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Sandler NJEdge 6.0


Published on Conference 6.0, November 2009

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Sandler NJEdge 6.0

  1. 1. IT as a Value Added Component of Teaching and Learning: Excerpts from a Path Analysis Martin E. Sandler, Ph.D., Assistant Director, Assessment, TLTC, Seton Hall University Abstract: Student attitudes and perceptions of the use of instructional technology are mapped in a structural equation model derived from survey data and an instrument developed in part on the principles of undergraduate practice of Chickering & Gamson (1999) and the capacity of computer environments as a tool for enhancing learning (Azevedo 2005). IT Academic Use, Course Learning Management, and IT as a Value Added component of teaching and learning are focally examined. Introduction and Conceptual Framework Excerpts from a path analysis are focally examined including Discussion and Conclusion IT Academic Use (R2 = .158) with eight total effects, Course A path analytic procedure was conducted on survey data yielding a Managements Improves Learning, (R2 = .584) with five total effects, By employing path analysis derived from reliable measures, structural equation model as a means to map the experience of and IT Value Added for learning (R2 = .195), the criterion dependent assessment can assist practitioners and faculty in understanding the role of undergraduate learners using technology. The survey examined how variable in the model, with six total effects (see Figure 1). Total IT as a value added component of undergraduate education. Path analysis technology affected learning inside and outside the classroom and was in part effects are assumed to be causal such that the cause precedes the empowers the assessment professional with skills to address the call for based on the principles of undergraduate practice of Chickering & Gamson effect in direction and temporal order. accountability made by accrediting bodies with reliable measures. In this (1999) and the capacity of computer environments as a tool for enhancing instance clear evidence was obtained about the focal variables examined learning (Azevedo 2005). Accordingly, the largest total effect arose from students’ and in particular of IT as a value added component of academic life. Math and Verbal SAT Score AT .859 (p < .001), that is, for every unit A structural model was determined from measurement data to increase in Cumulative GPA there is a corresponding eighty-six (86) Importance and Relevance to Other Institutions explore the impact of technology on Academic Performance (Cumulative GPA), percent increase in students’ SAT scores. The SAT proved to be a very By mapping and tracing effects using path analysis, structural IT Academic Usage, Course Learning Management, and IT Value Added, four strong contributor/predictor of the explained variance of Cumulative equation modeling enables assessment professionals, instructional designers, focal variables in a path analysis that included twenty-four (24) variables. For GPA totaling seventy-nine (79 )percent. and faculty to explore survey data through a powerful new lens close to the ease of understanding the reader may interchange the meaning of IT to respondents’ experience, represent Information Technology or Instructional Technology. thereby explaining elements of GENDER teaching and learning with Methodology, Sample and Data Reduction X1 technology with greater clarity. ETHNICITY/ The findings confirm the Eleven hundred fifty-two (1152) undergraduate students were RACE - .120 Endogenous ongoing importance of the Variables X2 IT USE NON - included in an on-line survey administration during the spring 2008 semester. IT SKILL LEVEL ACADEMIC .317 Y5 .119*** COURSE MGMT. principles of Chickering & PARENTS’ With a forty-four (44) percent online survey response rate, the sample for EDUCATION 2 Y1 R2 = 0.114 .261 IMPROVES Gamson (1999) and the utility AL LEVEL X3 R = 0.078 analysis included (N=509). LEARNING/Bb 2 of computer environments as a .665 Y9 R = 0.584 HOUSEHOLD .104 .666 .212 .228 tool for enhancing learning INCOME X4 .179 After a principal components analysis of the survey data, eleven .081*** (Azevedo 2005). CLASSROOM .072 .081*** reliable measures were determined with coefficients between .68 and .89. PREFERRED IT DISTRACTS/ MEDIA IMPRS. .163 .497 .079*** Subsequently, twenty-four (24) variables were included in the path analysis LEVEL IT IN COURSE X5 IMPEDES LEARNING Y2 - .112 LEARNING Y6 2 - .108 .085 References R = 0.246 that included twelve (12) endogenous; eleven arose from the subscale factors R2 = 0.001 .443 IT TEAM IT-VALUE addressed. Cumulative GPA was added as the twelfth endogenous variable; VERBAL & -.133 COORDINATION - .173 ADDED Azevedo, R. (2005). MATH SAT Y12 twelve (12) exogenous variables denoting student background were included. X6 .072 Y10 .062** R2 = 0.195 “Computer Environments as R2 = 0.047 ACADEMIC - .391 - .436** .162 .212 .505 MetacognitiveTools for SATISFACTION .331 ASPIRATION WITH WIRELESS Enhancing Learning,” Structural Equation Modeling X7 IT USE ACADEMIC .308 NETWORK Y7 Educational Psychologist, Y3 R2 = 0.027 Descriptive, transformational, and inferential statistics were YEARS TO COLLEGE R2 = 0.158 - .296 40(4), 193-197. DEGREE X8 .108 CUMULATIVE .253 obtained using SPSS 16. Structural Equation Modeling (SEM) using a weighted - .164 .165 GRADE POINT least squares (WLS) estimator followed by employing LISREL 8.80 after a CUMULATIVE .859 AVERAGE - GPA b Chickering, A.W. and HOURS The conventional syntax used in Y11 R2 = 0.785 pretreatment phase with PRELIS 2.50. As a simple indication of Model Fit, the PASSED X9 DIVERSE path diagrams may be deviated Gamson, Z.F. (1999). - .162 from in order to simplify ratio of Chi-Square / degrees of freedom = 50.335/193 = 0.261 providing TALENTS Y8 representation. “Development and HOURS SATISFACTION R2 = 0.035 evidence of a fine fit; ratios below 2.00 are recognized as very good. STUDY X10 WITH LAPTOP Adaptations of the Seven Y4 Principles for Good Practice in R2 = 0.008 Chi-Square with 193 degrees of freedom = 50.335 (p = 1.000). All total effects represented Findings HOURS EMPLOYED are significant atp <.001 with the exception of those marked * at p <.01, ** at p < .02, Undergraduate Education,” X11 and *** at p < .05 ; total effects <.060 are trimmed and not represented. A dashed line New Directions for Teaching Total effects are mapped in the course of a Path Analysis. Six (6) out represents a non-significant effect. The figure presented serves as a final structural model. and Learning, no. 80, Winter HOUSING/ of twelve (12) endogenous variables of the structural model had notable levels Exogenous 1999. COMMUTING Figure 1: 2008 Teaching and Learning with Technology Survey of variance explained, between eleven (11) and seventy-nine (79) percent. In X12 Variables Model: Total Effects on Six Principal Endogenous Variables b addition, a robust number, ninety-eight (98) percent of the hypotheses 24_var_T&LwT_2008LIS _9 a_cent _EM_Centered_3_BEST.LS8 explored were confirmed. The total effects on three focal endogenous variables in a structural equation model are exclusively featured. Martin E. Sandler, Ph.D. has several years experience as a researcher, faculty member and administrator in higher education. He has published in Research in Higher Education and the Journal of College Student Development and is currently Assistant Director, Assessment, TLTC, Seton Hall University.