Adaptive learning

Abstract: Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used now in several application areas where the hyperspace is reasonably large and where a hypermedia application is expected to be used by individuals with different goals, knowledge and backgrounds. This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set of identified methods and techniques of AH. It introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.

Abstract: Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.

Abstract: The interactions between levels of learner prior knowledge and effectiveness of different instructional techniques and procedures have been intensively investigated within a cognitive load framework since mid-90s. This line of research has become known as the expertise reversal effect. Apart from their cognitive load theory-based prediction and explanation, patterns of empirical findings on the effect fit well those in studies of Aptitude Treatment Interactions (ATI) that were originally initiated in mid-60s. This paper reviews recent empirical findings associated with the expertise reversal effect, their interpretation within cognitive load theory, relations to ATI studies, implications for the design of learner-tailored instructional systems, and some recent experimental attempts of implementing these findings into realistic adaptive learning environments.

Abstract: This review of literature and research into the effectiveness of distance education systems deals with a number of factors which affect their success or failure. These include the influence of distance learning theory upon instructional design and delivery, redefining the roles of partners in distance education teams, media selection, technology adoption, change implementation, methods and strategies to increase interactivity, inquiry, and active learning, learner characteristics and modes of learning, teacher mediation and learner support, operational issues, policy and management issues, and cost/ benefit tradeoffs. It is intended as a companion piece to Sherry and Morse’s (1994) training needs assessment.