The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37 accepted as short papers. This year's conference features three invited talks: Tiffany Barnes, Professor at North Carolina State University in Raleigh, North Carolina; Jodi Forlizzi, Geschke Director of the HCI Institute and Professor at Carnegie Mellon University; and Jim Larimore, Chief Officer of Center for Equity in Learning at ACT, Inc. Together with the "Journal of Educational Data Mining" ("JEDM"), the EDM 2018 conference supports a "JEDM" Track that provides researchers a venue to deliver more substantial mature work than is possible in a conference proceeding and to present their work to a live audience. Three such papers are featured this year. The papers submitted to this track followed the "JEDM" peer review process. The main conference invited contributions to an Industry Track in addition to the main track. The EDM 2018 Industry Track received ten submissions of which six were accepted, a tangible improvement over last year, with only four submissions total, all of which were accepted. This expansion of the industry track represents an intentional goal to better connect industry researchers with the academic research community. The EDM conference continues its tradition of providing opportunities for young researchers to present their work and receive feedback from their peers and senior researchers. The doctoral consortium this year features 14 such presentations, more than double compared to the prior year. In addition to the main program, there are four workshops: (1) Educational Data Mining in Computer Science Education (CSEDM); (2) Proposal Policy & EDM: Norms, Risks, and Safeguards; (3) replicate.education: A Workshop on Large Scale Education Replication; and (4) Scientific Findings from the ASSISTments Longitudinal Data.