchronously, at the same workstation or across networked computers. The purpose of Computer-Supported Collaborative Learning is to scaffold or support students in learning together successfully [START_REF] Stahl | Computer-supported collaborative learning: An historical perspective[END_REF].

The scope of our work is to merge the field of collaborative learning with the field of semantic web and develop a script ontology as a framework for the formalization of collaboration scripts that can be used by various computer-supported learning platforms. In order to achieve that, we reviewed the literature and developed the ontology using the OWL language. The ontology presented is an original one and, as far as we know, no other ontology on collaboration scripts has been reported to the literature so far. An ontology is a semantically enriched data model that represents a set of concepts within a domain and the relationships between those concepts [START_REF] Antoniou | A Semantic Web Primer[END_REF]). It provides a common vocabulary to refer to the concepts of a domain; it specifies relationships using logical statements that describe how the concepts are related and also provides rules for combining concepts and their relations to define extensions to the vocabulary.

The Web Ontology Language OWL is a semantic markup language for publishing and sharing ontologies on the World Wide Web. It uses the XML/RDF exchange syntax [START_REF] Mcguinness | OWL Web Ontology Language Overview[END_REF]. It is the most recent development in standard ontology languages, certified by the World Wide Web Consortium (W3C) to promote the Semantic Web vision. OWL is used by applications that need to process the content of information instead of just presenting information to humans [START_REF] Mcguinness | OWL Web Ontology Language Overview[END_REF]. OWL goes beyond the languages XML and RDF in their ability to represent machine interpretable content on the Web, because it has more facilities for expressing meaning and semantics than the latter. [START_REF] Mcguinness | OWL Web Ontology Language Overview[END_REF]).

In the following, we present the theoretical background of computer-supported collaborative learning scripts, we describe how our ontology is designed, analyzing every class and property, and, finally, we illustrate how this ontology could be beneficial for users when embedded in a knowledge-based system for supporting collaborative learning.

CSCL SCRIPTS

Scripted collaboration differs from other collaborative learning methods mostly in the scripts' ability to specify the cognitive activities that learners are expected to engage in, while the majority of other methods leave these activities unspecified or indistinguishable (O'Donnel, 1999). Dillenbourg (2004a) defines CSCL scripts as "instructional sequences in which peer interactions are expected to constitute the core learning mechanism". Scripts are collaboration didactic scenarios (O'Donnell & Dansereau, 1992) that organize learning activities by specifying tasks and subtasks, their duration, the role of each collaborating learner in the group and other relevant parameters of the collaboration. Each script is analyzed into a number of phases and each phase prescribes the tasks and the activities that learners and tutors have to perform and the goals that have to be achieved. The reason for employing scripts to guide collaboration is that usually unguided collaboration among team members can lead to detrimental learning. Kollar, Fischer & Slotta (2005, p. 332) argue that "the lack of explicit scaffolds for collaboration could result in unequal participation of learning partners and ineffective argumentation". Scripts are expected to facilitate learning by guiding peer collaboration and engaging all participants in activities that trigger the activation of their cognitive and metacognitive processes.

Schemata

Based on script common features one can classify them into script-schemata, which are patterns that describe the script structure at a more abstract level. Our work refers to eight major script-schemata. The Jigsaw schema prescribes the partition of the knowledge or information necessary to solve the task, either by forming pairs who have complementary knowledge [START_REF] Hoppe | Can analytic models support learning in groups[END_REF] or by providing them with complementary information or by asking them to play complementary roles [START_REF] Dillenbourg | Overscripting CSCL: The risks of blending collaborative learning with instructional design[END_REF]. The Conflict schema triggers argumentation among group members by forming pairs of learners with conflicting opinions [START_REF] Dillenbourg | Elaborating new arguments through a CSCL scenario[END_REF]. The Negotiation schema involves justifying and entitling viewpoints during collaborative interaction, negotiating about them and trying to convince other team members [START_REF] Dillenbourg | Introduction: What do you mean by "collaborative learning[END_REF]. The Reciprocal schema defines two roles in teams, one of the peers regulating the other and then switching roles (Palincsar & Brown, 1984). The Regulation schema urges learners to set goals for their learning and then attempt to plan, monitor, regulate and control their cognition, motivation, behaviour and context [START_REF] Pintrich | Educational psychology at the Millennium: A look back and a look forward[END_REF]. The Inquiry schema organizes a classroom to function as a scientific research community and guides learners to participate in practices of progressive scientific dialogue [START_REF] Scardamalia | Computer support for knowledge-building communities[END_REF]. The Competition schema pushes learners to work together and makes them responsible for their teammates' learning as well as for their own one [START_REF] Slavin | Using student team learning. Baltimore: The center for social organization of schools, The John's Hopkins University[END_REF]. Finally, the SWISH (Split Where Interaction Should Happen) schema triggers the interactions that teachers want to foster by splitting tasks to facilitate a shared understanding (Dillenbourg, 2004b).

