The Development of a Collaborative Virtual Environment for 3D Reconstruction of Cuneiform Tablets

Ch'ng E., Woolley S.I., Munoz L.H., Lewis A., Gehlken E. and Collins T. (2014) The Development of a Collaborative Virtual Environment for 3D Reconstruction of Cuneiform Tablets, 20th International Conference on Virtual Systems and Multimedia, VSMM 2014, Hong Kong, 9-‐12 December 2014.

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The Development of a Collaborative Virtual Environment for 3D Reconstruction of

Cuneiform Tablets

Abstract—The efficient reconstruction of ancient cuneiform tablets has been a challenging problem over many decades, which if an approach is found to speed up the rate of potential fragment joins, will lead to a discovery of rich historical records of knowledge inscribed 5,000 years ago. This paper describes The Leverhulme Trust funded development of a crowd-sourcing collaborative virtual environment via the identification of cooperative personalities. We conducted experiments in which 12 groups of 3 participants collaborated to reconstruct fragments in a laboratory 3D puzzle virtual environment. Personality types were analysed together with NASA TLX workloads and task performance. We discovered that patterns exist for each of the virtual interaction modalities, demonstrating a potential for profiling systems to group personality types for maximising the effective reconstruction of cuneiform fragments.

Keywords—Virtual Environment; 3D Reconstruction, Cuneiform Tablets; Virtual Collaboration

I. INTRODUCTION For many decades, the efficient reconstruction of ancient

cuneiform tablets has eluded the cuneiform research community. Cuneiform is mankind’s first information technology, the first ever script that made recording information possible 5,000 years ago. Sumerians, Babylonians and Assyrians used clay tablets for recording and communicating information for thousands of years across the entirety of the Ancient Middle East. Unlike the chemical, optical or magnetic mediums that we use today, clay, the medium in which cuneiform was inscribed on is one of the most durable to-date with thousands of years of recorded information on their surfaces. However, cuneiform tablets are susceptible to degradation from a number of sources, including mineral salts. Preservation is sometimes more a product of the environment than by the resilience of the medium. Furthermore, large un-catalogued collections of broken clay

tablets from different collections and periods has meant that the information could not be easily accessed which includes knowledge of society, culture, politics, science, history, accounting and even jokes of the time.

The reconstruction of cuneiform tablets could be achieved in a number of ways. However, apart from pockets of research delving into imaging and storage databases, there appears to be a lack of progress in the area. In our research, we attempted to solve the problem using two different approaches: 1) a computer assisted crowd-source approach, and 2) an automated approach which made use of algorithms that reduce the search space as well as minimise the required level of human intervention by reducing the probability of generating false-positive matches (details are in two separate publications [1] and [2]).

This article focuses on the study of cooperative behavior in potential crowd-sourcing work, which will lead to a personality profiling system that strategically matches participants for crowd-sourcing the cuneiform tablet reconstruction process. The success of the project has direct relevance to the reconstruction of all types of ancient fragments, from bones to pottery using crowd-sourcing technology.

II. BACKGROUND The reconstruction of broken ancient cuneiform tablets

scattered across large geographical locations has been a persistent problem to-date. Methodical approaches in development since the first systematic cataloguing of cuneiform fragments have not significantly increased the speed of the reconstruction process. The reconstruction of cuneiform tablets from fragment joins is important, mainly because the information locked within these ancient scripts will lead to important knowledge from the Ancient Middle East. Present methods in practise in almost all archives and museums

Eugene Ch’ng School of Computer Science

International Doctoral Innovation Centre University of Nottingham Ningbo China

199 Taikang East Road, Zhejiang Ningbo, 315100 China Centre for Creative Content and Digital Innovation,

University of Malaya, 50603 Kuala Lumpur, Malaysia [email protected]

Sandra I. Woolley, Luis Hernandez-Munoz, Tim Collins Electronic, Electrical and Systems Engineering, University

of Birmingham, Edgbaston, Birmingham, B15 2TT, UK [email protected] [email protected] [email protected]

Andrew Lewis

Digital Humanities Hub, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

[email protected]

Erlend Gehlken Institut für Archäeologische Wissenschaften

Goethe-Universität, Grüneburgplatz 1 60629 Frankfurt a. M., Germany [email protected]


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housing the collections involved browsing through paper catalogues of photographic or lithographic representations in order to match fragments from a page with fragments from other pages or catalogues. One can immediately see the difficulty and inefficiency of such a method, on top of the fact that if a pictographic match were found, the curator of the museum housing the fragments would have to be notified before a manual reconstruction takes place.

