Population characteristics and the decision to convert to organic farming

Keywords: organic farming, decision making, major change, Theory of Reasoned Action, agent-based model, social influence, credibility

We revisit some ideas of why farmers do not convert to organic farming from our previous article with a dynamic individual based model. In this model, an agent's decision on transitioning to organic is based on the comparison between the satisfaction with its current situation and the potential satisfaction with an alternative farming strategy. A farmer agent's satisfaction is modeled with the Theory of Reasoned Action. It is computed by comparing the agent's outcomes over time and comparing its current outcome against those of other agents to whom it lends great credibility ('important others'). The first study is based on prototypical farm populations. In this paper, the predicted conversion rate is studied with some French "cantons" having different practice intensities. The model is initialized with dairy farmers' data in these "cantons" in 2000. The results show that the "cantons" characteristics have great impact on the virtual adoption rate. Intensive "cantons" convert less than extensive ones. Extensive farms having not very good environmental outcomes seem to convert the most.

Introduction

The recent dairy crisis combined with increasing consumer demand for organic food have made conversion to organic farming socially and economically interesting for dairy farmers [START_REF] Dedieu | Les exploitations en agriculture biologique: quelles performances économiques?[END_REF], [START_REF] Sainte-Beuve | Performance économique des exploitations biologiques et conventionnelles: Levier économique à la conversion[END_REF]. However, many farmers still do not convert. Why? Recently, the conversion to organic farming has been qualified as a major change [START_REF] Sutherland | Triggering change: Towards a conceptualisation of major change processes in farm decision-making[END_REF] or a transformational adaptation [START_REF] Rickards | Transformational adaptation: agriculture and climate change[END_REF], [START_REF] Dowd | The role of networks in transforming Australian agriculture[END_REF], as well as a social movement [START_REF] Sutherland | Triggering change: Towards a conceptualisation of major change processes in farm decision-making[END_REF], [START_REF] Fairweather | Are conventional farmers conventional? Analysis of the environmental orientations of conventional New Zealand farmers[END_REF]- [START_REF] Pavie | Etude des freins à la conversion à l'agriculture biologique des exploitations laitières bovines[END_REF]. Conversion often implies strong changes in a farmer agent's worldview and social network, and generally begins with a strong need for change [START_REF] Sutherland | Triggering change: Towards a conceptualisation of major change processes in farm decision-making[END_REF], [START_REF] Barbier | Cours de vie d'agriculteurs allant vers l'économie en intrants: les plaisirs associés aux changements de pratiques[END_REF]. Such a change that engages a number of social processes involving the agent, its peers and its environment has been rarely studied [START_REF] Burton | Modelling the adoption of organic horticultural technology in the UK using duration analysis[END_REF].

Agent-based modeling [START_REF] Goldstone | Computational models of collective behavior[END_REF] or individual-based modeling [START_REF] Grimm | Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future?[END_REF] appear to be relevant and well-geared to help identifying the main drivers that can explain the observed dynamics. However, as pointed out in [START_REF] Huet | Toward Modelling of Transformational Change Processes in Farm Decision-Making[END_REF], [START_REF] Xu | Why do farmers not convert to organic farming? Modelling conversion to organic farming as a major change[END_REF], none of current agent models is well fit to represent the decision process about a major change that is at stake in the conversion to organic farming. This is the reason why [START_REF] Xu | Why do farmers not convert to organic farming? Modelling conversion to organic farming as a major change[END_REF] has proposed a dynamic version of the Theory of Reasoned Action (TRA); and showed its potential to explain why farmers do not convert to organic farming in a prototypical farmer population. This paper aims at evaluating this model when running on more realistic farmer populations designed from the French Agricultural Census data (RGA) 2000. The purpose is mainly to study how the conversion rate evolves differently in various farmer populations. The main original features of the model are the following:

 Like [START_REF] Kaufmann | Simulating the diffusion of organic farming practices in two New EU Member States[END_REF], we propose a dynamic model of TRA [START_REF] Ajzen | Belief, attitude, intention and behavior: An introduction to theory and research[END_REF] to compute an agent's satisfaction. In the model, a decision on conversion is based on comparing the agent's satisfaction with its current strategy against its potential satisfaction with an alternative one. These satisfactions are computed with the attitude and the subjective norm related to the current or alternative strategy. Both attitude and subjective norm are dynamic and based on the difference in practice outcomes. Consistently with TRA [START_REF] Ajzen | Belief, attitude, intention and behavior: An introduction to theory and research[END_REF], our model integrates decomposed variables: attitude and subjective norm-instead of global variables as in [START_REF] Kaufmann | Simulating the diffusion of organic farming practices in two New EU Member States[END_REF]. A farmer agent's evaluation is thus based on its concrete strategy and practice instead of an abstract general opinion.

The evaluation relates to its own experiences and its peers' strategies and practices.  An agent's attitude about its current strategy is modelled as the difference between its past outcomes with current strategy and its current outcome. This choice is grounded in [START_REF] Mintzberg | The Structure of 'Unstructured' Decision Processes[END_REF], [START_REF] Öhlmér | Understanding farmers' decision making processes and improving managerial assistance[END_REF] who argue that a farmer agent's earlier practices are very influential for its decision on a future practice. Regarding an alternative strategy which can be new to the agent, the attitude is the difference between its past outcomes with current strategy and the outcomes of other peers with a similar farm and adopting the alternative or the outcome of the alternative in the media.  The subjective norm involves a comparison with peers, weighted by their credibility. This credibility is based on the difference between outcomes (i.e. total production).  As [START_REF] Darré | La parole et la technique: l'univers de pensée des éleveurs du Ternois[END_REF] showed that farmers co-construct their practices. An agent updates its practice by copying the performances of its credible peers with a similar farm.  Finally, an important feature of our model is tied to the decision process about a "major change". It is assumed that a major change is only considered in critical situations where an agent faces high costs (economic, cognitive, emotional, etc.). Otherwise, agents do not even consider changing their major options. In a stable period, if a farmer agent is satisfied enough with its current farming strategy, it does not envisage an alternative one. Only a certain level of frustration or critical event (succession, major change in the farm, etc.) will evoke a consideration of an alternative to a farmer's current strategy.

In [START_REF] Xu | Why do farmers not convert to organic farming? Modelling conversion to organic farming as a major change[END_REF], the model is studied with prototypical farm populations and shows different reasons for the lack of conversion to organic farming. In this work, the model is explored further by an initialization with six French "cantons" (about 100 farmers) varying in their dairy farm type distributions. The adoption degree is compared among these populations to clarify the effect of population features. In general, intensive "cantons" convert less than extensive ones. Particularly, extensive farms having not very good environmental outcomes seem to convert the most.

After presenting the model's principles with partially ODD (Overview, Design concepts, Details) protocol [START_REF] Grimm | ODD protocol : A standard protocol for describing individual-based and agent-based models[END_REF], we outline the model's behaviors and some explanations before going on to synthesis, and discuss our conclusions.

2

Materials and Methods

2.1

The model Basic elements.

Farmer.

The model studies the evolution of a population with N farmer agents. Each agent is characterized by its farm; its farming strategy; its practice defined on several dimensions i which are evaluated through performances; the importance W i given to each dimension of practice; the credibility (C(f,v)) it lends to each other agent; its memory of applied strategies and practices during the last M periods; its satisfactions with current farming strategy (I C ) and with an alternative one (I A ); and its duration for staying with a strategy (DC) and for being dissatisfied for a strategy after being stable (DD). DC and DD capture the duration between two events related to the decision process. DD counts an agent's dissatisfaction duration with its current strategy. In the model, an agent has to be dissatisfied long enough with its current strategy to change it. DC counts the duration since last strategy change. An agent cannot consider changing strategy again even if the agent is dissatisfied with it during the confirmation period. This is consistent with the theory of innovation diffusion [START_REF] Rogers | Diffusion of innovations[END_REF] in which an agent has a confirmation period just after adopting a new strategy. Both counters are necessary to simulate an agent's stability and consistency. The corresponding delayed action of both counters can only occur when the counter is above the parameter TD.

Except for W i , all these attributes of a farmer agent are dynamic during the simulation, and are described in detail below.

Credibility. Each agent f gives a credibility C (f, v) to another agent v by comparing their outcomes. Credibility is between 0 (not credible at all) and 1 (very credible).

Satisfaction. Each agent has a satisfaction with its current farming strategy (I C ) that corresponds to an evaluation of that strategy. It may also evaluate an alternative strategy in certain cases and have a satisfaction for it (I A ). Satisfaction with a farming strategy lies between 0 (not satisfied at all) and 1 (very satisfied).

If an agent is satisfied with its current farming strategy, it does not consider an alternative. Otherwise, its satisfactions with its current farming strategy (I C ) and with an alternative one (I A ) are computed and compared. If I A is higher enough than I C , the agent will change its farming strategy. I C is thus computed at every iteration, whereas I A is only computed when a stable agent is dissatisfied with its current farming strategy.

