publications
publications by categories in reversed chronological order. generated by jekyll-scholar.
2024
- RERThe Effects of Educational Technology Interventions on Literacy in Elementary School: A Meta-AnalysisRebecca D. Silverman, Elena M. D. Hammond, Kristin Keane, and Saurabh KhannaReview of Educational Research, 2024
Educational technology is ubiquitous in schools, but there is insufficient information about the effectiveness of interventions that use educational technology to support literacy in elementary school. Given the importance of literacy development in these grades, we conducted a meta-analysis on the effects of educational technology interventions on reading comprehension and writing proficiency as well as the constrained and unconstrained skills that underlie these two literacy constructs. We also conducted analyses to determine whether effects of educational technology interventions differ according to characteristics of the educational interventions or participants in the studies in our corpus. Findings indicate positive effects of educational technology interventions on constrained skills, unconstrained skills, reading comprehension, and writing proficiency though effect sizes differed dramatically across outcomes. Findings also suggest that effects differ according to particular characteristics, providing direction for future research and development of educational technology interventions to support literacy in elementary school.
- EDCCJob Training, English Language Skills, and Employability: Evidence from an Experiment in Urban IndiaPrashant Loyalka, Dinsha Mistree, Robert W Fairlie, and Saurabh KhannaEconomic Development and Cultural Change, 2024
Low-income individuals in developing countries are often inadequately prepared for employment because they lack key labor market skills. We explore how employability and wage outcomes are related to English language skills in a novel, large-scale randomized field experiment conducted in Delhi, India involving 1,260 low-income individuals. Experimental estimates indicate that a job training program that emphasizes English language skills training substantially increases English language skills as well as employability and estimated wages (as assessed by hiring managers through interviews) for regular jobs and employability for jobs that specifically require English language skills. Program effects hold regardless of gender, social class, or prior employment. We furthermore find that participants enjoy improved employability and estimated wage outcomes because the program improves their English language skills. Taken together, our results suggest that English language skills training, which is surprisingly underutilized in developing countries, may provide considerable economic opportunities for individuals from lowincome backgrounds.
2023
- Psych MethodsA graph theory based similarity metric enables comparison of subpopulation psychometric networks.Esther Ulitzsch, Saurabh Khanna, Mijke Rhemtulla, and Benjamin W DominguePsychological Methods, 2023
Network psychometrics leverages pairwise Markov random fields to depict conditional dependencies among a set of psychological variables as undirected edge-weighted graphs. Researchers often intend to compare such psychometric networks across subpopulations, and recent methodological advances provide invariance tests of differences in subpopulation networks. What remains missing, though, is an analogue to an effect size measure that quantifies differences in psychometric networks. We address this gap by complementing recent advances for investigating whether psychometric networks differ with an intuitive similarity measure quantifying the extent to which networks differ. To this end, we build on graph-theoretic approaches and propose a similarity measure based on the Frobenius norm of differences in psychometric networks’ weighted adjacency matrices. To assess this measure’s utility for quantifying differences between psychometric networks, we study how it captures differences in subpopulation network models implied by both latent variable models and Gaussian graphical models. We show that a wide array of network differences translates intuitively into the proposed measure, while the same does not hold true for customary correlation-based comparisons. In a simulation study on finite-sample behavior, we show that the proposed measure yields trustworthy results when population networks differ and sample sizes are sufficiently large, but fails to identify exact similarity when population networks are the same. From these results, we derive a strong recommendation to only use the measure as a complement to a significant test for network similarity. We illustrate potential insights from quantifying psychometric network similarities through cross-country comparisons of human values networks.
2022
- QSSRecalibrating the scope of scholarly publishing: A modest step in a vast decolonization processSaurabh Khanna, Jon Ball, Juan Pablo Alperin, and John WillinskyQuantitative Science Studies, 2022
By analyzing 25,671 journals largely absent from common journal counts, as well as Web of Science and Scopus, this study demonstrates that scholarly communication is more of a global endeavor than is commonly credited. These journals, employing the open-source publishing platform Open Journal Systems (OJS), have published 5.8 million items; they are in 136 countries, with 79.9% in the Global South and 84.2% following the OA diamond model (charging neither reader nor author). A substantial proportion of journals operate in more than one language (48.3%), with research published in a total of 60 languages (led by English, Indonesian, Spanish, and Portuguese). The journals are distributed across the social sciences (45.9%), STEM (40.3%), and the humanities (13.8%). For all their geographic, linguistic, and disciplinary diversity, 1.2% are indexed in the Web of Science and 5.7% in Scopus. On the other hand, 1.0% are found in Cabell’s Predatory Reports, while 1.4% show up in Beall’s (2021) questionable list. This paper seeks to both contribute and historically situate the expanded scale and diversity of scholarly publishing in the hope that this recognition may assist humankind in taking full advantage of what is increasingly a global research enterprise.
- Ed ResThe Effect of Faculty Research on Student Learning in CollegePrashant Loyalka, Zhaolei Shi, Guirong Li, Elena Kardanova, Igor Chirikov, Ningning Yu, Shangfeng Hu, Huan Wang, Liping Ma, Fei Guo, Ou Lydia Liu, Ashutosh Bhuradia, Saurabh Khanna, Yanyan Li, and Adam MurrayEducational Researcher, 2022
Whether faculty research affects college student learning has long been the subject of debate. Previous studies use subjective measures of student learning; focus on correlation rather than causation; and typically focus on one college, thus lacking generalizability. Using unique, large-scale survey and assessment data that we collected from nationally representative samples of STEM undergraduates in China, India, and Russia, as well as a causal identification strategy that accounts for differential sorting of students to faculty, we present generalizable estimates of the effect of faculty research on objective, standardized measures of student learning. Results show that faculty research has a negative effect on student learning, suggesting direct trade-offs between the university’s dual mission of producing research and learning.
