Rosen, Y., Rushkin, I., Rubin, R., Munson, L., Ang, A., Weber, G., Lopez, G., et al. (2018). The Effects of Adaptive Learning in a Massive Open Online Course on Learners’ Skill Development. Learning at Scale. Publisher's Version adaptivelas2018.pdf
Obradovich, N., Tingley, D., & Rahwan, I. (2018). Effects of environmental stressors on daily governance. Proceedings of the National Academy of Sciences , 115 (35), 8710-8715. Publisher's Version
Yeomans, M., Kindel, A., Mavon, K., Reich, J., Stewart, B., & Tingley, D. (2018). Engagement Across Political Differences in Massive Open Online Courses. International Journal of Artificial Intelligence in Education , Publisher's Version engagementacrossdifference.pdf
Williams, J., Rafferty, A., Tingley, D., Ang, A., Lasecki, W., & Kim, J. (2018). Enhancing Online Problems Through Instructor-Centered Tools for Randomized Experiments. Computer Human Interaction . 36th Annual ACM Conference on Human Factors in Computing Systems. Publisher's Version
Olivola, C., Tingley, D., & Todorov, A. (2018). Republican Voters Prefer Candidates Who Have Conservative-Looking Faces: New Evidence from Exit Polls. Political Psychology , 35 (9), 1157-1171.
Spilker, G., Bernauer, T., Kim, I. S., Milner, H., Osgood, I., & Tingley, D. (2018). Trade at the Margin: Estimating the Economic Implications of Preferential Trade Agreements. Review of International Organizations , 13 (2), 189-242. tradeatmargin.pdf
Kertzer, J., & Tingley, D. (2018). Political Psychology in International Relations: Beyond the Paradigms. Annual Review of Political Science , 319–339. psyir.pdf
Whitehill, J., Mohan, J., Seaton, D., Tingley, D., & Rosen, Y. (2017). MOOC Dropout Prediction: How to Measure Accuracy? Proceedings of the Fourth (2017) Association for Computing Machinery on Learning @ Scale. whitehill-mooc.pdf
Ang, A., Rosen, Y., Fredericks, C., Rushkin, I., Lopez, G., Tingley, D., & Blink, M. J. (2017). Designing Adaptive Assessments in MOOCs . Proceedings of the Fourth (2017) Association for Computing Machinery on Learning @ Scale.
Williams, J., Rafferty, A., Lasecki, W., Ang, A., Tingley, D., & Kim, J. (2017). Connecting Instructors and Learning Scientists via Collaborative Dynamic Experimentation . ACM CHI Conference on Human Factors in Computing Systems.
Williams, J., Rafferty, A., Maldonado, S., Ang, A., Tingley, D., & Kim, J. (2017). ``Tools for Dynamic Experimentation and Personalization. Proceedings of the Fourth (2017) Association for Computing Machinery on Learning @ Scale.
Lopez, G., Seaton, G., Ang, A., Chuang, I., & Tingley, D. (2017). `Google BigQuery for Education: Framework for Parsing and Analyzing edX MOOC Data' . Proceedings of the Fourth (2017) Association for Computing Machinery on Learning @ Scale.
Rushkin, I., Rosen, Y., Ang, A., Fredericks, C., Tingley, D., Blink, M. J., & Lopez, G. (2017). Adaptive Assessment Experiment in a HarvardX MOOC. Educational Data Mining . International Conference on Educational Data Mining.
Tingley, D., & Wagner, G. (2017). Solar geoengineering and the chemtrails conspiracy on social media. Palgrave Communications , 3. Publisher's VersionAbstract
Discourse on social media of solar geoengineering has been rapidly increasing over the past decade, in line with increased attention by the scientific community and low but increasing awareness among the general public. The topic has also found increased attention online. But unlike scientific discourse, a majority of online discussion focuses on the so-called chemtrails conspiracy theory, the widely debunked idea that airplanes are spraying a toxic mix of chemicals through contrails, with supposed goals ranging from weather to mind control. This paper presents the results of a nationally representative 1000-subject poll part of the 36,000-subject 2016 Cooperative Congressional Election Study (CCES), and an analysis of the universe of social media mentions of geoengineering. The former shows ~ 10% of Americans declaring the chemtrails conspiracy as “completely” and a further ~ 20–30% as “somewhat” true, with no apparent difference by party affiliation or strength of partisanship. Conspiratorial views have accounted for ~ 60% of geoengineering discourse on social media over the past decade. Of that, Twitter has accounted for >90%, compared to ~ 75% of total geoengineering mentions. Further affinity analysis reveals a broad online community of conspiracy. Anonymity of social media appears to help its spread, so does the general ease of spreading unverified or outright false information. Online behavior has important real-world reverberations, with implications for climate science communication and policy.
Chilton, A., Milner, H., & Tingley, D. (2017). Trump just blocked a Chinese takeover of a sensitive U.S. company. Here's what's going on. Washington Post: Monkey Cage. Publisher's Version
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E., LotsofOtherPeople,, & Tingley, D. (2017). Redefine Statistical Significance. Human Nature Behavior. sig-naturehumanbehaviour.pdf
Chaudoin, S., Milner, H., & Tingley, D. (2017). A Liberal International American Foreign Policy? Maybe Down but Not Out. H-Diplo/ISSF. cmt-hdiplo-2017.pdf
Renshon, J., Lee, J., & Tingley, D. (2017). Emotions and the Micro-Foundations of Commitment Problems. International Organization , 71 (S1), S189-S218.
Tingley, D. (2017). Rising Power on the Mind. International Organization , 71 (S1), S165-S188. risingpowers.pdf
Osgood, I., Tingley, D., Bernauer, T., Kim, I. S., Milner, H., & Spilker, G. (2017). The Charmed Life of Superstar Exporters: Survey Evidence on Firms and Trade Policy. Journal of Politics , 47 (1), 133-152.Abstract

What factors determine firms’ attitudes towards trade policy? Building off the literature on firms in trade, this paper considers producers’ policy preferences and political behavior in light of two key patterns in modern international trade: industries that face import competition often have many exporters; and, foreign sales are concentrated in the hands of a small number of ‘superstar’ exporters.
Using a new survey of Costa Rican firms matched to systematic firm-level data on export behavior, we find that firm features are generally more important predictors of attitudes toward trade liberalization than industry-wide comparative advantage. We also show that export intensity is strongly associated with interest and lobbying activity on trade policy. The largest exporters, who are the
strongest supporters of global integration, dominate trade politics.