The channels of communication between scholars (and between institutions) are subject to the same inefficiencies of geographical dispersion, specialisation, and linguistic incompatibility that beset all international projects. Given the large number of fragments, and these unavoidable impediments to the reconstruction process, it is not surprising that there are still thousands of fragments in need of further analysis.

Even where fragments with a higher probability of candidate joins – those from the same collection for example, i.e., tablets of a single period and in the same geographical region, such as the 2,000 unpublished pieces housed in the Heidelberg collection, a calculated time for completing the manual task would take a single expert close to 20 years to complete (see [3], p.45). Whilst good efforts to digitise archives were attempted [4]–[7], which made access easier for international audiences, related work that attempts to create an environment physical or virtual for reconstructing cuneiform fragments were not found. However, our work in analysing the digital archives consisting of over 8,000 complete tablets revealed certain trends in the sizes and ratios of tablets from different periods [8]. This is a step forward in the reconstruction process. The discovery of information using digital means can be useful for the filtering process when automating reconstructions.

This Leverhulme Trust funded project seeks new digital approaches for speeding up the reconstruction of cuneiform fragments. One of the ways in which reconstruction could be made more efficient in terms of speed is to create a virtual environment from which cuneiform scholars and interested groups can work together cooperatively. Apart from challenges in human-computer interaction particularly in 3D manipulation, a difficult barrier remains. Decades of research in the field have prompted the development of faster reconstruction processes, and a deeper look at the problem suggests that cooperation is lacking. One plausible reason for the lack of cooperation could be self-interest. Sets of new

fragment joins imply new discovery, resulting in elevated position in publications in the cuneiform community, which, including students, consists of over 600 scholars who are capable of reading cuneiform scripts. In order to build such an environment, the understanding of cooperation and coordination is necessary. In this research, we conducted cooperation experiments with 36 participants grouped into 12 sets of 3 individuals each. This provided an opportunity to investigate how different personality profiles collaborate in groups when presented with 3D digital fragments in a laboratory virtual environment. We analysed personality types using a questionnaire constructed with 163 items of the International Personality Item Pool (IPIP) [14],[15], containing validated questions equivalent to Cattels’ “16 Personality Test” [9], [10]. We used a questionnaire to investigate the types of information that participants found most useful in the reconstructions including the grouping and orientation of the fragments and the levels of cooperation. We also calculated NASA TLX workloads [16]. Finally, we designed a laboratory collaborative environment that will eventually allow us to crowd-source efforts from people who could be grouped according to profile and/or personality types, i.e., from groups who might collaborate together more successfully.

The creation of an online user interface and collaborative virtual environment is another challenge, but the state of technological development has prepared a pool of resources from which to integrate suitable technology. Inexpensive laser scanning systems that are a fraction of the cost of a full scanner with a sufficiently high-resolution for this type of work can be built as highlighted in [3], p.55-57. The custom-built system integrates SLR cameras with the DAVID laser scanner software [11] for scanning cuneiform clay replicas that we reproduced and baked for our experiments. In addition, real cuneiform fragments have been scanned at the Heidelberg University collection. The user interface was evolved via participatory design exercises performed using the PICTIVE approach (Plastic Interface for Collaborative Technology Initiative through Video Exploration) [12] in a focus group that involved a cuneiform scholar (an Assyriologist), user interface designers and expert programmers to create a paper mock-up prototype. The web-based collaborative virtual environment was implemented using client-side and server-side JavaScript technology, integrating a combination of scalable non-blocking I/O asynchronous Node.JS, socket.io, express.js, Jade and web 3D libraries such as three.js.

Fig. 1. Overview of the process needed for developing a collaborative virtual environment for crowd-sourcing cuneiform fragment reconstruction


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An overview of the development of the collaborative virtual environment can be seen in Fig. 1. The majority of the time was focused on acquiring information from experiments for the design. In 1.1, fragment replicas were laser scanned and processed before importing the 3D models into the Vizard environment (1.2) where our experimental studies (section IV) and the results presented here (section V) were based. The two modes of cooperation between participants – Virtual Synchronous and Virtual Asynchronous were experimented with using the Vizard ‘laboratory’ virtual environment.

