Helping Students FIG-ure It Out: A Large-Scale Study of Freshmen Interest Groups and Student Success

Aulck, Lovenoor; Malters, Joshua; Lee, Casey; Mancinelli, Gianni; Sun, Min; West, Jevin D.

doi:10.1177/23328584211021857

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…The article by Lovenoor Aulck, Joshua Malters, Casey Lee, Gianni Mancinelli, Min Sun, and Jevin West (Aulck et al, 2021;"Helping FIG-ure 2021) article, they use data science tools to discern why an experimental condition has its observed effects (e.g., the seminars offer integration, belonging and information).…”

Section: Insight Into Interventionsmentioning

confidence: 99%

“…The article by Lovenoor Aulck, Joshua Malters, Casey Lee, Gianni Mancinelli, Min Sun, and Jevin West (Aulck et al, 2021; “Helping FIG-ure It Out: A Large-Scale Study of Freshmen Interest Groups and Student Success”) studies the impact of freshmen seminars on 76,000 University of Washington students over 22 years. They use propensity scores to find matched samples of freshmen who were in the freshmen seminar (or freshmen interest groups, FIG) versus those who were not, and to see if the seminars have a positive effect.…”

Section: Presentmentioning

confidence: 99%

“…Whereas the Dowell et al (2021) and Aulck et al (2021) articles use NLP methods to understand how and why a treatment is effective or not, the article by Kylie Anglin, Vivian Wong, and Arielle Boguslav (Anglin et al, 2021; “A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity”) uses NLP to determine whether an educational reform is being delivered with fidelity. The work argues that the success of most reforms and interventions depends on whether it is actually implemented as proposed.…”

Section: Presentmentioning

confidence: 99%

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Education Data Science: Past, Present, Future

McFarland¹,

Khanna²,

Domingue

et al. 2021

AERA Open

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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.

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Section: Insight Into Interventionsmentioning

confidence: 99%

Section: Presentmentioning

confidence: 99%

Section: Presentmentioning

confidence: 99%

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Education Data Science: Past, Present, Future

McFarland¹,

Khanna²,

Domingue

et al. 2021

AERA Open

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Addressing Threats to Validity in Supervised Machine Learning: A Framework and Best Practices for Education Researchers

Anglin

2024

AERA Open

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Given the rapid adoption of machine learning methods by education researchers, and the growing acknowledgment of their inherent risks, there is an urgent need for tailored methodological guidance on how to improve and evaluate the validity of inferences drawn from these methods. Drawing on an integrative literature review and extending a well-known framework for theorizing validity in the social sciences, this article provides both an overview of threats to validity in supervised machine learning and plausible approaches for addressing such threats. It collates a list of current best practices, brings supervised learning challenges into a unified conceptual framework, and offers a straightforward reference guide on crucial validity considerations. Finally, it proposes a novel research protocol for researchers to use during project planning and for reviewers and scholars to use when evaluating the validity of supervised machine learning applications.

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First-year seminars through the lens of Vincent Tinto's theories of student departure. A systematic review

Samoila

Vrabie

2023

Front. Educ.

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IntroductionVincent Tinto is a towering figure in the study of dropout in higher education whose theory regarding student departure is a widely accepted contribution that explains how students become involved in the academic community and how their level of engagement impacts their decision to persist or to drop out. The aim of this study is to advance understanding of how Vincent Tinto's theories influence the current practices and support services offered by higher education institutions to freshman students through first-year seminars (FYS).MethodsA systematic review was conducted based on peer-reviewed articles published between 2020 and 2022, selected through a multi-stage process. With reference to Tinto's contribution, this systematic review aims at providing answers to the following research questions: What are the methodological approaches identified in the studies under analysis? What knowledge can be identified in recent studies related to the categories of students attending FYS? What knowledge about students' personal and psychological characteristics with relevance for FYS can be found in recent studies? What knowledge about academic pedagogical and counseling factors with relevance for FYS can be found in recent studies? What knowledge about social and relational factors integration with relevance for FYS can be found in recent studies?ResultsThe findings indicate three categories of studies that capitalize on Tinto's theories with regard to the design, conduct and evaluation of first-year seminars: a. studies that assess the role of students' personal and psychological variables, considered within FYS; b. studies that analyze the pedagogical and counseling interventions destined to students within FYS; c. studies that analyze the role of the social and relational dimension within FYS.DiscussionThe literature on the topic capitalizes on Vincent Tinto's contributions in relation to a student corpus which is extremely diverse in terms of age, socio-economic status, racial and cultural characteristics, attended specialization (a significant number of studies are destined to students who attend STEM specializations), generational characteristics (the example of first-generation students). Therefore, the interventions aimed at facilitating the integration of first-year students are carefully tailored and customized according to the needs of these categories of beneficiaries.

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Helping Students FIG-ure It Out: A Large-Scale Study of Freshmen Interest Groups and Student Success

Cited by 3 publications

References 28 publications

Education Data Science: Past, Present, Future

Education Data Science: Past, Present, Future

Addressing Threats to Validity in Supervised Machine Learning: A Framework and Best Practices for Education Researchers

First-year seminars through the lens of Vincent Tinto's theories of student departure. A systematic review

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