NJEDge.net Conference 6.0, November 2009 http://njedge.net/conference2009/a-focal-examination/

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.