Observations of how users interacted with the 3D objects via the Vizard environment gave us an idea of what features to include. The information gained was combined together with outcomes from PICTIVE participatory design (1.3). Data recorded from the experiments (1.3 and 1.4) were processed and analysed for making informed decisions for designing the virtual environment (1.5) and the Personality Profiling Information System (PPIS) (1.6) that automatically groups participants into the most efficient personality combinations. At the time of writing 1.6 is being designed and will not be reported here.

III. THE COLLABORATIVE VIRTUAL ENVIRONMENT The design of the collaborative virtual environment

involves a number of phases. In the observation of how participants interacted with the ‘laboratory’ virtual environment in Worldviz’s Vizard IDE, we noted a number of important features that will be necessary in crowd-sourcing work. We conducted participatory design exercises using the PICTIVE (Plastic Interface for Collaborative Technology Initiative through Video Exploration) paper mock-up approach [12] and implemented the user interface for the web-based collaborative virtual environment. The collaborative environment is implemented using both client-side and server-side JavaScript technology as we are expecting to recruit hundreds of participants (there are over 600 cuneiform scholars besides interest groups). The gamification for crowd-sourcing fragment reconstruction is also part of our future plan.

The volume of data is expected to be large and therefore, building a stable platform for future expansion is important. As such, we will need a scalable network layer that is event-driven, with a non-blocking I/O model that is data-intensive and real-time with the ability to push and pull data from

Fig. 2. Correlations between gender, age and other factors. Axes scales: Gender (Male: 1, Female: 0) i.e., groups with one male participant had a gender value of 0.33, two

male participants 0.66 and three male participants a gender value of 1. Reasoning and Abstractedness from Cattel Personality Factors (0: Min – 50: Max). Workload from NASA TL:X (0: Min – 100: Max). Self-reported Performance (0: Best – 100: Worst. As per NASA TLX). Undone (0% - 100%).

Fig. 3. Performance correlations between different personality and cooperation factors. Personality types were gathered using a questionnaire constructed

with 163 items of the International Personality Item Pool (IPIP) containing validated questions equivalent to Cattels’ “16 Personality Test”. Workloads were calculated with the NASA TLX. “Learned from Annotations” refers to user’s self-assessment of how much they learned from the annotations left by others, and “Left Traces” refers to how much information (in the way they grouped and oriented fragments )participants felt they left for others. Axes scales: -Temporal demand and Effort from NASA TLX (0: Min – 100: Max). Reasoning and Openness to Change from Cattel Personality Factors (0: Min – 50: Max). Learned from Annotations and Left Traces (1: Very little – 5: Very much). Correct and Incorrect (0% - 100%).


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multiple platforms and devices on the fly. Node.js, a server side technology built on Google’s V8 JavaScript runtime, an open source JavaScript engine developed for Google Chrome’s web browser is a suitable candidate. User interface supporting modules such as Kinetic, express.js and jade were used on the client side. The 3D aspect of the virtual environment uses Three.js, a web 3D library that sits on top of HTML. Socket.io acts as an information transfer portal between the server and clients. Metadata is currently stored within an SQL database field, linking to multi-material OBJ files. These will eventually be stored within MongoDB, an open source freeform document database that scales horizontally. The virtual environment can eventually be deployed in the Cloud using services such as Amazon AWS EC2 (Amazon Elastic Compute Cloud).

IV. METHODS FOR DISCOVERING COOPERATIVE PERSONALITIES Cooperation is seen to be a major hurdle in the traditional

system of fragment reconstruction. In building a collaborative virtual environment for crowd-sourcing such tasks, our objective was to investigate personalities and behaviours that will contribute and cooperate well within small groups in specific tasks. We constructed a functional but intentionally simple virtual environment using Worldviz’s Vizard IDE and populated the virtual environment with 3D laser scans of laboratory-fabricated cuneiform fragments. A total of 36 participants forming 12 groups of 3 individuals consented to their participation and received £15 Amazon vouchers. Two modes of virtual environment cooperation were observed:

Virtual Asynchronous (VA) – Group participants worked alone in sequence collectively carrying out the fragment reconstruction one after another, building on the last state of work of the previous participant. 90 seconds were given for each participant session cycle, until the group could no longer proceed with the reconstruction or when the fragments had all been completely reconstructed. Annotation on post-its notes were the mode of communication. Communication was also in

the form of the position, orientation and grouping of the fragments on the virtual display.