In accordance with TRA, the satisfaction I S with a farming strategy S depends on two elements: attitude A S and subjective norm SN S toward S. In the original theory, the interaction between these two elements varies with different agents facing different situations. In order to keep the model simple, satisfaction is assumed as the average value of these two elements.

𝐼 𝑆 = 𝐴 𝑆 + 𝑆𝑁 𝑆 2 (1)
Both attitude and subjective norm lie between -1 (very negative attitude/subjective norm concerning the farming strategy to evaluate) and 1 (very positive attitude/ subjective norm concerning the farming strategy to evaluate). They are computed with farms' outcomes, farmers' strategies, and credibility. See the section "Farmers' dynamics" for the computation details.

Considering the value range of attitude and subjective norm towards a farming strategy, the satisfaction should also lie between -1 and 1. However, to facilitate other calculations, the satisfaction is normalized between 0 and 1.

Practice. As stated above, the term "practice" in the model is not really an agent's actual practice, but the way the agent evaluates it, i.e. its performance. A practice is evaluated over two dimensions: the level of output production (i.e. the productivity impact, in our case milk production), and the level of environmental amenities production (i.e. the environmental impact), respectively called productivity performance (P 0 ) and environmental performance (P 1 ) in the paper. Both P 0 and P 1 lie between 0 (very bad on this practice dimension) and 1 (very bad on this practice dimension).

Importance given to each dimension. The importance given to productivity dimension is termed W 0 , and the one given to environmental dimension is termed W 1 . W 0 and W 1 lie between 0 (not important at all) and 1 (most important). They sum to 1.

W 0 + W 1 =1 (2)
Importance defines an agent's personal values. An agent uses its own lens to judge the information it receives and the other agents it meets. In this model, both W 0 and W 1 are kept constant if an agent does not change its farming strategy. Farming strategy. It is defined by the importance that a farmer gives to each dimension of practice. Two farming strategies are considered: organic and conventional. The organic strategy means lending more importance to environmental dimension and less to productivity dimension, whereas the conventional strategy does the contrary. It is assumed that when a farmer agent changes its strategy, it changes accordingly the importance given to each dimension.

Farm.

A farm has three attributes: its farming total production (productivity outcome) T 0, its environmental amenities outcome T 1 and its reference R. R is the maximum possible productivity performance considering a farm's all characteristics and evolution. Interviews and experts' arguments show that conventional farms' references are grounded on the negotiations with dairy enterprises (often expressed by "quota" in Europe in the past). Organic farms have more constraints in terms of reference due to stricter regulations. So a conventional farm f's reference is considered as its farmer f's initial productivity performance P 0 f (t=0) and that for an organic farm is a function l of P 0 f (t=0).

𝑅 𝑓 = { 𝑃 0 𝑓 (t = 0) if 𝑓 is a conventional farm/farmer 𝑙 (𝑃 0 𝑓 (t = 0)) otherwise (3)
The implementation of a farm may need more attributes for different use cases. The detailed computation of T 0 and T 1 are defined in the model's implementation (see 2.2).

Media.

When an agent is dissatisfied with its current farming strategy and looks for an alternative, it first searches in the population for other agents having similar characteristics but applying an alternative strategy. If it cannot find one, it has access to the media for an stereotype alternative model which depends on the farm's current outcomes.

Dynamics.

Overview of a farmer's dynamics over years.

One time-step (iteration) t -> t+1 represents one year, i.e. farmers decide their farming strategies, their practices, and so on, once a year. During an iteration, farmers' update order is picked up at random by a uniform law. Compute P 0 , P 1 } } before considering the alternative. TA is threshold of I C to consider an alternative. TO is the threshold of I A to change strategy. W 0 is the importance given to productivity performance, W 1 is the importance given to environmental performance. C (f, v) is the credibility that agent f gives to agent v. P 0 is the productivity performance, P 1 is the environmental performance. As shown in Figure 1 and the pseudocode of algorithm 1, during each iteration, an agent evaluates its satisfaction with its current farming strategy. If the agent is in a stable period and is satisfied with its current strategy, it does not consider a change. Otherwise, the agent looks for an alternative and evaluates it. If the agent has been dissatisfied for long enough and the alternative is good enough, it will change. Otherwise, the agent stays with its current farming strategy. It will then update its credibility given to other agents and its practice. See the detail in the following.

Credibility update.

Every two agents' relationship is characterized by the credibility one gives to another, and depends on an agent's personal view of its difference to another in outcome (i.e. total production). For agent f, its difference to agent v is the sum of difference on each outcome dimension weighted by the importance given to that dimension.

𝐷 𝑣 𝑓 = ∑(𝑊 𝑖 𝑓 (𝑇 𝑖 𝑣 -𝑇 𝑖 𝑓 )) 2 𝑖=1 (4)
The credibility that agent f gives to agent v is calculated with f's difference to v:

𝐶 𝑣 𝑓 = 1 1 + 𝑒 -𝛼 𝐷 𝑣 𝑓 (5)
with α the parameter to characterize the slope of logistic function.

In Figure 2, agent f's difference and credibility to v are respectively plotted on the x-axis and y-axis. When the difference is negative, it means that v has a worse outcome than f, thus f gives little credibility to v. When the difference is positive, v has a better outcome than f, thus f gives big credibility to v.

The lines with different colors represent α variations to characterize the bias degree that an agent has for others with better outcomes. When α is small, the bias is small. An agent tends to give same credibility to others, whether or not with better outcomes. If α is big, the bias is strong. Only others with better outcomes are credible.

In the model, every two agents are connected. The credibility depends on an agent's perceived difference in outcome to another and it is then used to update the agent's outcome which can change the perceived difference. Thus, these elements are dynamic. An agent changes its farming strategy according to its satisfaction evaluations with its current strategy (I C ) and with the alternative one (I A ). If an agent is in a stable state (its confirmation duration DC > threshold TD) and it is still dissatisfied with its current strategy (I C < threshold TA), it will consider an alternative one. If the agent is dissatisfied long enough (its dissatisfaction duration since being stable DD > TD) and its satisfaction evaluation of the alternative is better enough than that with its current one (I A > I C (1+ threshold TO)), it will change strategy. As stated in equation ( 1), satisfaction I with a strategy is the average sum of the related attitude A and subjective norm SN.

In equation (1), attitude (A S ) represents an agent's personal view of the difference between its experience and the (potential) outcome of evaluated strategy S. The agent f's experience is its average outcome on the farm (𝑇 𝐶,0 ̅̅̅̅̅ , 𝑇 𝐶,1 ̅̅̅̅̅ ) with its current farming strategy (S f ) in memory (M). It is computed like this:

𝑇 𝐶,𝑖 ̅̅̅̅ = ∑ 𝑇 𝑖 𝑡 𝑀 𝑡 𝑎𝑛𝑑 𝑆 𝑡 =𝑆 𝑓 𝑁𝑏(𝑆 𝑡 = 𝑆 𝑓 ) (6)
The evaluated outcome depends on the strategy to be evaluated. For agent f's current farming strategy evaluation, the evaluated outcome is f's current outcome (𝑇 𝑖 𝑓 ).

Agent f's attitude toward the current farming strategy (A C ) is like this:

𝐴 𝐶 𝑓 = ∑(𝑊 𝑖 𝑓 (𝑇 𝑖 𝑓 -𝑇 𝐶,𝑖 𝑓 ̅̅̅̅ )) 2 𝑖=1 (7)
If an agent's outcome on its farm changes and this change is considered better than its experience, A C will be positive and strengthen the agent's decision to keep its current strategy. Otherwise, A C will be negative and may influence I C . Then the agent may be dissatisfied and evaluate I A .

For agent f's evaluation of an alternative farming strategy, the evaluated outcome is the average outcome (𝑇 𝐴,0 𝑓 ̅̅̅̅̅ , 𝑇 𝐴,1 𝑓 ̅̅̅̅̅ ) of other agents having similar characteristics as f but applying the alternative. The similarity is defined by a function Y over the farm's characteristics and is compared with a distance threshold (simi). Y is designed in the model's implementation (see 2.2). The evaluated outcome is computed as follows:

𝑇 𝐴,𝑖 𝑓 ̅̅̅̅ = ∑ 𝑇 𝑖 𝑣 𝑁 𝑌 𝑣 𝑓 <𝑠𝑖𝑚𝑖 𝑎𝑛𝑑 𝑆 𝑣 ≠𝑆 𝑓 𝑁𝑏(𝑌 𝑣 𝑓 < 𝑠𝑖𝑚𝑖 𝑎𝑛𝑑 𝑆 𝑣 ≠ 𝑆 𝑓 ) (8)
If there is no corresponding peer (no other agents similar to f and applying the alternative strategy), agent f will search the media for a stereotypical farm as the alternative. 𝑇 𝐴,𝑖 𝑓 ̅̅̅̅ = 𝑇 𝑚𝑜𝑑𝑒𝑙,𝑖 𝑓 (9) Therefore, agent f's attitude toward an alternative is:

𝐴 𝐴 𝑓 = { 0 𝑖𝑓 (𝐼 𝐶 𝑓 > 𝑇𝐴) ∑(𝑊 𝑖 𝑓 (𝑇 𝐴,𝑖 𝑓 ̅̅̅̅ -𝑇 𝐶,𝑖 𝑓 ̅̅̅̅ )) 2 𝑖=1 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (10)
Another component of satisfaction: the subjective norm, represents how an agent considers others' opinions on the evaluated farming strategy through outcomes, i.e. the strategy's implementation results. It is thus an agent's perceived difference between the outcome to be evaluated and the average of other agents' outcomes.

