Katherine Farrow
email: katherine.farrow@parisnanterre.fr

Gilles Grolleau

Lisette Ibanez

Designing more effective norm interventions: The role of valence

Keywords: social norms, framing, behavioral intervention, social information JEL codes: C90, D03

Social norm interventions represent a low-cost and effective policy tool that have been shown to generate behaviour change in a number of contexts. We investigate whether valence framing impacts the effectiveness of a social norm intervention on prosocial behaviour. We use Amazon Mechanical Turk in conjunction with oTree to conduct an experiment in which we manipulate descriptive beliefs and original endowments in the context of a dictator game. We find that the impact of a social norm intervention appears to be significantly greater in a frame of negative valence vs. a frame of positive valence. Loss aversion and positional preferences could play a role in these findings. Regression results furthermore indicate a differences in the determinants of allocation decisions across frames, suggesting that normative beliefs matter more in a positive frame, whereas descriptive beliefs matter more in a negative frame. This work contributes to a better understanding of framing effects as well as the conditions under which normative considerations are most salient. On a practical level, it points to an opportunity for policymakers to substantially improve upon the design of social norm interventions.

Introduction

Social norms have been shown to impact behaviour in a wide variety of contexts (Bicchieri, 2006;[START_REF] Young | The evolution of social norms[END_REF][START_REF] Gintis | The bounds of reason: Game theory and the unification of the behavioral sciences[END_REF] and can constitute an important informal enforcement mechanism for achieving beneficial collective outcomes [START_REF] Nyborg | Social norms as solutions[END_REF].

The dynamics of norm proliferation within populations has therefore become of increasing interest to researchers in diverse fields pertaining to taxation, health, and environmental behaviors. Social norm interventions, or the disclosure of information regarding the behaviour or attitudes of others, have also attracted attention from policymakers as a promising low-cost strategy to encourage the adoption of welfare-improving behaviour and facilitate the spread of corresponding norms (Allcott, 2011;[START_REF] Cialdini | Managing social norms for persuasive impact[END_REF][START_REF] Schultz | Using normative social influence to promote conservation among hotel guests[END_REF]. [START_REF] Farrow | Social norms and pro-environmental behavior: A review of the evidence[END_REF] survey experimental work on social norm interventions in the context of environmental behaviors and find that a large body of field evidence demonstrates their effectiveness in this context, as well. Normative considerations are particularly important in the context of social dilemmas in which purely self-interested individuals have, according to traditional neoclassical economic theory, no intrinsic incentive to cooperate.

While most social dilemmas are typically thought of as either problems of provision or appropriation, it is possible to characterize the same dilemma within the context of either frame [START_REF] Cox | Provision versus appropriation in symmetric and asymmetric social dilemmas[END_REF]. The atmosphere, for example, is typically conceived of as a shared resource that must be preserved through refraining from engaging in carbon-intensive activities. However, the resource can equally be conceived of as one that must be created and maintained by preferentially engaging in carbon-neutral and carbon-enhancing activities. While in both frames of this dilemma, prosocial behaviour may amount to the same action (e.g. traveling by bike or taking public transit), the prescriptive emphasis of the prosocial norm evoked in each frame manifests differently according to whether the dilemma is characterized as a problem of appropriation or provision. In the first instance, the prosocial norm prescribes that one refrain from extracting wealth from the resource (e.g. by refraining from driving a car), whereas the prosocial norm in the second instance consists of an injunction that one contribute to the shared wealth of the resource (e.g. by choosing to bike or take public transit). When the issue of clean air is conceived of as a problem of appropriation, prosocial behavior amounts to refraining from contributing to a public bad, whereas when clean air is conceived of as a provision problem, normative behavior amounts to contributing to the public good. It follows that messages encouraging certain proenvironmental behaviors (e.g. driving less), could be accomplished either by an appeal to contribute to maintaining the shared resource of clean air, placing an emphasis on one's responsibility to behave prosocially, or to refrain from degrading the resource, placing emphasis rather on one's responsibility to not behave antisocially. We contend that the social ramifications of deviating in these two scenarios are, accordingly, quite different -so much so as to make a behavioral intervention differently effective across these scenarios, as well.

Given that normative messages have been shown to be effective tools to encourage pro-social behaviors, and to the extent that environmental issues can be characterized as either problems of provision or appropriation, it seems pertinent to investigate whether social norm interventions are more or less effective when applied in either type of situation. To investigate this possibility, we implement normative interventions in decision scenarios that differ in valence. In the context of a simple dictator game with no role or payoff uncertainty, we manipulate empirical expectations and original endowments to isolate the impact of valence framing on the effectiveness of a social norm intervention that mimics those often used by policymakers and the proponents of environmental campaigns in the real world. By advancing our knowledge of the conditions under which social norm interventions generate the greatest behavioral impacts, we add to the capacity of policymakers to become more effective 'behavioral architects.' Contrary to the conventional selfishness assumption advanced by standard economic theory, studies in experimental and behavioral economics have demonstrated that people systematically behave in prosocial ways [START_REF] Engel | Dictator games: A meta study[END_REF][START_REF] List | On the interpretation of giving in dictator games[END_REF] and furthermore, that these prosocial behaviors are sensitive to a range of experimental manipulations. These observations suggest that a variety of low-cost interventions could serve as promising policy tools for encouraging prosocial behaviors (Kinzig et al., 2013;[START_REF] Scarlett | Catalysts for conservation: Exploring behavioral science insights for natural resource investments[END_REF][START_REF] Schultz | Using normative social influence to promote conservation among hotel guests[END_REF]. In this paper, we examine the confluence of two of these manipulations in order to gain insights on how to optimize the effectiveness of a behavioral intervention to improve environmental outcomes, and find that valence framing has a dramatic effect on the effectiveness of a normative behavioral intervention.

Norms are supported by a variety of enforcement mechanisms [START_REF] Young | The evolution of social norms[END_REF] that can consist of rewards for compliance or punishment for deviation, and be either material or immaterial (i.e. psychological) in nature, and intrinsically or extrinsically enforced. Guilt, for example, can be considered a psychological, intrinsically-enforced punishment for deviating from a social norm, whereas shame could be considered an extrinsically-enforced psychological punishment. In contrast to a personal or moral norm, the behaviors prescribed by social norms are inherently consensus-driven, which emphasizes the particular importance of extrinsic factors (i.e. factors that are social in origin) as enforcement mechanisms (e.g. shame, esteem). As a result, another feature of social norms is that they are incredibly context dependent. The relative acceptability or prevalence of a behavior such as littering, for example, can differ widely across cultures and social groups. We expand the consideration of social context to include valence framing. Specifically, we explore whether the valence of the decision frame has a systematic impact on the effect of social feedback on behavior. Camerer and Thaler (1995) were early proponents of the idea that dictator game giving arises from norms. They reason that behavior in these one-shot games reflects the manners that people have become accustomed to following in the real-world, which prescribe similar prosocial behavior even in one-shot interactions (e.g. tipping). An explanation based on manners is also supported by experimental evidence that even those who are fairness-minded behave selfishly in the contexts where this is considered socially acceptable (e.g. markets). More recent evidence indeed suggests that social norms can be responsible for the changes in behavior detected across framing conditions, according to which different frames and choice sets serve to activate different social norms, along with the beliefs and preferences that accompany those norms [START_REF] Bardsley | Dictator game giving: Altruism or artefact?[END_REF][START_REF] Dufwenberg | The framing of games and the psychology of play[END_REF][START_REF] Gächter | Norm compliance in the presence of peers[END_REF]Krupka andWeber, 2009, 2013). Studying pro-social behavior in the context of a positive valence frame (a dictator game), Bicchieri and Xiao (2009) found that manipulating participants' empirical expectations yielded a higher impact on giving behavior than manipulating normative expectations. In contrast, in the context of a negative valence frame (a resource dilemma), [START_REF] Cialdini | Managing social norms for persuasive impact[END_REF] find that an injunctive normative message is more successful than a descriptive normative message at encouraging visitors to a national park to refrain from taking petrified wood. [START_REF] Krupka | Identifying social norms using coordination games: Why does dictator game sharing vary[END_REF] find differences in injunctive social norms across standard vs. bully dictator games (which extends the valence to include negative frame, introducing a taking option). Even when final outcomes are equivalent, they find that taking is relatively less acceptable than giving, and that the bully dictator game yields higher average cooperation than the giving game. [START_REF] Alevy | Gender-and frame-specific audience effects in dictator games[END_REF] find that introducing observability to a dictator game increases prosocial behavior in a giving game, and decreases taking in a bully dictator game. Using Amazon Mechanical Turk, Raihani and McAuliff (2014) find that an injunctive message increases giving, but a descriptive message has not effect. In another series of online experiments using the MTurk platform, [START_REF] Dreber | Do people care about social context? framing effects in dictator games[END_REF] find no significant difference in amounts transferred to receivers in give and take dictator games.

