Developing a Technoself System to Improve Lifelong Learning Engagement

Lifelong learning exists today in the context of a cultural and societal shift to a knowledge-based, technology-enhanced, and rapidly-changing economy. It has a significant impact on people’s lives and has become of vital importance with the emergence of new technologies that change how people communicate, collect information, and collaborate with others. The emerging technologies, such as social networking, interactive media and game technology, have expanded a new dimension of self – ‘technoself’ driven by socio-technical innovations and taken an important step forward in lifelong learning through the Technology Enhanced Learning (TEL). The TEL encourages learners as producers to embed personalized knowledge and collective experience on individualized learning within professional practice. It becomes more personal and social than traditional lifelong learning, especially about the ‘learning as socially grounded’ aspects. This paper studies the development of technoself system during lifelong learning and introduces technoself enhanced learning as a novel sociological framework of lifelong learning to couple the educational dimension with social dimension in order to enhance learner engagement by shaping personal learning focus and setting. We examine how people construct their own inquiry and learn from others, how people shift and adapt in these technoself-enhanced learning environments, and how learner engagement is improving as the involvement of learners as producers in lifelong learning. We further discuss the barriers and the positive and negative unintended consequences of using technology for lifelong learning.

Context-aware Approach for Determining the Threshold Price in Name-Your-Own-Price Channels

Key feature of a context-aware application is the ability to adapt based on the change of context. Two approaches that are widely used in this regard are the context-action pair mapping where developers match an action to execute for a particular context change and the adaptive learning where a context-aware application refines its action over time based on the preceding action’s outcome. Both these approaches have limitation which makes them unsuitable in situations where a context-aware application has to deal with unknown context changes. In this paper we propose a framework where adaptation is carried out via concurrent multi-action evaluation of a dynamically created action space. This dynamic creation of the action space eliminates the need for relying on the developers to create context-action pairs and the concurrent multi-action evaluation reduces the adaptation time as opposed to the iterative approach used by adaptive learning techniques. Using our reference implementation of the framework we show how it could be used to dynamically determine the threshold price in an e-commerce system which uses the name-your-own-price (NYOP) strategy.