Virtual Synchronous (VS) – participants worked synchronously on the fragment reconstruction, collectively building on the work.

The two modes of cooperation were designed to simulate our future crowd-sourcing virtual environment where participants could either work together simultaneously within a small group in a session, or due to time differences work on a collection of tablets separately but sequentially. Initially, each group was trained for several minutes on the use of the virtual environment. They used a training fragment set (comprising fragments different from those used in the test tasks) and were trained on how to drag, drop and rotate fragments, how to change the perspective of the screen and how to zoom-in and zoom-out.

TABLE I. ORDER OF THE RECONSTRUCTIONS

Task 1 Task 2 Virtual Asynchronous Groups 1, 3, 5, 7, 9, 11

Fragments F1

Virtual Synchronous Groups 1, 3, 5, 7, 9, 11

Fragments F3 Virtual Synchronous

Groups 2, 4, 6, 8, 10, 12 Fragments F1

Virtual Asynchronous Groups 2, 4, 6, 8, 10, 12

Fragments F3

The groups carried out the Virtual Asynchronous and the Virtual Synchronous tasks in a counterbalanced order as illustrated in table 1. They used two virtual set of fragments of similar complexity: Fragments F1 (F1-1 to F1-6) and fragments F3 (F3-1 to F3-6). Each set of fragments were supplemented with an orphan fragment from another tablet, i.e., a fragment that did not belong to the fitting tablet pieces. The orphan fragment increased the difficulty of the reconstruction in the experimental tasks. The participants were informed that the fragments for each task would make complete or partially

Fig. 4. Correlations between workload and personality and cooperation factors. Axes scales: Frustration, Effort and Workload from NASA TLX (0: Min – 100: Max). Self-reported Performance (0: Best – 100: Worst. As per NASA TLX). Reasoning and Openness to Change from Cattel Personality Factors (0: Min – 50: Max). Orient. of Frag, as Clue: Fragment orientation as a clue for other participants (1:Low Information – 5: High Information). Group. of Frag, as Clue: Fragment grouping as a clue for other participants (1:Low Information – 5: High Information). Left Traces: Participant assessment of traces left (in the way they grouped and oriented fragments) (1: Very little – 5: Very much). Difficulty in Cooperation: Participant assessment of difficulty of task cooperation (1: Very easy – 5: Very difficult). Contribution to Success: Participant assessment of their contribution to the task (1: Contr. very little – 5: Contr. very much). Collaborating: Participant assessment of their collaboration during the task (1: Very little – 5: Very much).


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complete tablets and that there could be one or more pieces that would not fit at all.

A. Measurement Approaches Here we comment on data processing and measurement

approaches that were particular to this research. A scoring system was designed to gauge how well our participants did in the reconstruction based on correct (the percentage of reconstruction where fragments had proper joins), incorrect (the percentage of reconstruction where fragments were incorrectly joined) and undone (the percentage of reconstruction where fragments were neither correct nor incorrect, e.g., fragments that were remotely positioned or ambiguous in the assembly). The scores were assessed independently by 3 researchers, groups were then assigned the average of the 3 scores. There was good consistency between the assessing researchers.

The measure for gender is simple. We scored the number of males as incremental counts of 1 and females as 0 before summing and averaging the score (i.e., groups with one male participant had a gender value of 0.33, two male participants 0.66 and three male participants implied a gender value of 1).

In the collection of participant scores, the personality profiles, NASA TLX workload and cooperation measures for each individual were collected and scores assigned to individuals based on their group’s performance in the VS and VA tasks.

V. OBSERVATIONS AND RESULTS In the statistical analysis of our experiments we observed

that the Virtual Asynchronous (VA) mode of cooperation yielded more interesting insights than the Virtual Synchronous mode. The effects of VA was much more distinct than in VS and there could be a number of reasons as to why this is the case:

Group Presence: In VA mode, users worked cooperatively by continuing each other’s work but were not in the presence of the other participants and therefore the distinction between various factors were much more prominent.