For agent f's evaluation of current farming strategy, the subjective norm is:

𝑆𝑁 𝐶 𝑓 = ∑ (𝑊 𝑖 𝑓 (𝑇 𝑖 𝑓 - ∑ (𝐶 𝑣 𝑓 𝑇 𝑖 𝑣 ) 𝑁 𝑣≠𝑓 ∑ 𝐶 𝑣 𝑓 𝑁 𝑣≠𝑓 )) 2 𝑖=1 (11)
An agent will be socially satisfied if it perceives that other agents, especially those to whom it lends great credibility ("important others"), consider it as a 'good farmer'. The agent may be so satisfied to have a good social image that it will never consider a major change. Otherwise, if the agent feels socially bad, it may try to become more similar to others in the group or to change of group. This can be done with a change of strategy.

For the evaluation of an alternative farming strategy, the subjective norm is:

𝑆𝑁 𝐴 𝑓 = { 0 𝑖𝑓 (𝐼 𝐶 𝑓 > 𝑇𝐴) ∑ ( 𝑊 𝑖 𝑓 (𝑇 𝐴,𝑖 𝑓 ̅̅̅̅ - ∑ (𝐶 𝑣 𝑓 𝑇 𝑖 𝑣 ) 𝑁 𝑣≠𝑓 ∑ 𝐶 𝑣 𝑓 𝑁 𝑣≠𝑓 )) 2 𝑖=1 otherwise (12)
If in other agents' opinions, especially those to whom agent f lends great credibility ("important others"), the alternative is not better, then it is judged not good enough to improve the situation. Agent f will tend to keep its current strategy. Otherwise, the agent's subjective norm strengthens its intention to change its strategy.

If an agent changes its strategy, it also changes the importance given to each dimension of practice. The parameter W is the conventional farmers' initial importance given to the productivity dimension.

For conventional agents: W 0 = W; W 1 = 1-W [START_REF] Grimm | Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future?[END_REF] For organic agents:

W 0 = 1-W; W 1 = W (14)

Practice update.

As farmers co-construct their practices [START_REF] Pimentel | Environmental, Energetic, and Economic Comparisons of Organic and Conventional Farming Systems[END_REF], at each time t, a farmer agent updates its practice by copying the performance of its credible peers with a similar farm.

∆𝑃 𝑖 𝑓 = ∑ 𝐶 𝑣 𝑓 (𝑃 𝑖 𝑣 -𝑃 𝑖 𝑓 ) 𝑁 𝑣≠𝑓 𝑎𝑛𝑑 𝑌 𝑣 𝑓 <𝑠𝑖𝑚𝑖 ∑ 𝐶 𝑣 𝑓 𝑁 𝑣≠𝑓 𝑎𝑛𝑑 𝑌 𝑣 𝑓 <𝑠𝑖𝑚𝑖 (15)
Both dimensions of practice are between 0 and 1. A farmer's productivity performance is also limited by the reference on its farm.

𝑃 0 𝑓,𝑡+1 = { 0 𝑖𝑓 (𝑃 0 𝑓,𝑡 + ∆𝑃 0 𝑓 < 0)

𝑅 𝑓 𝑖𝑓((𝑃 0 𝑓,𝑡 + ∆𝑃 0 𝑓 ) > 𝑅 𝑓 𝑃 0 𝑓,𝑡 + ∆𝑃 0 𝑓 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 )

A special case: if agent f looks for an alternative and cannot find a similar peer applying an alternative strategy, it will look for an alternative in the media. If after evaluation, agent f adopts the alternative found in the media, it will also copy the practice.

The design of use cases based on agricultural data

The model is implemented on data from Agricultural General French Census (RGA 2000) about six French "cantons" with strong variations in terms of practice intensity and homogeneity: 1516, Ruynes-en Margeride; 4202, Boën; 4224, Saint-Genest-Malifaux; 2208, Chèze; 2235, Plouagat; 2522, Quingey. The initialization of population distributions in each "canton" is deterministic. For sake of simplicity and according to expertise and literature [START_REF] Pavie | Evaluations et comparaisons des performances environnementales, économiques et sociales des systèmes bovins biologiques et conventionnels dans le cadre du projet CedABio[END_REF], [START_REF] Chambaut | Profils environnementaux des exploitations d'élevage bovins lait et viande en agriculture biologique et conventionnelle: enseignements du projet CedABio[END_REF], a farmer's farm and practice are defined by three variables: the utilized agricultural area (UAA), the number of dairy cows (NC), and the quota (Q) which is a synthetic indicator of the farm's maximum milk volume.

Figure 3 shows the average value of these variables in each "canton". The extensity (versus intensity) is here measured as the average UAA/average NC. "Cantons" 2208 and 2235 are the biggest producers and most intensive, while 4202, 4224 and 1516 have the least milk production. Despite its average milk production, 2522 is the most extensive "canton", also having the largest average UAA.

The implementations of a farmer, a farm and the media are presented as follows.

Farmer.

A farmer agent is designed by its practice with productivity performance P 0 and environmental performance P 1 designed from data. P 0 is directly deduced from the farm's initial characteristics and corresponds to the normalized average milk volume pro-duced by one cow in one year. For farmer/farm f, at the initial time t=0, P 0 f =Q f /NC f . At every time t, P 1 f =T 1 f /SAU f , the equation ( 15 < simi. The threshold simi is a parameter and supposed as 0.1 in the model.

Farm.

As shown above, each farm is initialized by the crossed distribution of discretized utilized agricultural areas (UAA) and quotas (Q) of its "canton" from the RGA 2000. They remain constant all along the simulation. The number of dairy cows (NC) is computed from a law extracted from data (with a regression r 2 =0.9563): NC = 0.2463 UAA + 0.0001106 Q (17) From databases regarding farmers' production and various sources1 , a law is built to compute the potential maximum milk production of an organic farm starting with the conventional strategy, knowing its initial productivity performance P 0 after the normalization and P 0 ' before the normalization. A farm f's normalized reference R and reference R' before the normalization are computed as follows:

For a conventional farm: R = P 0 (t = 0) [START_REF] Mintzberg | The Structure of 'Unstructured' Decision Processes[END_REF] For an organic farm: R' = 0.6046 P 0 '(t = 0) + 1913 NC [START_REF] Öhlmér | Understanding farmers' decision making processes and improving managerial assistance[END_REF] The environmental amenities outcome T 1 is computed at every time by an aggregated function of literature [START_REF] Pavie | Evaluations et comparaisons des performances environnementales, économiques et sociales des systèmes bovins biologiques et conventionnels dans le cadre du projet CedABio[END_REF], [START_REF] Chambaut | Profils environnementaux des exploitations d'élevage bovins lait et viande en agriculture biologique et conventionnelle: enseignements du projet CedABio[END_REF]. It considers mineral impacts and energy consumption related to the total milk production and the farm's agricultural surface:

For a conventional farm: T 1 = (53 UAA + 2.918 T 0 )/2 [START_REF] Darré | La parole et la technique: l'univers de pensée des éleveurs du Ternois[END_REF] For an organic farm: T 1 = (-10 UAA + 2.588 T 0 )/2 [START_REF] Grimm | ODD protocol : A standard protocol for describing individual-based and agent-based models[END_REF] Using French dairy farms' database in RGA 2000, R' is to be normalized between 0 (very low production) and 1 (very high production). 53 UAA, 2.918 T 0 , -10 UAA and 2.588 T 0 are normalized values between 0 and 1. The normalization is:

x = (x' -min) / (max -min) (22)
With: min = minimum real value in the database; max = maximum real value in the database; x is the normalized value of real value x'.

The media.

We use laws extracted from data to design farmers' alternative models. When a conventional farmer f wants to evaluate the organic strategy at time t+1, it computes T 0 '(t+1) as follows and its related T 1 '(t+1) with the equation [START_REF] Darré | La parole et la technique: l'univers de pensée des éleveurs du Ternois[END_REF]. Noting that T 0 ' is the farmer's real productivity outcome on the farm (before the normalization).

T 0 '(t+1) = 0.6046 T 0 '(t) + 1913 NC (23) When an organic farmer f wants to evaluate the conventional strategy at time t+1, it computes T 0 '(t+1) as follows and its related T 1 '(t+1) with the equation [START_REF] Grimm | ODD protocol : A standard protocol for describing individual-based and agent-based models[END_REF].

