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Education

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Publications and Conference Proceedings

• Lang, D., Tan, Wei; Du, Lan; Buntine, Wray; Gašević, Dragan; Chen, Guanliang. (2023). Enhancing educational dialogue act classification with discourse context and sample informativeness. IEEE Transactions on Learning Technologies.

• Lin, Jionghao; Tan, Wei; Nguyen, Ngoc Dang; Lang, D.; Du, Lan; Buntine, Wray; Beare, Richard; Chen, Guanliang; Gašević, Dragan. (2023). Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets. In Proceedings of the International Conference on Artificial Intelligence in Education.

• Tan, Wei; Lin, Jionghao; Lang, D.; Chen, Guanliang; Gašević, Dragan; Du, Lan; Buntine, Wray. (2023). Does informativeness matter? Active learning for educational dialogue act classification. In Proceedings of the International Conference on Artificial Intelligence in Education.

• Lin, Jionghao; Raković, Mladen; Li, Yuheng; Xie, Haoran; Lang, D.; Gašević, Dragan; Chen, Guanliang. (2023). On the role of politeness in online human–human tutoring. British Journal of Educational Technology.

• Kizilcec, René F.; Baker, Rachel B.; Bruch, Elizabeth; Cortes, Kalena E.; Hamilton, Laura T.; Lang, D. Nathan; Pardos, Zachary A.; Thompson, Marissa E.; Stevens, Mitchell L. (2023). From pipelines to pathways in the study of academic progress. Science, Vol. 380, No. 6643, pp. 344-347. Publisher: American Association for the Advancement of Science.

• Lang, D.; Wang, Alex; Dalal, Nathan; Paepcke, Andreas; Stevens, Mitchell L. (2022). Forecasting Undergraduate Majors: A Natural Language Approach. AERA Open, Vol. 8.

• Lin, Jionghao; Rakovic, Mladen; Lang, D.; Gašević, Dragan; Chen, Guanliang. (2022). Exploring the politeness of instructional strategies from human-human online tutoring dialogues. In Proceedings of the LAK22: 12th International Learning Analytics and Knowledge Conference.

• Domingue, Benjamin W.; Dell, Madison; Lang, D.; Silverman, Rebecca; Yeatman, Jason; Hough, Heather. (2022). The effect of COVID on oral reading fluency during the 2020–2021 academic year. AERA Open, Vol. 8. Publisher: SAGE Publications.

• Lin, Jionghao; Singh, Shaveen; Sha, Lele; Tan, Wei; Lang, D.; Gašević, Dragan; Chen, Guanliang. (2022). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems, Vol. 127, pp. 194-207. Publisher: North-Holland.

• Lang, D., Esbenshade, Lief; Willer, Robb. (2021). Did Ohio’s vaccine lottery increase vaccination rates. A Pre-Registered, Synthetic Control Study.

• Domingue, Benjamin W.; Hough, Heather J.; Lang, D.; Yeatman, Jason. (2021). Changing Patterns of Growth in Oral Reading Fluency during the COVID-19 Pandemic. Working Paper. Policy Analysis for California Education, PACE. Publisher: Policy Analysis for California Education, PACE.

• Soland, James; Domingue, Benjamin; Lang, D. (2020). Using Machine Learning to Advance Early Warning Systems: Promise and Pitfalls. Teachers College Record, Vol. 122, No. 14, pp. 1-30. Publisher: SAGE Publications.
• Lang, D. N., Chen, Y., Paepcke, A., & Stevens, M. (2020). Course reviews reveal gender differences and other scientific insight about the students who submit them. EdArxiv.
• Lang, D., Chen, G., Mirzaei, K., & Paepcke, A. (2020, March). Is faster better? a study of video playback speed. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 260-269).
• Lang, D. Stenhaug, B. Kizilcec, R. 2019. Keystrokes, Edit Distance, and Grading Rules: Psychometric Properties of Short Answer Items. AERA 2019.

• Mongkhonvanit, K., Kanopka, K., & Lang, D. (2019, March). Deep Knowledge Tracing and Engagement with MOOCs. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 340-342). ACM.

