Quantitative data analysis[[abstract]]This study aims to explore the latent impact of college student engagement on learning development. We have considered students’ background to determine the effect of student engagement and learning development. Based on the Student Engagement Questionnaire, this study collected 2,651 valid questionnaires from public and private universities in Taiwan. Factor analysis, t-test, one-way ANOVA, and SEM were used to analyze the data. The result reveals that the relevant factors of learning will promote student engagement and their future development. The findings may provide useful information for policy and practice in campus.[[sponsorship]]IMIP 2014[[conferencetype]]國際[[conferencedate]]20141023~20141024[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

[[corresponding]]Chang, Dian-Fu

Chang, Dian-Fu

Quantitative data analysisThis study aims to explore the latent impact of college student engagement on learning development. We have considered students’ background to determine the effect of student engagement and learning development. Based on the Student Engagement Questionnaire, this study collected 2,651 valid questionnaires from public and private universities in Taiwan. Factor analysis, t-test, one-way ANOVA, and SEM were used to analyze the data. The result reveals that the relevant factors of learning will promote student engagement and their future development. The findings may provide useful information for policy and practice in campus.IMIP 2014國際20141023~20141024紙本YTaipei, Taiwa

Tamkang University Institutional Repository

		 1		IMIP2014 at Taiwan on Oct.23-25thDetermining the relationship between college student engagement and learning development by SEMDian-Fu, Chang 1 and Shu-ching, Wu 21 Graduate Institute of Educational Policy and Leadership, Tamkang UniversityNo. 151, Yingzhuan Rd., Tamsui Dist., New Taipei City 25137, Taiwan 140626@mail.tku.edu.tw2 Department of Educational Policy and Administration, National Chi Nan UniversityNo. 1, Daxue Rd., Puli Township, Nantou County 54561, Taiwan shiela@wagor.tc.edu.twAbstract. This study aims to explore the latent impact of college student engagement on learning development. We have considered students’ background to determine the effect of student engagement and learning development. Based on the Student Engagement Questionnaire, this study collected 2,651 valid questionnaires from public and private universities in Taiwan. Factor analysis, t-test, one-way ANOVA, and SEM were used to analyze the data. The result reveals that the relevant factors of learning will promote student engagement and their future development. The findings may provide useful information for policy and practice in campus.Keywords: higher education, student engagement, learning development, SEM 1. Introduction. Previous studies indicated that college students’ time and energy devoted to purpose activities in campus has become one of useful predictors to explain their learning and personal development [1-4]. Chickering and Gamson demonstrated that “Seven Principles for Good Practice in Undergraduate Education” has become well known engagement indicators. Their principles include student-faculty contact, cooperation among students, active learning, feedback, time on task, high expectations, and respect for diverse talents [5]. Student engagement is defined as a concept that requires psychological connections within the academic environment (e.g., positive relationships between adults and peers) in addition to student’s active behaviors (e.g., attendance and effort). Effective interventions have also addressed related to student engagement comprehensively. The focusing has found not only on academic or behavioral skills, but also on social and interpersonal activities, particularly the need for supportive connections to other adults and peers [6]. Student engagement is not conceptualized as an attribute of the student, but rather a state of being that is highly influenced by contextual factors, like home, school, and peers, which will provide consistent support for student learning [7].1.2 Conceptual framework of student engagement. Various studies agreed that student engagement is a relevant and multidimensional conception with well construction. It can be used to integrate students’ thoughts, feelings, and behaviors [8-10]. Generally, previous studies have incorporated the concept into affective, behavioral, and cognitive engagement [8, 11-12]. Our conceptual research framework of student engagement argued that students demonstrate their levels of engagement through a variety of behavioral, emotional, and cognitive engagement. Participation, in terms of behavioral engagement, includes basic behaviors such as amount of reading and writing, frequency of learning activities of campus life, which will explain by active effort, student-faculty interaction, and civic issues. Affective component of student engagement refers to the student’s feelings of belonging in campus and valuing the outcomes that college provided, which will explain by student’s interest, motivation, and concentration on courses. Approaches, in the cognitive