Person: Nesterko, Sergiy O.
Loading...
Email Address
AA Acceptance Date
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Nesterko
First Name
Sergiy O.
Name
Nesterko, Sergiy O.
6 results
Search Results
Now showing 1 - 6 of 6
Publication HarvardX and MITx: The First Year of Open Online Courses, Fall 2012-Summer 2013(2014) Ho, Andrew; Reich, Justin; Nesterko, Sergiy O.; Seaton, Daniel Thomas; Mullaney, Tommy; Waldo, James; Chuang, IsaacHarvardX and MITx are collaborative institutional efforts between Harvard University and MIT to enhance campus-based education, advance educational research, and increase access to online learning opportunities worldwide. Over the year from the fall of 2012 to the summer of 2013, HarvardX and MITx launched 17 courses on edX, a jointly founded platform for delivering massive open online courses (MOOCs). In that year, 43,196 registrants earned certificates of completion. Another 35,937 registrants explored half or more of course content without certification. An additional 469,702 registrants viewed less than half of the content. And 292,852 registrants never engaged with the online content. In total, there were 841,687 registrations from 597,692 unique users across the first year of HarvardX and MITx courses. This report is a joint effort by institutional units at Harvard and MIT to describe the registrant and course data provided by edX in the context of the diverse efforts and intentions of HarvardX and MITx instructor teams.Publication PH207x: Health in Numbers and PH278x: Human Health and Global Environmental Change: 2012-2013 Course Report(2014) Reich, Justin; Nesterko, Sergiy O.; Seaton, Daniel Thomas; Mullaney, Tommy; Waldo, James; Chuang, Isaac; Ho, AndrewIn the 2012-2013 academic year, the first two Harvard School of Public Health courses were offered through HarvardX on the edX platform: PH207x: Health in Numbers and PH278x: Human Health and Global Environmental Change. They were taught by Professors Earl Francis Cook and Marcello Pagano, and Aaron Bernstein and Jack Spengler, respectively. This report describes the structure of these two courses, the demographic characteristics of registrants, and the activity of students. This report was prepared by researchers external to the course teams and is based on examination of the courseware, analyses of the data collected by the edX platform, and interviews and consultations with the course faculty and team members.Publication Heroesx: The Ancient Greek Hero: Spring 2013 Course Report(2014) Reich, Justin; Emanuel, Jeff; Nesterko, Sergiy O.; Seaton, Daniel Thomas; Mullaney, Tommy; Waldo, James; Chuang, Isaac; Ho, AndrewCB22x: The Ancient Greek Hero, was offered as a HarvardX course in Spring 2013 on edX, a platform for massive open online courses (MOOCs). It was taught by Professor Greg Nagy. The report was prepared by researchers external to the course team, based on examination of the courseware, analyses of the data collected by the edX platform, and interviews and consultations with the course faculty and team members.Publication ER22x: JusticeX: Spring 2013 Course Report(2014) Reich, Justin; Nesterko, Sergiy O.; Seaton, Daniel Thomas; Mullaney, Tommy; Waldo, James; Chuang, Isaac; Ho, AndrewER22x was offered as a HarvardX course in Spring 2013 on edX, a platform for massive open online courses (MOOCs). It was taught by Professor Michael Sandel. The report was prepared by researchers external to the course team, based on an examination of the courseware, analyses of data collected by the edX platform, and interviews with the course faculty and team members.Publication Bias–Variance and Breadth–Depth Tradeoffs in Respondent-Driven Sampling(Informa UK Limited, 2013) Nesterko, Sergiy O.; Blitzstein, JosephRespondent-driven sampling (RDS) is a link-tracing network sampling strategy for collecting data from hard-to-reach populations, such as injection drug users or individuals at high risk of being infected with HIV. The mechanism is to find initial participants (seeds), and give each of them a fixed number of coupons allowing them to recruit people they know from the population of interest, with a mutual financial incentive. The new participants are again given coupons and the process repeats. Currently, the standard RDS estimator used in practice is known as the Volz–Heckathorn (VH) estimator. It relies on strong assumptions about the underlying social network and the RDS process. Via simulation, we study the relative performance of the plain mean and VH estimators when assumptions of the latter are not satisfied, under different network types (including homophily and rich-get-richer networks), participant referral patterns, and varying number of coupons. The analysis demonstrates that the plain mean outperforms the VH estimator in many but not all of the simulated settings, including homophily networks. Also, we highlight the implications of multiple recruitment and varying referral patterns on the depth of RDS process. We develop interactive visualizations of the findings and RDS process to further build insight into the various factors contributing to the performance of current RDS estimation techniques.Publication Respondent-Driven Sampling and Homophily in Network Data(2013-02-13) Nesterko, Sergiy O.; Blitzstein, Joseph Kalmon; Liu, Jun; Airoldi, EdoardoData that can be represented as a network, where there are measurements both on units and on pairs of units, are becoming increasingly prevalent in the social sciences and public health. Homophily in network data, or the tendency of units to connect based on similar nodal attribute values (i.e. income, HIV status) more often than expected by chance is receiving strong attention from researchers in statistics, medicine, sociology, public health and others. Respondent-Driven Sampling (RDS) is a link-tracing network sampling strategy heavily used in public health worldwide that is cost efficient and allows us to survey populations inaccessible by conventional techniques. Via extensive simulation we study the performance of existing methods of estimating population averages, and show that they have poor performance if there is homophily on the quantity surveyed. We propose the first model-based approach for this setting and show its superiority as a point estimator and in terms of uncertainty intervals coverage rates, and demonstrate its application to a real life RDS-based survey. We study how the strength of homophily effects can be estimated and compared across networks and different binary attributes under several network sampling schemes. We give a proof that homophily can be effectively estimated under RDS and propose a new homophily index. This work moves towards a deeper understanding of network structure as a function of nodal attributes and network sampling under homophily.