Anne Kasperski

Alicia Weibel
email: weibel@chimie.ups-tlse.fr

Claude Estournès

Christophe Laurent

Alain Peigney

⇑ C Estourne

Multi-walled carbon nanotube-Al 2 O 3 composites: Covalent or non-covalent functionalization for mechanical reinforcement

Keywords: Ceramic matrix composites, Alumina, Carbon nanotubes, Microstructure, Toughness

come

Carbon nanotube (CNT)-ceramic composites have been studied for about 15 years, the key objective being to increase the fracture toughness compared to the corresponding ceramic [1][2]. Many reports are related to CNT-Al 2 O 3 composites. Most authors prefer to use multi-walled CNTs (MWCNTs), which are less prone to damage during the preparation process than single-and double-walled CNTs, and also are cheaper. However, their main characteristics (internal and external diameters, number of walls, length and defect proportion) vary greatly from one study to the other, which can result in large variations in the microstructure and properties of the composite. In particular, the MWCNT/ceramic interface area, which depends on both external diameter and length, is one of the key parameters in the toughening mechanisms, as in the case of micrometric fibre-ceramic composites. Moreover, differences in number of walls and length induce different amounts of MWCNTs for a given carbon content present in the composite [3]. Amounts that are too low or too high result in a poor degree of homogeneity of the MWCNT distribution in the composite. MWCNT characteristics also affect their ability to be dispersed and stabilized in a liquid medium, generally water, prior to the preparation of the MWCNT-ceramic powder, by a commonly used colloidal process [1,2]. The functionalization of the MWCNTs is one of the key steps towards stabilization of the suspension [4]. Both covalent and non-covalent functionalization have been investigated, but the superiority of the latter method, which is less prone to induce damage to the CNTs, has not been clearly established because of the variety of MWCNTs used in the preparations [1,2] and the contested validity of some measurements of the fracture toughness [5,6]. Therefore, in this work, we investigate the influence of the functionalization type on the microstructure, mechanical properties and possible reinforcement mechanisms for MWCNT-Al 2 O 3 composites prepared by colloidal mixing, freeze-drying and spark plasma sintering (SPS).

Two kinds of MWCNTs were used for this study. The samples, designated 8WCNT (Nanocyl, Belgium) and 20WCNT (Nanothinx, Greece), have been studied in detail elsewhere [7]. For the 8WCNT sample, CNTs with 8 walls are dominant (30%), with CNTs with 7 and 9 walls (both 16%) being the next most abundant species. The average number of walls (N) is 8.5, which was rounded down to 8 (hence the codename). The CNT length is less than 1.5 lm (supplier data), the average external diameter (d ext ) is 10.2 nm and the aspect ratio is around 150. For the 20WCNT sample, the number of walls varies widely, with most being within the range 8-37; CNTs with 17 walls are dominant, but only slightly (at only 10%). N = 19.8, which was rounded up to 20. The CNT length is over 10 lm (supplier data), d ext = 19.6 nm and the aspect ratio is around 500. For both samples, the MWCNTs are not bundled and most MWCNTs present important structural defects, such as kinks, incomplete walls, a bamboo-like structure and variations in diameter along the length. The MWCNT samples were divided into two batches, which were submitted to a covalent or non-covalent functionalization treatment. For covalent functionalization, the MWCNTs were poured into a nitric acid aqueous solution (3 mol l À1 ) using 1 mg of CNT per ml of solution. After refluxing overnight (130 °C, magnetic stirring), the suspension was neutralized, filtered and redispersed (ultrasonic bath, 80 W, 15 min) in an NaOH aqueous solution (4 mol l À1 ) in order to remove any disorganized carbon. After neutralization and filtration, the covalentfunctionalized MWCNTs obtained were dispersed again in deionized water (Vibra Cell 75042 sonotrode, 500 W operated at 30% power, with 5:5 s on:off pulses, 15 min), yielding a stable suspension (2.5 g l À1 ) as required for the study. For non-covalent functionalization, the CNTs were dispersed (sonotrode, 30 min) in an aqueous solution of gum arabic (GA), a water-soluble polysaccharide, using the same concentration of 1 g l À1 for both CNT and GA.

A commercial a-Al 2 O 3 powder (TAIMEI Chemicals Co. Ltd., 99.99% purity, average primary grain size 140 nm) was used for the preparation (15 min sonication and 1 h mechanical stirring) of a suspension in an aqueous solution adjusted to pH 12 by the addition of ammonia [8,9]. The CNT suspension was then poured into the alumina suspension (30 min sonication under mechanical stirring) and the vessel was immersed in liquid N 2 until freezing, whereupon it was freeze-dried (Christ alpha 2-4 LD, Bioblock Scientific) at À84 °C for 48 h in a primary vacuum (12 Pa) [10,11]. Four CNT-Al 2 O 3 composite powders were prepared, labelled 8C, 20C, 8NC and 20NC (with 8 and 20 reflecting the average number of walls of the CNTs and C and NC reflecting covalent and non-covalent functionalization, respectively). The CNT suspension volume required to obtain a 2 wt.% carbon content in the composite powder, for the 8WCNTs or the 20WCNTs, was determined from calculations using the CNT density chart [3]. The carbon content (Table 1) in the powders was measured by flash combustion. It corresponds solely to CNTs for the 8C and 20C samples. For the 8NC and 20NC samples, the carbon present in GA (determined by TGA under air) was subtracted to the flash-combustion measured values to obtain the carbon content corresponding to CNTs. All values are slightly lower than 2 wt.% and the corresponding volume fractions of CNTs (Table 1) are in the range 3.0-3.8 vol.%.

From each powder, two pellets were prepared by SPS (Dr. Sinter 2080, SPS Syntex Inc., Japan). First, green samples were prepared by compacting the powders in a stainless steel die (inner diameter 20 mm) under a uniaxial pressure of 200 MPa. The greens were loaded into a 20 mm inner diameter graphite die. A sheet of graphitic paper was placed between the punch and the sample as well as between the die and the sample for easy removal. The SPS treatment was performed in vacuum (residual cell pressure <3 Pa). A pulse pattern of 12 current pulses followed by 2 periods of zero current was used. The temperature was controlled using a thermocouple placed in a little hole (5 mm deep) located on the outer surface of the die. Heating rates of 150 and 100 °C min À1 were used from room temperature to 650 °C and from 650 to 1350 °C, respectively, where a dwell (6 or 11 min) was applied. For the 8C and 20C samples, a uniaxial charge (corresponding to 150 MPa) was gradually applied within the first minute of the dwell, maintained for a further 5 min and gradually released during the cooling down to room temperature (100 °C min À1 ). For the 8NC and 20NC samples, a 5 min dwell time was added at 1350 °C before the application of the uniaxial load, to ensure the decomposition of GA in the still porous composite (i.e. the total dwell time was 11 min). For the sake of comparison, an Al 2 O 3 sample was prepared by SPS (using the as-received powder) at only 1150 °C (6 min dwell), in order to obtain a dense sample with an average alumina grain size similar to that of the alumina matrix in CNT-Al 2 O 3 composites [12].

The sintered specimens were in the form of pellets 20 mm in diameter and about 2 mm thick. The density was determined by the Archimedes method after removal of the graphitic surface contamination layer by light polishing. The relative densities, calculated using 1.8 g cm À3 for the 8WCNTs and 2.1 g cm À3 for the 20WCNTs [3], are about 100% for Al 2 O 3 , 8C and 20C and in the range 97-98% for 8NC and 20NC, probably because some porosity produced by the decomposition of GA could not be fully resorbed during sintering.

Raman spectroscopy analyses (Jobin-Yvon Lab-RAM HR800, laser excitation at 632.82 nm) were performed at different steps in the study. The ratio between the intensities of the D band and the G band (I D /I G , Table 2) was calculated from the spectra (not shown). A higher ratio is generally attributed to the presence of more structural defects in the CNTs. There is not much change in the I D /I G ratio for the 8WCNTs (or the 20WCNTs), whether raw, covalent-functionalized or in the composite powder, showing no drastic Table 1. Carbon content (corresponding to CNTs, i.e. corrected for carbon present in gum arabic for 8NC and 20NC) in the composite powders (C n ), alumina average grain size (G), electrical conductivity (r e ),Vickers microhardness (H V ), bending fracture strength (r f ), SENB fracture toughness (K Ic ) of alumina and CNT-alumina composites (standard deviations are indicated for the mechanical properties). deterioration to the structure of CNTs -which, as noted above, are nevertheless quite defective. By contrast, the I D /I G ratio for the CNTs in the dense composites is slightly lower, probably indicating the annealing of some defects involving sp 3 carbon by the SPS treatment at 1350 °C.

Specimen C n G r e H V r f K Ic (wt.%) (vol.%) (nm) (S.cm À1 ) (GPa) (MPa) (MPa.m 1/
The fracture surfaces of the pellets (Fig. 1) were observed by field emission gun scanning electron microscopy (FESEM; JEOL JSM 6700F). For all samples, the fracture mode is intergranular. The average alumina grain size (G, Table 1) was determined from such images by the mean linear intercept method. For the Al 2 O 3 sample (Fig. 1a), G = 320 nm. For 8C (not shown) and 8NC (Fig. 1b), it is significantly lower (170 nm), in agreement with earlier results showing that the matrix grain growth is inhibited by the CNTs [1][2]. Indeed, CNTs that do not appear to have been damaged are well distributed at the matrix grain boundaries, without forming agglomerates. For 20C and 20NC, the matrix grain size is about 300-330 nm (Fig. 1c andd), reflecting the lesser influence of the 20WCNTs due to their lower number and lower total length (for the same carbon content, the number of 20WCNTs in the sample and their total length are 25 and 4 times lower, respectively, than for 8WCNTs), and their less homogeneous distribution. Indeed, CNTs and CNT imprints are observed (arrowed in Fig. 1c andd) at most alumina grain boundaries, though some CNT agglomerates, up to 30 lm long, are also evident. This could, in turn, reflect that 20WCNTs are more difficult to disperse than 8WCNTs because of their higher aspect ratio (500 vs. 150). Note that grain-growth inhibition is still active, because the average grain size is similar to that of Al 2 O 3 despite the higher sintering temperature (1350 vs. 1150 °C).