Indirect Cooperation: In VA mode, participants were aware that they were building on each other’s work and would look for traces left by other participants; in this mode there is no direct mode of communication.

Gender Balance: In VA mode, gender balance did not have any effect as compared to VS where the presence of different ratio of gender would induce different cooperation strategies (see table 2 and 3).

Our observation is that correct reconstructions in VA and VS demanded different behaviours and personalities because the two modes of cooperation produced different results within the groups. Only group 1 was notably successful and only group 2 was notably unsuccessful in both tasks.

Since the best and the worst reconstruction performances were counterbalanced between the groups, in average, there were not significant differences between VA and VS (p>0.05).

TABLE II. VIRTUAL ASYNCHRONOUS BEST AND WORST GROUPS

VA Best groups

Group Correct (%)

Incorrect (%)

Undone (%) Gender ratio Age

(min;Max) 5 100 0 0 0.66 (2M/1F) 20.7 (18;20) 8 90 10 0 1.00 (3M/0F) 25.3 (18;37) 1 78.3 11.6 10 0.66 (2M/1F) 31.7 (26;42)

11 70 10 20 0.33 (1M/2F) 30.3 (24;38) 12 60 0 40 0.33 (1M/2F) 29.7 (26;35)

VA worst groups

Group Correct (%)

Incorrect (%) Undone Gender ratio Age

(min;Max) 2 0 0 100 0.33 (1M/2F) 28.7 (23;38) 3 0 0 100 0.66 (2M/1F) 34.7 (29;41) 4 0 0 100 0 (0M/3F) 24.3 (22;27) 7 0 0 100 1.00 (3M/0F) 23.3 (22;24) 9 0 0 100 0.66 (2M/1F) 22 (20;25)

10 0 0 100 0.33 (1M/2F) 28 (20;44)

A. Age and Gender Age and gender were observed to have different effects in

the two modes of cooperation (Fig. 2). Gender appears to correlate with reasoning and abstractedness (Fig. 2A and 2B), these are qualities which were hypothesised to work well with the methodological capabilities needed in 3D puzzles.

More male participants correlated with fewer undone fragments (Fig. 2E), however, the most successful groups, as shown it Tables II and III, were mixed gender. In VA, the effect of age is much more distinct than in VS. For example, younger VA groups reported less workload (Fig. 2C) and better self-reported performance (Fig. 2D).

TABLE III. VIRTUAL SYNCHRONOUS BEST AND WORST GROUPS

VS Best groups

Group Correct (%)

Incorrect (%)


(min;Max) 1 100 0 0 0.66 (2M/1F) 31.7 (26;42) 3 95 0 5 0.66 (2M/1F) 34.7 (29;41) 9 65 0 35 0.66 (2M/1F) 22 (20;25)

10 65 0 35 0.33 (1M/2F) 28 (20;44) 7 60 0 40 1.00 (3M/0F) 23.3 (22;24)

VS Worst groups

Group Correct (%)

Incorrect (%)


(min;Max) 2 0 10 90 0.33 (1M/2F) 28.7 (23;38) 6 0 0 100 0.66 (2M/1F) 26.3 (19;35)

12 0 0 100 0.33 (1M/2F) 29.7 (26;35)

B. Performance based on Personality and Collaboration We measured performance as the percentage of correct,

incorrect and undone fragments that were used in the virtual environment at the end of each session as depicted in tables II and III. Fig. 3 illustrates the correlation between our measures of performance and other factors.

In the VS mode, the number of undone fragments positively correlates with temporal demand (Fig. 3A) and the number of correct fragments also negatively correlates with effort (Fig. 3F), showing that the struggle that participants experienced correlates with how complete the reconstructions were.


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In the VA mode, the lower the average levels of reasoning and openness to change of the groups, the more undone the reconstruction were (Fig. 3B and 3C). As might be anticipated, the less they collaborated and communicated, the more the fragments were in a state of undone. Collaboration is highly important, but for example, the graphs indicated that the less participants perceived that they have learned from the annotations (Fig. 3D), the more undone the fragments were, and the less that they deliberately left traces to their teammates the more undone the fragments were (Fig. 3E). Reasoning and openness to change positively affect the correctness of joins (Fig. 3G and 3H). Lastly, in VA learning from annotations (i.e., from notes indicated in 'post-its') and leaving traces to their teammates (i.e., in the way they grouped and oriented the fragments) increased the correctness of the joins (Fig. 3I and Fig. 3J).

