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This paper is divided into three main parts. In the first part of the study, we captured, collected and formatted an event log describing the handling of reviews for proceedings of an international conference in Thailand. In the second part, we used several process mining techniques in order to discover process models, social, organizational, and hierarchical structures from the proceeding’s event log. In the third part, we detected the deviations and bottlenecks of the peer review process by comparing the observed events (i.e., authentic dataset) with a pre-defined model (i.e., master map). Finally, we investigated the performance information as well as the total waiting time in order to improve the effectiveness and efficiency of the online submission and peer review system for the prospective conferences and seminars. Consequently, the main goals of the study were as follows: (1) to convert the collected event log into the appropriate format supported by process mining analysis tools, (2) to discover process models and to construct social networks based on the collected event log, and (3) to find deviations, discrepancies and bottlenecks between the collected event log and the master pre-defined model. The results showed that although each paper was initially sent to three different reviewers; it was not always possible to make a decision after the first round of reviewing; therefore, additional reviewers were invited. In total, all the accepted and rejected manuscripts were reviewed by an average of 3.9 and 3.2 expert reviewers, respectively. Moreover, obvious violations of the rules and regulations relating to careless or inappropriate peer review of a manuscript—committed by the editorial board and other staff—were identified. Nine blocks of activity in the authentic dataset were not completely compatible with the activities defined in the master model. Also, five of the activity traces were not correctly enabled, and seven activities were missed within the online submission system. On the other hand, dealing with the feedback (comments) received from the first and the third reviewers; the conference committee members and the organizers did not attend to those feedback/comments in a timely manner.

Interactive tabletops alone cannot automatically analyze and interpret students' digital footprints (event logs). Moreover, the final artifacts created by groups provide imperfect information about each individual's contribution to the group task. This research is divided into two main parts. In the first part of the study, a quantitative survey was conducted in order to identify the most significant indicators affecting the collaboration process in an online and networked context-aware multi-tabletop environment. In the second part of the study, several process mining techniques such as social network mining, basic performance analysis, role hierarchy mining, and dotted chart analysis were used with the purpose of increasing the instructor's awareness/knowledge about the collaborative dynamics in each group. The empirical findings showed that the levels of symmetry of actions (or similarity of tasks) and symmetry of roles (or low division of labor) were much higher in the high-performance groups compared with the low performance groups. Consequently, high performance groups showed increased tendencies to work on the same range of actions ‘together'. Quite the opposite, low performance groups showed increased tendencies to work on a dissimilar range of actions ‘individually'.

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