Note also that neither the average grain size nor the CNT distribution depends on the functionalization method.

The pellets were polished down to 1 lm using diamond slurries. The electrical conductivity was measured at room temperature with direct currents applied to (1.8 Â 1.8 Â 1.5 mm 3 ) specimens parallel to their length, i.e. perpendicular to the pressing axis. The current densities used were lower than 160 mA cm À2 (Keithley 2400). All composites present an electrical conductivity of at least 0.1 S cm À1 (r e , Table 1), reflecting that the carbon contents are higher than the percolation threshold measured on similar composites [1][2]. However, the values are higher for 8C and 8NC than for 20C and 20NC, reflecting the higher CNT number and total length, and the better distribution of the 8WCNTs, as noted above. The electrical conductivity for 8NC (1.7 S cm À1 ) is amongst the higher values reported for well-densified MWCNT-Al 2 O 3 composites with a similar CNT content [1][2], slightly higher than that (1.3 S cm À1 ) reported by Inam et al. [12], which confirms the high degree of homogeneity of the CNT distribution. The lower electrical conductivity for 8C (1.0 S cm À1 ), despite a similar CNT distribution could indicate that the CNTs sustained some structural damage during the covalent functionalization.

The indentation tests (300 g for 10 s in air at room temperature) were performed on the polished surface of eight specimens by loading with a Vickers indenter (Shimadzu HMV 2000). The corresponding diagonals of the indentation were measured using an optical microscope attached to the indenter. The calculated microhardness values (Table 1) are the average of 10 measurements. The Vickers microhardness of the composites (17.8-19.8 GPa) is lower than that of the Al 2 O 3 sample (21.3 GPa), in good agreement with earlier results [13,14]. The transverse fracture strength (r f ) was measured by the three-point bending method on eight specimens with dimensions of about 1.8 Â 1.8 Â 18 mm 3 . The fracture toughness (K Ic ) was measured by the SENB method on similar specimens notched with a diamond wire 0.17 mm in diameter and calculated using a calibration factor [START_REF] Brown | ASTM Special Technical Publication[END_REF]. For each composition, eight samples were tested for r f and eight for K Ic . The cross-head speed was fixed at 0.1 mm min À1 . No composite shows both a higher fracture strength and a higher fracture toughness than Al 2 O 3 (Table 1). The 8C and 8NC samples have a slightly higher fracture strength (456-468 vs. 413 MPa) but a lower toughness (4.1-4.2 vs. 5.4 MPa.m 1/2 ), which could be correlated with the smaller alumina grain size (170 vs. 320 nm) [START_REF] Kovar | [END_REF], without any direct effect of the CNTs being clearly observed. For 20C, both r f and K Ic are lower than for Al 2 O 3 despite a similar grain size. By contrast, 20NC also shows a lower r f , though K Ic is similar to that for Al 2 O 3 (5.2 vs. 5.4 MPa.m 1/2 ). The presence of CNT agglomerates in 20C and 20NC could account for the low fracture strength because the larger agglomerates (up to 30 lm in length) have a size comparable to the critical-flaw size, even taking into account the higher K Ic for 20NC. Indeed, these agglomerates are very porous and easily deformable, presenting the same behavior as large pores. To investigate the possible mechanisms of crack deflection or crack bridging by the MWCNTs in more detail, Vickers indentations were performed on the polished surfaces of the composites using a high enough load (2 kg) to produce cracks, which were observed by FESEM (Fig. 2). For 8C (Fig. 2a) and 20C (Fig. 2b), some CNTs are observed on the edges of the crack, but not bridging the crack. In contrast, large-scale crack bridging is observed for 8NC (Fig. 2c) and 20NC (Fig. 2d). This could reveal that covalent functionalization weakens the MWCNTs whereas non-covalent functionalization does not.

As discussed elsewhere [17,18], the crack propagation by decohesion at the grain boundaries causes the CNTs, which are mechanically locked by adjacent ceramic grains, to become progressively unfolded and taut, one by one, as the crack widens, thus ensuring some load transfer. At a certain point, all the CNTs which are thus unfolded do bridge the crack. Some bridging CNTs are broken, therefore absorbing a fraction of the fracture energy. The elastic deformation energy absorbed by one CNT for unfolding is weak but the cumulated energy necessary for the fracture of all bridging CNTs is much higher and this would thus likely limit the crack propagation. Moreover, we observe that crack bridging CNTs are mostly perpendicular to the crack direction (Fig 2c andd), which could correspond to pull-out, whereas oblique crack bridging could correspond to unfolding. Thus, pulling-out of CNTs could also limit the crack propagation. The poor toughness (4.2 MPa.m 1/2 ) for 8NC, despite crack bridging, could reflect that the 8WCNTs are short and have a low aspect ratio, and thus that the mechanical locking by the alumina grains would not extend very far, allowing for pull-out with only a low tensile stress. For the much longer 20WCNTs, which also have a much higher aspect ratio, the pull-out critical stress would be sufficiently high to lead to crack bridging, thus bridging by CNTs and pull-ing-out of CNTs could both contribute to the reinforcement.

In conclusion, this work shows that MWCNTs (8 walls, 1.5 lm long or 20 walls, 10 lm long), with prior covalent functionalization, are not able to provide any crack bridging in the corresponding Al 2 O 3 -matrix composites, probably because the chemical treatment was detrimental to their mechanical properties, which is correlated with low fracture toughness. In contrast, the non-covalent functionalization (here performed with gum arabic) preserves the mechanical properties of the MWCNTs, thus allowing for crack bridging on a large scale in the corresponding composites. Moreover, it is shown that, in order for this to provide toughening, long MWCNTs (>10 lm) are to be preferred to shorter ones (<1.5 lm), although they are more difficult to disperse homogeneously because they permit the establishment of sufficiently long mechanical interlocking of the CNTs by the matrix grains.

The FESEM observations were performed at TEM-SCAN, the "Service Commun de Microscopie Electronique a `Transmission", Universite ´Paul Sabatier, Toulouse. The authors thank G. Chevallier for assistance with the SPS, which was performed at the Plateforme Nationale CNRS de Frittage Flash (PNF 2 , Toulouse).

Figure 1 .

 1 Figure 1. FESEM images of the fracture surface of the Al 2 O 3 sample (a) and the 8NC (b), 20C (c) and 20NC (d) composites.

Figure 2 .

 2 Figure 2. FESEM images of cracks made by Vickers indentation using a high load (2 kg) on the 8C (a), 20C (b), 8NC (c) and 20NC (d) composites.

Table 2 .

 2 Ratio between the intensities of the D band and the G band (I D /I G ) calculated from the Raman spectra for the CNTs at different steps in the study: raw, covalent-functionalized, in the composite powders and in the dense composites.

	Step	8C	8NC	20C	20NC
	Raw	1.86	1.86	1.27	1.27
	Covalently functionalized	1.88	-	1.42	-
	In composite powder	1.77	1.85	1.28	1.41
	In dense composite	1.28	1.38	1.10	1.19


S Melzi

A Abdaoui
email: abdaoui@lirmm.fr

J Azé

S Bringay
email: bringay@lirmm.fr

P Poncelet
email: pascal.poncelet@lirmm.fr

F Galtier
email: f-galtier@chu-montpellier.fr

Patient's rationale: Patient Knowledge retrieval from health forums

Keywords:

Online health forums are areas of exchange where patients, on condition of anonymity, can speak freely on their personal experiences. These resources are a gold mine for health professionals-giving them access to patient to patient, patient to health professional and even health professional to health professional exchanges. In this study, we used text mining techniques to analyse health forums in order to extract emotions (e.g., joy, anger, surprise, etc.) expressed by patients. After a study of real messages, we demonstrate the difficulty of manual annotation due to the low level of agreement between humans. We propose a method to identify the polarity of a message and extract one or several emotions. This method was validated on a substantial real dataset.

INTRODUCTION

Online health forums are areas of exchange where patients, on condition of anonymity, can speak freely about their personal experiences. Some examples are the very active forums, including healthforum.com [START_REF]healthforum[END_REF], ehealthforum [START_REF]ehealthforum[END_REF], which allow internet users (often non-health professionals) to exchange opinions on their health situation. Hancock [START_REF] Hancock | The truth about lying in online dating profiles[END_REF] demonstrated that the ability to communicate anonymously via computers facilitates the expression of affective states such as emotions, opinions, doubts, risk fears, etc. These affective states are generally repressed in more traditional communication contexts, such as face to face interviews or when responding to surveys. These resources are a gold mine for health professionals-giving them access to patient to patient, patient to health professional and even health professional to health professional exchanges. For example, recently, the effects of new generation pills have been widely debated in French forums. This prompted some women to stop taking contraceptive pills, with a concomitant increase in abortions. Even if all patients do not use health forums, they represent a large and varied database of knowledge and patients' perceptions on their illnesses and any healthcare they have received. In this highly subjective setting, the characterization and understanding of these perceptions is difficult but nevertheless particularly relevant for complementing and improving public health programs.