C. Workload and Collaboration In the VS mode measuring workload and other factors

(Fig. 4), the lower the workload, the lower the frustration levels reported by the groups (Fig. 4A), participants also perceived their performance as resulting from the level of increased workload that they experienced, in other words, lower self-reported performance meant better perceived reconstructions results (as per NASA TLX definition), which in turn were correlated with low levels of workload (Fig. 4C). This was confirmed by the positive correlation between the amount of effort invested in collaborating with others and their perceived contribution to success to the collaboration. For example, clues received from the orientation and grouping of fragments were lesser when the workload was higher (Fig. 4D and 4E). This could mean that the information acquired from the orientation and grouping of fragments were not useful for

some groups, which resulted in a higher workload necessary to complete the task.

In terms of workload for VA, the more traces left to teammates in the way they grouped, ordered and oriented the fragments, the lesser the workload experienced by the participants (Fig. 4F). And the lesser the workload, the lesser the difficulty in the cooperation (Fig. 4G). Such improved collaboration correlated positively with the perception that participants contributed to success (Fig. 4H). In short, challenging tasks such as VA required good cooperation among individuals to produce positive reconstructions.

D. Personality In the relationships between personality and other factors

in the VS mode (Fig. 5A-C), openness to change is a positive personality trait since it affects how participants were receptive to clues from communicated gestures (Fig. 5A), and how much participants were willing to collaborate in the given task (Fig. 5B). Openness to change is a positive trait, the higher the level of the trait, the lesser the users found cooperation to be difficult in the collaborative task (Fig. 5C).

In VA (Fig. 5D-I), having a higher level of reasoning positively affects the participants’ determination to complete the task (Fig. 5D). A higher liveliness personality trait in the group positively promoted the ability of participants to collaborate with their teammates (Fig. 5E). The trait also affected how determined individuals were in completing the given task (Fig. 5F).

In VA, groups communicating with a higher number of annotations had higher levels of reasoning, openness to change and social boldness (Fig. 5G-I), which in turn could have

Fig. 5. Correlations between personality traits, cooperation information and other factors. Axes scales: Openness to Change, Reasoning, Liveliness, Social Boldness

from Cattel Personality Factors (0: Min – 50: Max). Clue from Gestures: Participant assessment of the gestures of others as clues for the reconstruction task (1:Low Information – 5: High Information). Difficulty in Cooperation: Participant assessment of difficulty of task cooperation (1: Very easy – 5: Very difficult). Contribution to Success: Participant assessment of their contribution to the task (1: Very little – 5: Very much). Collaborating: Participant assessment of their collaboration during the task (1: Very little – 5: Very much). Determination to Complete Task: Participant assessment of their determination (1: Very little– 5: Very much). # Annotations: Number of annotations made by participants in the task (0: Min – 3: Max).


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produced more correct and less undone reconstructions (Fig. 3B, 3C, 3D, 3G, 3H, 3I).

Again in VA, calculating a simple diversity of personalities metric for each group (by summing max-min(personality factors)), the top 3 performing groups were observed to be in the top 5 groups for this measure of diversity. The personality factors that made the largest contributions to this diversity in the top 3 performing groups were markedly different for each group with the exception of social boldness which all 3 successful groups had in common (i.e., all 3 successful groups had a large spread in social boldness). The 2 other groups (the non-successful groups) in the top 5 diverse groups did not have a large contribution from diversity in social boldness. But they did have in common a large variation in vigilance that was not common with the successfully diverse groups. More research would be needed to determine best group compositions, but these results do suggest that diverse groups work productively and that some combinations of personality diversity may be more successful than others.

E. Position, Orientation and Grouping of Fragments In VS, the information obtained by users in the orientation

of the fragments correlates with the information gained from the grouping of the correct fragments affecting the completion of the joins (Fig. 6). Gain from positional information of the fragments also positively correlates with information gain from grouping of the fragments (Fig. 6C). Information gained from the position and orientation of the pieces correlates (Fig. 6B). Such information produced by participants and recorded within the virtual environment will become a useful means to maximise information cues in our collaborative virtual environment. It will also assist in automatically determining the final stage of a collaborative task.

