22 SES 04 E, Student Engagement and Active Learning
In the last five decades, higher education systems and the student learning process have undergone a significant transformation under the influence of social, economic and political forces. The most influential trends for many national systems of higher education are massification (Trow, 1972; Marginson 2016b), globalization of higher education and dissemination of neo-liberal ideas (Olssen & Peters 2005; Zepke 2015). These institutional and system-level changes raise concerns about measurable indicators of the higher education institution’s educational activities, and effective policies enhancing student performance (Alexander 2000; Van Damme 2001; Hornsby & Osman 2014) and engagement (Trow 2006).
The student engagement approach in measuring university performance in undergraduate education is considered as the most acceptable for developing educational policies in higher education institutions (Pascarella 2001; Ewell & Jones 1993, 1996; Kuh 2009). A significant number of studies empirically prove the correlation between student engagement and academic achievement and student gains (Davis & Murrell 1993; Carini et al. 2006; Krause and Coates 2008; Kuh et al. 2008; Choi and Rhee 2014; Chi, Liu, & Bai 2017). This allows us to consider student engagement as a fruitful construct for exploring the learning process at university and institutional assessment (Kuh 2009).
As a rule, student engagement research is compiled from one university from one country (see, for example: Peng et al. 2017; Tadesse et al. 2018; Sawang et al. 2017; Asare et al. 2017) or a number of universities, in one particular region of a country or in one particular country (see, for example: Hu et al. 2012; Chiu et al. 2016). Some studies explored the relation of student engagement with the personal characteristics of the students. For example, Kinzie et al. (2007) found significant differences in student engagement between male and female students. Salmela-Aroa and Reada (2017) identified that students in Finland are more engaged in the beginning of the study at an university or a polytechnic. Hu and Kuh (2002) also found the set of personal characteristics of students significantly related to the level of student engagement.
This series of empirical studies found institutional differences in student engagement. Thus, J.F. Ryan (2005) analyzed data about 142 US colleges and universities and found a negative correlation between institutional expenditures and student engagement. Pike and Kuh (2005) constructed the typology of higher education institutions on the basis of the data about student engagement of their students. Previously, it demonstrated the effects of teaching and the context of learning on student engagement (Umbach & Wawrzynski 2005; Almarghani & Mijatovic 2017).
Regardless of the huge amount of scientific and practical research of student engagement prevalence and relation with personal and institutional characteristics, there is a lack of research concerning differences in engagement styles across nations. However, cross-national research in learning differences showed that national and cultural has an context influence and can define learning patterns and approaches (Katz 1988; Pratt 1991; De Vita 2001; Yamazaki 2005; Joy and Kolb 2009). The shortage of empirical evidence about student engagement patterns in various cultural contexts limits researchers and practices in implementing effective policy practices. Whether the practice, enhancing student engagement in one country, will be effective in other nation’s institutions? This research is aimed to answer two following research questions in order to promote our understanding in this area:
(1) Do national differences significantly relate to student engagement and contribute to their variance?
(2) Are there national differences that correlate a significance between personal and disciplinary characteristics of students and their student engagement?
The data from an undergraduate survey conducted by the international consortium the ‘Student Experience in the Research University’ (SERU) was employed. The sample includes leading universities from three countries with different trajectories of higher education systems development: the United States, China, and Russia. The US is a country that was involved in the first stage of the massification process that began in the 60’s. At this moment the US has one of the highest numbers of students enrolled in universal higher education, according to classification (Trow 1974). In Russia, the massification process began in Soviet times with sharp growth in enrollment in the 90’s. Now, Russia is a country with mass higher education, the gross enrollment ratio is accounted to be 77% (Froumin & Platonova 2017). Though, China is a country with mass higher education, the process of higher education expanding in this country began recently, in the 1990’s (Bie and Yi 2014; Chi, Liu, & Bai 2017). However, in contradistinction with Russia, the growth rate of higher education enrollment in China remains to be high. Despite differences at this stage of the higher education system development, the ideas of global competitions and performative discourse are traits of higher education policies of these countries. Thus, the US is a world leader by the number of universities taking top positions in international university rankings while China and Russia initiated special governmental projects encouraging national universities to improve global competitiveness. The data about twenty US and three Chinese research universities as well as about one Russian institution (National Research University Higher School of Economics) was derived from the SERU Web survey of undergraduate students. In the other eleven Russian universities, the survey was conducted by employing SERU methodology. The final sample consists of 17,065 undergraduate students from Russia (12 institutions, the HSE is included), 19,737 students from China (3 institutions), 130,117 students from the US (20 institutions). Universities participating in the research at present are the top universities in their countries. Twenty US universities take positions among the top-200 universities in the ARWU ranking. Three Chinese universities are members of the ‘Project 211’ that was initiated for promoting some Chinese universities in the global market. Finally, Russian universities in the sample included in the ‘5-100 Project’.
The data analysis was conducted in two phases. In the first phase, to examine national differences in student engagement in this research. The student engagement styles through factor analysis were constructed. To account values for student engagement styles the principal component analysis of 20 variables measuring student engagement with varimax rotation was employed. The number of factors (i.e., styles) extracted was determined using eigenvalues. Five factors representing 65.4% variance in indicators were extracted and rotated to identify student-engagement styles. The following engagement style factors were identified: 1) Cognitive activity, 9 items, Cronbach’s alpha=0.906. 2) Class Engagement, 3 items, Cronbach’s alpha=0.877. 3) Social Integration, 5 items, Cronbach’s alpha=0.745. 4) Disengagement, 3 items, Cronbach’s alpha=0.683. 5) Research Engagement, 2 items, Cronbach’s alpha=0.595. In order to explore national differences in student engagement, multiple linear regression analysis was conducted for each of the five engagement styles. For each of the engagement styles for three models were carried out: 1) a model without national variables; 2) a model with national variables; 3) a model with national variables and interactions between national variables and individual characteristics. The analysis of tolerance statistics indicated no multicollinearity problems. The analysis of the contribution of the factors to the dependent variable by comparison of the R2 values is widely used in papers with the same objectives (see, for example, Lundberg & Schreiner 2004). According to my empirical research questions mentioned in the introductory part, the following conclusions can be confirmed on the basis of this research: (1) National differences are significant predictors of student engagement styles. The most sizeable contribution of national differences was cognitive activity style and research engagement style. (2) National differences moderated relations between student engagement and personal and disciplinary characteristics employed in this research (gender, a field of study, plans after graduation).
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