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Psychological stress encompasses emotional tension and pressure experienced by people, which usually arises from situations people find challenging. However, more is needed to know about the pressures faced by international college students studying in China. The goal of this study is to investigate the various stressors that international college students in China face and how they cope with stress (coping mechanisms). Twenty international students were interviewed to gather data, which was then transcribed. Thematic analysis and coding were applied to the qualitative data, revealing themes related to the causes of stress. The following themes emerge from this data: anticipatory anxiety or future stress, social and cultural challenges, financial strain, and academic pressure. These themes will help understand the various stressors international college students in China face and how they try to cope. Studying how international college students in China cope with challenges can guide the development of targeted interventions to support their mental health. Research suggests that integrating aesthetics and connectivity into design interventions can notably improve the well-being of

Founded in 2007, the Foothill College Physics Show has served nearly a quarter of a million attendees in the two decades that have followed. This demo show features both performances for the public and field trips for students from local Title 1 schools. The college's students play an important role, acting as both on-stage talent, leading tours of the college, and helping build equipment. From a small beginning, it now hosts over twenty-five thousand attendees a year, and is an important part of the college's outreach efforts.

Minority college students face unique challenges shaped by their identities based on their gender/sexual orientation, race, religion, and academic institutions, which influence their academic and social experiences. Although research has highlighted the challenges faced by individual minority groups, the stigma process-labeling, stereotyping, separation, status loss, and discrimination-that underpin these experiences remains underexamined, particularly in the online spaces where college students are highly active. We address these gaps by examining posts on subreddit, r/college, as indicators for stigma processes, our approach applies a Stereotype-BERT model, including stance toward each stereotype. We extend the stereotype model to encompass status loss and discrimination by using semantic distance with their reference sentences. Our analyses show that professional indicated posts are primarily labeled under the stereotyping stage, whereas posts indicating racial are highly represented in status loss and discrimination. Intersectional identified posts are more frequently associated with status loss and discrimination. The findings of this study highlight the need for multifaceted

The study investigated the use of electronic resources/information by library users in selected colleges of Solapur University. Specifically, to investigate the awareness and level of use of electronic resources; perceived reliance, benefits and impact of use of electronic resources on the research activities. The research design for the study was a survey. Questionnaire schedule was used to collect data from 1022 library users from selected colleges of Solapur University. The result revealed that preponderance of users from aided 33.51% Self financing 26.10% and Education colleges 43.24 % preferred to visit the Library once in three days. While analyzing the entire college libraries regarding the frequency of visit, users gave first preference to once in three days i.e. 27.2%. College wise analysis reveals that mainstream of users from Aided Colleges 38%, Self financing Colleges 28.3%, Engineering Colleges 43%, Education colleges 53.2% and Pharmacy Colleges 23.4% are spending their time 1-2 hrs in libraries and 40.8%visit college libraries to issue and return books and in the device usage (33.9%) of users ranked mobile phone as the second device for accessing the e-resources. It i

Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main factors: the necessity for large-scale longitudinal datasets, the prevalence of black-box machine learning models lacking transparency, and the tendency of existing approaches to provide aggregated insights at the population level rather than individualized understanding. To tackle these challenges, this paper presents I-HOPE, the first Interpretable Hierarchical mOdel for Personalized mEntal health prediction. I-HOPE is a two-stage hierarchical model that connects raw behavioral features to mental health status through five defined behavioral categories as interaction labels. We evaluate I-HOPE on the College Experience Study, the longest longitudinal mobile sensing dataset. This dataset spans five years and captures data from both pre-pandemic periods and the COVID-19 pandemic. I-HOPE achieves a prediction accuracy of 91%, significantly surpassing the 60-70% accuracy of baseline methods. In addition, I-HOPE distills complex patterns int

We present OpenStaxQA, an evaluation benchmark specific to college-level educational applications based on 43 open-source college textbooks in English, Spanish, and Polish, available under a permissive Creative Commons license. We finetune and evaluate large language models (LLMs) with approximately 7 billion parameters on this dataset using quantized low rank adapters (QLoRa). Additionally we also perform a zero-shot evaluation on the AI2 reasoning challenge dev dataset in order to check if OpenStaxQA can lead to an improved performance on other tasks. We also discuss broader impacts relevant to datasets such as OpenStaxQA.

