Background: Learning Management Systems (LMS) represent one of the main technology to support learning in HE institutions. However, every educational institution differs in its experience with the usage of these systems. South East European University’s LMS experience is longer than a decade. From last year SEE – University is adopting Google Classroom (GC) as an LMS solution. Objectives: Identifying factors which encourage LMS activities, with special emphasis on SEEU, might be of crucial importance for Higher Education academic leaders as well as software developers who design tools related to fostering LMS. Methods/Approach: This paper introduces new approach of investigating the usage of LMS, i.e. identifying the determinants of increasing usage of LMS activities, by conducting empirical analysis for the case of SEEU. We apply appropriate estimation technique such as OLS methodology. Results: Using SEEU Usage Google Classroom Report & Analysis Data for spring semester (2016–2017) and winter semester (2017–2018) - SUGCR dataset 2017, we argue that (i) LMS activities are affected by demographic characteristics and (ii) the students’ LMS usage is affected by level and resources of instructors’ LMS usage. Conclusions: The empirical results show positive relationship between student and instructors’ LMS usage.

Semantic technologies have been extensively used for integrating stream data applications. However, using SWRL, which has become the de facto standard rule language in Semantic Web, has never been used in stream data applications. Its open world assumption and monotonic nature makes SWRL powerless for doing continuous inference over stream data. For example, using aggregate functions on a particular window of streams cannot be expressed in SWRL. C-SPARQL is a framework which supports continuous querying over data streams. We introduce here C-SWRL, a unified Semantic Web stream reasoning system that further supports continuous reasoning over stream data. C-SWRL utilizes C-SPARQL filtering and aggregation of RDF streams to enable closed-world and time-aware reasoning with SWRL rules. Moreover, the non-monotonic behavior is supported with the use of OWLAPI constructs. The system is presented by means of examples in water quality monitoring.

Stream data knowledge bases modelled with OWL are a proved natural approach. But, querying and reasoning over these knowledge bases is not supported with standard Semantic Web technologies like SPARQL and SWRL. Query processing systems enable querying, but to the best of our knowledge, Semantic Web rules are still unable to handle the required reasoning features for effective inference over stream data, i.e. non-monotonic, closed-world and time-aware reasoning. In absence of such a system, we showed in our previous work how Jess can be used for monitoring water quality, but by bringing input data manually. In this paper, we enable stream data support and thus a timely detection of faulty water quality statuses by also extending our ontology with the pollutants module. C-SPARQL abilities to filter and aggregate RDF streams are utilised to enable closed-world and time-aware reasoning with Jess rules. Moreover, Jess Tab functions are used to enable non-monotonic behaviour.