As part of the French project called "Parlons de nous" ("Let's talk about us"), we tried to combine different markers (emotions, risk fears, uncertainty, etc.) with respect to medical items (drugs, treatments, etc.) in order to identify common collocations (e.g., between mediator and fear). In this article, we focus on the identification of emotions as a specific sentiment analysis task. While many approaches have been proposed for the analysis of text polarity (positive and negative), few approaches focus on the analysis of feelings (joy, anger, sadness, etc.). From a corpus of messages collected on the English-language Spine Health website, we used the vocabulary of emotions of Mohammad and Turney [START_REF] Mohammad | Emotions Evoked by Common Words and Phrases : Using Mechanical Turk to Create an Emotion Lexicon[END_REF] to automatically annotate messages. A part of the corpus was manually annotated by 60 annotators. Based on a study of the agreement between annotators, we were able to show that it was difficult, even for humans, to associate a particular emotion to a message. We decided to give two information items to health professionals: the polarity of the text (positive or negative) and associated emotions (e.g., joy). We looked for the best descriptors for these two items. Experiments on real datasets revealed the effectiveness of this approach and discussions with health professionals have shown the medical importance of identifying such information.

The rest of this paper is organized as follows: in Section 2, we identify the medical issues. In Section 3, we propose a first sentiment analysis categorization and recent methods. In Section 4, we describe the corpus used in our approach. The method we used is described in Section 5. In Section 6, we describe the main results. Finally, in Section 7, we conclude and give the main prospects.

II. MOTIVATIONS

As pointed out by Siegrist [START_REF] Siegrist | Emotions and Health in Occupational Life: New Scientific Findings and Policy Implications : Inauguration Speech Belle Van Zuylen Professorship[END_REF], one of the great challenges for health professionals is to capture patients' satisfaction to answer the question "How can we improve our practice?". With this objective, Siegrist studied patients' feedbacks after their stay in large American hospitals and turned them into raw data that could be tapped by the medical authorities for decision making. Using the forums as an object of study, we are getting closer to the patient private sphere. Indeed, patients express things in posts, they does not express in comments (even anonymous). However, precisely identifying the emotional state of patients through these messages is a difficult objective task and not always verifiable, as discussed by Quirk [START_REF] Quirk | A Comprehensive grammar of the english language[END_REF]. However, we could consider using these large amounts of emotionally-charged texts to construct indicators that are relevant for health professionals. An example of such an application is "We feel fine" [START_REF] Kamvar | We feel fine and searching the emotional web[END_REF]. This tool queries the web with the aim of assessing users' moods. Every 10 minutes, the application considers sentences with emotional words and performs statistical calculations based on the type of feelings, age, gender, etc. An example of application is dedicated to pharmaceutical companies. They monitor the social web in general to identify texts in which patients talk about their medications and measure the associated emotional states. This feedback can help them improve their products or their communication about these products. Another example concerns the physicians who want to know the patients fears about the prescribed treatments. This feedback can help them to improve their communications to patients.

The (semi-)automatic analysis of forums is difficult from a technological standpoint. Most (semi)-automatic methods used in the health domain are applied to publications and hospital reports. Adapting these methods to messages from social media like forums is not simple at all. Such messages are written by patients in rather a loose style. They vary in size (between a hundred and a thousand characters). They contain non-standard grammatical structures, many misspellings, abbreviations, emotion-rich expressions as well as emotion-rich words (I love, I hate), unconventional layout, e.g., repeated use of capital letters (TIRED), unconventional spelling (enoooooough), and punctuation repetition (!!!!!!!!), slang words that are (or not) specific to the forum or the topic (LOL vs. IVF) and emoticons (:-)). Message volumes are generally very high (in the French forum dedicated to breast cancer on the Doctissimo site, there are more than 3,300 threads, some of which contain more than 2,000 replies). Finally, the processing of health forum data based on semi-automatic information extraction methods is a significant technological challenge.

III. STATE OF THE ART

Sentiment analysis has been widely studied since the early 2000s. Many communities are interested in this area and their definitions and interpretations are highly varied (e.g., psychology, social sciences, computational linguistics, natural language processing, data mining, etc.). Sentiment analysis involves the extraction of emotional states expressed or implied in texts [START_REF] Liu | Sentiment Analysis and Opinion Mining[END_REF]. It includes the following tasks:

1. Subjectivity analysis [START_REF] Wiebe | Annotating expressions of opinions and emotions in language[END_REF] focuses on the detection of feelings based on subjective expressions or words;

2. Polarity analysis [START_REF] Boiy | Automatic sentiment analysis in on-line text[END_REF] focuses on the detection of positive and negative polarity of texts;

3. Emotional analysis [START_REF] Lu | Emotion Detection in Textual Information by Semantic Role Labeling and Web Mining Techniques[END_REF] focuses on the emotional category of texts (e.g., anger, disgust, fear, etc.)

4. Intensity analysis [START_REF] Mulder | A lexical grammatical implementation of affect[END_REF] focuses on different levels of polarity or emotion intensity (e.g., very positive, very sad, etc.). These approaches offer a more precise granularity of expressed opinions and emotions.

We focus on the third task. Like most semi-automatic methods in the literature, we use the typology of emotions defined by Ekman [START_REF] Ekman | An argument for basic emotions[END_REF], which describes six emotions, but many other typologies also exist ( [START_REF] Plutchik | A general psychoevolutionary theory of emotion[END_REF]; [START_REF] Pearl | Identifying emotions, intentions, and attitudes in text using a game with a purpose[END_REF]; [START_REF] Francisco | Automated mark up of affective information in english texts[END_REF]).

The methods used to analyse feelings are numerous and generally specific to the text type, e.g., tweets [START_REF] Roberts | EmpaTweet: Annotating and Detecting Emotions on Twitter[END_REF], press titles [START_REF] Strapparava | Learning to identify emotions in text[END_REF], etc., and application areas, e.g., social media analysis [START_REF] Balahur | Sentiment Analysis in Social Media Texts[END_REF], gender impact in negotiations [START_REF] Boneva | Using E-mail for Personal Relationships The Difference Gender Makes[END_REF], identification of suicidal emails [START_REF] Pestian | Sentiment analysis of suicide notes: A shared task[END_REF].

For all studied sentiment analysis tasks (polarity and emotions), most previous studies focus either on the creation of resources to describe feelings or on the use of these resources to classify texts according to sentiments. In the first category, most methods associate texts with emotional word resources. Most of these resources have been compiled for English texts and polarity analysis, e.g., General Inquirer [START_REF] Stone | The General Inquirer: A Computer Approach to Content Analysis[END_REF], Linguistic Inquiry and Word count [START_REF] Tausczik | The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods[END_REF], MicroWNOp [START_REF] Cerini | Language resources and linguistic theory: Typology, second language acquisition, English linguistics[END_REF], sentiwordnet [START_REF] Baccianella | SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining[END_REF]. More specific resources, such as the DAL dictionary [START_REF] Whissell | The dictionary of affect in language[END_REF], Wordnet affect [START_REF] Strapparava | WordNet-Affect: an affective extension of WordNet[END_REF] or the lexicon of Mohammad and Turney [START_REF] Mohammad | Emotions Evoked by Common Words and Phrases : Using Mechanical Turk to Create an Emotion Lexicon[END_REF] were created for emotional words. There are also approaches for extending these vocabularies for specific application domains by building manual rules [START_REF] Neviarouskaya | Affect analysis model: Novel rule-based approach to affect sensing from text[END_REF], or identifying co-occurring words with words already identified as denoting emotions through large corpora [START_REF] Harb | Web Opinion Mining : How to extract opinions from blogs ? Categories and Subject Descriptors[END_REF] or the web [START_REF] Kozareva | UA-ZBSA: a headline emotion classification through web information[END_REF]. For classification, most approaches use machine learning techniques based on specific attributes, including emotion words ( [START_REF] Chaumartin | UPAR7 : A knowledge-based system for headline sentiment tagging[END_REF]; [START_REF] Strapparava | Learning to identify emotions in text[END_REF]) to build a statistical model from a corpus of texts and use it to detect feelings in other texts.

While many of these methods are effective on large text corpora, they are limited in the case of short texts such as tweets or specific texts as in health forums. In our study, these limitations were mainly due to the subjectivity of the annotation task, as we describe in Section 4.2.

IV. CORPUS

A. Data collection and annotation

We built a corpus from 17,000 messages collected in the English-language Spine-health forum. We automatically annotated the corpus with the vocabulary of emotions of Mohammad and Turney [START_REF] Mohammad | Emotions Evoked by Common Words and Phrases : Using Mechanical Turk to Create an Emotion Lexicon[END_REF]. This lexicon consists of more than 14,000 entries characterized by their polarity and associated with 8 emotions. In this work, we consider only 6 emotions [START_REF] Ekman | An argument for basic emotions[END_REF]): anger, disgust, fear, joy, sadness and surprise.

Each word in the lexicon could be associated with several emotions (e.g., the word abandoned was associated with the emotions fear, anger and sadness). This automatic annotation enabled us to filter 22% of the messages (not containing emotion words). In order to focus only on emotions associated with medical items, we used MeSH to identify medical units in the text, which allowed us to filter messages without any medical references (6% of messages). In a message, many emotions were usually expressed because the messages were relatively long. We therefore chose to segment the messages in sentences. We finally kept 3,000 sentences to constitute an Automatically Annotated Corpus (AAC) and labelled sentences with several emotions by the most frequent one (re-annotation step).

A subset of this corpus (600 sentences) was manually annotated by 60 non-health professionals, i.e. basically Master's students and computer science researchers from our lab. We called this the Manually Annotated Corpus (MAC). We thus set up a web-based platform. Unlike Strapparava and Mihalcea [START_REF] Strapparava | Learning to identify emotions in text[END_REF] who used an interface to annotate and capture many emotions through an emotion-intensity cursor, we decided to simplify the task and asked the annotators to identify only the presence of emotions expressed in the texts. Each sentence was pre-labelled automatically via the lexicon, the corresponding emotion was shown by default but could be unchecked if the annotator believed that it was not expressed in the sentence. If the sentence did not express any emotion, all emotions were to be unchecked. Finally, if the annotator could not decide, "I do not know" was selected.