If, as previous evidence suggests, norms determine the landscape of the material and non-material incentives that agents face, and if the same actions take on a different normative significance across valence frames, then it follows that normative considerations should impact behavior differently across these frames. Interestingly, there appears to be no work that examines the impact of valence framing on the effectiveness of an intervention that brings cognitive attention to the prevailing social norm. We design an experiment with the aim of systematically testing the effect of valence framing on normative considerations. Should a social norm intervention indeed be more effective in one frame versus another, this insight would provide policymakers a strategy by which they could improve the design of social norm interventions aiming to incite behavior change.

Research questions and hypotheses

To understand the potential role of valence in the behavioral differences across these two types of situations, we employ a simplified framework using the dictator game.

In this way, we eliminate potential strategic differences between the two types of situations (notably rivalry) as potential drivers of the effect of valence on behavior. In this paper, we consider the typical dictator game to be one of positive valence. The payoff outcomes in this game are Ei -x for the dictator, and x for the receiver, where Ei is the dictator's original endowment, and x represents their transfer to the receiver. In a dictator game of negative valence, in contrast, the dictator also has sole decision-making capability, but it is the receiver who possesses the initial endowment, Ei. The payoff outcomes in this game are thus x for the dictator, and Ei -x for the receiver, where x represents the amount taken from the receiver by the dictator. Thus, although the strategic considerations and range of possible outcomes for both players remain the same across frames, the nature of the dictator's action changes from one in which he transfers money from himself to the other worker (i.e. giving) to one in which he transfers money from the other worker to himself (i.e. taking). 1Although this distinction is of little consequence to traditional economic theory, it is considered quite important by psychologists (Beckenkamp, 2006). In order to investigate the impact of valence on normative interventions, we design a 2 (positive vs. negative valence) by 3 (no, low, and high social norm intervention) between-subjects experiment. The broad experimental design is summarized in Table 1. While some studies find no significant difference in baseline levels of prosocial behaviour across frames that differ in valence [START_REF] Apesteguia | The role of rivalry -public goods versus common-pool resources[END_REF][START_REF] Dreber | Do people care about social context? framing effects in dictator games[END_REF], others indicate that social dilemmas of positive valence, in which group members must contribute to the creation of a resource, tend to generate higher levels of cooperation than dilemmas of negative valence, in which group members must exercise self-restraint in order to preserve a resource [START_REF] Andreoni | Warm-glow versus cold-prickle -the effects of positive and negative framing on cooperation in experiments[END_REF][START_REF] Bougherara | Eviter le mal ou... faire le bien: Gestion des biens environnementaux et politiques de développement durable. une etude experimentale[END_REF][START_REF] Khadjavi | Doing good or doing harm: Experimental evidence on giving and taking in public good games[END_REF][START_REF] Willinger | Framing and cooperation in public good games: An experiment with an interior solution[END_REF][START_REF] Sonnemans | Public good provision and public bad prevention: The effect of framing[END_REF]. Evidence demonstrating that valence framing appears to affect the normative significance of the actions in question suggests that normative considerations may have a different impact on behavior across frames. The results found by Krupka andWeber (2009, 2013) and Bicchieri and Xiao (2009) lead us to expect that a social norm intervention will have a positive effect on transfer amounts in the traditional dictator game of positive valence, forming our first hypothesis:

Hypothesis 1. A social norm intervention increases prosocial behavior in the dictator game of positive valence relative to baseline conditions (T3, T5 > T1), and a high social norm intervention increases prosocial behavior to a greater degree than a low social norm intervention (T5 > T3).

Theories regarding the mechanisms that support conformity to social norms emphasize that motivation to conform can arise not only from the pursuit of the positive psychological benefits that may accompany conforming, but also the avoidance of anticipated negative consequences that may result from deviating [START_REF] Young | The evolution of social norms[END_REF]Bicchieri, 2006). Sugden (2000)'s resentment hypothesis proposes that people rely on behavioral strategies that focus on reducing the distance between their own behavior and others' expectations in order to avoid the negative fallout that disappointing others may entail. Taken together, these works indicate that the avoidance of negative consequences serves as an important motivation for conformity, which is supported by results indicating that a decision scenario involving taking elicits more cooperative norms relative to a scenario that involves only giving [START_REF] Krupka | Identifying social norms using coordination games: Why does dictator game sharing vary[END_REF]. Unlike [START_REF] Krupka | Identifying social norms using coordination games: Why does dictator game sharing vary[END_REF], who elicit normative beliefs and use these normative beliefs to predict behavior of separate players, we seek to manipulate descriptive beliefs to evaluate the impact of information provision on behavior. Since deviating in a frame of negative valence (i.e. taking too much) is arguably a more severe infraction than deviating in a frame of positive valence (i.e. giving too little), we expect prosocial behavior to be more sensitive to the social norm intervention in the taking game. Our second hypothesis regarding the relative efficacy of descriptive norms compared across these two games is therefore the following:

Hypothesis 2. Both levels of social norm interventions will have a greater impact on prosocial behavior when implemented in a dictator game of negative valence compared to a frame of positive valence (T4 -T2 > T3 -T1, T6 -T2 > T5 -T1).

To facilitate an investigation of not only whether, but how valence framing may lead to different reactions to social norm interventions, we elicit participants' beliefs (i.e.

their normative and empirical expectations) as well as their social preferences. To elicit beliefs, we follow incentivized methods in the experimental economics and social psychology literature (Bicchieri and Xiao, 2009;[START_REF] Krupka | Identifying social norms using coordination games: Why does dictator game sharing vary[END_REF], and to elicit preferences we use the incentivized Social Value Orientation measure developed by [START_REF] Murphy | Social value orientation: Theoretical and measurement issues in the study of social preferences[END_REF]. Thus, in addition to the main research question, our experimental design will allow us to shed light on the potential impact of valence framing and social norm interventions on preferences and beliefs, as well as the impact of framing on the relationship between beliefs and preferences and behaviour.

Further avenues of exploration made possible by this dataset include effects related to gender, age, education, income, geography, and political affiliation, as well as the possible impacts of descriptive and injunctive beliefs on social preferences, of role (dictator vs. receiver) on expectations and preferences, and of empirical expectations on normative expectations. We may expect those indicating more liberal political ideology to express the belief in the appropriateness of greater allocation amounts than those with more conservative ideologies. Additionally, we might also expect that a participant's role in the game may influence their normative beliefs -specifically that being in the position of dictator may lead participants to consider lower allocation amounts as more socially appropriate, which would suggest that people tend to form self-serving perceptions about social norms according to their social position. Though we have no specific expectations regarding the impact of most of the demographic variables on susceptibility to normative interventions, some previous evidence suggests that we might expect women to be more affected by a normative intervention than men [START_REF] Rand | Social heuristics and social roles: Intuition favors altruism for women but not for men[END_REF]Jones and Linardi, 2015;[START_REF] Dreber | Do people care about social context? framing effects in dictator games[END_REF]. Since descriptive and injunctive beliefs are theorized to be important determinants of norm-related behaviour, we would expect that these (and especially descriptive beliefs) will be a significant determinant of allocation decisions in the multivariate analysis we carry out.

Methods

Our experiment consists of two variations of the dictator game, one of positive valence and the other negative, that are structurally equivalent in the range of payoff outcomes and their respective theoretical predictions. In the game of positive valence, a worker is endowed with $1.00 and given the opportunity to allocate this amount between himself and another worker with whom he is randomly paired. In the negative valence frame, a worker is randomly paired with another worker who is endowed with $1.00, and the first worker is given the opportunity to make the same allocation decision. In each of these games, the task of the dictator is to distribute this amount between himself and the worker with whom he is paired. 2

In each frame, we introduce a social norm intervention in which dictators are informed that most of the dictators in a previous session of the experiment transferred an average of either $0.30 (low condition) or $0.50 (high condition) to the participant with whom they were paired. Unlike previous studies, we vary the intensity of the intervention, as we introduce treatments designed to elicit both high and low empirical expectations in each decision frame. Both intervention levels are based on past behavior of actual workers in a baseline condition. The low condition ($0.30) reflects actual average behavior across the baseline sample, and as such constitutes a relatively accessible target behavior. The allocation level chosen for the high intervention condition is also based on data from the baseline sample, but presents this information in such a way so as to foster the perception of a different descriptive norm. Following a strategy developed in Bicchieri and Xiao (2009), we accomplish this experimentally without the use of explicit deception. 3 For the high intervention, we chose an allocation of $0.50, which represents the most that any dictator allocated under baseline conditions in the negative frame. This leads us to believe that $0.50 is indeed relatively much to ask of most dictators and thus represents a rather challenging and costly behavior to conform to. For the low [high] conditions, the intervention consists of the following: "In a scenario exactly like this one 2 Because we use identical neutral vocabulary in both frames (i.e. 'allocate' rather than 'give' and 'take'), any evidence of a main effect associated with valence framing suggests that the use of stronger frame would conceivably generate an even greater impact.

