Students make varying choices regarding how to allocate their time between a range of activities, which has important implications for their learning and development (Pace, 1981). Some studies find that undergraduate students are not sufficiently engaged in their studies and spend considerable amounts of time partying and other leisure activities (Armstrong & Hamilton, 2013; Arum & Roksa, 2011). In contrast, some other studies indicate that college students fall into a state of "poverty" during the time of independent exploration, spending "all the peak time" studying (Lingo & Chen, 2023), especially students in highly selective universities are facing overwhelming time demands (Haktanir et al., 2021). What is more, it is much harder for firs-year college students to manage time conflicts due to experiencing a critical turning point in knowledge and psychology (Armstrong & Hamilton, 2013).

    There is a difference between "natural time" and "social time" according to Adam (1994), the "natural time" is fixed and divisible units that can be measured, while quality, complexity, and mediating knowledge are preserved exclusively for the conceptualization of "social time". The "social time" is organized around values, goals, morals, and ethics, whilst simultaneously being influenced by group tradition, habits, and legitimized meanings, which can explain cross-cultural and historical differences in the allocation of time. At the same time, individuals also allocate their time based on their preferences, rather than allocating their time to comply with the requirements of "social time" (Hartmut, 2010).

   The concept of time provides basic theoretical clues for us to describe and understand the possible differences in time allocation among students (Fosnacht, McCormick, & Lerma, 2018; Toutkoushian & Smart, 2001). Compared with students in primary and secondary schools, the time discipline of college is weakened and has the characteristics of flexibility, although college students' time allocation is still subject to compulsory discipline. It is worth noting that flexibility is both an opportunity and a challenge for students. For instance, previous studies suggest that certain groups of students, such as low-income and disadvantaged students from underdeveloped areas, may face more constraints in discretionary time (Jaeger, 2009). Moreover, students with different level of academic performance may differ in their understandings of activities as well as differ in how they make plans and arrange priorities (Cambridge-Williams et al., 2013). In short, previous studies imply that students’ time allocation might be influenced by various factors, such as individual characteristics, family background, previous experiences in high school, and peers’ behaviors in college.

    Although previous studies offer valuable insights into the influence factors of time allocation(Crispin & Kofoed, 2019), it remains unclear the characteristics of students' time allocation. Additionally, previous studies simplify comparisons between the duration students spending on different activities in a cluster or discriminant analysis(Innis & Shaw, 1997; Pike & Kuh, 2005), overlooking the push and pull of various activities that force students to make trade-off on time allocation, especially for first-year students from elite or research universities.

    This paper attempts to investigate the characteristics of first-year college students’ time allocation and divide students into different types according to their time allocation. Furthermore, this paper will deeply investigate the characteristics of different types of students and analyze what factors affect students’ time allocation.

To answer the above research questions, we conducted two rounds of surveys among first-year undergraduates at a top research university in China. The baseline survey was carried out as soon as these students were enrolled in the university and the information were collected about their family background, previous high school experience and self-evaluation of ability development. The follow-up survey was conducted when these students finished their first-year study, it collected information about their time allocation, ability development and peers’ behaviors. A total of 1021 students participated in the two rounds of surveys. We began by analyzing students’ self-reported time allocation in a typical week and calculating the percentage of time spending in each activity such as class preparation, socializing and exercising, taking part in co-curricular activities and community service, working for pay. Then we classified students into different types according to the characteristics of their time allocation by using the latent profile analysis (LPA). The advantage of LPA is a probabilistic framework to describe the latent distribution rather than simply analyzing the difference between individuals (Crispin & Kofoed, 2019; Vermunt & Magidson, 2003). We categorized students into mutually exclusive and exhaustive subgroups based on their time-use behavior (Lanza & Cooper, 2016) and determined how well the model fits by taking fitting indexes such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted Bayesian information criterion (aBIC), and entropy values into consideration (Lubke & Muthen, 2007). Next, we performed the Lo-Mendell-Rubin (LMR) test and the parametric bootstrapped likelihood ratio test (BLRT) to compare the candidate models. Furthermore, we developed a multinomial logistic regression model to examine what factors were related to different types of students with different characteristics of time allocation. Specifically, we added into the regression equation students’ demographic characteristics (such as gender, whether the child has any brothers or sisters), family background (such as family income, father’s education level and occupation type, hometown province), and previous experience (such as college entrance examination scores, types of high school, the graduation year of high school, and college majors)

On average, students spent about 57.53% of their spare time on class preparation, 29.45% of their spare time socializing and exercising, 9.83% of their spare time taking co-curricular activities and community service, 3.19% of their spare time working for pay. However, the standard deviations indicated that there was considerable variation in how students allocated their time to these activities. We further found that all the students could be classified into four types: positive scholar (62.48%), social expert (15.70%), active volunteer (12.98%), and enthusiastic worker (8.83%) by fitting models that identified between two and six latent classes. Regression results show that students’ gender, major fields, and family income were predictive of students’ time allocation. Specifically, females were more likely to be active volunteers rather than positive scholars; students majoring in physical and life science fields, as well as mathematics and computer science compared to students majoring in social science, were less likely to be enthusiastic workers rather than positive scholars. Notably, students from low-income families were less likely to be active volunteers relative to positive scholars, while students from high-income families were more likely to be social experts. Additionally, we found no significant relationship between previous experience and students’ types of time allocation. As Kuh et al. (2005) have argued, what students do during colleges counts more in terms of desired outcomes than who they are or where they go to college (Pike & Kuh, 2005). The analyses on college students’ time allocation would help us gain a clearer insight into student development. What is more, the heterogeneous types of students also showed that social time had both structural and dynamic characteristics, which was of great significance for administrators to help first-year students better adapt to college life and achieve academic success in the future.

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