Approaches to the use of sensor data to improve classroom experience

quipping classrooms with inexpensive sensors can enable students and teachers with the opportunity to interact with the classroom in a smart way. In this paper an approach to acquiring contextual data from a classroom environment, using inexpensive sensors, is presented. We present our approach to formalising the usage data. Further we demonstrate how the data was used to model specific room usage situation as cases in a Case-based reasoning (CBR) system. The room usage data was than integrated in a room recommendations system, reasoning on the formalised usage data. We also detail on our on-going work to integrating the systems presented in this paper into our Smart University vision

LEAP: a precise lightweight framework for enterprise architecture.

This paper proposes LEAP: a simple framework for Enterprise Architecture (EA) that views an organization as an engine that executes in terms of hierarchically decomposed communicating components. The approach allows all aspects of the architecture to be precisely dened using standard modelling notations. Given that the approach is simple and precisely dened it can form the basis for a wide range of EA analysis techniques including simulation, compliance and consistency checking. The paper denes the LEAP framework and shows that it can be used to represent the key features of ArchiMate whilst containing fewer orthogonal concepts. We also show that the precision of LEAP, achieved through the use of OCL, can be used to verify both the claims made for inter-layer relationships in EA models and for extensions to ArchiMate

Use data mining to improve student retention in HE - a case study

Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention (National Audition Office, 2007). Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing technologies to monitor student, analyze student academic behaviour and provide a basis for efficient intervention strategies. Our aim is to identify potential problems as early as possible and to follow up with intervention options to enhance student retention. In this paper we discuss how data mining can help spot students ‘at risk’, evaluate the course or module suitability, and tailor the interventions to increase student retention

Automatic generation of data merging program codes

Data merging is an essential part of ETL (Extract-Transform-Load) processes to build a data warehouse system. To avoid rewheeling merging techniques, we propose a Data Merging Meta-model (DMM) and its transformation into executable program codes in the manner of model driven engineering. DMM allows defining relationships of different model entities and their merging types in conceptual level. Our formalized transformation described using ATL (ATLAS Transformation Language) enables automatic generation of PL/SQL packages to execute data merging in commercial ETL tools. With this approach data warehouse engineers can be relieved from the burden of repetitive complex script coding and the pain of maintaining consistency of design and implementation

An e-learning support toolkit for social work students on placement

Students of the social work professions generally are required to be placed in social work settings and to undergo assessment in the workplace during their training. These students are usually supported by qualified practice tutors who regularly meet with them and give feedback on their practice performance and progress. The support procedure sometimes is fragile and affects the quality of the students’ learning experience. Through a user centered design approach, the Remora project aims to provide an integration of mobile software toolkits and social software applications to support work-based learning and assessment for social workers. Two main applications are created and deployed on two categories of portable devices to help practice workers in their administration, information sharing and collection of documents linking with competency learning resources. The applications are extendible to be applicable to any work-based learning situation

Using data mining to improve student retention in HE: a case study.

Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention
 (National Audition Office, 2007).
Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing technologies to monitor student, analyze student academic behaviour and provide a basis for efficient intervention strategies. Our aim is to identify potential problems as early as possible and to follow up with intervention options to enhance student retention. In this paper we discuss how data mining can help spot students ‘at risk’, evaluate the course or module suitability, and tailor the interventions to increase student retention

Applied metamodelling to collaborative document authoring

This document describes a domain specific language tailored for collaborative document authoring processes. The language can support communication between content management systems and user interfaces in web collaborative applications. It allows dynamic rendering of user interfaces based on a collaboration model specified by end users. The construction of the language is supported by a metamodel. We demonstrate the use of the proposed language by implementation of a simple document authoring system

A portable document search engine to support off-line mobile learning

Some modern mobile devices have the capability to store thousands of documents and therefore have the potential to be used as powerful offline learning tools. The traditional graphical file browser does not scale well on physically constrained devices. In addition, documents obtained from random sources on the Internet may contain ambiguous names and therefore may be difficult to relocate on the device. This paper describes a solution to these problems using a traditional search engine approach. A powerful open source document search engine was ported to mobile architectures. Support for adding documents to the mobile device was developed using an Internet search enhanced to highlight domain specific results. The software was developed as part of the Remora project to provide mobile learning support for trainee social workers in the UK