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Online courses, particularly in the massive open online course (MOOC) format, have been lauded for their potential to democratize access to educational opportunities but criticized for their markedly low completion rates. Yet educators continue to enroll in online courses, including MOOCs, in high numbers. For teachers at under-resourced schools, free online courses may be the only professional development option. It thus remains important to understand whether online courses, in their various formats, can serve as vehicles to support teacher learning and whether this can happen on a large-scale. Extending prior research that explores the relationship between contextual factors, engagement, and learning in online settings, this mixed-method study examines outcomes in a MOOC designed for teachers of English learners (ELs). In particular, the study identifies and examines structural and social supports that were available to some course participants (release time, stipends, participating with colleagues, and having a facilitator who convened face-to-face meetings) and investigates whether these local conditions were significantly related to completion and learning. Findings indicate that participants who received more supports were significantly more likely to complete the course. While participants, on average, showed evidence of learning, participants receiving supports did not show evidence of learning more than other participants. This is potentially due to omitted variable bias because participants who completed the course without supports may differ from participants who completed the course with supports in important, unaccounted for ways. This study extends prior research about how learning environments impact online learning experiences and suggests that structural and social supports may be useful in facilitating MOOC completion.

Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom’s Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.

We draw on two studies of silence as a social achievement: one from a Quaker Meeting in which adult congregants use five kinds of silence to orchestrate their situation (Steinbock, 2012); the other from a second grade classroom in which children and their teacher employ 16 kinds of silence to juggle a question–answer exchange on arithmetic (Rutherford-Quach, 2013). Silence is not ‘an empty space,’ said Charles Goodwin (2018, p. 201), ‘but a place occupied by its own relevant activity.’ We stress the productive nuances of silence, illustrating its use as a flexible resource in the relations among people in specific learning situations. At Quaker Meeting, silence as ‘relevant activity’ is both goal and method for arranging a space for reflection and spiritual reorientation. In a second grade classroom, silence gains its relevance in the rougher interactional waters of schools where aspects of ability, attention, and intelligence are negotiated and turned into institutional identities.

Assuring that English learners (ELs) receive the support services to which they are entitled requires accurately identifying students who are limited in their English proficiency. As a first step in the identification process, students’ parents fill out a home language survey. If the survey indicates a language other than English is spoken in the home, students are then assessed for English language proficiency. The home language survey thus plays an important gatekeeping role. In July, 2009, the Arizona Department of Education reduced the number of questions on the state’s home language survey from three to one. This article addresses whether this reduction in questions can lead to under-identification of students who, by the state’s own criterion (i.e., performance on the Arizona English Language Learner Assessment, or AZELLA), are entitled to EL services. Analyses of data from two Arizona school districts clearly show that use of a single home language survey question will under-identify substantial numbers of English learners. Further, it is highly unlikely that a fail-safe mechanism established by the state, whereby teachers can nominate potential ELs for language testing, will successfully identify more than a small number of students missed by the single question survey.