The rich data that Massive Open Online Courses (MOOCs) platforms collect on the behavior of millions of users provide a unique opportunity to study human learning and to develop data-driven methods that can address the needs of individual learners. This type of research falls into the emerging field of learning analytics. However, learning analytics research tends to ignore the issue of the reliability of results that are based on MOOCs data, which is typically noisy and generated by a largely anonymous crowd of learners. This paper provides evidence that learning analytics in MOOCs can be significantly biased by users who abuse the anonymity and open-nature of MOOCs, for example by setting up multiple accounts, due to their amount and aberrant behavior. We identify these users, denoted fake learners, using dedicated algorithms. The methodology for measuring the bias caused by fake learners' activity combines the ideas of Replication Research and Sensitivity Analysis. We replicate two highlycited learning analytics studies with and without fake learners data, and compare the results. While in one study, the results were relatively stable against fake learners, in the other, removing the fake learners' data significantly changed the results. These findings raise concerns regarding the reliability of learning analytics in MOOCs, and highlight the need to develop more robust, generalizable and verifiable research methods.

Having good mentors and role models is important for personal growth. However, they are not always available at the time of need. Some of our personal heroes have passed away leaving only their wisdom through writings and other artifacts. We present Wearable Wisdom, an intelligent, audio-based system for mediating wisdom and advice from mentors and personal heroes to a user. It does so by performing automated semantic analysis on the collected wisdom database and generating a simulated voice of a mentor sharing relevant wisdom and advice with the user. The results show that our platform is statistically superior in delivering relevant, yet abstract wisdom as well as providing more inspiration compared to control. We describe the implementation of the Wearable Wisdom system, report on a user study, and discuss potential applications of wisdom computation for supporting personal growth and motivation.