Table 1 shows the distribution of sentences in both corpora according to six emotional categories. The AAC corpus was clearly unbalanced and both fear and sadness emotions were best represented. In the MAC corpus, 45% of the sentences were annotated as neutral (no emotion) and 9% were undecidable. We also noted after the re-annotation that the MAC corpus was better balanced than the AAC corpus. However, surprise was very poorly represented.

B. Between-annotator agreement

We used the Kappa measure to assess the betweenannotator agreement. For this, 150 sentences from the MAC were annotated by two non-professional annotators. We got a Kappa of 0.26, which clearly shows that the agreement between annotators was very low. In addition, we measured the agreement between health professional annotators and non-professionals for the same 150 sentences and obtained a moderate agreement of 0.46. This preliminary experiment highlighted the difficulty of the manual annotation task. Moreover, the disagreement between annotators was mainly due to the variability between people and not their sensitivity to health (professional vs. non-professional).

A first bias, already identified by [START_REF] Sokolova | Sentiments and Opinions in Health-related Web messages[END_REF], was in considering the perspective of the annotator/reader likely differed markedly from that of the author of the message. Indeed, health forum posts treat topics such as disease, treatment, etc. This information is negative by nature and most of the annotators, by empathy, associated an emotion such as sadness to factual information such as the description of a diagnosis. For example, the sentence "I am also HLA-B27 negative, so was diagnosed with a spondyloarthropathy" was annotated as sad in our corpus, although it contained a factual diagnosis. A second bias concerned the fact that the corpus was in English, while the annotators were native French-speakers. Furthermore, by studying sentences with gaps in the annotations, we noticed that it was easier to identify the polarity than the emotion. It was also easier to predict positive emotions than negative emotions because negative emotions share very similar vocabulary. We also noted that surprise was the hardest emotion to identify, as also noted by Strapparava and Mihalcea [START_REF] Strapparava | Learning to identify emotions in text[END_REF] who argue that surprise is not often taken into account in studies of emotions as it is neutral in nature. For example, the sentence "I discovered its effect on me the hard way, hugging the toilet after a painful back procedure, ugh!" None of our annotators considered the emotion surprise, despite the presence of the word "discover".

The quality of our annotated corpus was actually quite questionable. Indeed, the annotators were not sufficiently coached with specific instructions to avoid the biases mentioned above. Tests of internal consistency (interindividual reproducibility) would have to be done to assess if the annotators were consistent over time. Another possibility would be to get several annotators to annotate sentences and choose labels by majority vote. Finally, even with its drawbacks, this study gave a relatively clear picture of the difficulties involved in obtaining a qualitative corpus and the methodology to improve its quality. Based on these findings, we then decided to compare the results obtained with the AAC and MAC corpus, knowing that most of the methods of the state of the art only use AAC corpus. We evaluate different methods to characterize forum texts based on: 1) a two-category classification to identify the polarity of emotions; 2) a multi-category classification for six emotions: a sentence could only be associated with a single emotion class. Surprise was eliminated because of its neutrality. This typology is similar to that described by Roberts [START_REF] Roberts | EmpaTweet: Annotating and Detecting Emotions on Twitter[END_REF]; 3) a multi-label classification allowed us to associate a sentence to several emotion classes.

V. EXPERIMENTAL PROCEDURE

Our approach relies on a classification method based on attributes such as unigrams, bigrams and specific attributes, defined to capture traces of emotions in messages. It consists of two steps: 1) pre-processing of sentences from messages, and 2) classification of these sentences. Evaluation of the results depends on the classification performed.

Pre-treatments: forum posts are specific in the sense that the words used are not necessarily found in conventional dictionaries (slang, special formatting, abbreviations, emoticons, etc.). It is therefore necessary to standardize them by generalizing their content. To do this, we applied the preprocessing procedure outlined in Table 2 and corresponding to the chain set up by [START_REF] Balahur | Sentiment Analysis in Social Media Texts[END_REF] for tweets. Classification: We used the following attributes in order to find the best emotion descriptors:

-Attributes based on N-Grams (U, U+B): Unigrams, Unigrams and Bigrams; -Emotion words (EW): if a sentence contains two words corresponding to the emotion joy, it takes the value 2 for the corresponding attribute; -Smileys (SMI): all emoticons (:-):-(...) were classified according to the six emotions. If a sentence contains a smiley related to joy, it takes the value 1 for the corresponding attribute;

-Amplifiers (AM): These attributes correspond to punctuation (!,?...), repeated letters (looool) and capitalized words (HATE). If a sentence contains such elements, it takes the value 1 for the corresponding attribute;

-The emotion context (CONT): we used two attributes that we call neighbour emotion and overall emotion. The first attribute is true if the sentence that precedes or follows the sentence expresses the same emotion and the overall feeling is the true value if there is another sentence in the message that expresses the same emotion.

Like Bechet [START_REF] Béchet | SDMC : un outil en ligne d'extraction de motifs séquentiels pour la fouille de textes[END_REF], we enriched the attributes with patterns obtained using a sequential pattern algorithm (PAT). For this, we used the MeSH medical thesaurus to identify medical words (labelled MW), the lexicon of emotion words (labelled EW) to identify traces of emotions and a lemmatizer for grammatical category words (labelled JJ, NN, VV, MD, etc.). Each sentence was then considered as a sequence of itemsets corresponding to a combination of these three labels. We then used the GSP algorithm [START_REF] Zhang | A GSPbased efficient algorithm for mining frequent sequences[END_REF] to obtain frequent patterns, i.e. frequent sequences. We used only those containing at least one label for a medical entity and another label for an emotion word. These patterns were then used as attributes. A sentence was labelled true for a pattern if its syntactic form fit the pattern. Figure 1 summarizes the protocol for obtaining patterns.

Evaluation:

The quality of the two-class classification was evaluated using the standard precision measurements P (percentage of correct predictions), recall R (percentage of correct labels found by the system) and F-measure F (harmonic mean of precision and recall). For multi-class classification, we calculate both the average Fmi at a micro level (R and P were calculated by constructing the overall contingency table) and Fma at a macro level (R and P were calculated for each class and averaged). For multi-label classification, other metrics were needed. Indeed, if we took, for example, a sentence belonging to both classes sadness and anger, the system could predict: sadness and anger (the prediction was correct), sadness (the prediction was partially correct) and disgust (the prediction was wrong). So there were degrees of possible misclassification. Other measures [START_REF] Read | Classifier Chains for Multi-label Classification[END_REF] were then used such as the Hamming loss HL (accuracy for each class averaged per class), accuracy A (averaged for all examples) and macro F-measure Fma.

VI. RESULTS AND DISCUSSION

We used implementations of Weka for bi-class and multi-class classification and Meka for multi-label classification. We used the SMO implementation of the SVM classifier of Weka with default settings. We used the CC chain classification implemented in Meka for multi-label classification. We used two datasets: MAC and AAC corpora (considering that the majority emotion label set after automatic annotation was the class to predict). We carried out a cross-validation (10-fold) and used the attributes described in Section 5. Table 3 presents the AAC corpus results. We do not present the MAC corpus results which were similar that seems to suggest that even with the previsous limitations mentioned in Section IV.B, the MAC corpus has at least the same quality as the corpus used in the literature and obtained automatically from emotional word ressources. The bi-class classification gave the best results, which seemed fairly consistent because the two classes were better represented. This task was also easier for human annotators. Multi-label classification gave better results than the multiclass classification because one example could be associated with multiple classes. Moreover, we detected only little differences between the micro and macro F-measures for multi-class classifications, which suggests that all classes were hard to identify. These results should be compared with the inter-annotator agreement (see section 4.2). In both cases, the task was difficult, but the semi-automatic method seemed to detect patterns more systematically, except in specific cases such as irony.

We could also conclude that the best descriptors were a combination of unigram and bigram with emotion words (U + B + EW). Taking smileys and amplifiers into account did not improve the classification. Indeed, smileys were often used for irony, which was not captured by considering their presence in the sentence. For example, the sentence "I stopped working in 1/09 and kind a thought that at some point I would get better, in hindsight, also rather dumb:-) instead of picking up the pace of getting worse significantly" was automatically associated with the label joy because of the smiley:-), even though it was used by the patient to indicate his bitterness. A simple improvement consists in changing the polarity of the smiley if in the near context there is a contradictory polarity.

Similarly, the context was not an attractive attribute. Indeed, messages were often long (7 sentences on average in our corpus) and contained many feelings (more than 6 emotions in 41% of the messages). Two consecutive sentences often contained different uncorrelated emotions. Finally, patterns were also ineffective because they were too general. They could easily be used to improve the accuracy if we define them by class of emotion.

Note that when the classifiers were wrong, they often placed the sample in a "close" class, with the same polarity. These incorrect predictions were due to the fact that the classes shared many words (such as anger, disgust and sadness). Dictionaries and lemmatizers are used as resources, so the method could easily be applied for other languages using similar resources.

VII. CONCLUSIONS AND PERSPECTIVES

Here we describe a method for analysing emotions in health forums. The main challenge was the acquisition of data, and this step will be further improved. For the extraction of emotions, we compared different attributes for different classification tasks (two-class, multi-class and multilabel) and showed that the most effective were a combination of unigrams and bigrams and emotion words for classification bi-classes. However, suggesting a precise label, despite the precision obtained, could be relevant for the health care professionals involved in such studies.

[36]Prospects associated with this work are numerous. From the emotion analysis standpoint, we will apply our method on larger datasets not specific to health, such as the SemEval challenge [START_REF] Strapparava | SemEval-2007 Task 14: Affective Text[END_REF] and compare our method with other published methods such as SWAT [START_REF] Katz | SWAT-MP: the SemEval-2007 systems for task 5 and task 14[END_REF], uPAR [START_REF] Chaumartin | UPAR7 : A knowledge-based system for headline sentiment tagging[END_REF] and UA [START_REF] Kozareva | UA-ZBSA: a headline emotion classification through web information[END_REF]. We will also take shifters into account. Indeed, Smith and Lee [START_REF] Smith | A CCG-Based Approach to Fine-Grained Sentiment Analysis in Microtext[END_REF] showed that the polarity of a term is often modified by the context surrounding it, including markers of negation. In the sentence "This treatment does not make her the negation changes the polarity of the sentence from positive to negative. In the case of emotions, it is harder to understand the impact of these shifters because there are close links between emotions, such as between the failure to be happy and to be sad. For this, more complex emotion models should be used, such as SentiSens [START_REF] Carrillo De Albornoz | SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis[END_REF] which takes the relationship between emotions into account (e.g., hate vs. love). In addition, we will build a lexicon of emotion words specific to our area, as proposed by Carrillo de Albornoz [START_REF] Carrillo De Albornoz | SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis[END_REF]. For this, several options are considered. Smith and Lee [START_REF] Smith | A CCG-Based Approach to Fine-Grained Sentiment Analysis in Microtext[END_REF] used Wordnet [START_REF] Miller | WordNet: A Lexical Database for English[END_REF] to associate new words with words already associated with an emotion based on the relationship "similar to" for adjectives and hyponymy for nouns and verbs. Inspired by the Turney and Littman approach [START_REF] Turney | Measuring praise and criticism: Inference of semantic orientation from association[END_REF], it is also possible to search for frequently co-occurring adjectives (or other grammatical forms) using the web or large corpora [START_REF] Kozareva | UA-ZBSA: a headline emotion classification through web information[END_REF]. We will also validate the genericity of our approach on a French corpus. Indeed, our method relies solely on lexicons and a stemming tool. Finally, the attributes used in our study are focused on the expression of emotions through the lexicon and not through the syntax or through other discourse markers. An improvement would be to integrate these aspects, although complex rhetorical constructions are not frequent in the studied forums.

We also identified issues related to the field of application. The spine-health forum is specialised in the topic of "pain" and discusses a pathology which is a disease of the elderly. The nature of the text message is closely related to these two factors (little smileys or slang, little expressions of joy, etc.). To explore other feelings, we need to diversify the themes of the studied forums. Once the emotions are identified, many applications can be envisaged. The discovery of novelties such as medical associations between medical entities and emotional markers can be used for informational searches by laboratories (e.g., what patients think of this medicine?), physicians (e.g., what patients think of this operation?), patients, etc. More generally, emotion searches could be used to model variations in emotions over time. For example, over time we frequently observed changes in the emotions of patients, e.g., "fear and surprise", "surprised and angry", etc. Furthermore, we could study the influence of the media on the patients' emotion changes, e.g., the case of the third generation pill. Another application might be to identify patients communities based on the expressed emotions. For example, in the debate about the effects of new generation pills, it was noticed that many of the comments were related to religious beliefs. This information is essential for moderators. This applications list is not exhaustive. Identification of emotions is a step toward these applications.

  Figure 1. Sequence definition

TABLE I .

 I PERCENTAGE OF SENTENCES IN BOTH AAC AND MAC CORPORA ACCORDING TO 8 CATEGORIES (J -JOY, SU -SURPRISE, F -FEAR, A -ANGER SA -SADNESS, D -DISGUST, N -NEUTRAL, DK -DO NOT KNOW) BEFORE AND AFTER RE-ANNOTATION.

	AAC	J	Su	F	A	Sa	D	N	DK
	Before	22	14	39	22	39	18	/	/
	After	13	4	33	9	35	6	/	/
	MAC	J	Su	F	A	Sa	D	N	DK
	Before	10	4	17	13	19	14	45	9
	After	14	6	23	14	25	18	0	0

TABLE II .

 II

		APPLIED PRE-PROCESSING
	Pre-processing	Example	Resource
	Repeated punctuation	!!!!	/
	Specific layout	TIRED	/
	Emoticon	:-)	/
	Slang	2mr > Tomorrow	Chatslang.com
	Emotional words	Fear	Mohammad's lexicon [4]

TABLE III

 III

	.	COMPARISON OF RESULTS OBTAINED ACCORDING TO A SET
		OF ATTRIBUTES USING THE AAC CORPUS
			Bi-class			M-class		M-labels
	Attributes		R	P	F	Fmi	Fma	HL	A	Fma
	U		62.7	57.8	60.1	24.4	23.2	0.24	51.5	58.2
	U+B		65.9	61.5	63.6	24.8	22.3	0.14	58.3	59.3
	U+B+EW		66.3	64.1	65.1	27.3	25.1	0.12	61.4	61.8
	U+B+EW+SM+AM	53.4	52.4	52.9	23.4	20.7	0.25	55.5	55.8
	U+B+EW+CONT		53.6	45.5	49.2	25.2	22.5	0.22	55.4	57.6
	U+B+EW+PAT		66.2	65.2	65.7	26.1	25.8	0.15	58.2	62.4

ACKNOWLEDGMENT

This paper is based on studies supported by the Maison des Sciences de l'Homme de Montpellier (MSH-M) within the framework of the French project Let's talk about us (Parlons de nous) [42].


Patrice Bouvier
email: patrice.bouvier@liris.cnrs.fr

Élise Lavoué
email: elise.lavoue@univ-lyon3.fr

Karim Sehaba
email: karim.sehaba@liris.cnrs.fr

Sébastien George
email: sebastien.george@insa-lyon.fr

Identifying Learner's Engagement in Learning Games: a Qualitative Approach based on Learner's Traces of Interaction

Keywords: Game Based Learning, Learner Behaviour, Engagement Measurement, Qualitative Approach, Digital Gaming, Trace Theory

This paper proposes a qualitative approach for identifying learners' engagement from their traces of interactions performed in the learning game. Learners' engagement is an effective indicator of their motivation, acceptance and attachment to the learning activity. Engagement also informs about the relevance of the content and the effectiveness of the proposed interactive learning game. Designers, practitioners and teachers need information about engagement for analysing, designing and validating the learning game and also for modifying and adapting learning games in order to maintain their effectiveness. Currently, most of the approaches provide quantitative information about learner's engaged-behaviours. Thus, our objective is to extract qualitative information from learners-generated data. In this paper, we propose an approach in three stages that combines theoretical works on engagement and engaged-behaviours, Activity Theory and Trace Theory. By relying on traces of interactions, this approach enables to identify engaged-behaviours in low-constraint interactive games, directly, continuously, under ecological conditions and over a long time period. Then we present the results of a user study that demonstrate the feasibility and the validity of our approach. This study has been conducted on twelve traces composed of several thousands of learner-generated data.

INTRODUCTION

The features of digital games like multi-sensory immersion, interactivity and immediate feedback stimulate students' motivation and facilitate the development of skills like attention, problem-solving, decision-making or collaborative work [START_REF] De Aguilera | Video games and education: (education in the face of a "parallel school[END_REF]. Several studies [START_REF] Gee | What video games have to teach us about learning and literacy[END_REF][START_REF] Egenfeldt-Nielsen | Overview of research on the educational use of video games[END_REF] argue in favour of the use of digital games as efficient educational tools. This explains why computer supported education practitioners show a growing interest in digital game based learning [START_REF] Prensky | Digital Game-Based Learning[END_REF].

Student's engagement is considered as a useful indicator in order to prevent school dropout [START_REF] Reschly | Research leading to a predictive model of dropout and completion among students with mild disabilities and the role of student engagement[END_REF]. Identifying engagement may inform about learners' motivation, acceptance and attachment to the learning mediated activity. It can also inform about the relevance of the content and the effectiveness of the proposed interactive technology or service. These information may be used by designers and teachers for testing, modifying or adapting the content or features of the system. During classroom lessons, teachers have the opportunities to assess and influence students' engagement [START_REF] Skinner | Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year[END_REF] by adapting the form or the content of the lessons according to several students' characteristics such as personality, motivation, needs and affective states. With computer supported education, the relationship between teachers and students is limited, especially in the case of distance learning [START_REF] Greenhow | Learning, Teaching, and Scholarship in a Digital Age: Web 2.0 and Classroom Research-What Path Should We Take "Now[END_REF]. Two means are available for eliciting and maintaining learners' engagement in mediated learning activities: either the mediated activity is intrinsically engaging (this is what is expected with the learning games), or the teacher has some information about learners' engagement.

In this paper, we propose an approach for identifying learners' engagement in learning games from their traces of interaction (i.e. learner's actions ac-tually performed within the mediated activity). This approach, useful for both teachers and designers, enables the direct and continuous analysis of students' engaged-behaviours under ecological conditions. We applied our approach through the analysis of 12 traces of interaction in a digital game.

This paper is organised as follow. In section 2 we refine the concept of engagement and review the methods for identifying it. In section 3 we describe the three stages of our qualitative approach. In section 4 we present the results of a user study and discuss them. Finally, in section 5, we conclude by showing the implications for computer-supported education and introduce some future works.

BACKGROUND AND RELATED WORK

In this section we study the notions of engagement.

Our aim is to provide a definition that is applicable in both entertainment and learning fields. Based on this definition, we study the methods for identifying engagement in digital gaming.

2.1 Defining Engagement in Digital Gaming [START_REF] Boyle | Engagement in digital entertainment games: A systematic review[END_REF] observe in their systematic review on engagement in digital games, that the nature of engagement is still not well understood and that there is still a lack of a widely accepted definition of engagement. Defining an abstract concept like engagement is useful in order to facilitate scientific exchanges, especially when the concept is used in several fields. Also, it helps to be clear about what is aimed, or what is being measured. Thus, it improves the validity and effectiveness of the comparisons between several methods or approaches. [START_REF] Brown | A grounded investigation of game immersion[END_REF]) define engagement as "the lowest level of immersion" before "engrossment" and "total immersion". [START_REF] Brockmyer | The development of the game engagement questionnaire: A measure of engagement in video game-playing[END_REF] consider engagement "as a generic indicator of game involvement" which can evolve on a progressive scale whose levels are immersion [START_REF] Jennett | Measuring and defining the experience of immersion in games[END_REF], presence [START_REF] Tamborini | The role of presence in the experience of electronic games[END_REF], flow [START_REF] Csikszentmihalyi | Flow: The Psychology of Optimal Experience[END_REF] and psychological absorption (total engagement).