3 While explicit deception is not accepted in experimental economics, our strategy in this intervention is considered implicit deception, the use of which has precedent in the economic literature (see [START_REF] Hersch | Experimental economics' inconsistent ban on deception[END_REF] for a number of examples regarding conditional matching, role assignment, and surprise restart procedures). As [START_REF] Ariely | Psychology and experimental economics: A gap in abstraction[END_REF] recognize, deceptive procedures are often necessary in order to create experimental conditions that will yield meaningful results. We also note that our decision to include this ?high? social norm intervention level is born purely out of a research interest in the role that the costliness of conformity would play in the effectiveness of the SNI. As we will show, such an intervention only offers marginal improvement over the ?low? SNI level, and as such is not of great interest from a policy perspective anyways. in a previous study, most Individual A workers allocated an average of $0.70 [$0.50] to themselves and $0.30 [$0.50] to the worker with whom they were paired." Whereas the low condition describes the average allocation among all workers who took part in the baseline experiment (which by definition encompasses most workers), the high condition describes the average allocation of a deliberately chosen selection of at least 50% of the workers in the sample. These workers were selected such that the average allocation amount within the sample was $0.50. Using this formulation, we are able to promote the perception of modal behavior without strictly deceiving participants. To evaluate whether these interventions did indeed have an impact on descriptive beliefs, we elicit these beliefs in all conditions. The experimental design is displayed in Table 2.4 of the growth in the use of this platform, its internal and external validity have been the subject of a number of recent inquiries. [START_REF] Amir | Economic games on the internet: The effect of 1stakes[END_REF] find that one dollar stakes in an Amazon Mechanical Turk experiment yield similar results as higher stakes in the same laboratory experiment, lending credibility to the use of the medium and the stakes commonly employed. The downsides that have been identified, such as non-random attrition, less control over concurrent activities, and lack of attention, can be mitigated through careful experimental design (e.g. comprehension checks) and inspection of the resulting data (e.g. consistency, response times) [START_REF] Mason | Conducting behavioral research on amazon's mechanical turk[END_REF].

We note that this decision setting can be considered to produce conservative results for two reasons. First, we strove to create the most minimal differentiating conditions possible across treatments. Instead of using the words 'give' and 'take' to describe the transfer decisions made by dictators in the positive and negative valences, respectively, we elected to use neutral terminology, i.e. 'allocate,' to describe dictator decisions in both frames. We also described the initial conditions as neutrally as possible across treatments, explaining in the positive frame that Individual A had been allocated $1.00, while in the negative frame that Individual B had been allocated $1.00, rather than describing these endowments more ambiguously (e.g. Individual A/B has $1.00), which could lead dictators to assume that the individual may have earned that money or had some other type of inherent right to it. Describing this money as having been recently allocated to the individual at once creates a minimal claim to the money and, in being more descriptive, allows for a greater degree of experimental control. Finally, the anonymous online decision setting arguably maximizes the social distance between dictators and receivers as well as all but eliminates the extent to which dictators would expect to be subject to social punishment for deviating from social norms.

Results

After accounting for attrition 5 (99 players) and omitting those who repeated the game at least once more in subsequent experimental sessions (241), as well as those who provided incorrect answers to at least one of the three comprehension checks (308), we obtain a total of 716 valid observations across the ten experimental conditions (including the four strategy method treatments), and 492 observations among the six treatments of interest. Of these, 284 contain dictator choices. Overall, this sample was comprised of 58% men and reported an average age of 35.3 years. Most respondents in our sample are located in the US and India (52% and 33%, respectively), with 15% reporting residency in other areas. 6 These descriptive statistics indicate that our U.S. sample is generally representative with respect to gender (44% of the U.S. sample is male vs. 51% of the U.S. population7 ), but that Indian males are overrepresented (72% of the Indian sample is male vs. 52% of the Indian population8 ). Our U.S. sample also appears to be relatively representative with respect to age (sample median of 34 vs. population median of 35) and 5

Participants whose partners left the game before it was over were remunerated in the same way as everyone else, i.e. according to a random drawing of one of the incentivized tasks in the game, except that in their case the dictator game task did not figure among the tasks eligible to be selected.

education (approximately 39% the sample and 30% of the population hold a bachelor's degree or higher).

Regarding social preferences, 38% of the sample is individualist (maximizes own payoffs), 61% of the sample are prosocials (maximizes joint payoffs or minimizes the difference between payoffs), and less than 1% (2 people) exhibited competitive preferences (maximizes the positive difference between the payoff for oneself and the payoff for the other). We observe that beliefs about injunctive norms exhibit very little variation across valence frame, role (dictator vs. receiver), region, gender, age, educational level, political ideology, and income. We also note that average descriptive beliefs tend to be greater than average behavior in all conditions (though this difference is only significant in one condition), indicating that people expect a greater degree of prosociality from others relative to their own behavior in the game. 9 We also find evidence indicative of a selfserving bias in descriptive beliefs. 10 Figure 1 shows the distribution of allocation decisions by treatment across valence frames. Across all conditions, we observe a majority of dictators allocated $0.50 to the receiver, and we see that the high norm intervention in the negative frame greatly increases this tendency.

Our main findings are reported in Table 3. Regarding average treatment effects, our results show a similar qualitative pattern as those of [START_REF] Krupka | Identifying social norms using coordination games: Why does dictator game sharing vary[END_REF] under baseline conditions, namely that the amount shared with the receiver is higher in the frame that involves taking, although this difference is insignificant, as in [START_REF] Dreber | Do people care about social context? framing effects in dictator games[END_REF].

Qualitatively, our results indicate that the interventions appear to have an effect 9

This may be an artifact of a desire not to view oneself as a sucker, which has been demonstrated as an important motivation in strategic social dilemmas [START_REF] Bougherara | Dealing with aversion to the sucker's payoff in public goods games[END_REF]. 10 In the negative frame, dictators believe that most dictators in a previous session gave $0.43 to Individual B, while receivers believe that most dictators in a previous session gave $0.48 to Individual B (p = 0.05). on allocations with respect to baseline levels, with the low and high interventions raising average allocation amounts by $0.020 and $0.059 in the positive frame and by $0.073 and $0.094 in the negative frame. We do not, however, find strong support for our Hypothesis 1 in the positive frame, as mean allocation amount does not appear to differ significantly across intervention levels. In the negative frame, in contrast, we do find that average allocation levels in the social norm intervention treatments is greater than in the baseline

treatment. An analysis of variance in the positive frame indicates a slightly insignificant overall treatment effect of social norm interventions (F = 2.90, p = 0.089), while an analysis of variance in the negative frame indicates a significant intervention effect (F = 8.08, p = 0.005). These findings provide support for Hypothesis 2, indicating that a social norm intervention generates higher average allocation amounts when implemented in a frame of negative valence compared to a frame of positive valence. Although a 3x2 analysis of variance on the entirety of the experimental design, indicates a main effect of both social norm interventions (F = 4.32, p = 0.013) and negative valence (F = 6.94, p = 0.009), it does not indicate a significant interaction effect between the two (F = 0.43, p = 0.65). We explore these results in further detail through multivariate analysis.

To investigate the relative importance of the various determinants of allocation decisions, we perform a tobit regression on observations from the pooled sample, as well as on the samples from each valence frame separately. Left-censored at zero and right-censored at 1. Other multivariate models were explored, including a hurdle model and an ordinal regression model. The hurdle model failed to converge no matter the specification. This may be due to the fact that participants exhibited strong model behavior (many observations are either $0 or $0.50). A Brant test of an ordered logit regression reveals that the data violate the parallel lines assumption, and although a partial proportional odds model outperformed the ordered logit, it generated a non-trivial number of negative predicted outcome probabilities. From Table 5 we observe that several differences exist in the determinants of allocation amounts across frames. Interestingly, the dummy variables for the interventions themselves are not significant in any of the regressions. This may be due to the relatively greater importance of negative valence, descriptive beliefs, and prosociality on behavior. We note that beliefs about others' behavior is not a significant predictor of generosity in the positive frame, but it is strongly significant, and large in magnitude, in the negative frame. Furthermore, normative beliefs are weakly insignificant as a determinant of behavior in the positive frame (p = 0.06), whereas they are not at all significant in the negative frame (p = 0.77). We also observe that having a prosocial social value orientation is a significant predictor of generosity in all regressions.

The magnitude of the parameter estimate associated with prosocial social preferences suggests that a negative frame appears to induce prosocial people to be more generous than a positive frame. The age parameter is significant, but is not economically meaningful due to its low magnitude. A couple of regional effects also emerge. Those reporting residency in EU countries appear to be more generous than those in the U.S.

(our reference group). Those residing in India appear to be more generous overall in the negative frame than those in the U.S., implying a greater sensitivity to the change in frame. In the positive frame, those reporting residency in countries other than the U.S., India, and the EU, allocate significantly less of their endowment to the receiver.

Discussion

Confirming previous literature, our results partially support our Hypothesis 1, as we find that a social norm intervention can have a significant positive impact on allocation decisions in the positive frame, although this is true only for a high level of intervention.

