A New Pathway to University Retention? Identity Fusion With University Predicts Retention Independently of Grades

Talaifar, Sanaz; Ashokkumar, Ashwini; Pennebaker, James W.; Medrano, Fortunato N.; Yeager, David S.; Swann, William B.

doi:10.1177/1948550619894995

Cited by 18 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, 7.94% of the sample left the university. Note that a subset of this dataset was previously used in an article by Talaifar et al ( 28 ) exploring the links between self-reported fusion and college retention.…”

Section: Methodsmentioning

confidence: 99%

“…Study 2 sought to replicate Study 1’s main finding and additionally assess if the unquestioning affiliation index could predict an important real-world behavior . Following evidence that self-reported identity strength with university predicts future college retention ( 28 ), we tested whether unquestioning affiliation measured from students’ stream of consciousness essays could predict college retention.…”

Section: Study 2: Testing Unquestioning Affiliation With a Behavioral...mentioning

confidence: 99%

See 1 more Smart Citation

Tracking group identity through natural language within groups

Ashokkumar¹,

Pennebaker

2022

PNAS Nexus

Self Cite

View full text Add to dashboard Cite

To what degree can we determine people's connections with groups through the language they use? In recent years, large archives of behavioral data from social media communities have become available to social scientists, opening the possibility of tracking naturally occurring group identity processes. A feature of most digital groups is that they rely exclusively on the written word. Across 3 studies, we developed and validated a language-based metric of group identity strength and demonstrated its potential in tracking identity processes in online communities. In Studies 1a–1c, 873 people wrote about their connections to various groups (country, college, or religion). A total of 2 language markers of group identity strength were found: high affiliation (more words like we, togetherness) and low cognitive processing or questioning (fewer words like think, unsure). Using these markers, a language-based unquestioning affiliation index was developed and applied to in-class stream-of-consciousness essays of 2,161 college students (Study 2). Greater levels of unquestioning affiliation expressed in language predicted not only self-reported university identity but also students’ likelihood of remaining enrolled in college a year later. In Study 3, the index was applied to naturalistic Reddit conversations of 270,784 people in 2 online communities of supporters of the 2016 presidential candidates—Hillary Clinton and Donald Trump. The index predicted how long people would remain in the group (3a) and revealed temporal shifts mirroring members’ joining and leaving of groups (3b). Together, the studies highlight the promise of a language-based approach for tracking and studying group identity processes in online groups.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Study 2: Testing Unquestioning Affiliation With a Behavioral...mentioning

confidence: 99%

Tracking group identity through natural language within groups

Ashokkumar¹,

Pennebaker

2022

PNAS Nexus

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent work by Ashokkumar, Galaif, and Swann (2019), show that after a public transgression of the group, strongly fused individuals strive to protect the group's reputation. And Talaifar et al (2020), found that students who were strongly fused with their university were more likely to remain in school up to a year later. Alternative studies indicate that one of the positive consequences of being fused with a group could be self‐expansion (Besta, Jaśkiewicz, Kosakowska‐Berezecka, Lawendowski, & Zawadzka, 2018), and that being fused with the romantic partner could lead to more constructive ways of coping with relationship conflicts and reduced vigilance for relationship threats (Walsh & Neff, 2018).…”

Section: Main Advances and Discoveries Since 2015 For Identity Fusionmentioning

confidence: 99%

Recent advances, misconceptions, untested assumptions, and future research agenda for identity fusion theory

Gómez

Chinchilla

Vázquez

et al. 2020

Social & Personality Psych

View full text Add to dashboard Cite

Just a decade ago, two psychologists, Swann, and Gómez, developed a new theoretical framework to explain extreme pro-group behaviors: identity fusion theory. Identity fusion refers to a visceral feeling of oneness with a group that motivates individuals to do extraordinary self-sacrifices on behalf of the group or each of its members. Since the formulation of the theory, interdisciplinary researchers of the five continents have conducted dozens of studies on identity fusion, both in laboratory and field settings. Research has deepened into the causes, consequences, underlying mechanisms, and applications of identity fusion. The development of fusion-based research has been steadfast and very prolific. Hence, the first section of the current manuscript includes an updated overview of this fast growing literature. This increasing interest for the theory has, however, been accompanied by a series of misconceptions and untested research assumptions, which we address in the second and third sections of the paper, concluding with a final section suggesting a future research agenda. Our aim is to help those interested in knowing more about identity fusion or about the causal mechanisms that lead individuals to risk their life and personal well-being for a group discarding common misconceptions as well as formulating more precise and nuanced hypotheses for future research.

show abstract

“…Family commitments, financial strain, time management, expected study load, and work commitments were also found to be related to early dropout (Nieuwoudt and Pedler, 2021). A student's relationship to the educational institution also played a role as students who were strongly 'fused' with their university were more likely to not dropout (Talaifar et al, 2021). Institutional fit, high school performance, and financial aid were also significant predictors of dropout (Elder, 2021).…”

Section: Introductionmentioning

confidence: 91%

Exploring Predicting Performance of Engineering Students Using Deep Learning

2021

18th International Conference Cognition and Exploratory Learning in Digital Age 2021

View full text Add to dashboard Cite

A significant amount of research has gone into predicting student performance and many studies have been conducted to predict why students drop out. A variety of data including digital footprints, socio-economic data, financial data, and psychological aspects have been used to predict student performance at the test, course, or program level. Fairly good prediction results have been achieved using both traditional machine learning and more recently deep learning methods. While using diverse sets of data has achieved good results, this data is often difficult and expensive to collect, and may have privacy-related issues. This paper explores the extent to which only prior performance data readily available with registrars in most Universities can be used to predict student performance in future terms. Twenty term data from 789 students enrolled an engineering program at an American University were used to train long term short term (LSTM), Bi-directional LSTM and Gated Reference Units (GRU) models to predict student performance in future terms. The results are that all three types of models were able to reasonably predict the next term's performance (F1-score of about 0.70) regardless of the number of terms a student had spent the University. The models generally did not overfit. The prediction was reasonable until about trying to predict performance on seventh term in the future, but the performance dropped beyond this point primarily due to lack of sufficient data (F1-score of about 0.2).

show abstract

A New Pathway to University Retention? Identity Fusion With University Predicts Retention Independently of Grades

Cited by 18 publications

References 28 publications

Tracking group identity through natural language within groups

Tracking group identity through natural language within groups

Recent advances, misconceptions, untested assumptions, and future research agenda for identity fusion theory

Exploring Predicting Performance of Engineering Students Using Deep Learning

Contact Info

Product

Resources

About