DEL MODELO DE EVALUACIÓN DEL PLAN DE ACCIÓN AL BANCO DE PROYECTOS DEL MUNICIPIO DE MARSELLA

The designers found the grouping of content through the extracted ontology entities visualised in the semantic maps with ViewS Microscope very helpful. There was a strong desire from the simulator designers however to explore comments together with ontology concepts, as additional semantics were sought from an early point in the study. Of particular interest to the designers was whether an ontology entity, consequently a cluster of ontology entities,

was linked with a comment or a set of comments that were referring to the participant's personal experience or the simulated situation.

Tables 5.2 and 5.3 present example user comments linked with ontology entities from different clusters in the Body Language ontology and WNAffect taxonomy of emotions for different simulation episodes and user groups. Some of the comments were seen as helpful to enrich the feedback provided to the learner or to add more options for response in the simulated situations. For example, in different simulation episodes (Table 5.2) there are different aspects discussed with respect to the situation presented (e.g. #1 and #2) that can be used to evaluate the presented scenario, as well as personal norms and suggestions (e.g. #3 and #4) that can be integrated to enrich the simulator. Personal experiences of people in different gender groups (Table 5.3, #1 and #2) can also be used to enrich the simulated situation with different negative emotions, or augment the options given to the user for appropriate behaviour according to users' suggestions linked to positive emotions (Table 5.3, #3 and #4).

Table 5.2 Example comments and annotated entities from body language

signal meaning clusters in the Greetings and Bill simulation episodes. Episode # User Comment Semantic Tag

social interaction

1

("Greetings" episode)He is very polite and probably greets his partners as used in his culture.

respectful, greeting,

2

("Bill" episode)You can make a softer gesture with your palm when you want someone to hold and relax while you take care of things. caution, attention psychological process 3

("Greetings" episode)I think that it's pretty rough-mannered to arrive with a significant delay in every situation, most of all in a business one. So I would expect the person to apologize and to come with a very good justification.

anticipation, defensive

4

("Bill" episode)If I see that not everyone agrees to share the bill equally, I would never do so. I would propose that everyone pays what he/she has exactly to pay.

Table 5.3 Example comments and annotated entities from WNAffect

clusters in the male and female user groups. WNAffect

Cluster

# User Comment Semantic Tag

negative

1

(male user) One year ago I was obliged to order a chip dish in order to not embarrass the diners, eventually I was angry and hungry!

anger, wrath, fury

2

(female user) The gestures in multicultural environments are very risky, especially can be viewed as obscene or insulting. I always try to avoid them.

repugnance, abhorrence, contempt

positive

3

(male user) When people are more friendly you should never make them feel embarrassed for their behaviour. Especially when this is warm regards.

warm- heartedness,

friendliness

4

(female user) It is important in a team of different nationality to respect the request to order what they like to eat, so that it might suits everyone, even if it could cost extra money.

regard, admiration

5.6 Discussion

The semantic maps with ViewS Microscope provided a useful tool for the simulator designers to get an insight into the UGC. The designers were able to quickly sense the user reactions with the simulator, thus to evaluate the intended effect of the simulator and also to sense which parts of the simulator may need improvement. It was also useful to facilitate summarisation, comparison and contrast of different simulation episodes or users groups, as ViewS Microscope provided a fast way to quantify contributions as well as to qualitatively present diversity of social-signal aspects. This shows that ViewS can be used to capture and compare different viewpoints expressed in UGC.

Although the designers provided overall very positive feedback, more information was sought in the study regarding the textual content, which ViewS was unable to capture. This information concerned additional semantics to attribute the UGC related to the intention of the users' contribution - e.g. whether they were referring to personal experiences and rules/norms, or providing statements about the situation presented in the simulation. To further investigate the potential of using UGC to augment digital environments for learning a hybrid approach was instantiated in [141]

combining semantic analysis with ViewS and discourse analysis. While ViewS showcased its most prominent beneficial role - a gateway to UGC, the discourse analysis that aimed at annotating UGC with categories related to either improving the simulator (e.g. real-world stories and rules) or gathering information about the simulator (e.g. statements about the situation and feedback on the simulator) appeared very challenging: using three different content annotators the observed agreement in attributing UGC with discourse categories was classified as moderate to low, considering the subjectivity of assessing user contributions.

This research builds on the assumption that semantic augmentation of UGC with social signal related terms is helpful for getting an insight into the UGC to improve the simulated environment for learning. For this thesis, it is considered sufficient that ViewS acted as an effective and efficient gateway to UGC. Although promising, but yet challenging, as presented in [141], aiding the simulator designers with more sophisticated means to further examine UGC and its usefulness in improving the simulator (e.g. using discourse analysis) is out of the scope of this work.

The next Chapter concerns the support for the elicited requirements (summarised in Table 5.4) for viewpoint focus modelling with automatic computational methods.

Table 5.4 Viewpoint focus requirements (FRs) elicited during the

exploratory study in Chapter 5.

FR-1 Identify clusters of annotated ontology entities in the ontology space.

FR-2 Preserve the cardinality of the clusters of the annotated ontology entities.

FR-3 Extract aggregates of ontology entities given the clusters of annotated ontology entities in the ontology

spaces.

FR-4 Construct clusters and aggregates based on the distance of the annotated ontology entities.

FR-5 Allow for clustering and aggregation using different distances between ontology entities.

FR-6 Represent the breadth of annotated ontology entities.

FR-7 Preserve the ontology hierarchy to be able to reason about specificity and generality of ontology entities.

FR-8 Represent the composition of the clusters and consequently of the aggregates in the ontology space.

FR-9 Allow for selective data partitioning: the viewpoint model should be able to distinguish focus spaces between

e.g. different episodes, aspects and dimensions.