In contrast, we do find a significant average treatment effect of framing with respect to the social norm interventions in the negative frame. Specifically, the intervention yields an average allocation amount of $0.310 when implemented in the positive frame vs. $0.393 when implemented in the negative frame, amounting to an increase of approximately 27%. Analyses of variance within each frame also indicate a significant treatment effect of interventions in the negative frame, but not in the positive frame. Regression analysis furthermore suggests that there may be important differences in how decisions are made across frames. Whereas normative beliefs appear to be a relatively important decision factor in the positive frame, descriptive beliefs emerge as extremely important in the negative frame. Interventions that seek to influence descriptive beliefs in negative frames may therefore be more effective than those that seek to influence injunctive beliefs.

This novel result offers an important refinement of our understanding of how social norm interventions impact behavior that can be useful to policymakers seeking to design normative interventions that are maximally effective. Specifically, our results

suggest that a simple reframing of the decision context from positive to negative may increase its behavioral impact. This result resonates with [START_REF] Bohm | Outcome valence and externality valence framing in public good dilemmas[END_REF], who study the impact of pure frame change in the context of a public good game and find that cooperation levels are significantly higher in negatively-framed game than a positively-framed game. We note that the large magnitude of the impact of a social norm intervention in the negative frame in Table 3 is striking given the minimal conditions in which it arose -that is, in the context of neutral terminology and an anonymous online decision environment that minimizes the extent to which participants could expect to receive punishment for deviating from the social norm. 13 This suggests that the social norms identified here may be to some extent internalized by the participants in our sample, or that an experimenter demand effect may still exist despite the considerable social distance between participants and the experimenters. The issue of robustness and external validity is an important one, and should be explored in extensions to this work through 13

We strove to make it clear to participants that there were no right or wrong answers to the questions we pose in the tasks.

replications and field studies.

One possible reason why we observe such a dramatic difference in the effectiveness of the normative intervention across valence frames relates not to a difference in the social significance of compliance across these settings, but to a difference in the significance of deviation. In the positive frame, in which prosocial behavior could be succinctly described as 'giving', deviating from prosocial behavior would be implicitly described as 'not giving,' or failing to behave prosocially enough (a fairly innocuous crime). In the negative frame, however, in which prosocial behavior amounts to 'not taking,' antisocial behavior could be implicitly described as 'taking too much.' Deviants in this frame therefore face the option of behaving too antisocially, arguably a more serious infraction than failing to behave prosocially enough (the implication of deviation in the positive frame). Here, one could consider a form of loss aversion to emerge: the threat of losing social esteem, i.e. falling from grace, associated with deviating in the negative frame may be more motivating than the threat of not gaining additional social esteem, i.e. distinguishing oneself, in the positive frame. One of the most notable findings of the analysis suggests that descriptive beliefs play a much more important role in determining behavior in a context where people are more sensitive to the costs of deviating from socially-appropriate behavior than they are to the gains that stand to be obtained by conforming. The fact that normative beliefs appear to be a more important factor in decision-making in the positive frame, in contrast, suggests that intervention strategies could differ by frame.

Finally, we note we find no evidence of a boomerang effect similar to that which has been found with the use of social comparison interventions in other contexts. Indeed, it appears that receiving information about actual average behavior (i.e., an average allocation of about $0.30) induces participants to not give not simply as much, but indeed more than, this amount. Thus, another possible explanation, which is not mutuallyexclusive with the loss aversion hypothesis, is that social information activates positional preferences with respect to moral capital (a desire to be more prosocial than others), and that this effect is greater in the negative frame.

6 Conclusion [START_REF] Tversky | The framing of decisions and the psychology of choice[END_REF] observe that 'when framing influences the experience of consequences, the adoption of a decision frame is an ethically significant act.' Indeed, we echo others in emphasizing that a consideration of the ethicality of social norm interventions should necessarily precede considerations surrounding the design and implementation of these interventions. 14 Allcott and Kessler (2015), furthermore, show that ignoring the costs that consumers bear in engaging in energy-saving behavior leads to inflated welfare estimates of behavioral intervention designed to encourage energy conservation, and therefore stress the need to conduct comprehensive welfare analyses of these types of interventions in order to fully account for their benefits as well as their costs.

We note several additional avenues for interesting future work. [START_REF] Coffman | A model of information nudges[END_REF] have developed a model of information nudges in which the aggregate impact of information about others' behavior depends in part on the extent to which the intervention changes marginal agents' beliefs, and [START_REF] Frey | Social comparisons and pro-social behavior: Testing "conditional cooperation" in a field experiment[END_REF] have demonstrated that people who are indifferent to behaving prosocially react most strongly to information about others' behavior. In this regard, useful future work could examine social norm interventions in a within-subjects design in order to evaluate the impact of social interventions as a function of the degree to which they differ from 14 See, for example, [START_REF] Schubert | Green nudges: Do they work? are they ethical?[END_REF] for an approach to determining the ethical quality of green nudges. apriori beliefs, and to determine the factors that contribute to whether or not beliefs are updated in a significant way, as well as those influencing whether or not an agent acts on these changed beliefs. Another interesting extension to the present work, inspired by Harris et al. (2014) is exploring whether, in situations where people are confronted with a variety of social norms, they are more likely to follow the norm that is most selfserving. Another unexplored question is whether these findings extend to higher stakes (e.g. are people similarly willing to conform to a social norm when it becomes more costly to do so?) We also note that a change in valence frame that we study is be closely related to research on proscriptive vs. prescriptive messages (e.g. [START_REF] Winter | Choosing to encourage or discourage: Perceived effectiveness of prescriptive versus proscriptive messages[END_REF] and research demonstrating that the word 'no' elicits a unique cognitive response in the brain [START_REF] Alia-Klein | What is in a word? no versus yes differentially engage the lateral orbitofrontal cortex[END_REF]. Relatedly, the significance of framing the norm in terms of what is given/left to the receiver vs. what is taken/kept by the dictator constitutes a different type of frame change that may well have impacts on behavior and conformity to social norms.

We contend that the scale of the impact we find as a result of this simple frame change represents a significant advance in our knowledge of the conditions under which normative considerations are most salient and exposes potential sources of differences in the decision-making process across frames. Because these findings can conceivably have applications in a variety of domains, the extent to which they extend to other contexts should also be a priority for future research.

-Screen 3 -

Positive Valence [Negative Valence]

Part 1 Now, we will begin the 19 tasks.

Please consider the same scenario described before: Individual A is allocated $1.00 and has the opportunity to allocate this money between him or herself and Individual B. Individual A will make a choice, and then both individuals will be paid money based on the choice made by Individual A. [Please consider the same scenario described before: Individual B is allocated $1.00 and Individual A has the opportunity to allocate this money between him or herself and Individual B. Individual A will make a choice, and then both individuals will be paid money based on the choice made by Individual A.] In this task, you are Individual A and you have been randomly paired with another worker, Individual B. Please indicate the amount you would like to allocate to yourself and to the worker with whom you are paired. If this task is selected at the end of the study, both you and the worker with whom you are paired will be compensated according to the allocation you choose in this task. Your worker ID and the ID of the worker with whom you are paired will remain anonymous In a scenario exactly like this one in a previous study, most Individual A workers allocated an average of $0.70 [$0.50] to themselves and $0.30 [$0.50] 17 to the worker with whom they were paired. 18 Please indicate the amount you would like to allocate to yourself and to the worker with whom you are paired. For the 'low ' and 'high' conditions, respectively. 18 This sentence was not included in the baseline treatment.

-Screen 4 -

Positive Valence [Negative Valence]

Part 2

Please consider the same scenario described before: You are Individual A and have been allocated $1.00. You have the opportunity to allocate this money between yourself and Individual B. You will make a choice, and then both you and Individual B will be paid money based on the choice you make. [Please consider the same scenario described before: Individual B is allocated $1.00 and Individual A has the opportunity to allocate this money between him or herself and Individual B. You are Individual A. You will make a choice, and then both you and Individual B will be paid money based on the choice you make.]

In this task, we are interested in how you think most Individual A workers allocated money in a scenario exactly like this one in a previous study. In the Part 3

In the following six tasks, you will be making decisions about allocating resources between yourself and another worker. If one of these tasks is selected at the end of the study, you will be randomly paired with a different worker and both you and the worker with whom you are paired will be compensated according to the allocation you chose in the selected task. Your worker ID and the ID of the worker with whom you are paired will remain anonymous. All of your choices are completely confidential.

On each of the following sliders, please indicate the allocation you prefer most by marking the respective position along the midline. As you can see, your choices could influence both the amount of money that you receive as well as the amount of money that the other receives. There are no right or wrong answers. This is all about personal preferences.

CEE-M Working Papers

Figure 1 :

 1 Figure 1: Distribution of allocation decisions by treatment and frame

[

  Please indicate the allocation you prefer most by marking the respective position along the midline.19 There are no right or wrong answers. This is all about personal preferences. If this task is selected at the end of the study, then both you and the worker with whom you have been paired for this part of the study will be compensated according to your choice in this task.[Slider displayed to participants] 19 FollowingMurphy et al. (2011), the bounds of the sliders that follow are [ 100 cents, 85 cents], [ 100 cents, 15 cents], [ 50 cents, 100 cents], and [ 50 cents, 85 cents] for the Other and [ 50 cents, 85 cents], [ 50 cents, 85 cents], [ 100 cents, 50 cents], and [ 100 cents, 85 cents] for You.

