Coarse-Grained Detection for Personalized Online Learning Interventions

For many people, their first encounter with learning programming happens online. However, it is likely that these first-time learners will encounter obstacles that they cannot overcome on their own, especially as they progress to more complex concepts. Keeping these online learners engaged with the content is essential for them to learn programming, as their experience could have long-term implications for how they view computing. One way to address this issue is to detect when a learner is having difficulty with a concept, provide them with automated assistance and encouragement, and offer more opportunities to practice. For struggling learners, customized encouragement may be just what they need to re-engage with the task, and additional practice may help them better understand the concept(s) and prepare them for future topics. Many recent technologies advocate for the use of machine learning techniques to customize and personalize educational content for their users. However, less resource-intensive methods (and those not requiring time and other resources to train and test models) utilizing users’ historical interaction data may provide enough support to provide just-in-time customization and personalization to help learners. This chapter describes two studies where we created a simple, coarse-grained (instead of using complex machine learning detection methods) frustration detector to provide customized content for the user. We modified an existing programming game, providing encouraging messages and hints in the first study, and providing extra game levels for more practice when necessary in the second study. Based on our results, we show that simple, coarse-grained detection methods are sufficient to trigger adaptive interventions to benefit struggling learners.