Person:

Sadler, Philip

We report preliminary results from an NSF-funded project to build, test, and research the impact of a WorldWide Telescope Visualization Lab (WWT Vizlab), meant to offer learners a deeper physical understanding of the causes of the Moon's phases. The Moon Phases VizLab is designed to promote accurate visualization of the complex, three dimensional Earth-Sun-Moon relationships required to understand the Moon's phases, while also providing opportunities for middle school students to practice critical science skills, like using models, making predictions and observations, and linking them in evidence-based explanations. In the VizLab, students use both computer-based models and lamp + ball physical models. We present findings from the first two phases of the study---one in which we compared learning gains from the WWT VizLab with a traditional two dimensional Moon phases simulator, and another in which we experimented with different ways of blending physical and virtual models in the classroom.

Massive open online course (MOOC) studies have shown that precourse skills (such as precomputational thinking) and course engagement measures (such as making multiple submission attempts with assignments when the initial submission is incorrect) predict students' grade performance, yet little is known about whether these factors predict students' course retention. In applying survival analysis to a sample of more than 20,000 participants from one popular computer science MOOC, we found that students' precomputational thinking skills and their perseverance in assignment submission strongly predict their persistence in the MOOC. Moreover, we discovered that precomputational thinking skills, programming experience, and gender, which were previously considered to be constant predictors of students' retention, have effects that attenuate over the course milestones. This finding suggests that MOOC educators should take a growth perspective towards students' persistence: As students overcome the initial hurdles, their resilience grows stronger.

Participants’ engagement in massive online open courses (MOOCs) is highly irregular and self-directed. It is well known in the field of television media that substantial parts of the audience tend to drop out at major episodic, or seasonal, closures, which makes creating cliff-hangers a crucial strategy to retain viewers (Bakker, 1993; Cazani, 2016; Thompson, 2003). Could there be an analogous pattern in MOOCs—with an elevated probability of dropout at major chapter transitions? Applying disjoint survival analysis on a sample of 12,913 students in a popular astronomy MOOC that built participants’ cultural capital (hobbyist pursuits), we found a significant increase in dropout rates at chapter closures. Moreover, the latter the chapter closure was positioned in the course sequence, the higher the dropout rate became. We found this pattern replicated in a sample of 20,134 students in a popular computer science MOOC that introduced participants to programming.

One of the foundational assumptions in education is that greater teacher knowledge contributes to greater gains in student knowledge, but empirical evidence in support of this assumption is scarce. Using a U.S. sample of 79 biology teachers and their 2749 high school students, we investigate whether teachers’ subject matter knowledge (SMK) and knowledge of students’ misconceptions (KOSM) in high school life science are associated with students’ posttest performance on multiple-choice test items designed to reveal student misconceptions, after controlling for their pretest scores. We found that students were more likely to answer an item on the posttest correctly if their teachers could answer the question correctly, themselves (SMK). Teachers’ ability to predict students’ most common wrong answer (KOSM) for an item predicted even better student performance. Items for which a particular wrong answer rose above others in popularity saw an even greater benefit for teacher KOSM.

To encourage the formation of science identity among girls, many scholars and practitioners have suggested to assign samegender science teachers to students so that the teachers can serve as gender role models. However, direct evidence of any long‐term effect of gender‐matching is scarce. In a nationally representative survey of college students from the United States, we investigated if gender‐matching between students and their first high school science teachers was associated with students’ stronger identity in those science subjects in college. In physics, we found no gender‐matching effect. In chemistry, there was a gender‐matching effect only for women students. In biology, there were gender‐matching effects for students of both genders. In addition, we found that students in general had a lower science identity if they reported a negative influence of opposite gender domination (IOGD) on their career choices. However, for female students who were at the negative end of the IOGD scale, female biology teachers raised the level of biology identity to the grand average. Our findings suggested that the gender role model effect was strongest when the gender role models resonated with the overall disciplinary gender representation at the school or societal levels.

Massive Online Open Courses (MOOCs) provide opportunities to learn a vast range of subjects. Because MOOCs are open to anyone with computer access and rarely have prerequisite requirements, the range of student backgrounds can be far more varied than in conventional classroom-based courses. Prior studies have shown that misconceptions have a huge impact on students' learning performance; however, no study has empirically examined the relationship between misconceptions and learning persistence. This study of 12,913 MOOC-takers examines how students' misconceptions about the upcoming course material affect course completion. Using a survival analysis approach, we found that, controlling for the score in a pre-course test, students holding more misconceptions had a higher dropout rate at the start of the course, an effect that diminished over time. Other student variables were found to have a positive impact on survival that persisted throughout the entire course: U.S. location, higher age, an intention to complete, better English skills, prior familiarity with the subject, motivation to earn a certificate, and score and time spent on the previous problem set (homework). By contrast, student gender, education level, number of previous MOOCs completed, and motivation to participate in online discussion forums did not affect survival.

Massive open online courses (MOOCs) show highly irregular participation behaviour among users. In this study, using data from Computer Science 50x of HarvardX, we investigated one extreme, yet common strategy to foresee the endgame: taking the final problem set at the beginning of the course. We found such a strategy to be the only dominant trajectory alternative to following the sequence prescribed by the syllabus. Whereas all students who took and passed the final problem set at the beginning of the course subsequently completed the course, those who took and failed the final problem set at the beginning of the course finished the fewest number of milestones, even fewer than those who never attempted the final problem set. Moreover, students with a lower prior programming proficiency were more likely than better prepared students both to take the final problem set early and to fail it. This study revealed the disconcerting phenomenon that many students dropped out of a MOOC because, apparently, their confidence was crushed even before they learned any course content. The study suggests that future MOOC practices and policies should offer informative and constructive syllabi to accommodate students’ need for previewing the endgame.

Statistics skills are increasingly required for a wide range of careers, and Statistics courses and degrees have exploded in popularity in recent years. We estimate that 920,000 US students are now taking Statistics classes in high school each year. We present results from a nationally representative survey of 15,727 college first-years attending two- and four-year institutions, of whom 26% had taken Statistics while in high school. We are the first to describe in detail this population of US high school Statistics course-takers, and present data about the demographics, career interests and values, STEM identity, grades, and test scores of those who took Statistics in high school. Latent profile analysis is used to characterize the profiles of key subgroups, illustrating the diverse skills, interests, and values of this population.