Table 1 :

 1 Experimental design

	Social norm intervention	Valence Framing
		Positive Negative
	Control	T1	T2
	Low	T3	T4
	High	T5	T6

Table 2 :

 2 Dictator game proceduresThe experiment was carried out on a targeted sample of 1364 participants using Amazon Mechanical Turk in conjunction with oTree. Amazon Mechanical Turk was used to recruit participants who are then directed to the oTree experimental platform in order to participate in the experiment. Amazon Mechanical Turk is now used by many social scientists to perform incentivized experiments(Baumester et al. 2011, RAND) and has been favorably evaluated as a tool for research in social science(Berinksy et al., 2012;[START_REF] Crump | Evaluating amazon's mechanical turk as a tool for experimental behavioral research[END_REF]. Recruitment through Amazon Mechanical Turk offers a number of advantages: sample populations exhibit more variation in age, socioeconomic status, education, and geographic localization than typical laboratory experiments, a secure anonymous payment platform through which to transfer compensation from experimenters to participants, and rapid turnaround time. As a result

	Positive Valence	Negative Valence
	Instructions and comprehension check
	Baseline Low SNI High SNI Baseline Low SNI High SNI
	Elicit descriptive expectations
	Elicit normative expectations
	Elicit social preferences
	Collect demographic information

Table 3 :

 3 Generosity across conditions

	Condition	Positive	Valence Negative
	Baseline	0.290 (0.231) 0.320 (0.232) n = 132 n = 144
	Low social norm intervention High social norm intervention	0.310 (0.210) 0.393* (0.285) n = 58 n = 56 0.349 (0.216) .414** (0.233) n = 51 n = 51

* and ** indicate p-values of less than 0.05 and 0.01 when from a t-test with respect to the baseline condition in the same frame. Standard deviations are in parentheses.

  11 Descriptive statistics are presented in Table4and regression results are presented in Table5. The variables 'low social norm intervention' and 'high social norm intervention' are dummy variables equal to one if the participant was in the treatment condition of interest and zero otherwise. The remainder 11

Table 5 :

 5 Tobit regression results for generosity

	Independent variables	Parameter estimates (s.e.) Entire sample Positive valence Negative valence
	Normative belief	0.384 (0.216)	0.557 (0.294)	0.086 (0.306)
	Descriptive belief	0.342*** (0.087)	0.172 (0.114)	0.580*** (0.130)
	Low SNI	-0.047 (0.056)	-0.063 (0.052)	0.030 (0.057)
	High SNI	0.007 (0.056)	0.025 0.053)	0.004 (0.054)
	Negative valence Negative valence * Low SNI Negative valence * High SNI	-0.007 -(0.051) 0.089 (0.079) 0.003 (0.079)	---	--
	Prosocial type	0.132** (0.034)	0.133** (0.046)	0.326*** (0.050)
	Gender	-0.031 (0.035)	-0.007 (0.048)	-0.065 (0.045)
	Age	0.002* (0.001)	0.002 (0.001)	0.003 (0.002)
	Income Educational attainment Political ideology	-0.006 (0.008) 0.003 (0.014) 0.008 (0.009)	-0.021 (0.013) 0.012 (0.012) 0.014 (0.014)	0.007 (0.012) -0.008 (0.019) 0.008 (0.011)
	Financial ease	0.007 (0.020)	0.011 (0.028)	-0.011 (0.029)
	India	0.075 (0.046)	0.006 (0.068)	0.130* (0.061)
	EU	0.166** (0.071)	0.093 (0.113)	0.164 (0.087)
	Other regions	-0.113 (0.071)	-0.265* (0.116)	-0.015 (0.092)
	Constant	-0.279 (0.175)	-0.228 (0.236)	-0.303 (0.240)
	N =	217	105	112
	Left-censored observations	45	20	25
	Uncensored observations	170	85	85
	Right-censored observations	2	0	2
	Pseudo R 2	0.536	0.677	0.659
	*, **, and *** refer to associated p-values of less than 0.05, 0.01, and 0.001, respectively.

If this task is selected at the end of the study, you will receive a bonus of $1.00 if you choose the same allocation that was most frequently chosen by Individual A workers in the previous study.

  table below, please indicate the allocation that you think most Individual A workers in this scenario actually chose in a previous study.

		-Screen 6 -
		[All Players]
		Allocation
	Individual A	Individual B
	(you)	(worker with whom
		you are paired)
	$0	$1.00
	$0.10	$0.90
	$0.20	$0.80
	$0.30	$0.70
	$0.40	$0.60
	$0.50	$0.50
	$0.60	$0.40
	$0.70	$0.30
	$0.80	$0.20
	$0.90	$0.10
	$1.00	$0

  1 -2018 Lisette Ibanez & Gilles Grolleau « Designing more effective norm interventions: the role of valence »

	WP 2018 -13:	Brice Magdalou
		« An abstract model of welfare-improving transfers »
	WP 2018 -01:	Ilaria Brunetti, Mabbel Tidball, & Denis Couvet
	WP 2018 -14:	« Relationship Between Biodiversity and Agricultural Production » Mickael Beaud, Mathieu Lefebvre & Julie Rosaz
	WP 2018 -02:	« Other-regarding preferences and giving decision in risky environments: Phillippe Le Coent, Raphaële Préget & Sophie Thoyer « Do farmers follow the herd? The influence of social norms in the experimental evidence »
	WP 2018 -15:	participation to agri-environmental schemes » Kate Farrow,
	WP 2018 -03:	Ludivine Roussey & Raphaël Soubeyran
		« Overburdened judges »
	WP 2018 -04:	Nicolas Quérou
		« Interacting collective action problems in the Commons »
	WP 2018 -05:	Karine Constant & Marion Davin
		« Unequal vulnerability to climate change and the transmission of adverse
		effects through international trade »
	WP 2018 -06:	Henrik Andersson & Emmanuelle Lavaine
		« Nitrates and property values: evidence from a french market intervention »
	WP 2018 -07:	Mamadou Gueye, Nicolas Querou & Raphaël Soubeyran
		« Does equity induce inefficiency? An experiment on coordination »
	WP 2018 -08:	Douadia Bougherara & Laurent Piet
		« On the role of probability weighting on WTP for crop insurance with and
		without yield skewness »
	WP 2018 -09:	Douadia Bougherara, Carole Ropars-Collet & Jude Saint-Gilles
		« Impact of private labels and information campaigns on organic and fair
		trade food demand »
	WP 2018 -10:	Sylvain Chabé-Ferret, Philippe Le Coent, Arnaud Reynaud, Julie Subervie
		& Daniel Lepercq
		« Can we nudge farmers Into saving water? Evidence from a randomized
		experiment »
	WP 2018 -11:	Dimitri Dubois, Stefano Farolfi, Phu Nguyen-Van & Juliette Rouchier
		« Information sharing is not always the right option when it comes to CPR
		extraction management: experimental finding »
	WP 2018 -12:	Tristan Le Cotty, Elodie Maitre d'Hotel, Raphaël Soubeyran & Julie
		Subervie
		« Inventory credit as a commitment device to save grain until the hunger
		season »
	1 CEE-M Working Papers
	Contact : laurent.garnier@inra.fr/ 04 99 61 31 21

Note that the Nash equilibria are x * =0 in the positive frame, and x * = Ei in the negative frame.

In four additional treatments, we use the strategy method in order to investigate the extent to which the effectiveness of the social norm intervention is sensitive to the hypothetical nature of the information provided. The four additional strategy method treatments ask participants to imagine the hypothetical behavior of other workers in a past session, altering the order of the hypothetical average amount in each frame ($0.30/$0.50 and $0.50/$0.30). We find no significant difference in average allocation amounts between participants who were exposed to a low (high) social norm intervention and those who first considered a first a high (low) descriptive norm, followed by a low (high) descriptive norm.

 Rand (2012) indicates that self-reports are highly reliable using Amazon Mechanical Turk.

U.S. Census Bureau (2015), https://www.census.gov/quickfacts/table/PST045216/00

Indian Office of the Registrar General & Census Commissioner (2001), http://censusindia.gov.in/ Census And You/gender composition.aspx

of the independent variables used in the analysis are described in Table 4. 4.7 1 7 2 = high school graduate 3 = some college (no degree) 4 = associate's degree 5 = bachelor's degree 6 = graduate degree, (master's) 7 = graduate degree, (doctorate) Income 1 = less than $10,000 4.06 1 9 2 = $10,000 -$14,499 3 = $15,000 -$24,999 4 = $25,000 -$39,999 5 = $40,000 -$59,999 6 = $60,000 -$74,499 7 = $75,000 -$99,999 8 = $100,000 or more 9 = prefer not to answer Political 1 = extremely liberal 3.5 1 7 ideology 2 = liberal 3 = slightly liberal 4 = neutral 5 = slightly conservative 6 = conservative 7 = extremely conservative 8 = prefer not to answer Degree of 1 = very at ease 2.43 0 4 financial ease 2 = somewhat at ease 3 = somewhat uneasy 4 = very uneasy Prosocial type 0 = individualist 0.605 0 1 1 = prosocial India 0 = not a resident of India 0.355 0 1 1 = resident of India EU 0 = not a resident of the EU 0.06 0 1

Other 12 1 = resident of the EU 0 = not a resident of one of these 0.081 0 1 countries 1 = resident of one of these countries Supplementary materials: Experimental protocol

Instructions

This is a study on decision-making. You will receive some additional money based on the choices that you and other workers make during the study. In this study, we ask you to participate in 19 tasks. 16 At the end of the study, one of these tasks will be selected at random, and you will be compensated according to the choice that you and other workers made in this task. Please answer as honestly as possible. There are no right or wrong answers.