dimension, have divided into four cognitive strategies, namely analysis, integration, judgment, and application.1.3 The relationship of student engagement and learning development. Highly engaged students show that their behavioral involvement in learning activities accompanied by positive emotions. They usually demonstrate positive emotions during ongoing actions including enthusiasm, optimism, curiosity, and interest [13-14]. Furlong and Christenson pointed that student engagement has practical implications [9]. To determine the relationships, this study focuses on the variables that might have high relationships with student engagement.2. Research design. This study has followed the NSSE’s questionnaire to collect students’ view on their engagement [15-16]. The adapted questionnaire includes backgrounds, dimensions of student engagement, and learning development. The key variables are listed as Table 1.TABLE 1. Key variables in questionnaireVariables	Questionnaire contentsBackgrounds	student background	Gender, major field, grade, family SESuniversity background	Public/private, general/vocational, location of universitiesStudent engagement	participation	Amount of reading and writing, frequency of learning activitiesattitudes	Experiences or attitudes to coursesapproaches	Cognitive approachesLearning development	academic performance	GPA (score)learning satisfaction	Satisfaction of learning activitiescareer development	Career ambition (internships, study abroad, community service, working on a research project, preparing for exams)The participants were asked to fill their responses in the 5-point response scale (1=strongly disagree/never, 2=disagree/seldom, 3=no comments/sometimes, 4=agree/usually, 5=strongly agree/always). The original set of questions includes 53 items. After experts’ reviewing and pilot testing, the items were revised and reduced to 40. In the participation of reading and writing, Cronbach’s α is .779 (with 59.114% of variance explained). In participation frequency of learning activities, Cronbach’s α is .914 (with 54.422% variance explained). In attitudes of experiences or attitudes to courses, Cronbach’s α is .627 (with 49.720% of variance explained). In the cognitive approaches, Cronbach’s α is .796 (with 54.655% of variance explained). According to the Cronbach’s α test, the reliability of our questionnaire is quite fit.The target group is 1,038,041 full-time enrollment students of 162 universities or colleges in the academic year 2012 in Taiwan. The samples are 2,651 students from 13 universities or colleges. It represents the sampling at 95% confident level. Factor analysis, t-test, one-way ANOVA, and structural equation modeling were used to analyze the data. 3. Results.3.1 Engaged in reading and writing. Reviewing the number of assigned books to read per academic year, the result shows 84.0% of students reported they read fewer than 11, while the non-assigned books to read fewer than 11, there are 80.3% of students say yes. In the writing engagement, referring the number of papers written per academic year, there are 37.3% of students said "none" in the item of 20 pages or more, and 15.8% of students said "none" in the item of 5 pages or less. The detailed amount of reading and writing per academic year for the students has presented in Table 2.TABLE 2. Amount of reading and writing per academic year by students       (%)Reading/writing per academic year	None	1-4	5-10	11-20	20 or moreAmount of reading					1. Number of assigned books	3.7	31.9	48.4	11.9	4.12. Number of non-assigned books	8.5	40.2	31.6	12.4	7.0Amount of writing					1. Number of papers of 20 pages or more	37.3	37.9	18.1	4.9	1.62. Number of papers of 5-19 pages	16.4	48.9	25.2	7.4	1.83. Number of papers of 5 pages or less	15.8	47.1	23.0	9.1	4.73.2 Analysis of student engagement. The descriptive statistics of student engagement show that participation is weighted in mediocre level; attitudes and approaches are similarly weighted in middle level, see Table 3. The mean pf participation of student active effort is 3.03. While the participation of student-faculty interaction and civic issues are 2.36 and 2.39 respectively. In the attitudes dimension, the means of interest, motivation, and concentration are 3.09, 3.32, and 3.34. Furthermore, the mean of cognitive approaches dimension is 3.21, integration is 3.27, judgment is 3.13, and application also is 3.13, see Table 3. The result reveals about 36% of students expressed below average level in their learning development. Specifically, there are 54% of students reported their academic performance left behind; approximately 50% of students reported they are unsatisfied with learning activities. Besides, there are 40.6% of students expressed their career ambition was in middle-low or low level, see Table 4.TABLE 3. Descriptive statistics for student engagementDimensions	Factors	Means	S.D.Participation	Active effort	3.03	.68	Student-faculty interaction	2.36	.77	Civic issues	2.39	.87Attitudes	Interest	3.09	.68	Motivation	3.32	.87	Concentration	3.34	.54Approaches	Analysis	3.21	.90	Integration	3.27	.88	Judgment	3.13	.89	Application	3.13	.98Note: Means represent the data