The Data Flow Problem in Learning Design

A Case Study

Abstract

A teaching-learning process formalized through the IMS-Learning Design specification (IMS-LD) comprises a sequence of learning activities (learning flow) as well as a sequence of artifacts between tools or services (data flow) used to support the learning activities. According to the literature, the collaborative learning flow specification has been successfully achieved; however the automation of the collaborative data flow is still an open issue in IMS-LD. Nevertheless, no case studies have been reported in the literature in order to show with real data why the automation of the data flow is an important issue in Learning Design (LD).

In this paper an authentic case study which is significative and relevant to the problem is analyzed. Supported with real data, several findings related to the data flow problem in collaborative learning emerged: data management is error-prone for the users; data flow specification is error-prone for the course designer; users suffer an additional cognitive load during the data management; the course designer suffers an additional cognitive load during the data flow specification; and the need to include instance-level data flow specification within the learning design has also been identified. Furthermore, these findings also help us to understand the relevance of the problem: a data flow approach which is error-prone for both the users and the course designer may potentially affect the accomplishment of the users’ learning objectives; and a learning design which merge declarative-level learning flow with instance-level data flow affects the reusability of the whole unit of learning (UoL).

Based on the relation among these findings and literature, three dimensions of the IMS-LD data flow problem have been identified: the data flow automation problem already reported in the literature, which is related to the user’s data flow management issue; the data flow consistency problem, which is related to the issue of matching the different parts that comprise the data flow specification; and the UoL reuse problem, which is related to the instance-level collaborative data flow specification issue. Furthermore, these dimensions also help us to determine the necessary requirements in order to tackle these problems.

Since IMS-LD fails to specify the data flow in collaborative learning, we propose a separation of the data flow from the learning flow. For this purpose, a standard workflow language such as BPEL is used and a unit of data flow (UoDF), which is just a business process archive (BPR) understandable by a BPEL-compliant engine is created. Then, the learning design is specified in a unit of learning flow (UoLF), which is actually a UoL understandable by an IMS-LD compliant engine that follows the best practices in collaborative data flow specification, which means not specifying the data flow at all. Finally, for coordination of both engines, a coordination model has been developed and a prototype is currently under test and evaluation. Future work includes evaluation of more case studies in order to validate the proposal solution, and identify limitations and drawbacks. Interestingly is that our proposal which is based on a composition-based approach may be thought as the in-between approach that will provide a framework for future integration of LD and workflow streams.