Mobile Learning: location, collaboration and scaffolding inquiry

Critiques of mobile learning pedagogy are concerned with whether such approaches are technology led. This chapter discusses how the particular features of mobile learning can be harnessed to provide new learning opportunities in relation to collaboration, inquiry and location-based learning. Technology supported inquiry learning is a situation rich with possibilities for collaboration. In particular, mobile learning offers new possibilities for scaffolding collaboration together with its other better-known features such as scaffolding the transfer between settings and making learning relevant by making use of the possibilities of location-based learning. These features are considered as part of mobile learning models, in particular mobile collaborative learning models

How to make classrooms creative and open spaces: ARIS games, digital artifacts and storytelling

As part of long-term research into interviewing users and visualizing digital artifacts, we have created a parallel archives of projects in our classroom. Ethnography helps us to discover the temporal trends of interactions with students and with the virtual environment. The outcomes expected motived us to repurpouse stories we co-create with students in a new form, retelling motivations, design, narratives, into a gaming scenario where the use of experiences become more digital and less tangible but always snapshots of their social existence.Peer Reviewe

Strata Managers and Educational Mishaps

In Australia, educational qualifications are a prescribed requirement for licensing within various occupations and professions, and each state and territory has varying degrees of educational aims and objectives. This research paper examines the minimum standards of education and knowledge, which are imposed as a pre-requisite for the licensing of a Strata Manager. The paper traces the historical progression which occurred during the last century to the current decade, and includes an assessment of societies changing needs of the role within the profession. In this regard, it is argued that the educational requirements during the mid 1990s to the early 2000s best served the needs of the consumer in comparison to these last 10 years. The discussion is complemented with data from New South Wales, mapping the educational knowledge fields and comparing this information to the duties and responsibilities of a Strata Manager

Education in Undergraduate Construction Management Degrees - Is it "Construction" or "Management" that is in bold type?

The proposition of this study is that the content of education in construction management degree programs has changed over time. Content has moved away from construction technology and has moved more towards generic areas of management. Here, issues arise such as prescriptive versus principle-base teaching and the degree to which experiential learning can be provided. This study explores quantifiable data to test the above proposition over an extended period of time for a selected University in Australia. The study looks at course handbook data for the construction management degree including the likes of assigned subject credit points and contact hours. From the analysis, debate and related sources of supporting information are used to extrapolate themes demonstrating the resultant changes in graduate profile arising from the analysis. Comment is also provided on the impact of such changes including the differentiation of university graduates in the past and present. The industry perspective is also canvassed in terms of how changes have affected their expectations relating to employment of University qualified graduates

Optimization Modeling and Machine Learning Techniques Towards Smarter Systems and Processes

The continued penetration of technology in our daily lives has led to the emergence of the concept of Internet-of-Things (IoT) systems and networks. An increasing number of enterprises and businesses are adopting IoT-based initiatives expecting that it will result in higher return on investment (ROI) [1]. However, adopting such technologies poses many challenges. One challenge is improving the performance and efficiency of such systems by properly allocating the available and scarce resources [2, 3]. A second challenge is making use of the massive amount of data generated to help make smarter and more informed decisions [4]. A third challenge is protecting such devices and systems given the surge in security breaches and attacks in recent times [5]. To that end, this thesis proposes the use of various optimization modeling and machine learning techniques in three different systems; namely wireless communication systems, learning management systems (LMSs), and computer network systems. In par- ticular, the first part of the thesis posits optimization modeling techniques to improve the aggregate throughput and power efficiency of a wireless communication network. On the other hand, the second part of the thesis proposes the use of unsupervised machine learning clustering techniques to be integrated into LMSs to identify unengaged students based on their engagement with material in an e-learning environment. Lastly, the third part of the thesis suggests the use of exploratory data analytics, unsupervised machine learning clustering, and supervised machine learning classification techniques to identify malicious/suspicious domain names in a computer network setting. The main contributions of this thesis can be divided into three broad parts. The first is developing optimal and heuristic scheduling algorithms that improve the performance of wireless systems in terms of throughput and power by combining wireless resource virtualization with device-to-device and machine-to-machine communications. The second is using unsupervised machine learning clustering and association algorithms to determine an appropriate engagement level model for blended e-learning environments and study the relationship between engagement and academic performance in such environments. The third is developing a supervised ensemble learning classifier to detect malicious/suspicious domain names that achieves high accuracy and precision

Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend