Abstract:

“Learning” is an infinite process that one experiences in life. One's everyday life is surrounded by objects that could contribute to increase in knowledge. Yet, the exploitation of these objects is not scaled up to an end that meets the learners’ aspirations. The challenge is to maximize opportunities to match between learners’ goals and pervasive instructional resources. For example, in an academic institution, where instruction is abundant, a learner could be dynamically prompted to participate in a scheduled seminar about a preferred topic held in a location that is of close proximity. Similarly, a laboratory could broadcast the facility of an experimental session to respond to practical learning needs. Such instructional and experiential learning resources remain largely under-utilized, as traditionally, they are proposed in a pull format, where learners have to figure out the sources of learning. Creating a push format of notifications system that seamlessly integrates learning object instances into learners’ agenda based on personalized profiles and opportunistic locations will overcome the problems mentioned above and a scalable learning framework.

We propose an ambient learning environment that aims at combining physical components modeled following the Learning Object Metadata (LOM) model and instantiated as typical academic learning sessions, with personalized learner stereotypes modeled following the Learner Information Package (LIP) standard, to cooperatively guide learners through specific learning paths. Information about learners such as learning interests, goals, prior experiences, certificate credentials and level of education is exposed to ambient learning objects which proactively participate in building a personalized learning path to achieve LIP-specified goals. The adoption of standards aims at achieving interoperable solutions.

The proposed system creates a learning schedule in the learners’ calendar to keep record of planned learning possibilities in the surrounding environment that matches the learners’ profile attributes (like goals and prior competencies). A closed-loop learning optimization process is continuously engaged to achieve new goals and build new competencies. This flexible learning approach is an alternative to rigid pre-scheduled sessions or classes, which benefit only a predefined set of few learners. We envision an open institution, where learners are admitted to running sessions without prior assignment. The companion provides the learner with the information about surrounding sessions that match his profile (competencies and objectives). Once selected, the session is entered into the learner calendar pending approval of a supervising unit. Once approved, the learning session is confirmed into the learner calendar. The learning process is validated by a biometric attendance application, based on which new skills are appended to the learner’s profile.

Keywords:

Ubiquitous environments, learning technology, adaptive learning, learning objects, learner profile.