Phases

Each phase in a collaboration script is defined by five specific items: the activity that learners have to perform, the group composition, the activity distribution, the interaction mode and the timing of the phase [START_REF] Dillenbourg | Overscripting CSCL: The risks of blending collaborative learning with instructional design[END_REF]. Moreover, each phase is differentiated in the dimensions of granularity, coercion degree and locus of control (Dillenbourg, 2004a). The "granularity" of a script refers to the grain size of the scripted activity, in terms of time scale of the script (from minutes to whole semester) and to the grain size of sub-tasks definition. The "degree of coercion" refers to what extent learners are forced to follow the collaboration script, i.e. the extent that it constraints the actions of the student. Usually scripts with high granularity tend to be more coercive. For example, a script guiding closely the student through the whole session is considered as highly coercive. Finally, the "locus of control" refers to whether a script possesses some metacognitive value and should be internalized by learners (the locus of control is internal) or it is simply a step by step procedure to be executed by the group (the locus of control is external).

Roles and activities

The main purpose of roles in collaboration scripts is to prescribe privileges and obligations of students during collaboration [START_REF] Kobbe | Framework on multiple goal dimensions for computer-supported scripts[END_REF]. [START_REF] Baggetun | A review of schemes for modelling scripts[END_REF] suggested that a role can be described mostly by two parts. The first part is its responsibilities and privileges within the scope of the scripted collaboration. The responsibilities and privileges of a role are specified by assigning activities and by defining the performable actions within them. The second part is its membership. A role can be assigned to a single person or a group. Furthermore, a role can divided into sub-roles, which usually inherit the responsibilities and privileges of the role.

An activity refers to learning tasks and interactions between roles that take place during the scripted collaborative learning. Activities are conceptualized as forming a hierarchy; each activity can be decomposed into more fine-grained activities and any lower level activity can be subsumed by one or more higher level (low-grained) activities [START_REF] Kobbe | Framework on multiple goal dimensions for computer-supported scripts[END_REF].

ONTOLOGY DESCRIPTION

Figure 1 presents the current classes and subclasses of the ontology. The upper classes (top level) are Phase, Schema, Role and Activity. Class Schema has 8 subclasses (middle level) and each of these subclasses has its own subclasses (bottom level). The hierarchy has been designed according to the aforementioned theoretical framework of CSCL scripts. From the ontology it is apparent that some script subclasses may belong to more than one class (script-schemata). For example the class The_Escape_The_Maze_Script is an intersection of classes Competition and Regulation, and essentially this means that this script possesses features that belong to either of the two script-schemata. Respectively, there are scripts that inherit their features from only one script-schema. For example the classes The_ArgueGraph_Script and The_Construction_of_Argumentation_Sequences_Script belong solely to the Conflict class. property Fade_Out_Level have numeral values from 0 to 5 and it describes the level of "script persistence" to guide learners. This practically means that a "non-fading" script consistently appears to students while a "faded-out" script is available only if students ask for it.

Object properties RelateRoleToPhase and RelateScriptToPhase belong also to the Phase class. The purpose of the RelateRoleToPhase property is to correlate each role with the phase it participates.The RelateScriptToPhase property relates every phase to the script it belongs. This is important because scripts are described as a sequence of their phases.

Figure 3 presents the properties of any script class of the type The_......._Script (for instance The_Concept_Grid_Script) (the general naming format is denoted by the long underscore). Intellectual/Conceptual and Physical/Social are the two poles in dimension x and they position the script according to its major learning objective. If the script is nearer to the Intellectual/Conceptual pole then the objective is to obtain a piece of knowledge or a specific intellectual ability. By contrast, script positioning nearer to the Physical/Social pole means that the objective is to obtain a skill for being in the world. This objective is characterized by physical and mental attitudes in social situations [START_REF] Stegmann | Examples of CSCL scripts using mobile tools[END_REF].