Fig. 6. Correlations between fragments in the virtual synchronous task. Axes scales: Grouping, Orientation and Position: Participant assessment as a clue for the reconstruction task (1:Low Information – 5: High Information).

F. Summary Results The data gathered from the experiments have provided

some insights into how we can maximise group work in the collaborative environment. Here we propose initial data for the Personality Profiling Information System, which will be used to group users in the two modes (VS and VA) of the virtual collaborative environment for future experiments. We first look at a similar contributing factor for both VS and VA before proceeding to the individual modes of interaction.

Reasoning and abstractedness are two qualities that work well with the methodological capabilities needed in 3D

puzzles. This is affected by the ratio of gender on reasoning and abstractedness.

1) Virtual Synchronous Mode Reduce workload in the VS mode: Workload seems to

have a greater effect on the participants. There is the need to reduce temporal demand and effort in order to decrease the amount of undone fragments. In our observations and interviews of the participants, it seems that the user interface is a contributing factor in greater temporal demand and effort in this mode. A reduction in workload reduces frustration, which will effects motivation. This pattern is reinforced by the negative correlation between workload and perceived contribution to success. Clues received from the orientation and grouping of the pieces were lesser when the workload is higher, this suggests that the lesser the information gained from the cues, the higher the workload.

Maximise the profiles for openness to change: Openness to change increases the receptiveness of participants to communicated gestures. The personality trait also increases the willingness of cooperation between participants. The trait within personality profiles promotes collaboration.

Maximise cues from grouping, positional and rotational information: Participants received more information between orientation and grouping, positioning and grouping, and positioning and orientation of the fragments. The decrease in the entropy of fragments all contributed to the communication of information and therefore increases performance.

Reduce the risk of group task abandonment: Data showed that for VS, the incorrect score for the groups is most of the time 0. Moreover, the correct score correlated negatively with the undone score (better reconstructions implied less undone fragments). This may imply that groups produce either correct reconstructions or undone reconstructions. This demonstrated that groups preferred to do a good reconstruction or abandon the task altogether, perhaps because they moderated each other to avoid incorrect results.

2) Virtual Asynchronous Mode Balance the ratio of genders: The ratio of gender appears

to affect performance with mixed gender groups being more successful. This however, will need to be thoroughly tested with more experiments involving a larger population sample.

Manage age groups: The data reported that younger groups have lesser workloads and better self-reported performance. As the age range stands between 22 and 40, the conjecture will need to be tested in a larger range of age groups.

Maximise profiles for reasoning, openness to change, liveliness and social boldness: There are less undone fragments for groups with higher scores for reasoning and openness to change. Reasoning and liveliness boosts participant motivation for completing the task. Liveliness also promotes the ability to collaborate with teammates. Finally, reasoning, openness to change and social boldness contributed to a higher number of annotations, which increase


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performance – more correct fragments and less undone fragments.

Maximise cooperation: Participants who cooperated by leaving traces (group, ordered and structured orientation of fragments) for other participants reported that they contributed to the success of the group. This is reinforced by the fact that those who cooperated by communicating information (leaving traces) are far better at completing the task with good performance. Conversely, the data also showed that those who learned from annotations are far better at completing the tablets.

VI. CONCLUSION This paper gives an overview of the Leverhulme Trust

funded project which highlights important development and discoveries during the research that brings forward prospective digital heritage technology that could significantly increase the effectiveness of the process of fragment reconstruction. The experimental results reported here presented exciting opportunities for crowd-sourcing cuneiform reconstruction work, and supporting a stronger collaboration for improved performance. Crowd-sourcing is a very popular way of distributed problem solving. A popular example of crowd-sourcing is FoldIt, which gamifies protein structure prediction. As Cooper et. al. [13] puts it “The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.”

Although the evaluation of our test-bed virtual environment reconstruction application was limited to 36 participants in 12 groups, we are progressing in the design and evaluation of our collaborative virtual environment in the cloud, which is expected to be crowd-sourced by a significantly higher number of users supported by a personality profiling system. Future work builds upon the research reported here.

ACKNOWLEDGMENT The authors gratefully acknowledge the support of The

Leverhulme Trust research grant (F000 94 BP), and the multidisciplinary support of the University of Birmingham’s Digital Humanities Hub.

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The Development of a Collaborative Virtual Environment for 3D Reconstruction of Cuneiform Tablets

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