-Screen 2 -Positive Valence [Negative Valence]

Comprehension check

Please consider the following scenario: Individual A is randomly paired with another person, Individual B. Individual A is allocated $1.00 and has the opportunity to allocate this money between him or herself and Individual B. Individual A will make a choice, and then both individuals will be paid money based on the choice made by Individual A. The pairing is anonymous, meaning that neither individual will ever know the identity of the other individual with whom he or she is paired. [Please consider the following scenario: Individual B is allocated $1.00 and Individual A has the opportunity to allocate this money between him or herself and Individual B. Individual A will make a choice, and then both individuals will be paid money based on the choice made by Individual A. The pairing is anonymous, meaning that neither individual will ever know the identity of the other individual with whom he or she is paired.] Before beginning the compensated tasks, we would like to make sure that you understand this scenario. Below are several possible amounts that Individual A could choose to allocate to him or herself. For each of these amounts, please indicate how much money would be allocated to Individual B as a result. You must answer these questions correctly in order for your work to be approved. -Screen 5 -

Positive Valence [Negative Valence]

Part 2

Please consider the same scenario described before: You are Individual A and have been allocated $1.00. You have the opportunity to allocate this money between yourself and Individual B. You will make a choice, and then both you and Individual B will be paid money based on the choice you make. [Please consider the same scenario described before: Individual B is allocated $1.00 and Individual A has the opportunity to allocate this money between him or herself and Individual B. You are Individual A. You will make a choice, and then both you and Individual B will be paid money based on the choice you make.]

In the tasks below, please evaluate the different possible allocation choices available to Individual A and decide, for each possible allocation, whether you believe choosing that option is very socially inappropriate, somewhat socially inappropriate, somewhat socially appropriate, or very socially appropriate. By socially appropriate, we mean behavior that most people agree is the "correct" or "ethical" thing to do. If one of the tasks below is randomly selected at the end of the study, you will receive a bonus of $1.00 if your response matches the most common response given by the other Individual A workers who are also participating in this study.

For example, if you decide that choosing to allocate $0 to Individual A and $1.00 to Individual B is "very socially appropriate," you should mark "very socially appropriate" next to this possible allocation. If this allocation is the task that is selected at the end of the study, and if most other Individual A workers also marked this allocation as "very socially appropriate," then you will receive a bonus of $1.00.

Possible allocations Appropriateness

Individual


S Cikic

S Jeschke

N Ludwig

U Sinha

C Stelzer
email: stelzer@iwf.tu-berlin.de

E Uhlmann
email: uhlmann@iwf.tu-berlin.de

Stelzer Christian

Eckart

Uhlmann

Make Engineering Students Collaborate

Keywords: Cooperative Learning, Blended Learning, Virtual Knowledge Spaces

Even today, the majority of university courses still consist of lectures complemented with exercises. Lectures serve to convey theoretical knowledge, whereas in exercises students work on assignments to consolidate the knowledge they have acquired during lectures, and to relate their knowledge to daily practice. Cooperative Knowledge Spaces will support natural learning scenarios in a virtual environment. This paper describes the experiences of a study held to evaluate a cooperative knowledge space equipped with teaching material of a presence lesson.

Introduction

By the orientating the existing diploma curriculum merely to presence lessons, the attractiveness of the teachings and, as a result, the motivation of the students is low. This one-way, passive style of reception leads to a decline of learning success and an extended duration of study.

For the sector of engineering, hands-on practice is very important. At the beginning of the diploma there is a high gap to this experience. To train these technical contents in a didactically more apt way, learning tools has to be provided by the teachers. Especially for the training of groups with different knowledge, collaborative and communicative options has to be implemented in the learning process, also taking into account differences between male and female students in their preferred styles of knowledge transfer and their need for assistance on learning.

The study presented here originates from a cooperation project between the Institute for Machine Tools and Factory Management (IWF) and the MuLF Center (Center a for Multimedia in Learning and Research), both at Technische Universität Berlin (TU Berlin), funded by the TU Berlin's internal teaching improvement program, "Offensive Wissen durch Lernen (OWL)". The objective of this cooperation is to combine the concepts of the ViCToR-Spaces project and the concepts of presence lessons, attempting to improve educational and pedagogical concepts and learning outcomes.

Collaborative Learning

In educational research, there is a growing consensus about the positive effects of collaborative learning on student achievement. Collaborative learning can facilitate the process of knowledge building by requesting students to engage in activities that are beneficial for learning when cooperatively solving a problem task or discussing and elaborating text material. [START_REF] Slavin | Research for the future. research on cooperative learning and achievement: What we know, what we need to know[END_REF][START_REF] Weinberger | Facilitating collaborative knowledge construction in computermediated learning environments with cooperation scripts[END_REF] Many studies on collaborative learning have been conducted in the area of text processing and text comprehension. However, a recent study [START_REF] Harskamp | Structured collaboration versus individual learning in solving physics problems[END_REF] showed that also in a more technical domain ---physics in that case ---students learning collaboratively not only performed significantly better in a post-test than students learning individually, but also subjectively had a much more positive learning experience.

One important goal of research on (computer-supported collaborative learning (CSCL)) [START_REF] Stahl | Computer-supported collaborative learning: An historical perspective[END_REF] is to bring these positive effects of collaborative approaches into e-learning. Studies on the application of CSCL methods in technical disciplines have yielded encouraging results, most notably in the context of the "Virtual Math Teams" project [START_REF] Wessner | Designing an online service for a math community[END_REF].

ViCToR-Spaces (see below) is intended to be a collaborative e-Learning and e-Research platform for technical disciplines. Within the OWL-funded cooperation project mentioned above, the feasibility of using ViCToR-Spaces in the context of a large first-year engineering lecture is to be studied.

Course "Fertigungstechnik"

The Technische Universität Berlin has changed the diploma to bachelor and master study course. "Fertigungstechnik" (assembly technology) is a compulsory course for the first-year students of mechanical engineering and can also be elected by students of "IT in mechanical engineering" and for teaching post. About 200 students take the courses in each term.

The "Fertigungstechnik" is held as an upfront presence lecture in two periods per week each semester. Besides this, two more periods per week are occupied by a practical laboratory seminar ("Übung im Versuchsfeld"). Both parts are tested in a written exam.

With the addition material of the seminar the learning for the written test of the lecture could be easier, but the basis for a successful certification is the development of special didactive learning tools embedded in the courses.

ViCToR-Spaces

ViCToR-Spaces [START_REF] Cikic | Victor-spaces: Cooperative knowledge spaces for mathematics and natural sciences[END_REF] is a project currently under development at the Center for Multimedia in e-Learning and e-Research (MuLF) at the Technische Universität Berlin. The project aims at providing a cooperative knowledge space meeting demands of learning, teaching, and research in the fields of engineering, mathematics, and natural sciences. Amongst others these demands include the detection and presentation of formulae both in content and communication interfaces, as well as the integration of virtual and remote laboratories. Documents are not only simply presented to students, but may also be edited cooperatively and organized into virtual rooms. These rooms serve as a virtual meeting place where students and teachers may not only manipulate documents, but also discuss and share them. To this end, each virtual room is provided with optimized cooperation tools such as mathematicsenabled forums and chats.

As social aspects become more important in pedagogical concepts of e-learning [START_REF] Baumgartner | Social software and e-learning[END_REF], the concept of ViCToR-Spaces combines multiple types of social software in a room-based 3( 6) system [START_REF] Greenberg | Using a room metaphor to ease transitions in groupware[END_REF], such as Wiki concepts, social bookmarking, and social networking. But ViCToR-Spaces not only aim at crosslinking users, also every kind of content in the system can be linked to other objects. In this way rooms, content pages, documents, and users build a semantical network in the system.

Fig. 1 XML Schema of Meta-data description of laboratories

Content pages, documents and rooms provide meta-data regarding the respective scientific field and their semantic relation among one another. Virtual and remote Laboratories are equipped with meta-data regarding the (virtual) devices and the respective input and output parameters (see figure 1).

ViCToR-Spaces is based on CURE, a room-based CSCL platform developed at Fernuniverstität Hagen [START_REF]CURE website[END_REF][START_REF] Jörg | Supporting flexible collaborative distance learning in the CURE platform[END_REF].