Respectively Static and Mobile are the two poles in dimension y and they position the script according to the importance of being able to change or to choose the context or location of a learning interaction or cooperation. If a script is positioned nearer to the Static end of the range this means that learning occurs in a closed repeating interaction between learning tool and learner, while nearer to the Mobile end of the range learning occurs in a more open repeating interaction between learning tool and learner. This is achieved by changing the context while interaction happens [START_REF] Stegmann | Examples of CSCL scripts using mobile tools[END_REF].

Figure 5 shows the properties of the Role class. Data properties of this class are Role_Description, Expectation, Obligation, Privilege. The Role class also includes the object properties RelateActivityToRole, which connects activity to role, and RelatePhaseToRole, which is the inverse property of RelateRoleToPhase. The t-start property describes the time that an activity starts and property t-end describes the time that this activity ends. The Activity class also includes the object property RelateRoleToActivity, which is the inverse of RelateActivityToRole.

USE CASES

The main benefit from using an ontology is that a common format for developing machine-processable representations of knowledge can be used across various systems, thus enabling the exchange of information between systems and the proliferation of common tools to process the represented knowledge. The collaboration script ontology is proposed and developed with the purpose of formalizing scripts in order to promote their reusability and portability between different computer-based environments for collaborative learning. To better illustrate why such an effort can be beneficial for CSCL we comment in the following on the use cases of web-based systems that would take advantage of the script ontology.

Scripts are expected to be developed and used by educators and instructional designers. Appropriate software tools -like a script editor -can facilitate instructors to create and edit scripts (as instances of the script classes included in the ontology), thus reifying and communicating their ideas on how a collaborative learning session can be organized and scaffolded. Using scripts to further guide students' collaborative sessions can produce data on the effectiveness and efficiency of scripted collaborative learning interactions among the team members. Thus repositories where script-related information is archived can also include information on the effectiveness of the scripts in specific situations and for learners of specific profile. In order this information to be also exploited by any other computer-based system related to CSCL, it has to conform to a common representation format such as an ontology can define. If this is implemented then transferring scripts and using them to other systems could be easily accomplished; for example, a script developed and evaluated in a specific domain could be spotted by an instructor while searching for script-related resources, transferred to another web-based system and evaluated in another context and domain. Furthermore, an ontology-based system will have the ability to query the knowledge database and facilitate system users in their effort to pinpoint scripts with specific features that could be used in their learning design. For example questions like: "which scripts could be appropriate in a specific domain and for a specific user profile? Is ArgueGraph a Conflict or Jigsaw script? What is the locus of control of the ArgueGraph script?" could be easily answered by an ontology-based system.

Overall, we expect that by building ontology-based environments for collaborative learning we promote the formalization of tools for collaboration (such as scripts) in a way that improves our ability to search, retrieve and process data relevant to collaborative learning sessions. This may help system developers to establish transferability between their systems, educators to design and replicate fruitful collaborative activities in various systems and learners to engage in collaborative activities better adapted to their personal learning needs.

CONCLUSIONS

This paper presented an ontology-based framework for formalizing computer-based collaborative learning scripts. The ontology organizes semantically several script-schemata, script classes and also the phases, the roles and the activities that constitute a script. It is argued that the CSCL script ontology can become a key component in a knowledge-based system that aims to efficiently support students and instructors in collaborative learning tasks.

FIGURE 1 .

 1 FIGURE 1. Classes & subclasses of the CSCL script ontology

FIGURE 2 .

 2 FIGURE 2. Properties of phase class Data properties Coersion_Degree and Granularity take only the values High, Medium and Low. Property Locus_of_Control acquires only the values Internal and External andproperty Fade_Out_Level have numeral values from 0 to 5 and it describes the level of "script persistence" to guide learners. This practically means that a "non-fading" script consistently appears to students while a "faded-out" script is available only if students ask for it.Object properties RelateRoleToPhase and RelateScriptToPhase belong also to the Phase class. The purpose of the RelateRoleToPhase property is to correlate each role with the phase it participates.The RelateScriptToPhase property relates every phase to the script it belongs. This is important because scripts are described as a sequence of their phases.Figure3presents the properties of any script class of the type The_......._Script (for instance The_Concept_Grid_Script) (the general naming format is denoted by the long underscore).

FIGURE 3 .

 3 FIGURE 3. Properties of the_......_script class

FIGURE 4 .

 4 FIGURE 4. An example of how scripts are positioned on conceptual dimension space (adapted from Stegmann, 2004)

FIGURE 5 .

 5 FIGURE 5. Properties of Role class

FIGURE 6 .

 6 FIGURE 6. Properties of Activity class