T 0 '(t+1) = (T 0 '(t) -1913 NC) / 0.6046 (24)

2.3

Experimental design [START_REF] Xu | Why do farmers not convert to organic farming? Modelling conversion to organic farming as a major change[END_REF] has studied 625 parameter sets identifying all the qualitative behaviors of the model to diagnose reasons for the absence of conversion. This work aims to study conversion in six different French "cantons" using the same 625 parameter sets varying the main parameters: α (slope of logistic function) from 5 to 41, TA (threshold to consider an alternative) from 0.41 to 0.49, TO (threshold to consider an alternative) from 0.01 to 0.09, W (importance given to the dimension representing farming strategy) from 0.6 to 1. TD (threshold for two counters of duration) is kept constant with the value 5. Agents' memory is also kept constant as 10 years. The distance for similarity, simi, values 0.1. The evolution of each "canton" with a population of 100 conventional farmers (no organic farmers at first) is simulated for 30 years and replicate 100 times.

Model behaviors

The adoption situations in each "canton" are studied as follows.

3.1

The adoption rate and "cantons" Fig. 4. -Average percentage of organic farming adoption according to "canton" over 625 sets of parameters and their related 100 replications At the initialization, each "canton" has no organic farm. Figure 4 shows that farms in different "cantons" have different adoption percentages after 30 years. "Canton" 2522 convert the most, followed by 4202 and 1516, and then 4224. 2208 and 2235 convert the least. As these six "cantons" have different farm characteristics. The population distribution is studied to learn more about the conversion rate.

The characteristics of "cantons"

As shown in "Materials and methods" section, each farm has a couple of outcomes (total production) on two dimensions: productivity outcome (T 0 ) and environmental outcome (T 1 ). These two values indicate directly a farm's utilized agricultural area (UAA) and quota (Q) which are constant characteristics along the simulation. With another constant characteristic on the farm: number of cows (NC), a farmer's initial practice performance: productivity performance (P 0 ) and environmental performance (P 1 ) are also defined. In figure 5, for each canton, its average initial T 0 and T 1 are shown on the left; its average initial P 0 and P 1 are shown on the right. The black lines in each histogram show the variation. Consistently with figure 3, figure 4 and 5 show that "cantons" adopting the least: 2208 and 2235, are the most intensive and productive (having the smallest UAA/NC and the largest initial T 0 ). They are followed by 4224, 4202 and 1516, which have the largest initial environmental outcome T 1 and lower productivity outcome T 0 . The "canton" with the most adoption: 2522, is the most extensive (figure 3), but having the worst initial environmental performance P 1 (figure 5). Its outcomes: T 0 and T 1 are medium. Error bars indicates "more or less" one standard deviation.

The credibility a farmer gives to another depends on their relative outcomes (T 0 and T 1 ). The social evaluation of satisfactions depends also on their relative T 0 , T 1 and on the outcomes of its "similar" farmers (equation 11 and 12). Table 1 shows the initial farm distributions of each "canton" in terms of T 0 and T 1 .

The adoption rate and farm types

The adoption rate varies with "cantons". In fact, it varies also with initial outcome types in terms of productivity outcome (T 0 ) and environmental outcome (T 1 ). Generally, the probability to convert is high for cases with numerous farms at the initialization. Farms with a low initial T 0 or a high initial T 1 (first lines in Table 2) convert more than others. However, for the highest initial T 1 , the conversion rate is not the largest (except in 2208 and 2235).

The distributions of adoption vary with "cantons". 2522 has a disperse distribution and its adoptions center on medium outcomes. 4202, also having a disperse distribution, has adoptions in cases with high T 1 and low T 0 . 1516 and 4224 have a centered distribution and their adoptions are rather in cases with very low T 0 and high T 1 .

Table 2. Average percentage of organic farming adoption according to "canton" and farm types in terms of T 0 or T 1 over 625 sets of parameters and their related 100 replications "Cantons" converting the least, exhibit distribution profiles with one higher density for medium T 0 and another one for the highest T 0 . They are rather homogeneous in T 1 .

4

Conclusion discussion [START_REF] Huet | Toward Modelling of Transformational Change Processes in Farm Decision-Making[END_REF] and [START_REF] Xu | Why do farmers not convert to organic farming? Modelling conversion to organic farming as a major change[END_REF] argue that the organic farming adoption is strongly influenced by the imitation of "important others" practices and is very sensitive to the distance between farms. However, the model proposed and studied in [START_REF] Xu | Why do farmers not convert to organic farming? Modelling conversion to organic farming as a major change[END_REF] considers only prototypical populations and points out the reasons for non-adoption of organic farming. In this paper, we focus on the impact of population characteristics on the adoption degree. In this work, the model is explored further by an initialization with six French "cantons" (about 100 dairy farmers) varying in their farm type distributions from the French RGA 2000. At the farm level, we have observed that farms with low milk total production and high environmental total production convert more than others. Such farms are said having extensive practices and being more susceptible to convert according to ex- perts. Approximately one-third of global land under organic production is located in unfavorable areas where smallholder farmers, who often lack access to insurance or inexpensive credit, dominate agricultural production [START_REF] Ifoam | The World of Organic Agriculture: Statistics & Emerging Trends 2017[END_REF]. Keeping their extensive practices, or even increasing this extensity, by adopting for example organic farming, can be beneficial to eliminate their reliance on expensive, fossil fuel-derived chemical inputs [26] [27]. This increasing autonomy is taken into account in our environmental outcome which considers the decreasing of inputs and energy consumption. At the regional level, intensive regions convert less than extensive ones, especially those with not very good environmental outcomes. Indeed, we notice that the most intensive "cantons" almost do not convert (those located in the Brittany region). They are more likely to be satisfied with their current situation and do not consider a major change. Despite of having the least productivity outcomes, "cantons" 1516, 4202 and 4224 do not have the most conversion because of their already good environmental outcome. The adoption of organic farming cannot bring them much environmental gain. On the contrary, the most extensive "canton", 2522 has the largest adoption rate. It is less productive than farms in the Brittany region (2238 and 2235), but more than other "cantons". Its environmental production is not as good as the other extensive "cantons" (1516, 4202 and 4224). Thus, most farms from this "canton" can poorly decrease their productivity outcome and increase much their environmental outcome with the conversion to organic farming. This result can sound strange but it has been pointed out by [34] that most farmers having already chosen low-input strategy (i.e. strongly extensive) do not convert since they cannot improve more their environmental impact. The only reason they can convert is for economic purpose in case of strong crisis. We also show that more diverse regions, having high densities for different farm types, tend to adopt more but this point deserves more investigations. In fact, our model can also be applied to other contexts, for example, other forms of adoption in agriculture.

However, we cannot compare our adoption rates for the chosen French regions: firstly, because the data are not so easy to obtain; secondly very important dynamics are not present in the model and make the comparison having no sense. Adding to the model external factors such as economic crisis, but also demographic evolution implying an increasing average farm sizes, are the next steps for research about this model.

Fig. 1 .

 1 Fig. 1. -Overview of the farmer's update

Fig. 2 .

 2 Fig. 2. -The credibility (y-axis) agent f gives to v depends on f's difference (x-axis) to v for three values of parameter α (different-colored lines)

Fig. 3 .

 3 Fig. 3. Average number of dairy cows NC, average UAA (in are) and quota (in liters) for the six chosen French "cantons".

  ) is only used to update P 0 f . The Y function telling how two farmers are judged similar is based on a similarity of their farms' characteristics regarding UAA and NC. For agent f, agent v is a similar peer if |𝑈𝐴𝐴 𝑓 -𝑈𝐴𝐴 𝑣 | 𝑈𝐴𝐴 𝑓 < simi and |𝑁𝐶 𝑓 -𝑁𝐶 𝑣 | 𝑁𝐶 𝑓

Fig. 5 .

 5 Fig. 5. -Average of initial T 0 and T 1 (left panel) and P 0 and P 1 (right panel) in each "canton".Error bars indicates "more or less" one standard deviation.

Table 1 .

 1 Initial farm distributions according to "cantons" and farm types in terms of T 0 or T 1

	initial T0 canton 2208 2235 2522 4202 4224 1516	initial T1 canton 2208 2235 2522 4202 4224 1516
	0.05 0.15	3 3	3 2	5 12	16 17 37 6 5 27	0.85	3	3	3	13	14	19
	0.25	4	7	14 22 21 17	0.75	5	5	7	5	7	29
	0.35	8	2	16 14 18	9	0.65	14	5	15	22	21	21
	0.45	25 21 15 18 12	5	0.55	25	22	16	19	26	14
	0.55	18 12 16	9	14	2	0.45	24	19	23	24	23	10
	0.65	11	6	10	3	10	2	0.35	15	7	10	4	6	5
	0.75	9	3	2	2	2	1	0.25	5	23	11	6	1	2
	0.85	8	10	2	6	1	0	0.15	7	14	5	4	2	0
	0.95	11 34	8	4	0	0	0.05	2	2	10	3	0	0

Social simulation conference, August 20-24, 2018, Stockholm, Sweden

http://www.cantal.chambagri.fr/fileadmin/documents/Internet/Autres%20articles/pdf/2014/Bio /ABBL2008-2012.pdf http://www.tech-n-bio.com/ http://www.agrobio-bretagne.org/ Social simulation conference, August

20-24, 2018, Stockholm, Sweden


Cédric D'ham

Erica De Vries

Isabelle Girault

Patricia Marzin

Exploiting distance technology to foster experimental design as a neglected learning objective in labwork in chemistry

This article deals with the design process of a remote laboratory for labwork in chemistry. In particular, it focuses on the mutual dependency of theoretical conjectures about learning in the experimental sciences and technological opportunities in creating learning environments. The design process involves a detailed analysis of the expert task and knowledge, e.g., spectrophotometry as a method for the determination of the concentration of a compound in a solution. In so doing, modifications in transposing tasks and knowledge to the learning situation can be monitored. The remote laboratory is described, as well as the specific features that alter the degree of fidelity of the learning situation in comparison to the expert one. It is conjectured that these alterations might represent actual benefits for learning.

