Personalizing Learning from Data

Provided by guest blogger, Angelica Brodeur, graduate student University of St. Francis, MS Training and Development program

One of the main benefits of e-learning is the potential to offer learners a personalized learning experience, thereby moving away from a ‘one size fits all’ approach. The paradigm of Personal Learning Environments (PLEs) is one example,allowing learners to experience various tools, systems, and services as they access learning content, complete assessments, and interact with other learners as both learners themselves and users of technology. Of course, this approach can present challenges in catering to the individualized needs of all learners due to the complexity of interpreting the data of each users’ preferences. Thus, educators can use the Analytical Hierarchy Process (AHP), which is a multi-criteria decision-making method that can organize and analyze complex data provided by learners. Different techniques can be found within AHP to meet the needs of analyzing various forms of data.

Sendelj and Ognjanovic (2015) provide a compelling argument in using the Analytical Hierarchy Process (AHP) to collect the necessary data to offer learners a personalized learning experience by providing a variety of examples. While it is beneficial that the author provides the steps needed to use the AHP, this is still a complex subject that may challenge some educators not familiar with the level of data collection required through this approach. The biggest take away that can benefit all educators is questioning what data is being collected and analyzed from users that complete e-learning courses that can then be used to create future learning experiences that address individual learning needs. As a learner myself, I look forward to increasing my own knowledge on this subject to address this question as an adult educator.

Sendelj, R., & Ognjanovic, I. (2015). Personalized recommendation strategies for eLearning: An AHP approach. Applied Technologies and Innovations, 11.(1), 16+.

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