Start Date
12-13-2015
Description
Learner-centred, self-regulated learning approaches such as flipped classrooms or personalised learning environments are popular. This paper analyses personalised learning in collaborative, self-regulated e-learning approaches applying the theory of cognitive fit to explain the personalisation of learning tasks and learning tools. The personalised learning framework (PLF) is presented defining the core constructs of such learning processes as well as a method of personalisation. The feasibility of the framework is demonstrated using a thought experiment describing its possible application to a university course on electronic negotiations as part of an IS curriculum. Current learning methods used in the course and new learning methods matching the PLF are compared and discussed critically, identifying potentials to improve personalised learning as well as avenues for personalised learning research.
Recommended Citation
Melzer, Philipp and Schoop, Mareike, "A Conceptual Framework for Task and Tool Personalisation in IS Education" (2015). ICIS 2015 Proceedings. 6.
https://aisel.aisnet.org/icis2015/proceedings/ISedu/6
A Conceptual Framework for Task and Tool Personalisation in IS Education
Learner-centred, self-regulated learning approaches such as flipped classrooms or personalised learning environments are popular. This paper analyses personalised learning in collaborative, self-regulated e-learning approaches applying the theory of cognitive fit to explain the personalisation of learning tasks and learning tools. The personalised learning framework (PLF) is presented defining the core constructs of such learning processes as well as a method of personalisation. The feasibility of the framework is demonstrated using a thought experiment describing its possible application to a university course on electronic negotiations as part of an IS curriculum. Current learning methods used in the course and new learning methods matching the PLF are compared and discussed critically, identifying potentials to improve personalised learning as well as avenues for personalised learning research.