Meaning-Making Analysis and Topic Classification of SNS Goal-Based Messages

Setting learning goals enhances motivation and performance. However, a lack of motivation is still nowadays a large cause of education failure because learners often find difficulties to relate to the goals fixed in their formal education. Social Networking Services (SNS) offer a massive source of diverse information and represent an influential factor, including for learning. The purpose of this research consists therefore in 1) the construction of a large-scale dataset containing goal-based messages expressed by peers on SNS, and 2) the analysis of topics making the meaning of the different categories of goals included in the goal-based dataset. The massiveness of information available on SNS calls for a systematic text analysis. This study therefore introduced a Systemic Functional Grammar (SFG) approach to determine the linguistic features used to create the meaning of learning goals in SNS messages. This analysis resulted in the creation of a dataset containing 16,000 goal-based messages.