What make effective online learning?

What make effective online learning?

This is a key question everyone is asking now. Here is some research we did on the University of Auckland Online Learning System. The work was completed with Mr Don Wong. This was published in the Decision Sciences Journal of Innovative Education . Here is the full paper (DSJIE – Davis and Wong 2007).

While numerous studies have focused on the effectiveness and benefits of eLearning, few have focused on understanding and measuring the user experience and relating this to the actual student usage of the eLearning system. This study addresses this gap by conceptualizing and measuring the eLearners’ experience from two integrated perspectives: (1) the learners’ affective perceptions using the flow model and (2) the learners’ technology acceptance using the Technology Acceptance Model.

The integrated perspective proposes that the users’ acceptance and the affective responses toward a particular system are two important factors in determining the users’ intentional and actual behaviors, which in turn, influence user participation and engagement.

Validity and Reliability

It is highly valid work from many perspectives:

1. Real learning case site.
2. The data was collected directly from 964 students from the University of Auckland.
3. Rigorous two stage model development process using Confirmatory Factor Analysis and Structural Equation Modelling.
4. Conceptualising and measurement of two seminal user experience models.

Key Implications

1. The results indicate that intentional usage of an eLearning system is significantly affected by perceived usefulness and flow.
2. Hence, the practitioners seeking to facilitate the adoption of an eLearning system should emphasize how to better match learning relevance needs and requirements.
3. In addition, educational institutions might need to develop some strategies for gaining instructors’ involvement and support. Because they are the people who are important to the learners, they are in the best position to effectively exercise their social influence in affecting learners’ usage behavior.
4. Another important strategic consideration is the identification and acquisition of an eLearning system that is suitable to eLearners’ learning needs and able to be used to educate and entertain the users simultaneously.
5. The flow state is another important factor affecting eLearner behavioral intention. The data from the results suggested that an affective state, such as flow, is more important than eLearners’ beliefs about the usefulness of a particular eLearning system in determining their intention to use.
6. For this reason, eLearning content providers and system designers should work with instructors and innovative organizations collaboratively to come up with designs that can provide appealing learning experiences for their online learners.
7. While innovative organizations need to provide the infrastructure for successful implementation of eLearning systems, instructors need to upload the important and relevant learning materials online.
8. In addition, the importance of these learning materials should be brought to eLearners’ attention explicitly so that this message can act as a powerful driving force to motivate eLearners to use the system.
9. Finally, the model suggests that speed and involvement (importance) are two important factors that can be used to facilitate eLearners’ experience of flow.
10. Therefore, the most effective way to ensure active participation and deep involvement in the online lecture materials and courses is to develop systems that can interact spontaneously with the user while providing important and interesting content.