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Advances in Social Sciences Research Journal – Vol. 8, No. 6

Publication Date: June 25, 2021

DOI:10.14738/assrj.86.10443. Chun-Chieh, T., & Cheng-Ping, C. (2021). Exploring the Cross-Level Impact of Counseling Satisfaction from the Longitudinal Study of

Learning Outcomes: A Case Study of Economically Disadvantaged Students. Advances in Social Sciences Research Journal, 8(6). 515-

532.

Services for Science and Education – United Kingdom

Exploring the Cross-Level Impact of Counseling Satisfaction from

the Longitudinal Study of Learning Outcomes: A Case Study of

Economically Disadvantaged Students

Tseng Chun-Chieh

Assistant Professor, Department of Sport Health and Leisure

Chung Hwa University of Medical Technology, No. 89

First Wenhua Street, Rende Dis., Tainan City 71703, Taiwan

Chang Cheng-Ping

Professor, Department of Education, National University of Tainan

33, Sec. 2, Shu-Lin St., West Central Dist., Tainan City 70005, Taiwan

ABSTRACT

This paper discusses the connotations between the learning outcomes of

economically disadvantaged students and time factors. We recruited 1,053

economically disadvantaged students from a private university as participants and

collected their mean scores in professional courses for 4 years. After observing the

initial learning outcomes and academic growth rates of the students, this study

concluded that counseling satisfaction had a cross-level moderating effect on

learning outcomes. Additionally, the learning outcomes of economically

disadvantaged students in professional courses exhibited decelerating growth with

time, whereas cross-level counseling satisfaction had a significant influence and

moderating effect on academic growth rate.

Keywords: economically disadvantaged students, learning outcome, longitudinal

research, counseling satisfaction, cross-level influence

INTRODUCTION

In Taiwan, the rapid development of higher education has led to the wide establishment of

universities and increased enrollment rates. However, a long-lasting sluggish economy and

worsened unemployment rates have exacerbated the education burden of low- and middle- class families, increased the population of economically disadvantaged students, and prevented

economically disadvantaged students from acquiring higher education (Yang, 2007; Chang &

Kuan, 2008; Ministry of Education, 2015; Academia Sinica National Policy Report, 2017).

Income tax filing data in 2016 revealed the average family incomes of the bottom and top 5%

of households were NT$44,000 and NT$4,687,000, respectively, the higher value being 106.52

times greater than the lower value. Application information for the Financial Aid Project for

Economically Disadvantaged University Students revealed that in the 2017 academic year,

public and private universities received 13,339 and 51,498 students, respectively.

Furthermore, application information on the deduction of tuition and incidental fees indicated

that in the same academic year, 60,872 (25.3%) and 179,052 (87.3%) students from public and

private universities applied, respectively. This indicates that the severe wealth gap resulted in

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Advances in Social Sciences Research Journal (ASSRJ) Vol. 8, Issue 6, June-2021

Services for Science and Education – United Kingdom

lower enrollment rates of economically disadvantaged students in public universities, which

generally have higher-quality resources (Ministry of Finance, 2018; Help Dreams Website,

2018).

Because of greater proportions of economically disadvantaged students entering private

universities, differences in academic performance between students of different socioeconomic

status persist. Accordingly, scholars have posited that providing economic security and

assistance to economically disadvantaged students would improve their learning outcomes,

and they highlighted family backgrounds and learning durations as key factors behind

unsatisfactory learning outcomes (Walpole, 2003; Juvonen, 2006; Reimer & Pollak, 2010;

Bastedo & Jaquette, 2011; Doyle, 2011; Irvin, Meece, Byun, Farmer, & Hutchins, 2011; Morales,

2014; Wang, Yang, Liu, & Chang, 2013).

With domestic and overseas governments providing attention and support to economically

disadvantaged students, this study discussed whether funding support improves learning

outcomes. Domestic and overseas literature and observations have revealed that various

factors influence learning performance. From a young age, economically disadvantaged

students experience a shortage of family education management and generally lack motivation

to actively learn and participate in class. In addition, instructor bias toward students’ lifestyles

and required counseling methods results in students having poor learning outcomes. When

these students enter college, the increased number of courses and their difficulty may cause

them to lose confidence in learning. Moreover, social incentives may encourage students to skip

classes frequently and invest less time in learning, resulting in a lag in learning performance

compared with their peers (Hsieh, 2003; Li & Yu, 2005; Liu & Yang, 2009). Learning activity

experience is influenced by the environment; higher satisfaction with the learning environment

may encourage students to invest more time in the course or relevant activities and interact

with the instructor, thereby enhancing their learning performance (Astin, 1984; Tinto, 1975,

1993; Pascarella & Terenzini, 2005; Alm, Låftman, Sandahl, & Modin, 2019). Socioeconomic

status and academic performance exhibit a moderate-to-low degree of relationship (Sirin,

2005; White, 1982; Sun & Tsai, 2007). Relevant studies have indicated that socioeconomic

status and learning outcomes are related to social connotations, learning duration, counseling

facilities, and learning outcomes.

