Abstract

Abstract-Exercise training (ET) is one of the decisive and crucial factors for reducing and preventing unexpected cardiac events. The aim of this work is to implement a proactive eHealth system that will allow patients, following a Cardiac Rehabilitation (CR) program outside the hospital, to exercise safely, according to their recommended training zones. The eHealth system includes a smartwatch application, a smartphone application and several server-side applications, working on predefined personalised scenarios, which are alerting, guiding and supporting the patients. The heart rate (HR) of patients is continuously measured, recorded and analysed during ET with the help of wearable devices. While training, patients benefit from multiple levels of feedback. Communities of patients are created for stimulating and motivating patients to perform according to their CR program. Opposed to traditional home-based CR eHealth applications, profiles and training zones are created and handled dynamically for each patient.

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.

No abstract

Abstract-Three studies, conducted on graduate level, show findings that students with highly proactive behavior are more likely to succeed in a Learning Management System (LMS) environment. Statistical analysis techniques were used in the studies. In the second study the concept of continuous proactivity was introduced to observe if a study-group was more likely to succeed in the lesson compared to a control-group, and in the third study, proactivity was applied to the entire class. This paper reports the results of the second and third studies and introduces the concept of "continuous proactive learning strategies" to frame the future development of proactive rules in LMS.