Introduction

Nowadays, numerous learning environments are proposed and developed that involve some application of Information and Communication Technology (ICT). The design of such computersupported learning environments can be characterized as either technology-driven or theory-driven. On the one hand, technological progress influences the design of learning environments through the availability of new tools and techniques. On the other hand, advances in learning theory show the way to new roles and possible uses of technology for learning. Both technology and theory-driven development of learning environments tend to result in some adaptation of the expert domain knowledge and tasks that are the goal of the teaching. The general process of turning domain knowledge into an object of teaching has been called didactical transposition [START_REF] Chevallard | La transposition didactique[END_REF], and more specifically, it has been coined computational transposition [START_REF] Balacheff | Advanced educational technology: knowledge revisited[END_REF] when it comes to designing learning environments involving ICT. Computational transposition is the process which leads to the specification and the implementation of the knowledge model; i.e. adapting domain knowledge and tasks requires effort in order to fit the constraints of symbolic representation and computation imposed by the computer. On the other hand, the computer also allows new ways of manipulating representations, and these opportunities, in combination with knowledge from studies of expert tasks, should inform the design of new learning environments (see also [START_REF] Kozma | The roles of representations and tools in the chemistry laboratory and their implications for chemistry learning[END_REF].

The current article focuses on this mutual dependency of theoretical conjectures and technological opportunities in the particular case of the design of a remote laboratory for labwork in chemistry.

ICT for labwork in the experimental sciences

The literature shows a proliferation of computer programs for learning in the experimental sciences. Theories, concepts, laws, and phenomena in chemistry and physics can be presented and explained, but also documented, illustrated, modelled, depicted, etc. using a variety of computer programs and displays. A number of different theoretical frameworks on learning constitute the foundations of such environments (see [START_REF] De Vries | Educational technology and multimedia from a cognitive perspective: Knowledge from inside the computer, onto the screen, and into our heads[END_REF], for an overview). More specifically, regarding labwork in the experimental sciences, such environments include systems providing access to animations, simulations, pre-recorded experimental data and remote experimental devices. [START_REF] Trgalová | Systèmes de formation à distance. Proposition d'une typologie [Distance learning systems, a typology[END_REF] proposes a categorization on the basis of two criteria, i.e. the nature of experimental data and the degree of user control. Animations and simulations deal with simulated data, no real experiments are carried out, but the results of fictitious experiments are presented to the students. In the case of simulations, the learner can vary the value of input variables and observe the outcomes in terms of output variables. This type of computer programs is used to encourage discovery learning, i.e. students themselves discover the laws of a domain by carrying out virtual experiments on a model of that domain (de Jong and van Joolingen 1998). Animations do not allow such a high degree of user control, they merely present dynamic illustrations of laws and/or phenomena. Pre-recorded experimental data and remote experimental devices deal with data obtained from real experiments. They differ in the time of data collection, the former data are pre-recorded, whereas the latter are realtime collected. In addition, real-time collected data differ in the degree of user control [START_REF] Trgalová | Systèmes de formation à distance. Proposition d'une typologie [Distance learning systems, a typology[END_REF]: none (remote sensing devices), limited (simple remote operation devices) or full (remote laboratory). The appropriateness of each of these technologies for learning should be gauged in reference to pedagogical objectives.

Technology-induced changes in learning contexts, tasks and knowledge

Whereas remote laboratories dedicated to chemistry experiments may provide almost full student control via the Internet, such as for example in [START_REF] Senese | The Internet chemistry set: web-based remote laboratories for distance education in chemistry[END_REF] or in [START_REF] Cooper | Remote controlled experiments for teaching over the internet: a comparison of approaches developed in the pearl project[END_REF], students are often only allowed to control the parameters of a pre-designed experiment. Students in the latter situation are not expected to actually design the experiment, although it is in fact an essential subtask of a chemist to consider several alternative experimental settings. Moreover, the use of ICT might change the nature of labwork in chemistry in a number of other non-trivial ways. For example, tasks may either be formalized or remain implicit, manipulations can be taken over by a robot, students may or may not decide on the material used, security norms may be imposed by the system, etc. Still other aspects involve the educational setting, for example an artificial tutor might take over the role of the teacher, a robot might manipulate and carry out the experiment instead of the students themselves, the pedagogical strategy of the artificial tutor can be programmed to provide help or not. In order to control such technology-driven changes, a more systematic approach is needed that first analyzes domain tasks and knowledge, and then studies how they change in the process of implementing components of the learning environment. The adaptation of domain tasks and knowledge can then be monitored so as to produce presumably positive changes instead of mere arbitrary or even negative changes. One of our goals in designing the remote laboratory is therefore to coordinate and monitor modifications and adaptations of the expert domain knowledge and tasks.

The second section presents a review of the literature showing how the subtask of experimental design is often neglected in teaching practices to the advantage of other subtasks, such as carrying out an experiment, analysing data, etc. In contrast, our approach entails proposing a starting question and making the learner focus on the design of the experimental protocol that allows for data collection and finding an answer. The activity of designing a protocol is likely to produce a confrontation between theory, problem and experiment. This confrontation is an important way to encourage students to construct meaning and to acquire knowledge that can be reinvested in practical work. The third section then presents the design of Educaffix.net, a system that exploits the features of a remote laboratory for focusing on experimental design. This includes the systematic analysis of domain tasks and concepts in labwork in chemistry that guided the design process. A specific set of domain concepts is presented: the determination of the concentration of a solution with the help of photometry. In the fourth section, specific technology-based features will be highlighted in a comparison with the expert situation. The article concludes with perspectives for future research.

Labwork in science education

Practical work in a laboratory is essential for the learner in experimental sciences because some specific learning objectives can only be met in this kind of situation. According to Becu-Robinault (1997), there are experiments for seeing a phenomenon, experiments for introducing a concept, and experiments for verifying a scientific law.

Multiple pedagogical objectives

Several studies classified the learning objectives that teachers can associate with student labwork. Table 1 shows a number of possible learning objectives for labwork [START_REF] Tiberghien | An analysis of labwork tasks used in science teaching at upper secondary school and university levels in several European countries[END_REF].

Table 1. Learning objectives for labwork sessions [START_REF] Tiberghien | An analysis of labwork tasks used in science teaching at upper secondary school and university levels in several European countries[END_REF] 1 -Identify objects and phenomena and become familiar with them 2 -Learn a fact or facts 3 -Learn a concept 4 -Learn a relationship Content 5 -Learn a theory/model 6 -Learn how to use a standard laboratory instrument or piece of apparatus 7 -Learn how to carry out a standard procedure 8 -Learn how to plan an investigation in order to address a specific question or problem 9 -Learn how to process data 10 -Learn how to use data to support a conclusion Process For the purposes of the present article, two additional observations are necessary: the nature of objectives in the process category and the interdependency of objectives 7 and 8. Regarding the nature of the objectives, it can be noticed that process objectives are of two categories, some have a manual nature. In particular, objective 6 in Table 1 concerns the gestures that an expert chemist carries out in order to do experiments, whereas the objectives 7 through 11 on the other hand are largely methodological and concern a number of steps and their order for the activities mentioned. Regarding the objectives 7 and 8, planning an investigation (objective 8) additionally involves knowing how to apply a standard procedure (objective 7) or even how to design one oneself. The latter would involve learning the principles of a standard experimental procedure, which in itself might be more content like.

Among the process objectives, [START_REF] Tiberghien | An analysis of labwork tasks used in science teaching at upper secondary school and university levels in several European countries[END_REF] showed that "Learn how to use a standard laboratory instrument" is very common in chemistry in the countries studied, whereas "Learn how to plan an investigation to address a specific question or problem" is infrequent in chemistry. Furthermore, labwork sessions are usually found to be sequenced in four phases corresponding to the following tasks:

• Task 1: prepare / design the experiment

• Task 2: carry out the experiment (genuine practical labwork)

• Task 3: use the results to solve the scientific question

• Task 4: communicate the results

Whereas tasks 2 and 3 are constitutive of a labwork session and cannot be totally removed, task 4 is a general non-specific task. Thus, the main variables that can change the context of a labwork session are related to Task 1. The learning objectives shown in Table 1 are often preferentially present in one selected task.

