新冠疫情下大專生的家庭經濟狀況、心理狀態、學習困境與知覺到的學校資源

Undergraduate Students’Financial Condition, Mental Health, Remote Class Participation, and Perceived School Support during COVID–19 School Closures

COVID-19疫情大爆發對大專生造成哪些影響？而學校又提供了哪些線上資源？過往文獻多關注於遠距教學的成效，卻很少探究疫情對於學習面向以外的影響，也少有實證研究聚焦於經濟弱勢學生於疫情期間的處境。本文比較頂尖大學、公立大學、私立大學、公立技專與私立技專在疫情升溫時所提供的資源有哪些落差，而就讀不同類大專院校的經濟弱勢學生的心理狀態、學習狀況及其感受到的學校資源是否有異。分析對象橫跨92所大專院校在學生，於2021年7月進行調查。研究發現：一、經濟條件愈差的學生，愈會申請紓困補助。若他們的經濟狀況進一步因疫情下滑，將不利於心理健康。二、公立學校的經濟弱勢學生在參與遠距課程中所遭逢的困難比私校生多，而這現象在頂尖大學更明顯。三、和其他類型學校的經濟弱勢學生相比，就讀頂尖大學的經濟弱勢學生於疫情升溫期間，較能感受到學校有提供學習面向以外的資源（例如線上心理諮詢）。四、整體上私立技專於疫情時所提供的各種資源最少。

Policymakers, educators, and scholars are concerned with the extent to which educational inequality is worsened when schools are locked down during the COVID-19 pandemic. In this paper, we ask how school closures affect economically disadvantaged students, and whether there are variations in school support across different types (or tiers) of tertiary educational institutions in Taiwan. More specifically, we address how 1) students’economic background, 2) the type/tier of schools in which they are enrolled, and 3) the cross-level interaction between these two factors were associated with the probability of perceived school-level support and resources in response to the lock¬down period between May and June 2021. Our analyses are based on data from 5,904 undergraduate students across 92 universities and colleges who participated in an online survey in July 2021. We employ multilevel linear probability modeling (LPM), due to the hierarchical nature of the data, as students (Level 1) are nested within schools (Level 2). Because the data are from a nonprobability sample, we apply model-based weighting in the analyses, calculated by the Stata com¬mand“svywt”(Valliant and Dever 2018). Our focus is on comparing the differences between five types of schools, namely top universities, public four-year universities, private four-year universities, public technical colleges, and private technical colleges. Top universities refers to the 11 schools that received hundreds of millions of NT dollars in funding from the Ministry of Education to promote academic excellence in higher education between 2011 and 2015. We argue that tertiary educational institutions are highly stratified by school type, mainly because the expansion of higher education in Taiwan in the 2000’s had been too rapid and rushed, thereby creating a crisis for lower-ranked schools that experienced a lack of funds and shortage of educational resources. This problem had existed long before COVID-19 arrived in Taiwan and has been worsened during the epidemic.
There are several notable findings. First, when COVID-19 cases were surging and campuses were locked down during the studied period, students from lower income backgrounds were more likely to apply for financial aid than those from higher income backgrounds. Additionally, the level of mental stress increased among these economically under¬privileged undergraduates when they sensed that their family financial condition became worse due to the pandemic. Second, in line with previous literature, we find that economically underprivileged under¬graduates were less likely to enroll in higher-ranked universities in Taiwan. Third, our findings also reveal significant cross-level interac¬tions between students’income economic background (LV1) and school type (LV2). During the school lockdown, for instance, economically underprivileged undergraduates attending top universities were more likely to perceive their schools as having various online consultation services, such as career counseling and psychological counseling, compared to other economically underprivileged undergraduates who attended other types of schools. Interestingly, economically privileged undergraduates attending public four-year universities were more likely to report that their schools provided similar consultation services than their less privileged counterparts. Fourth, in comparison with eco¬nomically more privileged undergraduates, less privileged undergrad¬uates were less likely to report that their schools had provided enough e-learning resources or adequate remote teaching support. More importantly, this problem was more pronounced in public schools—especially among top universities—than in private schools. This sug¬gests a larger e-learning gap by family income within top universities. Fifth, private technical colleges, compared to other types of schools, provided less in the way of remote services or educational support during the school lockdown, although the within-school e-learning gap by family income was smaller among this type of school. In the conclusion, we further discuss these findings and other important implications of our research