Online experiments are growing in popularity, and the increasing sophistication of Web technology has made it possible to run complex behavioral experiments online using only a Web browser. Unlike with offline laboratory experiments, however, few tools exist to aid in the development of browser-based experiments. This makes the process of creating an experiment slow and challenging, particularly for researchers who lack a Web development background. This article introduces jsPsych, a JavaScript library for the development of Web-based experiments. jsPsych formalizes a way of describing experiments that is much simpler than writing the entire experiment from scratch. jsPsych then executes these descriptions automatically, handling the flow from one task to another. The jsPsych library is opensource and designed to be expanded by the research community. The project is available online at www.jspsych.org.

Behavioral researchers are increasingly using Webbased software such as JavaScript to conduct response time experiments. Although there has been some research on the accuracy and reliability of response time measurements collected using JavaScript, it remains unclear how well this method performs relative to standard laboratory software in psychologically relevant experimental manipulations. Here we present results from a visual search experiment in which we measured response time distributions with both Psychophysics Toolbox (PTB) and JavaScript. We developed a methodology that allowed us to simultaneously run the visual search experiment with both systems, interleaving trials between two independent computers, thus minimizing the effects of factors other than the experimental software. The response times measured by JavaScript were approximately 25 ms longer than those measured by PTB. However, we found no reliable difference in the variability of the distributions related to the software, and both software packages were equally sensitive to changes in the response times as a result of the experimental manipulations. We concluded that JavaScript is a suitable tool for measuring response times in behavioral research.

Half of the world's population has internet access. In principle, researchers are no longer limited to subjects they can recruit into the laboratory. Any study that can be run on a computer or mobile device can be run with nearly any demographic anywhere in the world, and in large numbers. This has allowed scientists to effectively run hundreds of experiments at once. Despite their transformative power, such studies remain rare for practical reasons: the need for sophisticated software, the difficulty of recruiting so many subjects, and a lack of research paradigms that make effective use of their large amounts of data, due to such realities as that they require sophisticated software in order to run effectively. We present Pushkin: an open-source platform for designing and conducting massive experiments over the internet. Pushkin allows for a wide range of behavioral paradigms, through integration with the intuitive and flexible jsPsych experiment engine. It also addresses the basic technical challenges associated with massive, worldwide studies, including auto-scaling, extensibility, machine-assisted experimental design, multisession studies, and data security. Keywords Online studies. Robust and reliable research. Massive online experiments. Citizen science Although some questions psychologists care about involve comparing only two conditions to each other, most require teasing apart the contributions of many intertwined variables. In the past, this has required hundreds, if not thousands, of studies across numerous laboratories, each targeting a specific variable, population, or stimulus set. In principle, we can now do this work many orders of magnitude more quickly. Given that half the world's population has internet access (ITU Telecommunication Development Sector, 2017), any study that can be run on a computer or mobile device can be run with nearly any demographic anywhere in the world, and in large numbers. This includes not just surveys, but studies involving grammatical judgments, reaction times, decisionmaking, economics games, eyetracking, priming, sentence completion, skill acquisition, and others-which is to say, most human behavioral experiments (Birnbaum, 2004;

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Psychology researchers have long attempted to identify educational practices that improve student learning. However, experimental research on these practices is often conducted in laboratory contexts or in a single course, which threatens the external validity of the results. In this article, we establish an experimental paradigm for evaluating the benefits of recommended practices across a variety of authentic educational contexts—a model we call ManyClasses. The core feature is that researchers examine the same research question and measure the same experimental effect across many classes spanning a range of topics, institutions, teacher implementations, and student populations. We report the first ManyClasses study, in which we examined how the timing of feedback on class assignments, either immediate or delayed by a few days, affected subsequent performance on class assessments. Across 38 classes, the overall estimate for the effect of feedback timing was 0.002 (95% highest density interval = [−0.05, 0.05]), which indicates that there was no effect of immediate feedback compared with delayed feedback on student learning that generalizes across classes. Furthermore, there were no credibly nonzero effects for 40 preregistered moderators related to class-level and student-level characteristics. Yet our results provide hints that in certain kinds of classes, which were undersampled in the current study, there may be modest advantages for delayed feedback. More broadly, these findings provide insights regarding the feasibility of conducting within-class randomized experiments across a range of naturally occurring learning environments.