In the field of education, engagement may be considered as the "behavioral intensity and emotional quality of a person's active involvement during a task" [START_REF] Reeve | Enhancing students' engagement by increasing teachers' autonomy support[END_REF]. After reviewing the concept of school engagement, [START_REF] Fredricks | School Engagement: Potential of the Concept, State of the Evidence[END_REF]) conclude that engagement is a "meta con-struct" which encompasses "behavioral" (participation, positive conduct, effort), "emotional" (interest, positive emotions) and "cognitive" (psychological involvement in learning, self-regulation) dimensions.

The first issue with these definitions is the reference to ambiguous concepts like involvement and immersion. The second issue is that these definitions are context-dependent (for example the definitions in the entertainment field seem to address more specifically the immersive games). As we aim to provide a definition that is valid in the both fields, we think more relevant to answer through a conceptual definition that focuses on the state of engagement rather than to its outcomes.

So we consider engagement as "the willingness to have emotions, affect and thoughts directed towards and determined by the mediated activity". Engagement occurs if players or learners' expectations (perceptual, intellectual, interactional) are fulfilled. Then, in a process similar to the suspension of disbelief1 [START_REF] Coleridge | The collected works of Samuel Taylor Coleridge[END_REF] players and learners may willing to get engaged in order to live more intensely the activity. Then they accept that, during a given time (perhaps beyond the duration of the mediated activity), their emotions, affect and thoughts will be mainly elicited by the mediated activity (here the digital game-based learning). The consequence of this engaged state, is that players' or learners' attention will remain on the game and their motivation will make them keep playing and coming back (again and again) in the game.

Identifying Engagement in Digital Gaming

Our objective is to analyse learner's engagement directly (i.e. from their actions and not a posteriori), continuously and over a long time period (i.e. session after session across weeks and months) and under ecological conditions (i.e. at home and without interfering with the learner's activity nor impacting the system). Our approach must allow to analyse the whole population of users rather than a selected sample and must be effective with interactive systems that offer a wide range of actions. Therefore, we do not address in this section psychophysiological or self-report, interview and observation methods (see respectively [START_REF] Kivikangas | A review of the use of psychophysiological methods in game research[END_REF] and [START_REF] Fredricks | The measurement of student engagement: A comparative analysis of various methods and student self-report instruments[END_REF] for recent reviews). Metrics approaches are used in industry and academics for meeting the previously mentioned con-straints. It consists in automatically collecting and storing any users' actions performed through input devices towards the system such as, users' choices, location changes, modifying character's characteristics, interaction with other character but also information like time spent or the level reached. Thus, it is possible to record all the users' course and process during the activity. Then, some data mining methods can be applied on these user-generated data in order to derive valuable (i.e. interesting, interpretable and useful) information. See [START_REF] Romero | Educational data mining: a review of the state of the art[END_REF] for a review addressing specifically learner-generated data. As metrics only inform on what users are doing but not why, one may see a limit in aiming to identify engagement (i.e. an abstract quantity) from metrics [START_REF] Canossa | Patterns of play: Playpersonas in user-centred game development[END_REF].

As engagement influences the behaviour, some measurable quantities can be considered for identifying engaged-behaviours [START_REF] Bauckhage | How players lose interest in playing a game: An empirical study based on distributions of total playing times[END_REF]. For studying the impact of tutorials on players' engagement in digital entertainment games, [START_REF] Andersen | The impact of tutorials on games of varying complexity[END_REF] collect some raw data like the number of unique levels completed, the total playing time and the number of times players have loaded the game. Expecting to predict when players will stop playing, [START_REF] Bauckhage | How players lose interest in playing a game: An empirical study based on distributions of total playing times[END_REF]) study how engagement evolves over time. They apply techniques from lifetime analysis on player's playing times information (when they play and for how long) collected from five AAA-games like Tomb Raider or Crysis. [START_REF] Weber | Using data mining to model player experience[END_REF] study players' engagement in terms of player retention within an American football game. For that purpose they collect preference data such as the game mode selected and behavioural data like average yards gained or ratio of possession. Dealing with learner's disengagement detection in web-based e-learning system, [START_REF] Cocea | Log file analysis for disengagement detection in e-learning environments[END_REF] compare eight machine learning techniques on several raw data. The latter are mainly related to reading pages (number of pages read, time spent reading pages) and quizzes events. The previous methods conduct quantitative measure on isolated (i.e. unlinked) data item. Thus, rather than addressing engaged-behaviour as a whole, they stay at a basic level by only considering some parts of an engaged-behaviour. Some approaches are considering user's engagedbehaviour through some sequences of actions. [START_REF] Beal | Classifying learner engagement through integration of multiple data sources[END_REF] propose a classification approach of learner's engagement within a mathematics ITS. For that purpose, they defined five student's timedependent patterns of actions based on time traces of actions within the ITS. More recently, [START_REF] Köck | Activity sequence modelling and dynamic clustering for personalized elearning[END_REF] adopt a clustering approach for detecting sequences of learner's actions in the Andes ITS. These studies only occur in high-constraint environment like ITS. In such environments, the variety of actions is tight and fully determined by the interactive system (attempts, request for hint, results etc.). In this case the number of items is limited. Thus, sequencemining may constitute an efficient method for discovering some statistically relevant sequences of actions. But, in low-constrained interactive systems like learning game, a wide range of actions may be possible. Then, sequence-mining could return a too large number of sequences. Also, as the temporal succession of actions does no imply that there is a coherence between these actions, these actions may be not useful in order to derive valuable information about a high-level engaged-behaviour. Moreover, machine learning for sequential data mining suffers from several issues like long-distance interactions [START_REF] Dietterich | Machine learning for sequential data: A review[END_REF]. This can be problematic if, within an engagedbehaviour, a long period occurs between items.

AN APPROACH FOR IDENTIFYING ENGAGED-BEHAVIOURS

In this section we present our qualitative approach for analysing directly, continuously and under ecological conditions learner's engaged-behaviours in lowconstraint interactive games and over a long time period. For that purpose we consider learner's behaviour as a chain of actions actually performed in the digital game. A chain of actions is an aggregation of learner's actions selected from temporal constraints and/or characteristics of the action. By aggregating several actions, we expect a comprehensive contextual information about learner's engagement. Also we adopt a theory-driven perspective by firstly determining some engaged-behaviours. Therefore, the challenge is to identify, among the wide variety of possible actions, those that are inherent to the engagement. These actions can refer to a high number of dimensions of the interactive systems and be collected at different granularity of temporal and spatial resolution. So, three main issues have to be overcome: 1. Determining some high-level engagedbehaviours. 2. Characterising these engaged-behaviours by identifying the underlying chains of actions. 3. Detecting these chains of actions among all the actions recorded.

Our strategy for tackling these three issues is in three stages. Each stage is described in the following three sub-sections.

Determining Engaged-Behaviours

In this section we describe how we determine some high-level engaged-behaviours. To decide whether a behaviour reflects, or not, an engagement, we consider the question of the learners' motives and needs that determine engagement. This is useful in order to give meaning, in relation to their engagement, to users' actions. [START_REF] Przybylski | A motivational model of video game engagement[END_REF] use the Self-Determination Theory (SDT) [START_REF] Ryan | Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being[END_REF] for explaining digital game engagement. The SDT identifies three basic psychological needs: competence (sense of efficacy), autonomy (volition and personal agency) and relatedness (social interaction). This perspective is different from other works based on empirical observations on players' behaviours. [START_REF] Lazzaro | Why We Play Games: Four Keys to More Emotion Without Story[END_REF] identifies four motivational factors for playing game labelled hard fun (challenge), easy fun (curiosity, fantasy), altered state (positive emotions) and people factor (social experiences). [START_REF] Yee | Motivations for play in online games[END_REF] observes three main components: achievement, social and immersion.

Even if these motivational factors are closed to the basic psychological needs previously mentioned. The basic psychological needs perspective has several advantages. As it is not based on empirical observation of players' behaviours on specific games, it does not depend on the gameplay2 of the game. Thus, basic psychological needs perspective can be applied on all current or future gameplay. By being more versatile, this approach enables to determine a wide and nonstereotyped range of behaviours. Our aim is neither to determine a user model nor to provide a model of engagement but to identify some behaviours that significantly reflect learner's engagement (i.e. that are not determined by the gameplay of the learning game). Thus, for qualifying a behaviour of being engaged, we link users' behaviours to the universal needs identified by the SDT that they satisfied.

For structuring the analysis of engagedbehaviours, we consider that digital gaming consists in performing some actions (decision-making process), directly or through a character, within an environment (or at least on a frame) which may involve social interaction with human or virtual agents. Thus, the gaming activity has the following four dimensions: environmental (in relation with the autonomy need), social (in relation with the relatedness need), self (in relation with the autonomy need) and action (in relation with the competence and autonomy needs). Of course, according to the type of learning game (simulation game, computer supported collaborative learning), the components will not always be present, nor with the same intensity. Each component encompasses several engaged-behaviours including those observed by [START_REF] Lazzaro | Why We Play Games: Four Keys to More Emotion Without Story[END_REF] and [START_REF] Yee | Motivations for play in online games[END_REF]. See Table 1 for an overall summary of the four components of players engagement.

Environmental Engagement

Player's engagement is directed towards the environment or the world depicted in the game. This engagement encompasses two main behaviours: the contemplation and the curiosity. Contemplatives like to stroll in the game area. Curious seek to know the physical and geographical boundaries of the game world. They may also be interested in configuring the features of the game. The goal is not to win but to increase their knowledge about the game.