The research was conducted to identify the factors that influence college students' satisfaction with their college experience. Firstly, the study was focused on the literature review to determine relevant factors that have been previously studied in the literature. Then, the survey analysis examined three main independent factors that have been found to be related to college students' satisfaction: Major Satisfaction, Social Self-Efficacy, and Academic Performance. The findings of the study suggested that the most important factor affecting students' satisfaction with their college experience is their satisfaction with their chosen major. This means that students who are satisfied with the major they have chosen are more likely to be overall satisfied with their college experience. It's worth noting that, while the study found that major satisfaction is the most crucial factor, it doesn't mean that other factors such as Social Self-Efficacy, Academic Performance, and Campus Life Satisfaction are not important. Based on these findings, it is recommend that students prioritize their major satisfaction when making college choices in order to maximize their overall satisfaction with t

Large-scale administrative data is a common input in early warning systems for college dropout in higher education. Still, the terminology and methodology vary significantly across existing studies, and the implications of different modeling decisions are not fully understood. This study provides a systematic evaluation of contributing factors and predictive performance of machine learning models over time and across different student groups. Drawing on twelve years of administrative data at a large public university in the US, we find that dropout prediction at the end of the second year has a 20% higher AUC than at the time of enrollment in a Random Forest model. Also, most predictive factors at the time of enrollment, including demographics and high school performance, are quickly superseded in predictive importance by college performance and in later stages by enrollment behavior. Regarding variability across student groups, college GPA has more predictive value for students from traditionally disadvantaged backgrounds than their peers. These results can help researchers and administrators understand the comparative value of different data sources when building early warning sy

This paper will illustrate the usage of Machine Learning algorithms on US College Scorecard datasets. For this paper, we will use our knowledge, research, and development of a predictive model to compare the results of all the models and predict the public and private net prices. This paper focuses on analyzing US College Scorecard data from data published on government websites. Our goal is to use four machine learning regression models to develop a predictive model to forecast the equitable net cost for every college, encompassing both public institutions and private, whether for-profit or nonprofit.

Motivated by studying the effects of marriage prospects on students' college major choices, this paper develops a new econometric test for analyzing the effects of an unobservable factor in a setting where this factor potentially influences both agents' decisions and a binary outcome variable. Our test is built upon a flexible copula-based estimation procedure and leverages the ordered nature of latent utilities of the polychotomous choice model. Using the proposed method, we demonstrate that marriage prospects significantly influence the college major choices of college graduates participating in the National Longitudinal Study of Youth (97) Survey. Furthermore, we validate the robustness of our findings with alternative tests that use stated marriage expectation measures from our data, thereby demonstrating the applicability and validity of our testing procedure in real-life scenarios.

The cost of attending college has been steadily rising and in 10 years is estimated to reach $140,000 for a 4-year public university. Recent surveys estimate just over half of US families are saving for college. State-operated 529 college savings plans are an effective way for families to plan and save for future college costs, but only 3% of families currently use them. The Office of the Illinois State Treasurer (Treasurer) administers two 529 plans to help its residents save for college. In order to increase the number of families saving for college, the Treasurer and Civis Analytics used data science techniques to identify the people most likely to sign up for a college savings plan. In this paper, we will discuss the use of person matching to join accountholder data from the Treasurer to the Civis National File, as well as the use of lookalike modeling to identify new potential signups. In order to avoid reinforcing existing demographic imbalances in who saves for college, the lookalike models used were ensured to be racially and economically balanced. We will also discuss how these new signup targets were then individually served digital ads to encourage opening college saving

College students with ADHD respond positively to simple socially assistive robots (SARs) that monitor attention and provide non-verbal feedback, but studies have been done only in brief in-lab sessions. We present an initial design and evaluation of an in-dorm SAR study companion for college students with ADHD. This work represents the introductory stages of an ongoing user-centered, participatory design process. In a three-week within-subjects user study, university students (N=11) with self-reported symptoms of adult ADHD had a SAR study companion in their dorm room for two weeks and a computer-based system for one week. Toward developing SARs for long-term, in-dorm use, we focus on 1) evaluating the usability and desire for SAR study companions by college students with ADHD and 2) collecting participant feedback about the SAR design and functionality. Participants responded positively to the robot; after one week of regular use, 91% (10 of 11) chose to continue using the robot voluntarily in the second week.

College students often face academic challenges that hamper their productivity and well-being. Although self-help books and productivity apps are popular, they often fall short. Books provide generalized, non-interactive guidance, and apps are not inherently educational and can hinder the development of key organizational skills. Traditional productivity coaching offers personalized support, but is resource-intensive and difficult to scale. In this study, we present a proof-of-concept for a socially assistive robot (SAR) as an educational coach and a potential solution to the limitations of existing productivity tools and coaching approaches. The SAR delivers six different lessons on time management and task prioritization. Users interact via a chat interface, while the SAR responds through speech (with a toggle option). An integrated dashboard monitors progress, mood, engagement, confidence per lesson, and time spent per lesson. It also offers personalized productivity insights to foster reflection and self-awareness. We evaluated the system with 15 college students, achieving a System Usability Score of 79.2 and high ratings for overall experience and engagement. Our findings sug

Colleges and universities are increasingly turning to algorithms that predict college-student success to inform various decisions, including those related to admissions, budgeting, and student-success interventions. Because predictive algorithms rely on historical data, they capture societal injustices, including racism. In this study, we examine how the accuracy of college student success predictions differs between racialized groups, signaling algorithmic bias. We also evaluate the utility of leading bias-mitigating techniques in addressing this bias. Using nationally representative data from the Education Longitudinal Study of 2002 and various machine learning modeling approaches, we demonstrate how models incorporating commonly used features to predict college-student success are less accurate when predicting success for racially minoritized students. Common approaches to mitigating algorithmic bias are generally ineffective at eliminating disparities in prediction outcomes and accuracy between racialized groups.