2021
- NatureSkill levels and gains in university STEM education in China, India, Russia and the United StatesPrashant Loyalka, Ou Lydia Liu, Guirong Li, Elena Kardanova, Igor Chirikov, Shangfeng Hu, Ningning Yu, Liping Ma, Fei Guo, Tara Beteille, Namrata Tognatta, Lin Gu, Guangming Ling, Denis Federiakin, Huan Wang, Saurabh Khanna, Ashutosh Bhuradia, Zhaolei Shi, and Yanyan LiNature human behaviour, 2021
Universities contribute to economic growth and national competitiveness by equipping students with higher-order thinking and academic skills. Despite large investments in university science, technology, engineering and mathematics (STEM) education, little is known about how the skills of STEM undergraduates compare across countries and by institutional selectivity. Here, we provide direct evidence on these issues by collecting and analysing longitudinal data on tens of thousands of computer science and electrical engineering students in China, India, Russia and the United States. We find stark differences in skill levels and gains among countries and by institutional selectivity. Compared with the United States, students in China, India and Russia do not gain critical thinking skills over four years. Furthermore, while students in India and Russia gain academic skills during the first two years, students in China do not. These gaps in skill levels and gains provide insights into the global competitiveness of STEM university students across nations and institutional types.
- AERA OpenEducation Data Science: Past, Present, FutureDaniel A McFarland, Saurabh Khanna, Benjamin W Domingue, and Zachary A PardosAERA Open, 2021
This AERA Open special topic concerns the large emerging research area of education data science (EDS). In a narrow sense, EDS applies statistics and computational techniques to educational phenomena and questions. In a broader sense, it is an umbrella for a fleet of new computational techniques being used to identify new forms of data, measures, descriptives, predictions, and experiments in education. Not only are old research questions being analyzed in new ways but also new questions are emerging based on novel data and discoveries from EDS techniques. This overview defines the emerging field of education data science and discusses 12 articles that illustrate an AERA-angle on EDS. Our overview relates a variety of promises EDS poses for the field of education as well as the areas where EDS scholars could successfully focus going forward.
2020
- RRQBeyond Decoding: A Meta-Analysis of the Effects of Language Comprehension Interventions on K–5 Students’ Language and Literacy OutcomesRebecca D. Silverman, Erika Johnson, Kristin Keane, and Saurabh KhannaReading Research Quarterly, 2020
The debate over the science of reading has focused primarily on decoding (i.e., connecting letters and sounds to read words) and whether to use phonics to teach it. However, research on reading has included much more than decoding. Language comprehension, which allows readers to derive meaning from text, is an equally critical component of reading. Research has suggested that explicit instruction on the components of language comprehension—vocabulary and semantics, morphology, and syntax—can support language and reading comprehension. To inform the field on the science of reading as it pertains to language comprehension, in this meta-analysis of recent language comprehension interventions (n = 43) in U.S. elementary schools, the authors examined whether effects vary depending on participant and intervention characteristics. Findings suggest positive effects on custom measures of vocabulary, listening comprehension, and reading comprehension but not on standardized measures of these outcomes. Results also indicate positive effects for English learners and promise for multicomponent interventions and those that include technology. Much more research is needed on how best to support language comprehension for underserved populations (e.g., students from low-income backgrounds) and how interventions can be optimized to support generalizable language and literacy outcomes. Implications for policy and practice are discussed.
2019
- PNASComputer science skills across China, India, Russia, and the United StatesPrashant Loyalka, Ou Lydia Liu, Guirong Li, Igor Chirikov, Elena Kardanova, Lin Gu, Guangming Ling, Ningning Yu, Fei Guo, Liping Ma, Shangfeng Hu, Angela Sun Johnson, Ashutosh Bhuradia, Saurabh Khanna, Isak Froumin, Jinghuan Shi, Pradeep Kumar Choudhury, Tara Beteille, Francisco Marmolejo, and Namrata TognattaProceedings of the National Academy of Sciences, 2019
The rapid proliferation of information and communication technologies in economic, political, and social life has led to an increasing demand for computing professionals worldwide. It has also seen a corresponding expansion in undergraduate computer science (CS) programs. However, despite rapid increases in the quantity of CS graduates, little is known about their quality. In particular, little is known about the major-specific competencies, knowledge, and skills of CS graduates from different countries, types of programs, and backgrounds. Such evidence can ultimately inform employers seeking to hire qualified computing professionals within a globally competitive labor market, as well as policymakers and administrators seeking to improve the quality and diversity of CS programs in an international context.We assess and compare computer science skills among final-year computer science undergraduates (seniors) in four major economic and political powers that produce approximately half of the science, technology, engineering, and mathematics graduates in the world. We find that seniors in the United States substantially outperform seniors in China, India, and Russia by 0.76–0.88 SDs and score comparably with seniors in elite institutions in these countries. Seniors in elite institutions in the United States further outperform seniors in elite institutions in China, India, and Russia by ~0.85 SDs. The skills advantage of the United States is not because it has a large proportion of high-scoring international students. Finally, males score consistently but only moderately higher (0.16–0.41 SDs) than females within all four countries.