Learning analytics

Educational Data Mining and Learning Analytics have emerged over the past decade as interdisciplinary fields encompassing learning (e.g., learning sciences), analytics (e.g., data science), and human-centered design (e.g., usability of dashboards). In this chapter, we provide a broad overview of research in these fields, with a focus on the area of modeling unstructured natural language data. We present key lessons learned and suggest potential steps forward for building a productive interdisciplinary community. We also discuss the need of learning from each other's perspectives and challenging the underlying assumptions and unintentional bias that we may bring into the process of mining educational data.