Coursework: ITP Seminar

Seminars are held weekly on Fridays in the Educational Sciences Building in Rm. 259, unless noted otherwise. (times vary)

While this is a required course for ITP fellows, members of the university and wider community are welcome to attend.

October 29, 2021
  • ITP Ed Sciences: Elise Marifian (ITP Fellow)

    October 29, 2021  12:00 pm - 1:30 pm
    259 Educational Sciences

    Elise Marifian (ITP Fellow)
    Economics

    "Financial Aid, Cost Uncertainty, and Student Enrollment Choices: Evidence from Bucky’s Tuition Promise"

    This paper examines how a four year, full-tuition guarantee for modest income residents affects their probability of accepting an enrollment offer at the state flagship. In recent years, colleges have increasingly adopted tuition promise programs to address informational and behavioral barriers to low-income individuals' college attainment. Despite evidence that high-achieving students from low-income backgrounds are less likely than their affluent peers to attend a selective college, few states offer tuition promise programs at their selective public flagship institutions. I study how the college enrollment choices of academically talented students were impacted by the 2018 introduction of one such program—Bucky’s Tuition Promise—at the University of Wisconsin-Madison, which promises four (two) years of grant aid to cover undergraduate tuition and fees for incoming first year (transfer) resident students whose household adjusted gross income (AGI) falls below the state median. To identify the causal effect of a BTP offer on admitted students' enrollment decisions, I adopt two strategies: a Regression Discontinuity (RD) design which leverages the discontinuity in BTP eligibility at the household AGI cutoff, and a Differences-in-Differences (DID) design which exploits the unanticipated nature of the policy and data from prior year cohorts. I find that BTP increases enrollment yield by 6 to 7 percentage points among low- and moderate- income residents, with the two designs generating similar results while shedding light on different aspects of the policy’s impacts.

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November 5, 2021
  • ITP Ed Sciences: Dr. Timothy Nokes-Malach

    November 5, 2021  12:00 pm - 1:30 pm
    259 Educational Sciences

    Dr. Timothy Nokes-Malach
    University of Pittsburgh

    "Using Large Educational Data Sets to Understand Factors that Affect Student Success in STEM" 

    Understanding both the barriers as well as the pathways to student success is critical to support all students to learn and achieve in STEM. In this talk, I will describe a research project that brings together an interdisciplinary team of learning scientists to understand the factors that affect student success in STEM disciplines. We are particularly concerned with questions about for whom educational innovations are effective, and their longitudinal outcomes. I will describe the overarching project and how we bring together different types of data and expertise to answer these questions. I will then describe one strand of the project in depth that has focused on understanding underrepresentation of women in physics. As a first step, we have begun to examine student motivational and performance patterns across multiple large introductory physics courses. The findings have implications for the development and implementation of pedagogies and resources to help all students learn.

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November 12, 2021
  • ITP Ed Sciences: Dr. Douglas Harris

    November 12, 2021  12:00 pm - 1:30 pm
    259 Educational Sciences

    Dr. Douglas Harris
    Tulane University

    Optimal College Financial Aid: Theory and Evidence on Free College, Early Commitment, and Merit Aid from an Eight-Year Randomized Trial

    Required readings

    Douglas N. Harris, Jonathan Mills

    We provide theory and evidence about how the design of college financial aid programs affects a variety of high school, college, and life outcomes. The evidence comes from an eight-year randomized trial where 2,587 high school ninth graders received a $12,000 merit-based grant offer. During high school, the program increased their college expectations and non-merit effort but had no effect on merit-related effort (e.g., GPA). After high school, the program increased graduation from two-year colleges only, apparently because of the free college design/framing in only that sector. But we see no effects on incarceration or teen pregnancy. Overall, the results suggest that free college affects student outcomes in ways similar to what advocates of free college suggest and making aid commitments early, well before college starts, increases some forms of high school effort. But we see no evidence that merit requirements are effective. Both the standard human capital model and behavioral economics are required to explain these results.

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November 19, 2021
  • ITP Ed Sciences: Grant writing workshop

    November 19, 2021  12:00 pm - 1:30 pm
    259 Educational Sciences

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December 3, 2021
  • ITP Ed Sciences: Dr. Daniel McCaffrey

    December 3, 2021  12:00 pm - 1:30 pm

    Dr. Daniel McCaffrey
    Educational Testing Service (ETS)

    "Are Your AI Scores Good Enough?"

    Use of computers to score performances from standardized evaluations, such as using artificial intelligence (AI) and natural language processing (NLP) to rate written or spoken responses to standardized test items, is growing rapidly in popularity. Currently, these methods are used in many testing situations to produce scores. For example ,tens of millions of responses from elementary and secondary students are scored using computer-based automated scoring and states like Ohio (Ohio Department ofEducation, 2018) are moving toward having all student responses from its elementary and secondary school testing program scored by such methods.Moreover, each year millions of responses from high stakes tests such as theGRER, TOEFLR, the Duolingo English Test, the Pearson Test of English and thePearson Test of English Academic are also scored by computer-based automated methods.

    Use of AI scoring for assessments invariably leads to questions about the ability of the scores to support the claims of the items and the tests and the fairness of the scores. Typically, evaluation of scores involves statistical analyses of the agreement between AI scores and human ratings of the same constructed responses or the accuracy of scores as predictors of the human ratings. In this talk I will discuss an alternative framework for evaluating AI scores that focuses on building evidence to support claims about the scores. I will discuss how to use statistical analysis of AI scores in this framework and methods for assessing the fairness of scores.

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December 10, 2021
  • ITP Ed Sciences: Noah Hirschl (ITP Fellow)

    December 10, 2021  12:00 pm - 1:30 pm

    Noah Hirschl
    ITP Fellow (UW-Madison)

    "Baccalaureate and post-baccalaureate degree prestige and top income attainment, 1990-2019"

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February 4, 2022
  • ITP Ed Sciences: Dr. Allison Atteberry

    February 4, 2022  12:00 pm - 1:30 pm
    259 Educational Sciences

    Dr. Allison Atteberry

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April 1, 2022
  • ITP Ed Sciences: Dr. Jessica Calarco

    April 1, 2022  12:00 pm - 1:30 pm
    Wisconsin Idea Room - 159 Education Building (Bascom Mall)

    Dr. Jessica Calarco
    Indiana University

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