The Study

The study was held over a period of two weeks in parallel to the theoretical lecture and the practical seminars as a "virtual course" in ViCToR-Spaces. The students were able to virtually rework their material and build virtual work and learn groups. Special rooms applied by the lecturers provided the digital material and videos for the according lecture (see figure 2). The students were able to jointly create lecture notes and discuss problems regarding the content in this room. A special exercise room provided practical task sheets and continuative material. Additionally the students could build work rooms to discuss problems in a smaller group.

Concluding the study we invited the students for a focus group discussion. In this discussion the students were asked about their experiences, their evaluation of navigation structures, awareness of the room concept, and suggestions for further development. They had no previous knowledge on working within virtual cooperative knowledge spaces but had already worked at least with some classical content management systems (amongst others with Moodle [START_REF]Moodle website[END_REF]).

4(6)

The results of the focus group discussion with the students will be outlined in the next paragraphs.

Functions -Navigation -Problems

As a first summary the reactions of the students on working with ViCToR-Spaces were partially very diverse.

In first place the students didn't realize the difference between a content management system such as Moodle and a room-based system such as CURE, but working with CURE they found intuitional.

Regarding e-Learning the students are used to a content-oriented behaviour: "What can I get here? What's in here for me? How can I get something fast?" That is why they didn't realize the cooperative aspect of the system. Due to the existence of a ready-made lecture note provided by the lecturer the students didn't see an advantage in writing down their own (if they have some) in a cooperative way. They balked the additional time expenses and regarded cooperative lecture notes as a waste of time. Also, in the students' opinion they have to solve the examination questions alone so they also want to learn alone. This is also the reason why they would decline cooperative learning scenarios such as scripted cooperation.

Suggestions

In the students' opinions, a quiz to control their own knowledge would give the system an added value. With this feature they can check the knowledge they just acquired. Also, they would like to know who else is in the system (not only in the same room). Imaginable is a "buddy search" to find out if a student's buddy is online and in which room he or she is Although the students assumed that in case online learning (and especially CSCWcomputer-supported cooperative work) will be established in the next years even fewer students would attend the lectures, they would appreciate an online registration system to handle all the organizational questions.

A special wish (and yet very visionary) was a so-called "Ask Dr. No": They ask a specific question and get a definite, correct answer described in detail. Or they have to answer a question and in case they give the wrong answer, they get the correct answer and a detailed description of the approach.

Lessons Learned

Due to different reasons we did not succeed in making the students collaborate. The most important one might be the mistiming of the study in the mid of the semester shortly (two weeks) before the students had to pass a written test as part of the practical laboratory ("Übung im Versuchsfeld"). But it was not only because for lack of time that the students adhered to their known learning and study techniques. As they were not used to work in a virtual cooperative environment they missed some sort of demonstrations, explanations and example scenarios to understand and learn more on the new possibilities. Furthermore since the room concept of ViCToR-Spaces is not yet (or only in a very poor way) visualized, the students did not experience a feeling of groups and rooms. It turned out that the course "Fertigungstechnik" was not a well chosen subject, because its issue is more or less a simple memorizing of facts and the provided learning material consisted mainly of explanative texts. It was something to consume rather than to work on.

The students did not see the differences to classic content management systems, so they were not motivated to adopt to a new system. Further developments have therefore to concentrate on creating an experience of rooms and groups, finding and integrating adequate "act-on" material and on developing a well understandable introduction.

Fig. 2

 2 Fig. 2 Start page of the entrance room of the study


Professor, PhD Cornelia Novac-Ududec
email: cornelia.novac@ugal.ro

lecturer, PhD candidate Diana Stefanescu
email: diana.stefanescu@ugal.ro

Patterns Instruction in Software Engineering

Keywords: Design patterns, software engineering, OOP design, multi-agent system, tutoring system

The paper deals with a training system with a client-server architecture created for students who are learning to design and implement object-oriented software systems with reusable components, and also targeting those users who wish become accustomed with (or improve their skills related to) software design. The paper briefly presents the design patterns which are to be found in the software library, as well as the manner of applying them. The user of such a system has five types of patterns at his/her disposal (Factory Method, Builder, Command, Mediator, Observer), out of which will be able to chose one/more than one, depending on the requirements of the system proposed for design.

Introduction

Motto: "The software design patterns help you learn more from other people's successes than from your own failures" [Mark Johnson] . A software system is a dynamic entity that undergoes multiple changes during its life-cycle, under the influence of factors such as: new user requirements, adding new functionalities, environment adjustment, etc. Therefore, apart from fulfilling those functions which it was designed to fulfill, a software application must be easily changeable, with some of its modules eventually re-used. Having this goal, the application must fulfill certain requirements regarding robustness, mobility, flexibility, scalability, etc. The software design principles are regarded as ways of obtaining the right and re-usable design. By applying abstractization and OOP languages polymorphic techniques, the open-shut principle and the substitution principle (Liskov) upon classes of problems, common solutions were reached -solutions which make up the so-called design patterns. The software patterns facilitate re-using design and architecture. If the design and/or architecture of a system were built with design patterns (techniques which have proven efficient), then they will be more accessible and easier to understand by those software developers who build new systems based on old design. The patterns help the software specialist chose that design that makes the system re-usable. In software engineering, "design pattern" (DP) [START_REF]Design Patterns in C# and VB.NET-Gang of Four (GOF)[END_REF] means a solution applicable to a certain type of problems. A design pattern is a description / model of how a problem should be solved. A pattern cannot be directly transformed in source code. The purpose of design patterns is to enable a "good" object-oriented design, and more than that -to make it reusable (which is more important than reusing the code, regardless of the fact that reusing the design often implies reusing the code). The quality of software design is proportional to the experience and previous knowledge of the one performing it. An expert applies solutions previously used and proven efficient for similar problems. When the expert finds a solution considered acceptable, he/she uses it again. Design patterns solve specific design problems, making the OO more flexible, elegant and reusable. DP help beginners with software design, proposing patterns based on previous experience. A user who is familiar with the DP-s can apply them instantly in order to solve specific problems, without having to rediscover them. By using the software design patterns while designing an object-oriented architecture, one is guaranteed to obtain system compliance to software engineering principles, while also preserving the qualities of "good design": reusable, flexible, robust [START_REF][END_REF].

Design Patterns

Depending on the DP purpose, the authors of GoF [START_REF] Gamma | Design Patterns: Elements of Reusable Object-Oriented Software (GOF)[END_REF] proposed three main categories of patterns: creational patterns, structural patterns and behavioral patterns. These are the most well-known DP, but not the only ones. Other types of patterns emerged -more or less popular -as well as variations of the GoF patterns. A pattern has four essential components:

1. Name: the name of the DP must describe in a few words a design pattern problem, its solutions and the solution's effects. 2. Problem: describes the situations when the pattern is applicable, explaining the problem as well as its context. 3. Solution: describes the components of the design, the relationships between them, the responsibilities of each component and the way they interact. The solution does not describe a specific design for a certain situation or a correct implementation, because a pattern has a general character, referring to a plethora of contexts. The DP presents an abstract description of a problem and a general arrangement of the components (classes and objects) that solve the described problem. 4. Effects: refer to the results of applying the pattern, which are very important when we evaluate the alternatives and we estimate the costs and benefits of applying the pattern. The effects of a pattern are also to be perceived in the flexibility, extensibility and portability of a system. DP identifies the classes and instances which participate, their role, collaboration and responsibility sharing between them.

The design patterns that the user of the proposed system has at hand are: Factory Method and Builder (from the Creational Patterns category), Command, Mediator and Observer, (from the Behavioral Patterns category).

Factory Method Pattern

Factory Method, also known as Virtual Constructor, defines an interface for creating an object, but allows the sub-classes decide which one of them will be instanced. Factory Method defers to the sub-classes the instancing operation. Factory Pattern is quite common to object-oriented programming. It returns an instance of a class out of many more classes possible, according to the data received. Ordinarily, all classes that can be returned have a common super-class and common methods, but accomplish tasks differently and accept different types of data. Figure 1 presents the general structure of the Factory Method pattern. In this diagram:

-Product is the interface of the objects created by factoryMethod(); -ConcreteProduct implements the interface Product; -Creator declares the method factoryMethod() which returns an object of the type Product. In some cases factoryMethod() can be a specific method which returns directly an object ConcreteProduct. -ConcreteCreator re-writes factoryMethod() so that it returns an object ConcreteProduct.

Figure 1. Factory Method Structure

The class Creator counts on its sub-classes, ConcreteCreator, for them to define the factoryMethod() so that it returns an instance ConcreteProduct. The Factory Method DP should be used when:

• A class can't anticipate which should be instanced;

• A class uses its own sub-classes to specify which objects are created;

• The decisions of creating objects are localized.

Conclusions: By using Factory Method:

-There is a separation of the code which represents the logic of the data program. The logic is established by Factory Method. By separating the logic from the program data, the extensibility of the programs improves; -Creating an object by Factory Method is much more flexible than creating an object directly; -The code of the Client class doesn't need to know the sub-classes, it works with the interface Product, so, if the programmer subsequently wants to add subclasses to Product, the class Client can use them without any alterations. One disadvantage of the Factory Method is that a class Client will have to derive from the class Creator, just to create an object ConcreteProduct.

