Learning Design

With regard to the discussions about PBL, three important characteristics of PBL can be extracted; the problem, the work process, and the solution, which can be used to distinguish between various theoretical and practical constructions of PBL – regardless initially of whether it is collaborative or cooperative. The three dimensions can then be thought of as stretched between two ends of a continuum between teacher and participant control. These fundamental questions of ownership and control seem also to be more generally applicable in relation to wider debates about social media and learning. The learning design model is based on the collaborative eLearning design (CoED) method. The CoED‑workshop methodology aims to support the design of targeted networked learning. The method scaffolds the design work of practitioners and has been developed and tried out in a number of different settings. Drawing on knowledge and theoretical concepts within the fields of design, systems development and collaborative learning, emphasis is on bringing focus and structure to the early stages of the design process. The method aims to develop design specifications and/or early prototypes within a few hours of starting work.

In his latest work Christopher Alexander describes 15 fundamental properties that make structures more alive and whole. These properties are already described for being applicable in many different domains including education. However, many of these applications seem to be highly speculative and therefore not reliable.

Topics

The workshop addresses three main topics.

Topic 1: The meaning of Christopher Alexander's 15 properties for education.

This topic aims at examining the meaning of the 15 properties described by Alexander in The Nature of Order for educational purposes with the focus on one property or a small subset of them. It is hereby important to make this applicability more specific and well-grounded in order to show that indeed the properties are – or are not – applicable. Possible questions to be addressed are:

• Can specific properties be used for designing educational actions and how can this applicability be supported?
• What do the properties mean in the field of education?
• Can we find examples of the properties in successful educational scenarios?
• Does this application still match with the original ideas of Alexander?

Topic 2: The specific volatile structures of educational situations.

A characteristic of patterns in the domain of education is their volatility, because the structures of a pattern instance which evolve based on social interactions are flexible and short-living. It is therefore difficult to document or measure them in a consistent way. Furthermore, the context of educational situations, ranging from short interactions to curriculum design, is always different, which makes the application of patterns less predictable. Possible questions to be addressed are:

• How can educational patterns be documented in a way which takes their volatility into account?
• What consequences does the volatility has for the conscious application of educational patterns?
• Can we generalize these volatile structures in the pattern format at all?

Topic 3: The empirical ground for educational patterns.

Educational situations are generally hard to generalize. Much empirical research in education are case studies which contain (too) many details and are therefore hard to generalize with respect to different educational situations. Other studies mainly contain general pedagogical principles and offer not much help in concrete situations. Patterns can connect these two views, but there still are open questions such as:

• How can educational patterns be empirically justified?
• Which pattern mining methods are appropriate for research in this field?
• As educational situations are highly dependent on their context, how can they be reliably generalized?

Interactive learning environments invite a broad spectrum of participation. On one end of are interactive models of complex phenomena. For example in science learning, simulations of natural and artificial (mechanical, electrical, etc.) phenomena can be an effective way to represent complex relationships underlying a scientific law. On the other end, social learning environments stimulate discussion of ideas and clarification of issues by a process of collaborative inquiry, often guided by more knowledgeable people (e.g. teachers, mentors, and parents). Whereas individual learning refers to the difference between what a learner can perform independently compared to what he or she previously could perform only by guidance (“knowledge in the head”), social learning refers to what a group of learners can produce together as well as the process leading up to it (“knowledge in the world”).

Social media have enabled interactive learning environments to more fully reach the “social end” of the spectrum, whereas advances in user interface and interaction techniques for ubiquitous computing and mobile devices have improved access to and manipulation of models of scientific phenomena. It is when users become content producers and not merely consumers of information and the power and control are shifted from designated experts to a broader network of competent practitioners that the learning potential of social media will be fully realized. Thus, we feel that by combining interactive models with social sharing (or applying wiki principles to interactive learning environments) may prove invaluable in designing collaborative learning systems. In other words we aim for multiple perspectives on collaborative learning as a design activity.

It covers the content, segmenting, sequencing and navigation, adaptation to target audience, design of text and graphics, learning tasks and feedback, and motivation. In the context of a project on designing elearning modules on renewable energies, this checklist was successfully used for providing formative feedback to the developers.