Social Engagement

Social engagement refers to the social connections within the game. In this case, the game is an opportunity to create and expand social relations toward other players. The purpose is to develop and/or to maintain her/his social network. This player will massively use the communication channels provided, will promote the game to increase the number of participants, will enjoy teamwork (collaborative work in a serious game, within a team or guild in a digital game).

Self Engagement

Self engagement concerns the connection between players and their character through identification and/or ownership aspects. These players like to customize their avatar and choose accessories for some other reasons than performance. Players experiencing this type of engagement will be particularly involved in shopping stage (for a new sword, costume, skills etc.). Thus, they may spend a long time to study the characteristics of accessories or skills. This player will take care about the role play.

Action Engagement

Player's engagement is directed towards the actions to perform in the situation depicted by the game. The core of the game is the main interest for these players. Players will try to quickly pass the levels, to win experience points, to complete challenge etc.

Characterising Engaged-Behaviours

The previous section on engagement enables to determine some engaged-behaviours by establishing the relationships between needs, motives and engagedbehaviours. This section describes how we characterise these engaged-behaviours. By characterising an engaged-behaviour we mean identifying the underlying actions and chains of actions performed by the learner within the learning game. Our aim is to select, among the various dimensions of the game, the relevant user-generated data i.e. the ones underpinning an engaged-behaviour. To reach this objective, we base on the Activity Theory to establish the relationship between the learners' needs, the object and motive of the activity and the actual realization of actions.

Basic Concepts of Activity Theory

Activity Theory [START_REF] Vygotsky | Mind in Society: The Development of Higher Psychological Processes[END_REF][START_REF] Leontiev | Activity, consciousness and personality[END_REF] aims to understand Human development through an analysis of the "genesis, structure and processes of their activities" [START_REF] Kaptelinin | Acting with Technology: Activity Theory and Interaction Design[END_REF]. Three different levels of analysis of the activity are distinguished :

• Activity An activity is performed by a subject, through a tool, in response to a specific need or motive in order to achieve an object (objective). The need generates the motive, the motive elicits the activity, the object structures and directs [START_REF] Kaptelinin | The object of activity: Making sense of the sense-maker[END_REF] the activity towards a desired and anticipated [START_REF] Bardram | Plans as situated action: an activity theory approach to workflow systems[END_REF] outcome.

Object is what characterises an activity and differentiates an activity from another [START_REF] Leontiev | Activity, consciousness and personality[END_REF].

The object has to be of high significance i.e. be self-sufficient.

• Action An action (or chains of actions) can be seen as the actual transcription of the activity. An action can be used by different activities in or-der to reach a goal. Thus, the goal of the action depends on the activity to which it is subordinated. The difference between objects (activity level) and goals (action level) is the significance.

Actions are performed consciously and with effort through operations.

• Operation An operation enables the actual realisation of the actions. Operations are automatized, that means performed without conscious thoughts or efforts. Operations are determined by the environmental and contextual conditions of the activity. Operations can be used by different actions.

Characterising Engaged-Behaviours with Activity Theory

We use Activity Theory as a hierarchical framework for conducting a comprehensive and structured analysis of the engaged-behaviours within the learning game. This structuring tool enables to deconstruct an engaged-behaviour in activity, chains of actions and chains of operations actually performed in the interactive learning game by the students. So, within each components of engagement, activities share the same motive but have a different object. For example, the activities "Expanding social network" and "Liven up the Group of Friends" share the same motive (Feeling emotions related to social interactions) which is generated by the relatedness basic need but have different objects (respectively Increasing the number of Friends and Maintaining a group activity within the Group). In this, we comply with Kaptelinin's call [START_REF] Kaptelinin | The object of activity: Making sense of the sense-maker[END_REF] by distinguishing between object and motive of the activity.

Let's consider an example (illustrated on Figure 1) extracted from the user study presented in section 4. In the social engagement component we identify the activity : Expanding social network whose motive is Feeling emotions related to social interactions and object is Increasing the number of Friends. In our All these actions are realised in the game through many operations actually performed with the input devices provided. These operations can consist in some mouse clicks or forms filling like for example typing the name of a town in the form for searching players or a button-pressed for launching a wave.

Detecting Engaged-Behaviours

In section 3.1 we determine some engagedbehaviours. In section 3.2 we characterise these engaged-behaviours by deconstructing them in activities, actions and operations. This section describes the last stage of our qualitative approach based on the Trace Theory. The objective here is twofold. First we detect within the recorded learner-generated data the chains of operations identified in the previous stage. Then, we reify the relationship between operations, actions and activities. The objective is to extract the identified activities from the raw learner-generated data.

Concepts of Trace Theory

The trace analysis approach is a framework for collecting, organizing and using user's activity traces (i.e. any player's actions performed towards the learning game) [START_REF] Clauzel | Enhancing synchronous collaboration by using interactive visualisation of modelled traces[END_REF]. At the lowest level, there is the observed elements (labelled obsels). Typically, an obsel corresponds to a player's raw action collected in the game (like a mouse click or a key pressed on the keyboard). An obsel contains a type of event, a timestamp and a set of contextual information useful for characterising the event and to derive meaning. A primary trace is a set of obsels temporally situated which may be connected. A primary trace may contain a very large number of obsels whose informational level may be too low. So, it may be difficult to derive knowledge from a primary trace.

The formalization proposed by [START_REF] Settouti | A trace-based system for technology-enhanced learning systems personalisation[END_REF] aims to facilitate the transition from primary traces to information that makes sense. This formalization uses a model of trace in order to organise and characterise the obsels within the trace. This model defines the types of the obsels and the types of the relation that compose the trace. It also considers a model of transformation which is a set of rules whose role is to transform a trace in a transformed trace of a higher level. A rule consists in temporal constraints or in operations on the contextual attributes performed between obsels. The transformed traces help to derive a more complex or abstract knowledge.

Detecting Engaged-Behaviours with Trace Theory

We use the Trace Theory for addressing the third issue: detecting the engaged-behaviours among all the actions recorded. We combine Activity Theory and Trace Theory by establishing the following correspondences between these two theories:

• operation ⇔ primary trace composed of obsels

• action ⇔ primary transformed trace

• activity ⇔ highest-level transformed trace

Trace Theory enables to detect the relevant operations among all the stored obsels and then to reify (through the transformation process) the relationship between operation, action and activity. The obsels which compose the highest-level trace correspond to the activities that belong to a specific engagedbehaviour.

Lets consider an example illustrated by the Figure 2. During a session we collect many obsels from several types (i.e. which correspond to specific events such as asking another player to become friend, launching a wave etc.). We define a model of primary trace in order to organise and characterise the obsels within the primary trace. Then we also define a transformation model. In the latter we create a rule labelled "Searching players by town" (see section 4.2.2 for an example of rule). This rule enables to detect in the primary trace, according to some temporal constraints, the presence of the pair of obsels "open the social search form" and "filling the players' town form". If they occurred within a certain time interval then the transformation rule generates a new obsel of higher-level (labelled "Searching players living in a specific town") in a new transformed trace. The transformation model has also a rule labelled "Proposing a challenge" and so on. A second transformation model loads the previously generated transformed trace in order to generate the highestlevel transformed trace. The latter may contain the obsel of highest-level that corresponds to the activity "Expanding social network".

USER STUDY

The objective of our study is twofold. We want to verify (1) the feasibility of the whole process (i.e. col-lecting the user-generated data and determining, characterising and detecting some engaged-behaviours) and ( 2) the validity of our approach i.e. do the behaviours we detect reflect an engaged-behaviour?

We apply our approach on an online game. This game enables to work on a base of 150 000 active players per month and so on 150 000 traces. Also, this game enables to analyse engaged-behaviours in lowconstraint interactive systems, directly, continuously and under ecological conditions and over a long time period. And finally the social and challenge dimensions are strong enough for providing a wide variety of engaged-behaviours. We first present the context of our user study. Then we detail the whole process for identifying some engaged-behaviours and present some results.

Context

For this research, we rely on the games developed by the company IntellySurf under the label YouRiding3 . We consider the YouRiding Bodyboarding games which take advantage of the Unity4 game engine. The Bodyboarding game consists in travelling from spot to spot all over the world in order to select the most effective waves for performing some maneuvers (like tube ride), completing a challenge or challenging other players. Players have to detect when their character reaches the best zone of the wave to perform a maneuver (lip or tube of the wave etc.) with the right key combination. Players must also adopt the right strategy for trying only maneuvers that their character's skills and equipment can perform. Players should also make the right choices concerning the improvements of character's skill and equipment.

Implementation of our approach

In this section we describe the implementation of our qualitative approach in the game used for the user study. We first show how we determine and characterise some engaged-behaviours. Then we explain how we detect these engaged-behaviours among all the interactions recorded.

Determination and Characterisation of Engaged-Behaviours

This is the theory-driven part of the process for identifying some engaged-behaviours. In this example we consider the engaged-behaviour Completing challenges. The latter belongs to the Action Engagement component presented in section 3.1.4. Then we characterise this engaged-behaviour through an Activity Theory perspective (see section 3.2.2). The table 2 details all the elements of this deconstruction. The analysis indicate that the activity Completing challenges is supported by the actions Obtaining information about the challenge -Improving the character's equipment -Improving the character's skills. A fourth action Paying for new credits is an option. Indeed, some players may buy some credits for improving their rider's equipment or skills. The action Obtaining information about the challenge is realized through the operations open profile improvements and open profile skills. These two operations indicate that the player has open the two pages that inform about the different challenges to complete.

