In a traditional classroom when a good teacher observes that a particular student is finding some learning material to be hard to comprehend, he/she offers simpler materials and simpler explanations. The teacher comprehends the complexity levels of the available learning materials, customizes the set of materials offered to a student based on the classroom or homework performance of that student. In the age of open digital learning, available learning materials have grown by orders of magnitude. Also, the likelihood of a good human teacher paying direct attention to an average individual learner is very low now. Thus, it has become necessary to build automated systems that can comprehend the complexity levels of learning materials, so as to auto-select and auto-suggest suitable sets of learning materials for each individual learner. This study is an attempt to bring personalization into massive online learning by auto tagging the content of topics and courses and also auto-suggesting suitable materials based on the performance of the learner. © Medwell Journals, 2016.