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Over two iterations of a Massive Open Online Course (MOOC) for school leaders, Launching Innovation in Schools, we developed and tested design elements to support the transfer of online learning into offline action. Effective professional learning is job-embedded: learners should employ news skills and knowledge at work as part of their learning experience. This MOOC aimed to get participants to plan and actually launch new change efforts, and a subset of our most engaged participants were able and willing to do so during the course. Required assessments spurred student actions, along with instructor calls to action and modeling and exemplars provided by course elements. We found that participants led change initiatives, held stakeholder meetings, collected new data about their contexts, and shared and used course materials collaboratively. Collecting data about participant learning and behavior outside the MOOC environment is essential for researchers and designers looking to create effective online environments for professional learning.

La creciente utilización de sistemas de mediación digital en la mayoría de espacios educativos —ya sean presenciales o no, formales o abiertos, y tanto en el nivel de educación básica como en situaciones de aprendizaje a lo largo de la vida— está acelerando el avance de la analítica del aprendizaje y haciendo que el uso de la información digital sea una práctica común en el campo de la educación. Las herramientas educativas digitales facilitan la interacción entre estudiantes, profesores y recursos de aprendizaje, y generan de manera continua un notable volumen de datos que pueden analizarse aplicando una variedad de metodologías. Esto ha hecho que aumenten exponencialmente las investigaciones que toman como referencia la información que procede de la actividad de los estudiantes en esos espacios digitales. Partiendo de esas evidencias, este número especial muestra un conjunto de estudios en el campo del aprendizaje digital y la investigación educativa basada en datos, que enriquecen el conocimiento sobre los procesos de aprendizaje y la gestión de la enseñanza en espacios mediados digitalmente.

The massive racial inequities exposed by the COVID-19 pandemic and the nationwide protests in response to the killings of unarmed Black people forced a reckoning among many educators about racial injustice in the educational system. In March 2020, we launched a massive open online course designed to support teachers in adopting antiracist equity mind-sets and practices. We used a mixed-methods approach to describe how the participants experienced the online course between March and July 2020. Participants in immediate post- and follow-up surveys reported statistically significant shifts in their mind-sets and practices toward equitable teaching practices (effect size = 0.18–58 SD ). In interviews, participants described how the course helped them change their practice through acquiring new language, reflecting collaboratively on practice, and engaging in calls to action. The findings provide insight to designers of online professional learning experiences focused on equity and open up new research areas on online professional learning for equity teaching mind-sets and practices.

Diversity, equity, and inclusion (DEI) issues are urgent in education. We developed and evaluated a massive open online course ( N = 963) with embedded equity simulations that attempted to equip educators with equity teaching practices. Applying a structural topic model (STM)—a type of natural language processing (NLP)—we examined how participants with different equity attitudes responded in simulations. Over a sequence of four simulations, the simulation behavior of participants with less equitable beliefs converged to be more similar with the simulated behavior of participants with more equitable beliefs ( ES [effect size] = 1.08 SD ). This finding was corroborated by overall changes in equity mindsets ( ES = 0.88 SD ) and changed in self-reported equity-promoting practices ( ES = 0.32 SD ). Digital simulations when combined with NLP offer a compelling approach to both teaching about DEI topics and formatively assessing learner behavior in large-scale learning environments.

Eliciting and interpreting students’ ideas are essential skills in teaching, yet pre-service teachers (PSTs) rarely have adequate opportunities to develop these skills. In this study, we examine PSTs’ patterns of discourse and perceived learning through engaging in an interactive digital simulation called Eliciting Learner Knowledge (ELK). ELK is a seven-minute, chat-based virtual role play between a PST playing a “teacher” and a PST playing a “student” where the goal is for the teacher to find out what the student knows about a topic. ELK is designed to be a practice space where pre-service and in-service teachers can learn strategies for effectively eliciting their students’ knowledge. We review the implementation of ELK in eight teacher education courses in math or science methods at six different universities and assess (a) patterns of interaction during ELK and (b) PSTs’ perceptions of ELK and their learning from the simulation. Our findings suggest that PSTs engage in effective practices such as eliciting and probing more often than less effective practices such as evaluating and telling. Results suggest that PSTs gain experience in practicing talk moves and having empathy for students’ perspectives through using ELK.