Detection of Engaged-Behaviours from the Traces of Interaction

We collect 89 types obsels (i.e. raw user-generated data raw) such as fb link account (a player connect her/his facebook account whith her/his account in the game), challenge wait (a player propose a challenge) or goto spot (a player go to a specific spot identified with its id) etc. The collect uses a classic architecture with JavaScript and PHP scripts. The collect is automatically triggered when the player performs a targeted action (typically a click). The obsel is then sent to a server for being stored, session after session, in a MySQL database. We use the tool D3KODE5 [START_REF] Champalle | Assistance to trainers for the observation and analysis activities of operators trainees on nuclear power plant full-scope simulator[END_REF] for analysing the traces and defining the transformation rules (see section 3.3.1 on the Trace Theory). D3KODE provides the following features: loading the data as a primary trace, creating the models of transformation and the rules associated and a graphical visualization of the (primary and transformed) traces (see Figure 3 for an illustration). So, the data are exported from the MySQL database in a CVS (Comma-Separated Values) file that is compatible with D3KODE. Each line of this file contains two timestamps (the date and time of the beginning and end of the event, most of the time it's the same value), the name of the obsel and at most three attributes which may provide some contextual information such as the name of the button pressed, the identification number of the spot or of the equipment etc.

Then we load the CVS file in D3KODE in order to obtain a primary trace.

In order to visualise the action level we apply a transformation to the primary trace. This transformation is a set of rules that enables to aggregate several operations in order to generate a high-level obsel. A rule can rely on temporal constraints or on the contextual attributes. This high-level obsel corresponds to an action. For example the obsel open profile improvements and open profile skills are aggregated in order to generate the action Obtaining information about the challenge. The following rule enables to select the instances where the two pages Improvements and Skill have been opened during an interval of 2 minutes. The analysis of several traces of interaction indicates that players consult these two pages in this interval. We observe that the action Obtaining information about the challenge occurs many time for some players in their whole traces and never for other players. In a similar manner we create all the rules that enable to generate the four actions. And we iterate the transformation process for aggregating the actions and then generating the obsel of highest-level (i.e. the one that indicates the activity Completing challenges). In this case the temporal constraint may be larger as for example the action Improving the rider's equipment occurs less often than the action Obtaining information about the challenge .

Results

We collected twelve player's traces on the period from January to April 2012. These traces from engagedplayers have been isolated by experts. A trace contains 89 types of obsels and can be composed of several thousands of obsels. We are able to detect 20 actions such as Promoting the game towards facebook, Being interested in other players' information, Improving the equipment, Paying for new credits etc. We identify six activities from the four components of engagement such as Expanding social network, Animate facebook group of friends, Completing challenges etc. These results show the feasibility of the whole process. This process is illustrated in Figure 3.

We also observe that the players who play the most (the sessions of play are spread over the whole period of four months) show several activities from several components of engagement. On the contrary, players who stop playing after only several sessions of play (typically spread on only one month) show no activities. This confirms that the behaviours we detect reflect an engagement. Also it seems to indicate that the variety of the performed activities is a relevant information regarding the engagement.

Discussion

Compared with a quantitative approach that would have done some statistical measures on the number of waves surfed by the player, our qualitative approach go beyond this information by identifying in which chains of actions a wave has been surfed. Indeed, we know if a wave has been surfed in order to complete a challenge or for challenging another player in order to be introduced with her/him.

For this user study we worked on twelve traces for implementing the transformation rules. The rules can be used on other players' traces of interaction from the game studied in this paper. Also theses rules could be applied to other digital games. From the designer point of view, the adaptability to various game engines would be fairly simple as few lines in JavaScript are needed in order to trigger the sending of an event.

The activities and actions level, and the rules allowing to infer activities from actions are broadly shared by different types of games. For example, the action Challenging another player has the same meaning as soon as there is a confrontation between learners. But, the operations are determined by the design and interface of each game. Therefore, only the operations level and the rules allowing to infer actions from operations depend on the game. Also, the scalability (number of players, volume of collected data) comes to the Big Data issue. Thus, two points may be improved: the asynchronous transmission (but features of html5 such as WebSockets will facilitate it) and the storage (NoSQL databases are a promising solution).

We already have done some preliminary comparisons with a sequence-mining algorithm. In the traces we analysed, the four obsels goto map, goto zone, goto spot and play start on spot represent more than the half of the obsels that compose a player's trace. The results obtained with this algorithm mainly refer to sequences combining these operations. These are what we call trivial sequences are they are fully determined by the gameplay of the game. These results seem to confirm the validity and effectiveness of our approach compared to sequences mining methods. This may be particularly true when the variety of actions within the interactive system is wide.

CONCLUSION AND FUTURE WORK

Summary of the Contribution

We propose a theory-driven and qualitative approach for identifying engagement from users' traces of interaction. This approach enables to identify engagedbehaviours in low-constraint interactive systems, directly, continuously and under ecological conditions and over a long time period. For using qualitatively, rather than quantitatively, learners-generated data, we propose an approach in three stages: (1) determination of high-level engagedbehaviours, (2) deconstruction, from an Activity Theory perspective, of these engaged-behaviours in activities, actions and operations, (3) detection of the chains of actions among all the stored learnergenerated data based on Trace Theory in order to extract the engaged-behaviours.

We present the results of our user study on twelve traces of interactions in order to demonstrate the feasibility of the whole process and to validate the approach.

Implications

Besides the psychological, physical, cognitive, social and cultural learner's factor, the effectiveness of the learning game depends on two categories of factors: the immersive and interactive features and the content (consistency and authenticity, narrative process, aesthetics) [START_REF] Dondlinger | Educational Video Game Design: A Review of the Literature[END_REF]. Our approach provides a structured tool for analysing the actual use of the game. The results may be different from designers' intuitions. It may also inform about what players want, what is missing in the game (like a wrong balance between the components of engagement). Thus, designers may modify the game in order to improve the player's experience.

Moreover, the process of engagement [START_REF] Brien | What is user engagement ? a conceptual framework for defining user engagement with technology[END_REF] (point of engagement, engagement, disengagement, reengagement) enables to deal with the aspect of temporality, i.e. how engagement evolves over time. Thus, the identification of learner's engagement during several sessions may be a useful in-formation for teachers for maintaining engagement by adapting the content or the form of the learning activity. Also this information could be directly used by the interactive system for an automatic adaptation or for giving a feedback to learners.

We consider that engagement might be a concept easier to grasp and to identify than the user experience6 in learning game. Thus, engagement and our approach for identifying it might provide a relevant information for designers, practitioners and teachers for analysing, designing and validating the learning game but also for modifying and adapting it in order to maintain the effectiveness of the learning games.

Also, our approach is not limited to immersive games like the one used for our user study. Indeed, our approach can be applied as soon as learners have some choices to perform (and thus there is some chains of action to analyse). Finally, this approach is not limited to engaged-behaviours and can be applied for identifying any evolution of behaviour from any type.

Future Works

Our approach is currently carrying out a posteriori and manually. But by conducting a regression analysis on players' actions, it could be possible to detect and then to select the most relevant (i.e. the most discriminant) activities and actions. Thus, by reducing the complexity of the calculations, it may be possible to automatise the method in order to identify engagement in real-time (i.e. during the mediated activity). The next step would be the automatic adaptation of the system to elicit and to maintain the engagement.

We plan to conduct a concurrent triangulation mixed-method by comparing and contrasting our results with subjective method like questionnaire or interview applied to "real" players. We should also compare our qualitative results with quantitative statistical ones such as number and duration time of the playing session, payments done etc.

Figure 1 :

 1 Figure 1: Social engagement in conjunction with Activity Theory hierarchy within the user study presented in section 4.

Figure 2 :

 2 Figure 2: Our approach combines a theoretical work on engagement and engaged-behaviour, Activity Theory and Trace Theory for identifying engaged-behaviours from traces of interaction. An example extracts from the user study presented in section 4.

Figure 3 :

 3 Figure 3: Graphical visualisation in D3KODE of the tranformation process from the obsels to a high-level engaged-behaviour.

Table 1 :

 1 Categorisation of (non-exhaustive) players' behaviours through the four components of engagement and some associated activities.

		Environmental en-	Social engagement	Self engagement		Action engagement
		gagement
	Learner's	Contemplative	Collaboration		Managing an avatar Surpassing oneself
	behaviours	Curious	Competition				Enhancing skills
			Social relatedness
	Activities	Virtual trip	Expanding social	Customizing	the	Be a top scorer
		Trying to reach the	network		character		Completing chal-
		limit of the game	Liven up the group	Developing	a	lenges
		Discovering extra-	of real friends		story around the
		content	Enjoyment	with	character
			others

Table 2 :

 2 Deconstruction of the activity Completing challenges in actions and operations. The activity Completing challenges is supported by the chain of actions To obtain information about the challenge -To improve the equipment -To improve the rider skills. The action To obtain information about the challenge is detecting through the operations game open profile improvements and game open profile skills.

	Activity			Completing challenges
	Actions	Obtaining	information	Improving the charac-	Improving the charac-	Paying for new
		about the challenge	ter's equipment	ter's skills	credits
	Operations open profile improvements	open shop	open tricks	open bank
		open profile skills	shop buy item	open key config	process bank
				shop buy with cash
				open profile quiver
				item equip
				item repair

Suspension of disbelief is the willingness to accept, despite the technical or narrative shortcomings, a fictional work as being the reality.

In digital gaming, gameplay is a blanket term which refers to the structure, the dynamics or the interactive aspects of a game.

YouRiding: http://www.youriding.com

Unity -Game Engine: http://unity3d.com

Define, Discover, and Disseminate Knowledge from Observation to Develop Expertise

According to ISO 9241-210, user experience refers to "a person's perceptions and responses that result from the use or anticipated use of a product, system or service" Lyon. The QuEJAnT project is labelled by the french competitiveness cluster Imaginove.

ACKNOWLEDGEMENTS

This research is conducted within the QuEJAnT project which brings together the LIRIS Laboratory and the video games companies Corexpert, Intellysurf and Kiniro. Funding for this project was provided by a grant from la Région Rhône Alpes and le Grand