Impact of teachers' view on science

The teacher, depending on his or her own personal view of experimental sciences, can emphasize some selected tasks, while minimizing others. In fact, teachers may adopt one of a variety of different points of view of science. [START_REF] Novak | Learning science and the science of learning[END_REF], [START_REF] Orlandi | Conceptions des enseignants sur la démarche expérimentale [Teacher conceptions on the experimental approach[END_REF] and [START_REF] Desautel | La formation à l'enseignement des sciences : le virage épistémologique [Teacher training in science: the epistemological turn[END_REF] studied these novice and expert teachers' images of science. They found that teachers hold a view according to which a scientific method is a linear and rational process in the pursuit of the truth. They also showed that a majority of teachers hold essentially positivist views of the nature of knowledge and knowledge production. These views are likely to influence teaching about science and the objectives that can be met in teacher-organised labwork sessions.

Focusing on experimental design

Sere and Beney (1997) described and analyzed intellectual activities of students in labwork activities; a majority of students were shown to call upon other references than scientific theories to conduct their experimental activities. Some of these references, notably those that differ from the way in which science operates, may contribute to the construction of misconceptions about science. The general objective of learning in the experimental sciences, as proposed by [START_REF] Darley | Exemple d'une transposition didactique de la démarche scientifique dans un TP de biologie en DEUG 2ème année [Example of a didactical transposition of a scientific approach in labwork in biology in the second year of university[END_REF] for example, is the ability for the students to define procedures for testing hypotheses and/or models initially proposed during a problem analysis phase. Thus, students should be involved in situations where they are novice researchers. Others [START_REF] Arce | Student-designed experiments in scientific lab instruction[END_REF]Betancourt 1997, Séré 2002) also emphasized the importance of the task of experimental design in a learning context.

Since experimental design task is complex in itself, errors are very likely to occur especially in beginners. Consequently, students are hardly ever allowed to design their own experiment and then carry it out with real chemicals and laboratory apparatus. The study by [START_REF] Tiberghien | An analysis of labwork tasks used in science teaching at upper secondary school and university levels in several European countries[END_REF] showed that the most relevant learning objective in labwork (objective 8: to learn how to plan an investigation in order to address a specific question or problem) was in fact the least frequent process objective, it was mentioned in less than 15 % of the sample studied. One of the reasons could be that errors in experimental design highly influence the actual labwork tasks 2 and 3. Errors in the experimental procedure might create dangerous situations during the experiment itself (Tasks 2: doing an experiment). Moreover, even if the execution of the experiment does not give rise to problems in itself, problems are likely to occur when interpreting the results, e.g., in case of inconclusive data (Task 3: interpreting the results). Therefore, it seems reasonable in a number of situations, to favour Tasks 2 and 3 at the expense of experimental design (Task 1). Moreover, correct performance of tasks 2 and 3 are considered of crucial importance in learning to manipulate (manual component) and in conceptualising about the domain (content knowledge component).

A psychological task analysis of experimental design

From an epistemological point of view, the task of designing an experiment in order to answer a research question is an important skill. Experimental design is the main procedure for accumulating evidence in traditional science domains (physics, chemistry, biology) but also in the social sciences, such as in psychology. High quality experimental design warrants validity and reliability of the results. Experimental design as an expert task in the social sciences has been studied by [START_REF] Schraagen | How experts solve a novel problem in experimental design[END_REF]. His theoretical task analysis shows two important sub-goals, 1) answer the research question and 2) control sources of variance [START_REF] Schraagen | How experts solve a novel problem in experimental design[END_REF]. These correspond to the main concerns to be monitored during task performance. Although the subgoals themselves are independent of the specific domain of investigation, the input in terms of content heavily relies on domain knowledge, e.g., what kinds of phenomena may cause error variance in a particular domain. For example, in designing an experiment on people's preferences regarding the flavour of different beverages, the administration procedure for tasting several beverages one after the other has to be designed so as to minimise carry-over effects. Equivalent problems can be found in other domains, e.g. in a chemistry lab, reusing flasks poses problems of polluted fluids. Different solutions will be adopted in the different domains. In the psychology domain, a solution would involve varying the order in which the beverages are administered and by giving something to eat in between beverages; in the chemistry domain, a solution would involve rinsing flasks with the right liquid before (re)using them. The construction of these different solutions to an experimental design problem thus is highly affected by domain knowledge.

Experimental design also shows commonalities with design processes in other domains (architecture, mechanical design, etc.). These commonalities can be illustrated by referring to the three defining characteristics of design processes (e.g. de Vries 1994). First, design involves the construction of something new as opposed to calculating a solution with the help of an algorithm or copying an old solution to the problem. In experimental design, existing paradigms, procedures or protocols can be reused, but they need to be adapted to the specificities of each new situation. Second, design involves constructing a representation or plan of something before it is manufactured, e.g. the drawing of a house. In experimental design, it is the procedure, e.g., a series of actions to be executed, that is developed before actually carrying it out. This means that there is a time lag between planning and execution and consequences of actions will be difficult to foresee (delayed feedback). Third, design involves creating something that fulfils some need and satisfies some requirements. In experimental design, the main goal is to answer the research question and to discard alternative explanations for results found once the procedure is executed. These features constitute some of the sources of difficulty in experimental design processes.

In conclusion, experimental design is an important but neglected activity in labwork. The current article aims at finding ways of encouraging experimental design activities through the use of computer networks. Although the present work does not focus on direct learning gains, there are reasons to belief that design activities in themselves may contribute to learning [START_REF] Hmelo | Designing to learn about complex systems[END_REF]. Design is thought to force students to think about structural, functional, and behavioural relationships between components of complex systems such as the human respiratory system. Similarly, experimental design in chemistry, besides its intrinsic value, could also deepen students understanding about chemical concepts, structures, functions and behaviours.

Educaffix.net: a remote laboratory

This remote laboratory is implemented to help students to design chemical experiments before performing them. Here, the expert chemical knowledge is first analyzed in order to implement the course content, a tutor and also to structure and organize the student activities. A concept mapping technique [START_REF] Giordan | Preliminary analysis to build an integrative conceptual network for biological education at university level[END_REF][START_REF] Novak | Learning science and the science of learning[END_REF]) is used to categorize, connect and display the relevant concepts in a comprehensive way.

Tasks (process knowledge)

Concepts (content knowledge)

Domain specific content

It is very common in the field of chemistry or biochemistry to deal with a solution in which a compound needs to be quantified. For this purpose, several methods can be used, such as conductimetry, potentiometry, pH-metry or spectrophotometry. These are all physical measurement methods that are appropriate in different situations. In Educaffix.net, students have to determine the concentration of the E124 dye in a solution of grenadine syrup by spectrophotometry. The chosen method, i.e. construction of a calibration curve, is widely used in chemistry. Learners have to resolve a technological problem, and in order to succeed, they need to mobilize chemistry knowledge to design the protocol, i.e. the procedure to be carried out in the specific context determined by the question. The French chemistry curriculum describes this kind of experiment for students enrolled in the last year of high school or in first year of college.

Figure 1 shows the tasks (upper part) and concepts (lower part) that play a role in the design and execution of an experiment to determine the concentration of a substance in a solution with a spectrophotometer. Each subtask is relevant for either one or both of the two main goals of experimental design: answer the question and control variance. For example, answering a research question about the determination of a concentration may involve the use of a spectrophotometer and a standard curve. Controlling variance involves optimally choosing the values of a number of parameters in the process (wavelength, reference solution, domain of validity, etc.). In addition, the tasks vary in the degree of requiring manual skill for their performance. For example, in order to obtain very precise measurements, the flasks need to be rinsed with the target solution. This subtask both needs to be specified in the experimental design (methodological component) and is essential once carrying out the experiment (manual component). Other manual components are present in the knowledge needed for manipulating the spectrophotometer, diluting a solution (changing its concentration) and homogenizing a solution. Although students need to acquire the knowledge concerning these tasks, the actual manual execution in Educaffix.net is taken over by the robot. The relation between tasks and concepts is as follows:

• Choose the wavelength for measurement by looking at the absorbance spectrum of the target chemical compound and taking the one that shows maximum absorbance of light.

• Choose the reference solution by using the additivity of absorbance spectra, i.e. taking the target solution minus the target compound.

• Choose the domain of validity according to Beer Lambert law, i.e. within the range of values for which there exists a linear relation between concentration and absorbance. • Choose the standard, i.e. a solution with different concentrations of the target compound.

• Rinse the equipment with the target solution in order to avoid inadvertent changes in concentration prior to measurement.

• Homogenize the solution in order to avoid measurement errors due to concentration errors.