• Benotti, L., Bhaskaran, J., Kjartansson, S., & Lang, D. (2018). Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text (pp. 121-131).

• Domingue, B. W., Lang, D., Cuevas, M., Castellanos, M., Lopera, C., Mariño, J. P., ... & Shavelson, R. J. (2017). Measuring student learning in technical programs: A case study from Colombia. AERA Open, 3(1), 2332858417692997.

• Lang, D., Kindel, A., Domingue, B., & Paepcke, A. (2017). Making the Grade: How Learner Engagement Changes after Passing a Course. Proceedings of Educational Data Mining 2017.

• Furlong, F., Lang, D., & Takhtamanova, Y. (2014). Drivers of mortgage choices by risky borrowers. FRBSF Economic Letter, 01.

• Furlong, F. T., Takhtamanova, Y., & Lang, D. (2014). Mortgage Choice in the Housing Boom: Impacts of House Price Appreciation and Borrower Type. Federal Reserve Bank of San Francisco.

• Hobijn, B., Krainer, J., & Lang, D. (2011). Cap rates and commercial property prices. FRBSF Economic Letter, 2011, 29.

• Lang, D., & Lansing, K. J. (2010). Forecasting growth over the next year with a business cycle index. FRBSF Economic Letter, 2010, 29.

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Unpublished Working Papers and Posters

Unpublished Working Papers

• Lang, D. (2017) Digital Canvases and Remote Tutoring.

• Anderson, R. Lang, D. Lee, H. (2017) Modeling Innovation Diffusion in an Online Tutoring Network.

Posters

• Kanopka, K. Lang, D.(2019) Adversarial Examples for Neural Automatic Essay Scoring Systems.

• Kanopka, K. Lang, D.(2018) Deep Knowledge Tracing and Engagement in MOOCs.

• Lang, D.(2018) Air Inequality: A Study of California Schools

• Bennoti, L. Bhaskaran, J. Kjartansson, S. Lang, D.(2018) Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues.

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Presentations

Recorded Presentations

• UW Data Science for Social Good 2019: Peer Support https://youtu.be/Sp61cGvxn00?t=5348

• Bay Area Learning and Analytics Conference (2019) https://youtu.be/ThRQPWcmfYE
  

Other Presentations

• Bay Area Learning and Analytics Conference (2019). "Clickstreams, Video Engagement, and Course Performance."

• Bay Area Learning and Analytics Conference (2018). "Teacher Professional Development: Social Networks and Influence Maximization."

• Bay Area Learning and Analytics Conference (2018). "Heterogeneous Effects of Cellphone Credit Incentives on Mobile Learning in Africa."

• Center for Education Policy Analysis Alumni Research Conference, Stanford University (2018). "Air Quality Inequality: A Study of San Francisco Schools."

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Research Partnerships

Yup- Yup is a text-messaged based tutoring company

• Completed Projects: Modelling student response time to tutor questions

• Completed Projects: A randomized controlled trial evaluation of a digital whiteboard in a remote tutoring environment

• Ongoing Projects: Diffusion of teacher practice using social network analysis

• Ongoing Projects: Measuring the impact of badges and gamification in a tutoring platform

Reup- Reup is a college-coaching company that encourages students to re-enroll in college after dropping out

• Ongoing Projects: Predicting college re-enrollment using coaching transcripts and natural language processing

Eneza- Eneza is a Kenyan learning platform that provides primary and secondary education via SMS and feature phones

• Ongoing Projects: Understanding the effect of microcredit incentives in educational platforms

• Completed Projects: Predicting psychometric features of items using natural language processing

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Awards

• Best Paper Award. (CS 230: Deep Learning) “Deep Knowledge and Engagement Tracing” Kanopka, K. Lang, D. Mongkhonvanit, K. (2018)

• Travel Scholarship. Berkeley Initiative for Transparency in the Social Sciences Research Transparency and Reproducibility Training (2018)

• Travel Scholarship. 21st Summer Institute in Statistical Genetics (SISG) (2015)