transform from 1-5 point scale. TABLE 4. Descriptive statistics for learning developmentDimensions	Factors	responses	Percent	Means	S.D.Academic performance	GPA (score)	low	2.8	3.22	.93		middle-low	15.6				middle	35.6				middle-high	31.8				high	4.0				missing values	10.2		Learning satisfaction	Satisfaction of learning activities	low	24.8	2.99	1.52		middle-low	11.2				middle	13.1				middle-high	22.5				high	18.8				missing values	9.6		Career development	Career ambition	low	22.4	2.80	1.29		middle-low	18.2				middle	25.0				middle-high	24.7				high	9.2				missing values	.5		Note: GPA represents low=under 60 points, middle-low=61-70 points, middle=71-80 points, middle-high=81-90 points, high= above 91 points".3.3 Differences between student engagement and learning development. The result reveals that main differences of student engagement significantly derived from students’ grade and location of universities. Senior students have significant student engagement, which can be explained by the factors of active effort (Mean=3.16, S.D.=.70, F-value =6.560, p < .001), student-faculty interaction (Mean=2.48, S.D.=.77, F-value =9.732, p < .001), civic issues (Mean=2.52, S.D.=.89, F-value =5.153, p < .01), motivation (Mean=3.45, S.D.=.91, F-value =3.725, p < .05), concentration (Mean=3.46, S.D.=.58, F-value =9.341, p < .001), and cognitive approaches (Mean=3.30, S.D.=.74, F-value =9.836, p < .001) respectively. In addition, college students in the central of Taiwan have shown better student engagement than do those in northern and southern areas. These differences also can be explained by the factors of active effort (Mean=3.19, S.D.=.63, F-value =14.262, p < .001), student-faculty interaction (Mean=2.45, S.D.=.71, F-value =25.797, p < .001), and motivation (Mean=3.40, S.D.=.80, F-value =4.466, p < .05) respectively.On the other hand, the main differences of learning development are derived from students’ major and sector to attend (public/private universities). Students majored in humanities and social sciences have better learning development than those majored in technology, which can be explained by the factors of academic performance (Mean=3.38, S.D. =.90, t-value=9.472, p < .001), learning satisfaction (Mean=3.39, S.D. =.79, t-value=5.974, p < .001), and career development (Mean=3.39, S.D. =.79, t-value=5.974, p < .001). Students in public universities have better learning development than those in private, which can be explained by the factors of academic performance (Mean=3.40, S.D.=.88, t-value=10.906, p < .001), learning satisfaction (Mean=3.33, S.D.=.80, t-value=1.984, p < .05), and career development (Mean=2.54, S.D.=.84, t-value=2.380, p < .05). 3.4 Students’ SES, engagement and learning development. This study has considered the impact of students with different social economic status (SES) on their engagement and learning development. We classified the students into four groups for comparing which are low, low-middle, high-middle, and high. The result reveals that students with low-middle SES exert better student engagement, which can be explained by the factors of active effort (Mean=3.11, S.D.=.73, F-value =4.236, p < .01), civic issues (Mean=2.49, S.D.=.94, F-value =4.309, p < .01), concentration (Mean=3.41, S.D.=.56, F-value =4.642, p < .01), and cognitive approaches (Mean=3.29, S.D.=.79, F-value =4.560, p < .001), see Table 5. Meanwhile, the result also demonstrates that students with low-middle SES have better learning development, which can be explained by the factors of academic performance (Mean=3.32, S.D. =.98, F-value=3.877, p < .01), and career development (Mean=2.62, S.D. =.84, F-value=6.450, p < .001), see Table 6.TABLE 5. Mean, variation, and difference of student engagement by SESDimensions	Factors	Responses	Means	S.D.	F-value	Post Hoc testParticipation	writing	low	2.24	.79	5.300**	high＜middle-low		middle-low	2.33	.85				middle-high	2.19	.76				high	2.13	.74			reading	low	2.72	.81	8.547***	low＜middle-low; middle-high and high＜middle-low		middle-low	2.92	.92				middle-high	2.74	.76				high	2.70	.76			active effort	low	2.98	.67	4.236**	low＜middle-low		middle-low	3.11	.73				middle-high	3.04	.66				high	3.04	.64			student-faculty interaction	low	2.34	.76	3.094			middle-low	2.45	.83				middle-high	2.39	.76				high	2.32	.72			civic issues	low	2.38	.85	4.309**	high＜middle-low		middle-low	2.49	.94				middle-high	2.40	.88				high	2.28	.82		Attitudes	interest	low	3.07	.70	.665			middle-low	3.09	.73				middle-high	3.12	.64				high	3.12	.64			motivation	low	3.29	.84	2.232			middle-low	3.40	.94				middle-high	3.33	.87				high	3.30	.81			concentration	low	3.32	.54	4.642**	low＜middle-low; middle-high＜middle-low		middle-low	3.41	.56				middle-high	3.30	.51				high	3.35	.54		Approaches	cognitive approaches	low	3.17	.75	4.560**	low＜middle-low; high＜middle-low		middle-low	3.29	.79				middle-high	3.17	.67				high	3.12	.67		Note: Values that are statistically significant are indicated in **p < .01. ***p < .001 (N=2,651).TABLE 6. Mean, variation, and difference of learning development by SESDimensions	Factors	Responses	Means	S.D.	