In considering the college admissions problem, almost fifty years ago, Gale and Shapley came up with a simple abstraction based on preferences of students and colleges. They introduced the concept of stability and optimality; and proposed the deferred acceptance (DA) algorithm that is proven to lead to a stable and optimal solution. This algorithm is simple and computationally efficient. Furthermore, in subsequent studies it is shown that the DA algorithm is also strategy-proof, which means, when the algorithm is played out as a mechanism for matching two sides (e.g. colleges and students), the parties (colleges or students) have no incentives to act other than according to their true preferences. Yet, in practical college admission systems, the DA algorithm is often not adopted. Instead, an algorithm known as the Boston Mechanism (BM) or its variants are widely adopted. In BM, colleges accept students without deferral (considering other colleges' decisions), which is exactly the opposite of Gale-Shapley's DA algorithm. To explain and rationalize this reality, we introduce the notion of reciprocating preference to capture the influence of a student's interest on a college's decisio

Medication literacy is integral to health literacy, pivotal for medication safety and adherence. It denotes an individual's capacity to discern, comprehend, and convey medication-related information. Existing scales, however, are time-consuming and predominantly cater to patients and community dwellers, necessitating a more succinct instrument. This study presents the development of a brief Medication Literacy Scale (MLS-14) utilizing classical test theory (CTT) and item response theory (IRT), targeting a college student demographic. The MLS-14's abbreviated version, a 6-item scale (MLS-SF), was distilled through CTT and IRT methodologies, engaging 2431 Chinese college students to scrutinize its psychometric properties. The MLS-SF demonstrated a Cronbach's α of 0.765, with three extracted factors via exploratory factor analysis, accounting for 66% of the cumulative variance. All items exhibited factor loadings above 0.5. The scale's three-factor structure was substantiated through confirmatory factor analysis with satisfactory fit indices (chi2/df=5.11, RMSEA=0.063, GFI=0.990, AGFI=0.966, NFI=0.984, IFI=0.987, CFI=0.987). IRT modeling confirmed reasonable discrimination and locatio

Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly. Prompting in-context is not robust to context distractions, and often fails to confer much information about the new concepts. Classic methods for few-shot word learning in NLP, relying on global word vectors, are less applicable to large language models. In this paper, we introduce a novel approach named CoLLEGe (Concept Learning with Language Embedding Generation) to modernize few-shot concept learning. CoLLEGe is a meta-learning framework capable of generating flexible embeddings for new concepts using a small number of example sentences or definitions. Our primary meta-learning objective is simply to facilitate a language model to make next word predictions in forthcoming sentences, making it compatible with language model pretraining. We design a series of tasks to test new concept learning in challenging real-world scenarios, including new word acquisition, definition inference, and verbal reasoning, and demonstrate that our method succeeds in each setting without task-specific training. Code and data for our project can be found a

In an effort to quantify and combat sexual assault, US colleges and universities are required to disclose the number of reported sexual assaults on their campuses each year. However, many instances of sexual assault are never reported to authorities, and consequently the number of reported assaults does not fully reflect the true total number of assaults that occurred; the reported values could arise from many combinations of reporting rate and true incidence. In this paper we estimate these underlying quantities via a hierarchical Bayesian model of the reported number of assaults. We use informative priors, based on national crime statistics, to act as a tiebreaker to help distinguish between reporting rates and incidence. We outline a Hamiltonian Monte Carlo (HMC) sampling scheme for posterior inference regarding reporting rates and assault incidence at each school, and apply this method to campus sexual assault data from 2014-2019. Results suggest an increasing trend in reporting rates for the overall college population during this time. However, the extent of underreporting varies widely across schools. That variation has implications for how individual schools should interpret

A growing number of college applications has presented an annual challenge for college admissions in the United States. Admission offices have historically relied on standardized test scores to organize large applicant pools into viable subsets for review. However, this approach may be subject to bias in test scores and selection bias in test-taking with recent trends toward test-optional admission. We explore a machine learning-based approach to replace the role of standardized tests in subset generation while taking into account a wide range of factors extracted from student applications to support a more holistic review. We evaluate the approach on data from an undergraduate admission office at a selective US institution (13,248 applications). We find that a prediction model trained on past admission data outperforms an SAT-based heuristic and matches the demographic composition of the last admitted class. We discuss the risks and opportunities for how such a learned model could be leveraged to support human decision-making in college admissions.