Builder Pattern

The Builder design pattern separates the process of building a complex object from its representation, so that the same building process can create different representations. In this diagram:

-Builder is the interface used to create the parts of the object Product; -ConcreteBuilder implements the method builderPart() of building and assembling the parts of Product and offers an interface to obtain the object Product built; -Director builds an object using the interface Builder, as this class is the one which knows the internal representation of the object to be built; -Product represents the complex structure of the object to be built. ConcreteBuilder builds internal representation and defines the process of assembling the parts of the object Product. The abstract class Product includes classes which define the components, as well as interfaces used for assembling the parts with a view to reach the final goal. This pattern is advisable when: -The algorithm of creating a complex object must be independent of its components and of the process of assembling them; -We need more representations for the same object which is to be built. The Builder pattern can be compared to the Abstract Factory pattern (another one of the Creational Patterns category), as they both return classes consisting of a certain number of methods and objects. The main difference is that, while Abstract Factory returns a family of kindred classes, Builder builds a complex object, depending step by step on the data received.

Command Pattern

The Command pattern encapsulates a request in an object which is submitted towards a specific module that deals with the request. The object that encapsulates the request implements a public interface. Thus, a class Client is given the possibility to make requests without knowing anything about the operation to be executed, because Client uses the public interface. Also, action implementation code changes can be made without needing Client program to be changed. -Receiver is the class that implements the operations of completing the request. Any class can be a Receiver. This pattern is advisable when: -An object is called as having as parameter the action it is about to perform. For example, if there is a request to open a document, this request is used as parameter in the call; -The incoming requests are placed in a pile and completed later, at different moments in time. For example, if one wants to copy a document in another document, one must open the first document, select the content, copy the content then Paste it into the second document, which must be open as well; -The application offers the user possibilities like Undo-Redo; -The application offers support for tracking changes in the system ("logging changes"), so that the system's status can be restored in case of failure. It is also possible to keep into a persistent log the actions performed against the system, so that -through Load and Restore operations implemented by means of the Command interface -to restore the status of the system. That restoration means loading the operations from the log and then re-executing them.

Mediator Pattern

When a system consists of more than one class, logic and algorithm are shared between the classes. The more the classes in a program, the more problems are regarding communication between them. If each class needs to know the methods of all the others, the structure of the program becomes very complicated, the program becomes harder to read and understand, and then, harder to maintain. The system will be hard to change, because a change will reflect upon the code of more than one class. The Mediator pattern solves this aspect by reducing the coupling between classes. Briefly, the classes inform the mediator when changes happen, then class Mediator informs all the classes that they must know the respective changes, as it will be the only one knowing the details of the methods of all classes, thus avoiding the class objects to call themselves directly. Mediator is a class that encapsulates the interactions between the objects of the system. The structure proposed by the Mediator pattern is shown in Figure 4: This pattern is to be used when:

-A group of objects communicates in a well-defined, complex manner; -Inter-dependencies between objects lack structure and are difficult to understand; -Re-using an object is hard because it refers/communicates directly with many objects; -A behavior distributed to more than one class must be customized without using too many sub-classes. Using the pattern Mediator is not limited to programs with visual interfaces, although this is the most common situation. It can be used in any situation that deals with a complex communication problem in a group of objects.

Observer Pattern

The Observer pattern defines an object inter-dependence of the type "one to more", so that, when one object changes status, the objects depending on it are automatically notified of the change and update their status. The Observer pattern proposes a solution for obtaining system consistency, simultaneously with component objects being re-used independently. The objects are shared between observers and subjects. A subject can have more observers (objects depending on the subject status). As soon as the subject status changes, all observers receive automatic announcements. In response, each observer synchronizes its status with the subject's. Figure 5 presents the general structure of the Observer pattern. -Subject declares an interface for adding or extracting the observer objects.

-Observer defines an interface used to notify the observers when there are changes in the subject's status; -ConcreteSubject preserves the status which is of interest for the observers and sends notifications when it changes; -ConcreteObserver implements the interface used to update, keeps a reference to the subject and preserves the status which must be consistent to the subject's status. Using the Observer pattern is recommendable in the following situations:

-When an abstractization has two aspects depending upon each other. Encapsulating these aspects in separate objects contributes to the possibility of re-using them in new contexts, independent of each other. Also, the system becomes elastic, allowing the modules to modify without a chain reaction of change; -When an object must notify other objects without knowing who they are. In other words, they are not characterized by strong, tight coupling; -When the changing of an object leads to changes in other objects, without knowing how many objects will have to be changed in the end.

The Architecture of the System

The platform is designed as a distributed application, providing the option of distance learning/tutoring by means of a computer network or the Internet. The application can be classified as having "three -tier" architecture, the Client part being represented by the configuration module and the tutoring material presentation module. The user connects to the system by means of a Login module, monitored by an Administrator agent (CerberusAgent). After authentication the user may have access to a configuring instrument (if the user connected to the system is the tutor) or to the user interface by means of which the learning material will be presented (if the user is a student). This part of the application will be monitored by an interface agent (InterfaceAgent).

The application server contains the agent platform that monitors the student activity, and a web server responsible for delivering the learning material to the user interface (Apache Tomcat).

The third level consists of a xml database (Apache Xindice), which stores information about the structure of the course, student models, etc. The MTS training system is designed for students which learn to design and implement object-oriented software systems, with re-usable components, but also for those users who wish to become familiar with (or improve) software design. We present the approach of a multi-agent intelligent platform for tutoring student which provides a Student Model. The "modeling student process" used the overlay architecture (common for many tutoring systems) combined with the Bayesian Theory of Probability [START_REF] Novac-Ududec | A Multi-Agent Intelligent Platform for Student Tutoring[END_REF]. The system's tasks are distributed among the intelligent agents (software agents), each having clearly specified individual roles as following: administrator (Cerberus Agent); interface (InterfaceAgent); tutor (Tutor Agent); student (Student Agent). Moreover, the system has "three-tier" architecture and was implemented in Java language [START_REF] Novac-Ududec | A Multi-Agent Intelligent Platform for Student Tutoring[END_REF].

We may well imagine software agents attached to every application or tutoring environment, each having an explicit representation of the users' objectives, of their plans and resources. These agents communicate and negotiate with each other in order to fulfill their individual or group objectives. The multi-agent tutoring system (MTS) was designed and implemented within the Computer Science Department, in which research focused upon "modeling" the student with a view to identifying the most suitable methods and tutoring strategies by taking into account individual knowledge and abilities. The user of this kind of system has all five types of previously described patterns at his/her disposal (Factory Method, Builder, Command, Mediator, Observer), out of which will be able to use one or more, depending on the requirements of the system that needs to be designed.

Depending on the needs of the user that must fulfill the requirements of the system, one selects from Tutor Agent the design patterns best suited for those functions. The patterns help build the menus of the application and the user interface. Then the database is built, as the user has permanent access to an example of application as similar as possible to the one which needs to be carried out. In order to develop an application, the student must know the Java programming language, must be familiar with MySql data servers and must be able to use the Eclipse platform. The Eclipse platform is an open source framework from IBM which aims to provide a variety of basic services for software developers [6].

Conclusions

The system wishes to be a useful working instrument for software application development training, for both students and other types of users. A design pattern together with an example of using that pattern may become a way of working in software design. Moreover, in this case applying design patterns -therefore design principles -in carrying out an object-oriented application results into a reusable design and reusable modules / components. Also, the resulting code enjoys more robustness and flexibility, these two characteristics being essential for developing new modules that would add new functionalities and operations to the application; the design/code of the application is open to extension and closed for changes, according to OCP. Another important aspect relates to the maintenance activities which the designed application will undergo along its life-cycle: they will more cost effective, as no change will disseminate through the entire system, the application code being built so that it isolates the volatile classes (according to the principle of reversing dependencies), the dependencies being in the way"details depend upon abstractization" and not the other way around.

The software patterns ensure compliance to design principles, as they contribute to obtaining a steady and efficient system, and they provide design discipline for students. Last but not least, using such a system in software engineering learning makes the process much more attractive and efficient. The design patterns approach in software engineering training fits the pedagogical Schemas Theory (created by Bartlett at the beginning of the 20 th century) [START_REF] Brewer | The Nature and Functions of Schemas[END_REF]. A brief representation of the particular knowledge of the training domain can help the trainee to easier absorb the new information, while the organization and structuring of the knowledge as patterns will guide him/her in finding (remembering) the necessary information in a certain context [START_REF] Gamma | Design Patterns: Elements of Reusable Object-Oriented Software (GOF)[END_REF]. We believe that within such an approach one can create a training ontology from Software Engineering.

  Figure 2 presents the general structure of the Builder as established by GoF.

Figure 2 .

 2 Figure 2. Builder Pattern Structure. Classes Diagram

Figure 3 .

 3 Figure 3. Command Pattern Structure

Figure 4 .

 4 Figure 4. Structure of the Mediator Pattern

Figure 5 .

 5 Figure 5. Structure of the Observer pattern