Data-driven learning design
Data-driven learning design means using data for decision making during the design process. Increasing access to live data from learning platforms allows us to use real-time data to iteratively design, measure and improve learning, rather than waiting till the end. This allows for making changes to the learning on-the-fly, and is well suited for modular, bitesize and continuous learning opportunities. Learning data can include traditional registration, completion and evaluation information, but can increasingly include realtime user engagement data such as views, time spent, drop-off, uploads, etc.
Data can be used to constantly assess how the users are interacting with the learning, in a cycle where insights are discovered, a response is drafted, and monitored to revise the impact of those responses (read more at http://www.loriniles.com/).
Common uses of learning analytics include the prediction of students’ success in a particular setting, and more specifically the identification of learners who are at risk of failing or dropping out of a course. However, learning analytics can be much more productive and powerful than that.
Practical applications of analytics in learning
Analytics serve as a real-time feedback loop, in which instructors are able to identify how learners interact with training content, learning activities and with each other. This information helps trainers (and increasingly platforms) understand accurately the learners needs and help identify gaps. Using analytics can aid in the improvement of the quality and success of any learning activity. Other benefits of learning analytics include:
Personalised learning and timely feedback
Trainers and platforms are able to use data to better understand how a learner is interacting, if they are succeeding and how best to adapt to the learner’s needs. This is to accompany the learner, provide timely feedback and alternate learning pathways to ensure a positive and productive learning experience.
Development of new methodologies
Analytics can help trainers and platforms identify the gaps in the methodologies implemented, and suggest new or different methodologies in order to tackle issues faced by learners. These could include participatory and collaborative processes between learners fostering better social and emotional learning.
Encourage learners self reflection
Where the learners are empowered with their own learning data, this can help the learners create cycles of practice, self assessment and self reflection.
Learning design tools that allow for iterative design
A key advantage of using analytics in learning is to constantly adapt the learning design based on the feedback and engagement. This allows for the learning experiences to evolve over time, instead of remaining static. Trainers can design and deploy initial learning experiences more quickly to their learners, and then use the live data to iteratively improve the learning designs. Ideally this data should be easily and readily available to the designers, e.g. within the learning authoring tool, and provide the right insights to make the appropriate design adjustments to improve learning outcomes.
The Gamoteca platform has integrated learning analytics to ensure that the design process is being informed by learner experience. Game creators can view aggregated analytics, drill down to session level reports with detailed player results and export data (CSV) for further analysis and visualisation. Game creators can view live game sessions and interactions, to understand screen or task-level learner interactions as they happen, and even adjust game flow if necessary. Learn more about the Gamoteca creator platform here.