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Projects and Code

• Data Science for Social Good https://uwescience.github.io/DSSG-Peer-Support

• Deep Knowledge Tracing github.com/klintkanopka/dkt2

• Tutor-Response-Time Models github.com/davidnathanlang/cs224u-project

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Skills and Programming Languages

• Expert: Stata, R

• Proficient: Matlab, Python, SQL

• Novice: TensorFlow, PyTorch

Technical Skills: Social Network Analysis, Natural Language Processing, Computer Vision, Machine Learning, Causal Inference, Regression Analysis, Econometrics, and Psychometrics

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Employment

August 2023- Present: Research Manager, Cradle-to-Career California

• Managed relations with numerous state-appointed officials, including the CIO of the State Department of Education, to establish a longitudinal data system linking birth, education, and workforce outcomes.
• Directed the development and execution of our research request process, attracting proposals from institutions such as the California Policy Lab, and Opportunity Insights.
• Composed policy briefs that resulted in “dear colleague” letters, elucidating the permissible use of FAFSA data for research purposes.
• Pioneered projects to improve public-facing data visualizations and craft advanced query-building tools.

June 2021- August 2023: Data Scientist, Western Governors University

• Led the allocation of $60 million in pandemic relief funds and conducted causal inference analysis to assess the impact of these funds on student degree progress with an estimated cost of 15,000 dollars per additional student graduated.
• Implemented machine learning and natural language processing techniques to analyze admission counselor and mentor notes, resulting in a 1.2% increase in forecasting accuracy for student retention.
• Spearheaded candidate search for key positions funded by a $5 million Gates grant, successfully hiring three individuals, including senior management and hard-to-fill roles.
• Managed and mentored three analysts on various research and reporting projects.
• Employed regression discontinuity analysis to evaluate the effects of multiple policies concerning financial aid, evaluation and assessment, and other areas.

September 2020 - April 2021: Data Engineer, Literably Technologies

• Reduced per-unit costs by 5% using advanced audio processing techniques.
• Evaluated potential vendors by comparing their models’ word error rates using non-parametric statistical methods.
• Developed and fine-tuned an XGBoost model to enhance classification accuracy by 8%, resulting in a 20% reduction in costs.
• Authored a policy brief on COVID-19 learning loss, which was featured in the New York Times.

Summer 2019-Present: Data Science Fellow, University of Washington

• Identified counseling and peer support strategies using natural language processing tools.
• Conducted social network analysis on network platform with more than 500,000 users.
• Met with platform stakeholders and recommended a set of interventions to improve users’ mental health.

2018-Present: Researcher, Stanford Graduate School of Business

• Conducted interviews regarding the XPrize competition, Adult Literacy, incentive prizes, and AI in educational settings.
• Assisted in the organization of a conference on Market Shaping.
• Wrote a summative white paper of findings and conference proceedings.

2015-Present: Ph.D. Candidate, Graduate School of Education, Stanford University

• Conducted natural language processing on text-message-based tutoring services to improve lesson quality and student engagement.
• Recruited and ran randomized controlled trials on a mobile tutoring network.
• Analyzed Stanford online course clickstream data and recommended policy and feature changes.
• Worked with San Francisco Unified School District to develop models.

2015-2018: Partner/Consultant, Data Dream Team Consulting LLC

• Worked with airlines to predict customer frequent-flier redemption.
• Cleaned a terabyte plus transactional database.
• Discussed and defended modelling choices with clients.
• Recommended changes to frequent-flier programs.

2014-2015: Workforce Analyst, University of California Office of the President

• Evaluated systemwide policies on faculty hiring.
• Reviewed and modeled faculty salary equity studies.
• Conducted survival analysis based on UC faculty data.
• Conducted longitudinal analysis of faculty advancement.

2008-2014: Research Analyst/Associate, Federal Reserve Bank of San Francisco

• Spoke to a wide range of audiences about the economic outlook.
• Fact-checked President Janet Yellen’s speeches.
• Used data-mining techniques on a joint project with McKinsey.
• Created and cleaned a variety of datasets.
• Created a data-retrieval add-in.