Implementation of Efficient Proactive Computing Using Lazy Evaluation in a Learning Management System

Abstract

In [1] we proposed a new kind of Learning Management Systems: proactive LMS, designed to help their users to better interact online by providing programmable, automatic and continuous analyses of users (inter)actions augmented with appropriate actions initiated by the LMS itself. The proactive part of our LMS is based on a dynamic rules-based system. But the main algorithm we proposed in order to implement the rules running system, suffers some efficiency problems. In this paper, we propose a new version of the main rules running algorithm that is based on lazy evaluation in order to avoid unnecessary and timecostly requests to the LMS database when a rule is not activated, that is: when its actions part will not be performed because preliminary checks failed.

Conflict Handling for Autonomic Systems

“…In this example several sensors relevant to capturing the vital parameters from a patient are analysed using Hidden Markov Models. The HMMs try to detect different types of diseases or conditions and save the results in a database, where they are periodically analysed by the proactive rules engine developed previously in our team [7]. The rule engine will check for different conflicts that may have happened.…”

Personalized, Adaptive and Intelligent Support for Online Assignments Based on Proactive Computing

“…This means that the interaction of our system with the LMS is done exclusively via its database, where the proactive engine checks for state changes which are relevant to the scenarios. The core of the proactive system is a rules-based engine, as described in [6]. The engine is responsible for the actual running of the proactive rules and their flow of control, while the rules encode the logic of the proactive scenarios.…”

“…LMS should tend to offer some personal, immediate and appropriate support like teachers do in classrooms. In [6] a new kind of LMS is proposed: proactive computing based LMS, designed to help their users to better interact online by providing programmable, automatic and continuous analyses of users' interactions, augmented with appropriate actions initiated by the LMS itself. Proactive systems, as defined in [5], adhere to two premises: working on behalf of, or pro, the user, and acting on their own initiative, without user's explicit command.…”

Evaluating the probabilistic side of proactive computing using cognitive modelling methodology and the statistical inference of user's cognitive states

“…The rules themselves are part of the proactive system, which is connected to a target system. The rules are processed by the Rules Engine, which is responsible for storing, executing and iterating the rules [19], [20]. Initially, several rules are launched at the start of proactive system with the objective to be continuously activated in order to search for the first instance of a data input.…”