This study recruited economically disadvantaged students from private universities as

participants to discuss the influences of the “growth” of learning outcomes (hereinafter

“academic growth”) in professional courses and counseling satisfaction on learning outcomes.

Because the research data comprise hierarchical data and changes, we used multilevel models,

hierarchical linear models, and growth curve models to examine the relationships and

moderating effects between variables of different hierarchical levels; this prevented the

analysis data being influenced by misspecified or misleading relationships among the

hierarchical levels (Ferron, Dailey, & Yi, 2002; Ferron et al., 2004; Raudenbush & Bryk, 2002).

In summary, this study explored the academic growth trajectory of economically disadvantaged

students in professional courses measured at different times as well as the influence and

moderating effect of counseling satisfaction on the academic growth of such students when the

variables are in different group levels.

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Chun-Chieh, T., & Cheng-Ping, C. (2021). Exploring the Cross-Level Impact of Counseling Satisfaction from the Longitudinal Study of Learning

Outcomes: A Case Study of Economically Disadvantaged Students. Advances in Social Sciences Research Journal, 8(6). 515-532.

URL: http://dx.doi.org/10.14738/assrj.86.10443

LITERATURE REVIEW

Despite the definition of economically disadvantaged students differing between disciplines,

the definitions collectively encompass students in low economic status, disability, and minority

groups (Li, 1997; Lee, 2004; Tseng, Chen, & Chen, 2006; Lu, 2006; Wang, Yang, Liu, Chang, &

Huang, 2014). In Taiwan and other countries, economically disadvantaged students are defined

as students from low-income families, families in special and unique conditions, indigenous

students, children with foreign parents, students with disabilities, and students who have

applied for financial aid (Ministry of Education, 2018).

An examination on how economically disadvantaged students spend their time revealed

learning duration to be an influential factor for unsatisfactory learning outcomes (Sun & Tsai,

2007). Studies have applied hierarchical linear modeling (HLM) in longitudinal research to

observe the contextual relationship between individuals and groups and to determine whether

the different measurement times are “nested” within individuals and multilevel data; the

analysis results have then been used to understand academic growth trajectories (Wu, 2005;

Lee & Wen, 2008; Lin, Chang, & Wu, 2008; Gao, 1999; Chen & Chou, 2009; Wu & Luh, 2010;

Wen, 2016; Lutz, Kolehmainen, & Bartholomew, 2010; Shapley, Sheehan, Maloney, & Walker,

2010). Therefore, a suitable method of verification analysis is to incorporate longitudinal

research for evaluating whether learning outcomes change by time and analyzing the

differences in data collected in separate time periods.

The literature review on the influences of academic growth trajectories and satisfaction with

learning outcomes revealed that learning performance changes progressively with time.

Economically disadvantaged students invest less time in learning, which contradicts the theory

that students who invest more time in studying display greater academic growth. Family

socioeconomic status and class interaction exhibit significant influences on the initial status

and growth rate of academic performance in middle-school students (Chang, 2010). Relevant

studies have indicated that the academic growth trajectory of adolescent students is a

nonlinear, decelerating growth curve (Lee, 2010; Lin, 2011). Time has a direct influence on

learning outcomes; assessments of learning outcomes made in single time periods have been

unable to portray the relationship between individual learning and environmental factors or

the influence of time changes (Ting, 2003; Steelman, Levy, & Sneel, 2004; Brooks, Schraw, &

Crippen, 2005; Goodman & Wood, 2005; Yorke, Knight, Baume, & Tait, 2006; He, Liu, & Wu,

2010; Liu, Tsai, & Li, 2016). Accordingly, this study proposed the following hypothesis:

H1: The learning outcomes of economically disadvantaged students exhibit growth

with time.

Overseas literature has indicated that learning outcomes are the accumulated results of

combining external teaching and internal learning efforts. Such studies have stressed that

learning outcomes are generally used as responses for assessing student capabilities; are the

result of converting conventional development in disciplines into growth in valuable

capabilities; are the expected qualifications after the learning process is completed; and,

through evaluation instruments, are used to understand learning results, thereby serving as a

reference for improving the teaching process (Mayer, 2001; Gallavara et al., 2008, Ko, 2011).

By contrast, domestic literature posits that learning outcomes are professional capabilities in

various domains attainable through learning and measurable using evaluation instruments.