F-value 	Post Hoc testAcademic performance	GPA (score)	low	3.16	.94	3.877**	low＜middle-low		middle-low	3.32	.98				middle-high	3.19	.82				high	3.22	.88		Learning satisfaction	Satisfaction of learning activities	low	3.26	.82	1.662			middle-low	3.34	.84				middle-high	3.33	.77				high	3.34	.78		Career development	Career ambition	low	2.50	.89	6.450***	high＜middle-low		middle-low	2.62	.84				middle-high	2.52	.87				high	2.38	.75		Note: Values that are statistically significant are indicated in **p < .01; ***p < .001; N=2,651.3.5 Effects of student engagement on learning development. Figure 1 shows a good fit to the data, χ2 (85) = 1296.579; GFI=.929; AGFI=.900; RMSEA=.073. The paths from the participation, attitudes, and approaches dimensions to student engagement have shown significantly differences. The result reveals that the participation is the most important factor (r2=.83), then approaches (r2=.72), and attitudes (r2=.63) in the model. There is also a significant path from student engagement to learning development. According to the standardized regression coefficient (r2=.90), it means that the correlation between student engagement and learning development is fairly high. The student engagement will directly affect on learning development in the model.FIGURE 1. Paths of student engagement to learning developmentNote: The figure showed standardized path coefficients; p<.05.4. Conclusion. This study demonstrates that the student engagement survey can be used to determine how well college students learn, what to learn, and why to learn. How much the undergraduate students engage reflected on not only the quality of higher education but the issue of student learning. The results reveal that the student engagement and learning development are in unsatisfied level; their participations are mediocre; attitudes and approaches are in middle level, and students’ learning development is below average. Enhancing student engagement could prompt to students’ critical thinking, problem solving, effective communication, and responsible citizenship. Therefore, institutions need well organized curriculum and meaningful campus activities for enriching students’ learning experiences.REFERENCES[1] 	A.W. Astin, What matters in college? Four critical years revisited, Jossey-Bass, San Francisco, 1993.[2] 	G. D. Kuh, Assessing what really matters to student learning: Inside the National Survey of Student Engagement, Change, vol.33, no.3, pp.10-17, 66, 2001.[3] 	C.R. Pace, Measuring the quality of student effort, Current Issues in Higher Education, vol.2, pp.10-16, 1980.[4] 	E. T. Pascarella and P. T. Terenzini, How college affects students: Findings and insights from twenty years of research, Jossey-Bass, San Francisco, 1991.[5] 	A.W. Chickering and Z.F. Gamson, Seven principles for good practice in undergraduate education, AAHE Bulletin, vol.39, no.7, pp.3-7, 1987.[6] 	J. Brophy, Motivating students to learn (2nd ed.), Lawrence Erlbaum, Mahwah, NJ, 2004.[7] 	K. R. Wentzel, Social relationships and motivation in middle school: The role of parents, teachers, and peers, Journal of Educational Psychology, vol.90, pp.202-209.[8] 	J. A. Fredericks, P. C. Blumenfeld and A. H. Paris, School engagement: Potential of the concept, state of the evidence, Review of Educational Research, vol.74, pp.59-109, 2004.[9] 	M. J. Furlong and S. L. Christenson, Engaging students at school and with learning: A relevant construct for all students, Psychology in the Schools, vol.45, no.5, pp.365-368, 2008.[10] 	M. J. Furlong, A. D. Whipple, G. St. Jean, J. Simental, A. Soliz and S. Punthuna, Multiple contexts of school engagement: Moving toward a unifying framework for educational research and practice, California School Psychologist, vol.8, pp.99-114, 2003.[11] 	E. Chapman, Alternative approaches to assessing student engagement rates, Practical Assessment, Research and Evaluation, vol.13, no.8, pp.1-7, 2003.[12] 	S. R. Jimerson, E. Campos and J. L. Greif, Toward an understanding of definitions and measures of school engagement and related terms, California School Psychologist, vol.8, pp.7-27, 2003.[13] 	L. H. Bass and A. S. Ballard, Student engagement and course registration methods as possible predictors of freshman retention, Research in Higher Education Journal, Student engagement and course, pp.1-13, 2012.[14] 	E. A. Skinner and M. J. Belmont, Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year, Journal of Educational Psychology, vol.85, pp.571-581, 1993.[15] 	NSSE, NSSE 2011 measures student engagement by major. Retrieved from http://nsse.iub.edu/2011_Institutional_Report/pdf/NSSE%20Overview_2011.pdf (​http:​/​​/​nsse.iub.edu​/​2011_Institutional_Report​/​pdf​/​NSSE%20Overview_2011.pdf​), 2011.[16] 	NSSE, Finances affect student academically, NSSE 2012 finds. Retrieved from http://www.insidehighered.com/news/2012/11/15/finances-affect-students-academically-nsse-2012-finds#sthash.4ufuHkHU.dpbs, 2012.[17] 	1

Determining the relationship between college student engagement and learning development by SEM

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Determining the relationship between college student engagement and learning development by SEM

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