Registered reports engender a review process in which reviewers evaluate the merits of the research question rather than the magnitude or direction of the results. In this process, authors first submit introduction, methods, and prespecified analyses sections of an article. Next, an initial peer-review process assesses the merits of the research question, in the absence of knowing the results. Worthy articles are granted an “in-principle acceptance.” The authors, then conduct their study, analyze their data, write up the remainder of their article, and resubmit it. The in-principle acceptance means that if the authors adhere to the research design as originally submitted, then the article will be accepted. Building from seven of the earliest registered reports in education research, our introduction describes the benefits, opportunities, and challenges of registered reports as a means to increasing transparency in research and reducing the number of illusory results.

The relationship between pricing and learning behavior is an important topic in research on massive open online courses (MOOCs). We report on two case studies where cohorts of learners were offered coupons for free certificates to explore how price reductions might influence behavior in MOOC-based online learning settings. In Case Study 1, we compare participation and certification rates between courses with and without free-certificate coupons. In the courses with a free-certificate track, participants signed up for the verified-certificate track at higher rates, and completion rates among verified students were higher than in the paid-certificate track courses. In Case Study 2, we compare learner behavior within the same courses by whether they received access to a free-certificate track. Access to free certificates was associated with lower certification rates, but overall, certification rates remained high, particularly among those who viewed the courses. These findings suggest that some incentives, other than simply the cost of paying for a verified-certificate track, may motivate learners to complete MOOCs.

Scientific progress is built on research that is reliable, accurate, and verifiable. The methods and evidentiary reasoning that underlie scientific claims must be available for scrutiny. Like other fields, the education sciences suffer from problems such as failure to replicate, validity and generalization issues, publication bias, and high costs of access to publications—all of which are symptoms of a nontransparent approach to research. Each aspect of the scientific cycle—research design, data collection, analysis, and publication—can and should be made more transparent and accessible. Open Education Science is a set of practices designed to increase the transparency of evidentiary reasoning and access to scientific research in a domain characterized by diverse disciplinary traditions and a commitment to impact in policy and practice. Transparency and accessibility are functional imperatives that come with many benefits for the individual researcher, scientific community, and society at large—Open Education Science is the way forward.

Identifying online users' contexts can help researchers understand their needs. However, the validity of different methods for identifying the location of online users has been underexplored. This paper proposes using multiple methods and examining their impact on different research questions to determine their validity. It then demonstrates this approach using data from six Massive Open Online Courses (MOOCs) by examining whether different methods produce different results regarding the relationship between SES and participation and engagement in MOOCs. We found that the choice of method impacted the estimated SES of the sample; IP geolocation placed participants in lower SES districts in comparison with their self-reported districts. Using all geolocation methods, we found that our MOOCs' learners tended to be located in high-SES districts, but the results were inconclusive regarding the relationship between SES and course engagement. Based on this case study, we suggest that using multiple methods produces more robust findings. However, when methods diverge in findings, researchers should consider which method is most suitable for their specific purposes.

Big data from massive open online courses (MOOCs) have enabled researchers to examine learning processes at almost infinite levels of granularity. Yet, such data sets do not track every important element in the learning process. Many strategies that MOOC learners use to overcome learning challenges are not captured in clickstream and log data. In this study, we interviewed 92 MOOC learners to better understand their worlds, investigate possible mechanisms of student attrition, and extend conversations about the use of big data in education. Findings reveal three important domains of the experience of MOOC students that are absent from MOOC tracking logs: the practices at learners’ workstations, learners’ activities online but off-platform, and the wider social context of their lives beyond the MOOC. These findings enrich our understanding of learner agency in MOOCs, clarify the spaces in-between recorded tracking log events, and challenge the view that MOOC learners are disembodied autodidacts.