Concerns over the quality of teaching in massive open online courses devoted to language learning (LMOOCs) have prompted extensive research on learning behavior in such courses. The purpose of this study is to gain a better understanding of autonomous learning check-ins – that is, individuals sharing their own learning records and/or other information about their learning-related experience – a novel behavior that has not been studied in previous LMOOC research. Using learning analytics, we found that just 6.2% (n = 699) of a sample of 11,293 learners autonomously engaged in check-in behavior, and that the content of these learners’ check-ins varied considerably according to their contexts and the language skills they were seeking to acquire. We further found (1) a positive association between check-in behavior and LMOOC completion; (2) that students who chose to check in earned relatively low grades on unit quizzes, especially in their early stage of learning, but outperformed the non-check-in group significantly in final exam scores; and (3) that those who checked in engaged with a significantly wider array of in-LMOOC learning components than those who did not, and thus accessed a wider system of language-learning experiences. Taken together, these results confirm that check-in behavior can aid the process of learning in an LMOOC and further highlight this behavior’s wider potential to aid self-directed autonomous online learning.

Commonly, low completion rates in massive open online courses have called into question the quality of their learning materials and instruction. This paper attempts to identify crucial factors of engagement and retention in language massive open online courses (LMOOCs) in the context of the open online program of Icelandic Online, a self-guided course for second language learners of Icelandic. The study seeks to explore the impact of factors associated with the course’s instructional design on engagement and retention and reveal crucial determinants of attrition. The study depends on survey and tracking data from 400 learners and qualitative data from 62 informants in one course. It builds on previous studies on student engagement and retention in LMOOCs (Friðriksdóttir, 2018, 2019). The present study identified six content-specific factors that the majority of participants considered to be important for their motivation. Some factors, such as gradual and scaffolded presentation of input, had a positive impact on retention. Furthermore, statements from learners in the study who disengaged before completing show that non-course-related factors, such as time constraints, affect LMOOC retention. The study provides a new framework for how to promote student engagement and suggests specific strategies for other LMOOC developers.

Introduction: In 2018, Canadian postgraduate specialist Emergency Medicine (EM) programs began implementing a competency-based medical education (CBME) assessment system. To support improvement of this assessment program, we sought to evaluate its short-term educational outcomes nationally and within individual programs. Methods: Program-level data from the 2018 resident cohort were amalgamated and analyzed. The number of Entrustable Professional Activity (EPA) assessments (overall and for each EPA) and the timing of resident promotion through program stages was compared between programs and to the guidelines provided by the national EM specialty committee. Total EPA observations from each program were correlated with the number of EM and pediatric EM rotations. Results: Data from 15 of 17 (88.2%) EM programs containing 9,842 EPA observations from 68 of the 77 (88.3%) Canadian EM specialist residents in the 2018 cohort were analyzed. The average number of EPAs observed per resident in each program varied from 92.5 to 229.6 and correlated strongly with the number of blocks spent on EM and pediatric EM (r = 0.83, p < 0.001). Relative to the guidelines outlined by the specialty committee, residents were promoted later than expected and with fewer EPA observations than suggested. Conclusion: We present a new approach to the amalgamation of national and program-level assessment data. There was demonstrable variation in both EPA-based assessment numbers and promotion timelines between programs and with national guidelines. This evaluation data will inform the revision of local programs and national guidelines and serve as a starting point for further reaching outcome evaluation. This process could be replicated by other national assessment programs.

Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.