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As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as 'Online Judges'. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for programming as CS students do. In this sense, contextualised assignment lists, with programming problems related to the students' major, could enhance engagement in the learning process. Thus, students would solve programming problems related to their academic context, improving their comprehension of the applicability and importance of programming. Nonetheless, preparing these contextually personalised programming assignments for classes for different courses is really laborious and would increase considerably the instructors'/monitors' workload. Thus, this work aims, for the first time, to the best of our knowledge, to automatically classify the programming assignments in Online Judges based on students' academic contexts by proposing a new context taxonomy, as well as a comprehensive pipeline evaluation methodology of cutting edge competitive Natural Language Processing (NLP). Our comprehensive methodology pipeline allows for comparing state of the art data augmentation, classifiers, beside NLP approaches. The context taxonomy created contains 23 subject matters related to the non-CS majors, representing thus a challenging multi-classification problem. We show how even on this problem, our comprehensive pipeline evaluation methodology allows us to achieve an accuracy of 95.2%, which makes it possible to automatically create contextually personalised program assignments for non-CS with a minimal error rate (4.8%). [For the full proceedings, see ED607784.]

The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks such as writing, design, and collaborative problem solving, and it has been used in new informal contexts where student actions are at best semi-structured. This iteration of the conference includes a range of work in these and other areas. This year's conference features three invited talks: Julita Vassileva, Professor at the Department of Computer Science, University of Saskatchewan, Canada; Steve Ritter, Co-Founder and Chief Scientist, Carnegie Learning Inc., Pittsburgh; and Michael Mozer, Professor Department of Computer Science and Institute of Cognitive Science University of Colorado. The number of accepted papers include 22 full papers and 42 short papers. An additional 47 papers were accepted to the poster track. The poster and demo track itself accepted 14 contributions out of 34 submissions. Together with the "Journal of Educational Data Mining" ("JEDM"), the EDM 2019 conference held 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. The papers submitted to this track followed the "JEDM" peer review process. Two such papers are featured in the conference's program. Additionally this year, papers that were regularly published in the journal in 2018 were invited for presentation at the conference. Two authors accepted this invitation. The main conference invited contributions to an Industry Track in addition to the main track. The EDM 2019 Industry Track received eleven submissions of which six were accepted. 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 eight such presentations. This year's conference includes also an invited talk by the authors of the 2018 winner of the EDM Test of Time Award. This year's talk is delivered by Mykola Pechenizkiy. In addition to the main program, there are three workshops: (1) Learning Analytics: Building bridges between the Education and the Computing communities; (2) Reinforcement Learning for Educational Data Mining; and (3) Workshop on EDM & Games: Leveling Up Engaged Learning with Data-Rich Analytics. Three tutorials were presented as well: (1) Sharing and Reusing Data and Analytic Methods with LearnSphere; (2) Causal Discovery with Tetrad in LearnSphere's Tigris and Designing and Developing Open; and (3) Pedagogically-Based Predictive Models using the Moodle Analytics API.

The advent of Massive Online Open Courses (MOOCs) has led to the availability of large educational datasets collected from diverse international audiences. Little work has been done on the impact of cultural and geographic factors on student performance in MOOCs. In this paper, we analyze national and cultural differences in students' performance in a large-scale MOOC. We situate our analysis in the context of existing theoretical frameworks for cultural analysis. We focus on three dimensions of learner behavior: course activity profiles; quiz activity profiles; and most connected forum peer or "best friends." We conclude that countries or associated cultural clusters are associated with differences in all three dimensions. These findings stress the need for more research on the internationalization in online education and greater intercultural awareness among MOOC designers.

The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of presentations all paper presenters pre-recorded their presentation as a video and then hosted it on YouTube with closed-captioning (CC). The official theme of this year's conference is Improving Learning Outcomes for All Learners. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to "improve" learning outcomes, not just predict them; (2) Using EDM to promote more "equitable" learning across diverse groups of learners, and to benefit underserved communities in particular. This year's conference features three invited talks: Alina von Davier, Chief Officer at ACTNext; Abelardo Pardo, Professor and Dean of Programs (Engineering), at UniSA STEM, University of South Australia; and Kobi Gal, Associate Professor at the Department of Software and Information Systems Engineering at Ben-Gurion University of the Negev, and Reader at the School of Informatics at the University of Edinburgh.