Prestructuring into four stages and eight elementary steps

In order to structure the task of the learner, the complete task of experimental design was divided into four main stages. These stages define the general method to be used: construction of a calibration curve in order to compare the results of the unknown sample to the standard. Other methods could have been chosen, such as the use of a unique standard solution to do the comparison or the direct determination of the E124 dye concentration by finding the value of its absorption coefficient in the literature. The four stages are the following:

• Selection of chemical compounds

• Preparation of a series of standard solutions

• Construction of the calibration curve

• Determination of the concentration of E124 in the grenadine syrup To design the experimental procedure, the learner has to choose amongst eight elementary steps and place them in a correct order in the notebook. These steps correspond to technical procedures that will be executed by the robot (i.e. record a spectrum). In addition, the student has to think about, calculate and/or choose the values of corresponding parameters (i.e. choose the concentration of the different solutions used to implement the standard curve). Eight actions are proposed and listed below: These actions were defined as general processes, independent of materials, and may be reused in other experimental situations. Each one is defined by a set of parameters that have to be specified by the learner before the action is recorded in the notebook. For example, when choosing the action "prepare a solution by dilution", the learner has to define the volume and the nature of the solution to be diluted, the total volume of the new solution and the nature of the solvent used to dilute. Specifying the parameters requires the process knowledge described above.

• Select a chemical compound • Consult a data safety sheet

The feedback of the artificial tutor

Before the robot performs the experiment, the procedure is checked and validated by a tutor. This system automatically tests the validity of the procedure according to the criteria mentioned below: (i) will the proposed procedure allow an adequate answer to the research question? (ii) does the procedure allow sufficient control of sources of variance so that the results will be significant? It has to be noted that the learner has some latitude to choose the actions and their parameters: there is not a single procedure that will be positively recognized as the right solution by the artificial tutor. However, different procedures will produce results more or less easy to interpret.

The feedback of the artificial tutor is given on the student's request and is separated into three levels. It has the advantage to structure the answer a teacher would give. A general level shows the degree of completion for each of the four predefined stages and details can be obtained for each stage. The second level of feedback is the number and the type of errors. The third level is the detailed description of the error with direct access to the appropriate theory part in the textbook. The artificial tutor does not propose a correction of the experimental procedure but points out problems that then might be corrected by the learner.

The interface

The Figure 2 shows an English translation of the Educaffix.net interface that is currently only available in French. This interface is provided to the student for designing the experimental procedure and watching the experiment run. It is divided into two parts. In the first part (on the right side) is the notebook, where the student-designed experimental procedure appears as a set of elementary steps categorized in the four pre-defined stages. Connected to the notebook are the buttons dedicated to the modification of its content: change the order of the elementary steps, modify or suppress an elementary step, print the experimental procedure or have it checked by the artificial tutor. When the latter button is used, a box dedicated to the artificial tutor's feedback appears below the notebook.

In the second part (on the left side of the interface), the learner can find the tools to build the experimental procedure and the panel to watch the experiment running. Three window tabs can be seen:

• Actions: elementary steps that can be chosen by clicking on one of the eight icons • Textbook: documents containing domain knowledge

• Bench: allows watching the robot performing the actions once the experiment starts

Experimental design in Educaffix.net

In Educaffix.net a number of features of the setting naturally render experimental design more salient than other common objectives of labwork. These features are presented below.

Robot reduces importance of gestures

A chemist makes gestures with arms and hands to operate devices and to handle fluids. However, it is not the handling of specific equipment, but the gathering of specific data that is at focus here. Therefore, the equipment used can as well be different from the one used in a regular laboratory. For example, in Educaffix.net, mixing of chemicals is done with a diluting robot and precise supplies of liquid are obtained with a syringe and needle instead of a graduated pipette as a human chemist would do.

Figure 3. The needles of the robot

Discrete steps replace continuous (perception-action) monitoring

When preparing a solution by dilution, a human chemist puts a known amount of concentrated solution in a volumetric flask and then adds the right amount of solvent by watching graduation marks on the flask (and stops when the required volume is reached). Transmission times would in fact make this kind of perception-action monitoring very error prone because of delays between student action and the execution by the robot. Again, prevalence is given to the content validity (amounts of fluid), rather than resemblance to the real laboratory. The technical solution adopted in Educaffix.net involves adding a known volume of solvent to a concentrated solution instead of visually adjusting to the required final volume.

Time sharing enforces serial task performance

The robot can be used for one experiment at a time. Therefore, the equipment has to be time-shared by the students such that when a learner is connected to the remote laboratory, the equipment is not available for use by other students. This feature in fact does mirror the reality of experiments in laboratories involving expensive apparatus. The costs of carrying out an experiment on the basis of an incorrect or imprecise design are high. This feature of the setting naturally enforces the construction of a high quality experimental design before actually trying it out with the apparatus.

System performs security check

Any experiment in chemistry involves security measures. Chemical compounds, even if they are not dangerous in terms of chemical reactions, can lead to problems. For example, water could damage electrical apparatus and cause explosion. It would not be feasible for a teacher to simultaneously attend to several students testing their experimental protocol. In Educaffix.net, a security check is performed and the system does not allow dangerous manipulations (cf. putting 5 mL of liquid in a 2 mL container).

These four features make the learning situation diverge from the real situation of expert chemist researchers. In other words, they reduce the fidelity of the learning situation [START_REF] Alessi | Computer-based instruction: methods and developments development[END_REF]. However, the same features might have a beneficial effect on student activities and learning.

Conclusions and future research

This article presents a system for concentrating students' activities on experimental design as a prelaboratory preparation. The particular topic of spectrophotometry and how to determine the concentration of a compound in a solution was analysed in order to pay attention to technologyinduced changes when transposing expert knowledge and tasks to the learning situation. The resulting system, Educaffix.net, is a remote laboratory in which learners have to design an experiment, with the help of the artificial tutor, before it is carried out by the distant robot.

The design process is the result of a balance between theoretical conjectures about learning with ICT and technological opportunities of ICT. The main pedagogical aims were maintained throughout this process: students have to answer a question, construct the experimental procedure themselves, look up theoretical references online, and check their procedures by soliciting the artificial tutor. Likewise, the study of actual student activities in the created situation will have to deal with theoretical notions about learning with technology and the influences of technology. Such a study into the use of Educaffix.net might reveal new issues to be considered in developing learning environments for labwork and allow comparisons with other forms of student activity. [START_REF] Rollnick | Improving pre-laboratory preparation of first year university chemistry students[END_REF] for example worked on the effectiveness of adequate student preparation before labwork. They asked students to write a synopsis of an experiment: this implies an appreciation of the theory underlying the experiment and a general view of the procedure to be used. However, this procedure also was shown to pose problems.

The use of Educaffix.net will be studied with about a hundred students in high school from the south east of France. Our aim is to study how the students design their experiment. The hypotheses are (i) that Educaffix.net will help students to design an experiment linked to the specific problem at hand; (ii) that the task of experimental design will help students to understand the relations between experimental procedures and the domain processes and knowledge necessary to solve the problem; (iii) and that students misconceptions will induce difficulties and errors in the activity of designing the experiment. The results of this study might inform theories of learning with technology as well as the design of future learning situations.

Figure 1 .

 1 Figure1. Relevant process and content knowledge. Arrows indicate that a task or concept is a prerequisite to another one, i.e. knowing about light is a prerequisite to knowing about wavelength.

•

  Prepare a solution by dilution • Set the baseline of the spectrophotometer • Record a spectrum • Record an absorption measure • Rinse • Homogenize

Figure 2 .

 2 Figure 2. Interface of the Educaffix.net system

Acknowledgements

The authors thank the French ministry of Education and Research for financial support, and the EDUCAFFIX company for the development of the software and the robot interfacing.


Mona Laroussi
email: mona.laroussi@insat.rnu.tn

NEW E-LEARNING SERVICES BASED ON MOBILE AND UBIQUITOUS COMPUTING: UBI-LEARN PROJECT

Keywords: Mobile learning, Ubiquitous learning, Wireless technology

Ubiquitous and mobile learning concerns building applications in highly dynamic and heterogeneous environments to bring computation into the real, physical world. This paper presents UbiLearn a distributed Learning platform with Nomads Objects and new e-learning services based on.

INTRODUCTION

The rapid and accelerating move toward the adoption and use of mobile technologies has increasingly provided students and teachers with the ability to study away from the classroom and on the move. Wireless and mobile technologies influence the evolution of current e-learning use and press forward the development of new mode of education enabling any time, anywhere and anyhow learning.

In this paper we present UBI-Learn a distributed learning platform with nomad objects and learning services related on.

