Background: With the increased attention on the need to retain students within STEM majors, it is important for STEM instructors to adopt evidence-based instructional practices that are student-centric and employ active learning techniques. However, traditional approaches for increasing student-centric, active learning practices such as workshops, seminars, and department or college mandates have been either inefficient or ineffective at motivating institutional change. This is particularly true for introductory STEM courses with large enrollments. One promising approach is to develop and support instructors in forming communities of practice around reforming introductory and foundational STEM courses. By engaging instructors within these communities of practice, and connecting the communities with each other, instructors may be more likely to experience an epistemological shift that will lead to adoption of active learning practices. To explore whether participating in a community of practice is related to the use of student-centered, active learning techniques, 25 undergraduate foundational STEM courses whose instructors were members of a community of practice were observed using the Classroom Observation Protocol for Undergraduate STEM Courses. The results were compared to a sample of 35 undergraduate foundational STEM courses whose instructors were not members of a community of practice. Results: Instructors who were members of a community of practice were much more likely to employ student-centric practices, such as asking questions, following up, and engaging in discussion, and much less likely to use instructor-centered practices, such as lecturing. In addition, students in these classes were more likely to be actively engaged in problem-solving activities rather than passively listening. We found that student-centric, active learning practices correlated with students attending and actively participating in class, an effect that is stronger for courses taught by instructors who were members of a community of practice. Conclusion: Communities of practice are a potentially effective mechanism for enhancing student learning and retention by increasing the use of active learning practices by STEM instructors. These communities are particularly effective when they consist of small, disciplinary teams working on the same course(s) and are linked with other individuals or groups that use evidence-based instructional practices.

Very little prospective research investigates how cannabis withdrawal is associated with treatment outcomes, and this work has not used the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) thresholds for cannabis withdrawal. The sample included 110 emerging adults entering outpatient substance use treatment who were heavy cannabis users with no other drug use and limited alcohol use. We used survival analyses to predict days to first use of cannabis and logistic regression to predict whether participants were abstinent and living in the community at 3 months. Those meeting criteria for cannabis withdrawal were more likely to return to use sooner than those not meeting criteria for cannabis withdrawal. However, the presence of cannabis withdrawal was not a significant predictor of 3-month abstinence. Emerging adults with DSM-5 cannabis withdrawal may have difficulty initiating abstinence in the days following their intake assessment, implying the need for strategies to mitigate their more rapid return to cannabis use.

Gestural recognition systems are important tools for leveraging movement‐based interactions in multimodal learning environments but personalizing these interactions has proven difficult. We offer an adaptable model that uses multimodal analytics, enabling students to define their physical interactions with computer‐assisted learning environments. We argue that these interactions are foundational to developing stronger connections between students' physical actions and digital representations within a multimodal space. Our model uses real time learning analytics for gesture recognition, training a hierarchical hidden‐Markov model with a “one‐shot” construct, learning from user‐defined gestures, and accessing 3 different modes of data: skeleton positions, kinematics features, and internal model parameters. Through an empirical comparison with a “pretrained” model, we show that our model can achieve a higher recognition accuracy in repeatability and recall tasks. This suggests that our approach is a promising way to create productive experiences with gesture‐based educational simulations, promoting personalized interfaces, and analytics of multimodal learning scenarios.

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Students' ability to effectively study for an exam, or to manage their time during an exam, is related to their metacognitive capacity. Prior research has demonstrated the effective use of metacognitive strategies during learning and retrieval is related to content expertise. Students also make judgments of their own learning and of problem difficulty to guide their studying. This study extends prior research by investigating the accuracy of novices' and experts' ability to judge problem difficulty across two experiments; here "accuracy" refers to whether or not their judgments of problem difficulty corresponds with actual exam performance in an introductory mechanics physics course. In the first experiment, physics education research (PER) experts judged the difficulty of introductory physics problems and provided the rationales behind their judgments. Findings indicate that experts use a number of different problem features to make predictions of problem difficulty. While experts are relatively accurate in judging problem difficulty, their content expertise may interfere with their ability to predict student performance on some question types. In the second experiment novices and "near experts" (graduate TAs) judged which question from a problem pair (taken from a real exam) was more difficult. The results indicate that judgments of problem difficulty are more accurate for those with greater content expertise, suggesting that the ability to predict problem difficulty is a trait of expertise which develops with experience.