Formalising the twofold structure of a proactive system: Proof of concept on deterministic and probabilistic levels

Doctoral thesis (2014)

In the present work, we investigate the domain of Proactive computing, which may be characterised as a comparatively new computer science research paradigm. In our study, we understand and use the notion ... [more ▼]

In the present work, we investigate the domain of Proactive computing, which may be characterised as a comparatively new computer science research paradigm. In our study, we understand and use the notion of Proactive Computing as it was defined by Dr. David L. Tennenhouse. Conceptually, the architecture and main framework of our software system is entirely based on underlying concept characteristics of the given approach. Being a new research paradigm, proactive computing doesn’t possess yet a clear methodological support and approach repository, which may subsequently provide us with the necessary research techniques and methods. An application of the proactive computing approach thus requires a thorough empirical and theoretical investigation. The main objective of the current research project is to design an effective methodological framework, which will subsequently allow us to provide our proactive system with a scientifically rationalised proof of concept. An acquisition of the scientific evidence is primarily accomplished through the design, implementation and testing of all composite theoretical, methodological and functional aspects of the proactive system’s framework. The present study aims to emphasise the importance of designing a coherent multidisciplinary methodological framework, which is initially based on the expertise of two research fields, computer science and cognitive science domains. The main composite aspects of our methodological structure are based on the specific multidisciplinary techniques from both research fields. We investigate the proactive computing paradigm within the defining principles of deterministic and probabilistic approach orientations, where we additionally apply the techniques of the cognitive modelling approach. All designed and implemented computational mechanisms, together with the realised empirical studies, collectively aim to formalise the concept characteristics of the proactive system. Ultimately, an effective validation of the employed methodological frameworks and their underlying computational techniques provide our system with the necessary, scientifically rationalised proof of concept. [less ▲]

Design of Proactive Scenarios and Rules for Enhanced e-Learning

in Proceedings of the 4th International Conference on Computer Supported Education, Porto, Portugal 16-18 April, 2012 (2012)

We show in this position paper how we designed Proactive Scenarios for an automatic and enhanced management of the online assignments on MoodleTM for both student and teacher users, through their ... [more ▼]

We show in this position paper how we designed Proactive Scenarios for an automatic and enhanced management of the online assignments on MoodleTM for both student and teacher users, through their implementation with Proactive Rules to be run on top of our prototype Proactive Engine developed for this LMS. According to the diversity of issues that arise from the users activity on LMS, Proactive Scenarios are of two main types, which differ in their main goals, features and complexity. Meta Scenarios are devoted to capture major events of interest and to trigger off the dedicated Target Scenarios, which will undertake the appropriate actions. These Proactive Scenarios will thus take care of specifically predefined situations such as notifications, reminders, problem prevention, user guiding etc. In our opinion, LMS supplemented by such capabilities could provide a boosted effect on the students’ learning process as it takes an individual approach for each user and therefore could be characterized as a type of intelligent tutoring system. However, in order to sustain or modify the direction of our research activity, we now consider to undertake empirical studies on real-life online courses using the Enhanced E-learning Platform, which runs our Proactive Scenarios. [less ▲]