UBIQUITOUS AND MOBILE LEARNING

Ubiquitous learning

An ubiquitous learning environment as mentioned in [START_REF] Shankar | Crafter: A Service Framework for Ubiquitous Computing Environments Proceedings of Ubicomp[END_REF] is an electronic global repository which has the potential to be accessed by anyone at any place and any time. Ubiquitous computing is about technology that is available to provide us information wherever and whenever we need it (laptops, personal digital devices like the electronic whiteboard, palm pilot, digital cameras, rocket e-book, mobile phones, PDAs (Personal Digital Assistants), etc.) Ubiquitous computing leads to ubiquitous learning. Technology provides abundant opportunities for sharing information, constructing knowledge and stimulating personal growth. Ubiquitous learning means that learning is not constrained by schedules and physical spaces; rather, it is pervasive and ongoing, prevalent in many interactions among students, faculty, parents, administration, staff, a wide variety of community stakeholders, etc." The term ubiquitous learning means that we are going to change the "culture of learning", that we are being continually surrounded by, and absorbed in learning experiences. Handhelds can be used anywhere inside an educational institution or outside. Handhelds can provide access to computing at the places where students' activities and learning occur. Flexible access means opportunities to integrate learning technology into pupil daily activities (success of Gameboy and the Tamagotchi). This new kind of technologies provides much greater degrees of freedom than the PC and single monitor set-up to design physical-digital interactions.

New applications for ubiquitous learning will provided us with facilities for creating learning environments. Applications will be able not only to transcend the constraints of space and time, but also to satisfy our changing educational needs. Handheld computers can empower students to take responsibility for their own learning. When using handheld computers, students are more engaged in learning, and often find their own ways to use handheld computers to support their learning, both in and out of class. Simulations, learning games, threaded discussions, and video presentations add an extra dimension to coursework. Since multimedia resources offer text, audio, and video, you can learn through different modes, making the entire process more student-centred and more effective

Mobile learning

Mobile learning: It's learning through mobile computational devices: Palms, Windows CE machines, even digital cell phone. The vision of mobile computing is that of portable computation with rich interactivity, total connectivity, and powerful processing. This small device is always networked, allowing easy input through pens and/or speech or even a keyboard when necessary (though it may be something completely different like a chord keyboard), and the ability to see high resolution images and hear quality sound. It may be that the image is overlaid on the world through glasses that act like a Heads Up Display. Mobile learning can be considered from two viewpoints The first one is technical oriented perspective regard traditional behaviouristic educational paradigm as given and tries to represent or to support them with mobile technologies. A main concern from this perspective is how to create, enrich, distribute and display learning material on mobile devices; the main benefits are to personalize the way of learning (where you want, when you want, what you want, as fast as you want, how you want; etc). The second one is pedagogical socio-cognitive and distributed cognition paradigms. In this viewpoint we face traditional designs of teaching and learning to push community oriented learning like collaborative learning, problem based learning; informal and ad-hoc learning, etc We are interest by both viewpoints and we try to present the Ubi-learn project and the new elearning services strongly coupled with this product.

Advantages of mobiles learning

What's the common characteristic of pupil between 15 and 20 years old? They can be genius or looser they can be from different social categories but all of them manipulate very easily a portable phone they exchange messages and they exploit all its functionalities. Mobile devices belong to a learner's personal sphere like glasses, wallet or key. This means the learner can take learning opportunities directly in the situation where they occur, because he has his learning environment always at hand. Mobile learning, offers the possibility to arrange learning settings flexibly and spontaneously, helps organisational skills , encourage a sense of responsibility, helps both independent and collaborative learning, and can be used to help track students' progress and for assessment.. Students and teacher can jump from and individual work, to a group work, to a classroom work, they can also change place without modifying the material.

UBI-LEARN

The general objective of the project Ubi-Learn is to design a complex learning "dispositif" as defined in [START_REF] Peraya | Médiation et médiatisation: le campus virtuel. Vers les campus virtuels[END_REF], taking into account the dimensions of ubiquity and mobility. Ubi-Learn leans on the emergence of a new shape of computer systems called Ubiquitous Computing as mentioned in W eiser (1991) or ambient Computing. The miniaturization of components and elements of Human-computer Interfaces, associated to the development of the connectivity (networks wireless telegraphy, network P2P 1 , etc.) allows to think to interactive environments where the interaction is completely distributed in a big quantity of communicating objects In the field of the education, we think that effectively the development of the advanced mobile telephony (GPRS 2 or UMTS 3 ) and local networks wireless telegraphy (Wifi 4 -802/11 or Bluetooth), have already begun to transform ways of learning, and bring to the creation of adapted info structures. If several scientific works have already allowed the investigation of these technological fields, it's necessary to notice that they have not yet allowed find out new educational modalities, which would really taking advantage of the mobility presented in Laroussi (2003).

RESEARCH ISSUES

We try through this study and this project to answer certain research questions, which are the following ones:

-How can we make interface for mobile learning more opened and powerful in [START_REF] Derycke | La Personnalisation de l'Interaction dans des Contextes Multimodaux et Multicanaux: une Première Approche pour le Commerce Electronique[END_REF] -How can we conceive resources adapted to the mobile learning? -What are pedagogical paradigms that can benefit from ubiquity and mobility? -How can we conceive distributed cognitive system by using nomads' objects? -What is the role of mobile devices in a learning environment? -What is the role of mobile devices in a collaborative environment? -Language of description for a mobile and interactive learning (Scorm, EML, etc.) -How should wireless technologies be designed to ensure that the needs of learners are met? -What are some learner-centered design methodologies?

1 Peer to Peer 2 General Packet Radio Service 3 Universal Mobile Telecomunications System 4 WIreless FIdelity

SCENARIO

In our research project, we are in an augmented virtual class; the students can be inside or outside of the virtual class, they are equipped with a computer or with any mobile device; and can interact and have a dialogue with their colleagues, their professors or another professor.

Anonymous questions

Dialogues comments

Summary Important Points urls

Anonymous questions

Dialogues comments

Summary Important Points urls

Fig1: Scenario of interaction in Ubi-Learn Let us take an example of a language course material. Generally in learning a foreign language we inherit all the accent of our professors. With such device we can have access instantly to all word pronunciations. In such a classroom, teacher can display adaptively and interactively his/her course slides. In a classical classroom students can interrupt him/her to ask questions. A white board can be used to display course and pertinent answers. In our augmented class, supplementary devices appear: a WIFI network, a management application for PDA and for the students. New possibilities appear argued in Laroussi (2003) -Differed learning of courses: When a slide is displayed, all PDA receive a text corresponding to the slide. Text is written by the teacher and maybe a summary, a list of the important points and/or list of useful links. Student can return back to a specific slide on their PDA during the classroom if they don't understand a term or a signification.

-Dialogue/Annotation: During a course, a discussion between students about one concept of the course can be useful. This kind of discussion consumes lot of attention and can generate a misunderstanding of the course material. The PDA can allow an asynchronous writing discussion; this asks less attention. Furthermore, the students can annotate the text accompanying each slide. These comments can be sent to colleagues (so avoiding a discussion).

-Cooperative and distributed course: Certain students' questions can exceed the teacher's competencies. If a question seems interesting, the teacher can ask the help of another teacher expert in the domain. The system can determine the adequate teacher connected at this time.

RESULT

This work should allow developing a methodological approach, based on the learning and social sciences contributions, for analysis and the conception of ubiquitous and mobile environments of learning. A prototype should allow technical and educational validation of the legitimacy and the efficiency of such learning environments

HIERARCHY OF SERVICES IN UBIQUITOUS AND MOBILE LEARNING

In Amoretti et al. ( 2003) authors define four levels of hierarchy for community-oriented services, the first level is a connectivity service, it can be provided to wired and wireless users. In all cases suitable authorisation and accounting facilities should be made available to allow Intranet and internet access. The second level in Amoretti et al. ( 2003) is the availability of traditional web-based services directly or indirectly covering many facets of e-learning. The third one should upon the second one and it's designed to support distributed computing and educational resources as an aggregated single environment. The last one is related to a scenario where resources and contents are not only provided by the official infrastructure but also by the whole university community. Ubi-learn offers also four levels of services, the first one Fig2: Hierarchy levels of services in Ubi-Learn Ubi-learn is mainly composed by four levels:

The first layer represents an association of system resources. Due to the dynamicity in the availability of physical devices, network bandwidth, connectivity and location, the system has a consistent description of the infrastructure available. The second level analyses the relation between the service description and the physical devices.

In exploiting such operation it classifies the system resources and, according with the service description, determines the suitable execution environment candidate to host the service The third layer defines a coordination space and interaction rules in order to allow the application to coordinates services as in any classical service-oriented model. Finally in the fourth layer is related to a scenario where resources and contents are joined together to model an adaptive pedagogical way of studying. This way will be the function of student profile, student background and student device.

CONCLUSION

In this paper we have outlined opportunities of ubiquitous and mobile computing in e-learning environment and specifically, a hierarchy of levels of services allowing distance access to mobile e-learning environment. The proposed infrastructure eases the construction of complex distributed services with both constraints: system heterogeneity and a dynamic application The approach presented in the form of a layered architecture shows the logical step form a heterogeneous scenario towards a homogeneous environment for executing services. One of the objectives of Ubi-Learn is to allow users to flexibly interact with the services in the environment.

The main points we are still exploring are: geo-localisation of the best person to solve a specific problem, how to insure a collaborative navigation through a course material and how to promote the collaborative work with nomad devices?