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LearnSmart, adaptive teaching, and student
learning effectiveness: An empirical investigation
Qin Sun, Yann Abdourazakou & Thomas J. Norman
To cite this article: Qin Sun, Yann Abdourazakou & Thomas J. Norman (2017): LearnSmart,
adaptive teaching, and student learning effectiveness: An empirical investigation, Journal of
Education for Business, DOI: 10.1080/08832323.2016.1274711
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2. LearnSmart, adaptive teaching, and student learning effectiveness: An empirical
investigation
Qin Suna
, Yann Abdourazakoub
, and Thomas J. Normanb
a
Trident University International, Cypress, California, USA; b
California State University, Dominguez Hills, Carson, California, USA
ABSTRACT
Facing the growing number of digital natives entering the classroom, business professors look for
innovative ways to enhance the student learning experience. The authors focus on the online
interactive learning tool LearnSmart (McGraw-Hill, New York, NY), and examine its impact on
student learning effectiveness by testing the direct and indirect relationships among perceived
competence, perceived challenge, instructors, perceived value, and satisfaction with LearnSmart.
Constructionism served as the theoretical foundation for this study. About 215 students at a public
university in the United States took the survey and 197 valid responses were received. Regression
analysis results showed that the use of LearnSmart improved students’ perceived competency, thus
increasing their perceived value of using LearnSmart, as well as their satisfaction with LearnSmart.
Perceived value was also found to mediate the impact of perceived competency on satisfaction
with LearnSmart, and the instructor played a significant role in facilitating and improving student
learning. Perceived challenge impacted student’s perceived value of using LearnSmart, but it did
not influence satisfaction with LearnSmart.
KEYWORDS
Adaptive teaching;
interactive learning tool;
perceived challenge;
perceived competence;
perceived value; student
learning effectiveness
Introduction
Student learning styles have been found to influence stu-
dent academic performance and thus it is important for
educators to adapt teaching methods to student learning
styles (Fleming, 2001; Sandman, 2014). As digital millen-
nial college students are more comfortable with the
online learning (Comer, Lenaghan, & Sengupta, 2015;
Ganesh & Sun, 2009), online teaching is increasingly a
part of course delivery. One major advantage of online
education is flexibility, especially to students with time,
distance or language constraints (Liu, Gomez, Khan, &
Yen, 2007; Steiner & Hyman, 2010). Therefore, it is not
surprising that more business professors intend to incor-
porate online learning into their curriculum.
Universities are also offering hybrid classes that inte-
grate online education into the traditional classroom set-
ting. In this way, professors combine the advantages of
online educations with traditional face-to-face teaching,
and thus enrich student learning. The effectiveness of
face to face teaching was found to be related to appropri-
ate instructional design and instructors’ subject knowl-
edge and capability to communicate clearly with the
students, to create rapport with the students, and deliver
the course enthusiastically (Sweeney, Morrison, Jarratt,
& Heffernan, 2009). On the other hand, online practices
could facilitate the teaching in classroom (Berger &
Topol, 2001).
With the rapid advancement of information technol-
ogy students have shown increasing interest in technol-
ogy-enhanced pedagogies (Jackson, Helms, Jackson, &
Gum, 2011), and business professors look for innovative
ways to teach business topics and enhance student learn-
ing effectiveness. Various high-tech devices and
technologies are employed in business courses to
enhance student learning effectiveness such as personal
digital assistants and video cameras (McGorry, 2006;
Smith & Fisher, 2006), multimedia (Sheppard & Vibert,
2016), online video (Lancellotti, Thomas, & Kohli, 2016;
Winch & Cahn, 2015), virtual communities (Luethge,
Raska, Greer, & O’Connor, 2016), class blogs (Ferguson,
Makarem, & Jones, 2016), and electronic conferencing
(Wood, Solomon, & Allan, 2008).
With the complement of technologies, experiential
learning can be promoted by interactive or adaptive
online teaching, which conforms to the requirements of
the Association to Advance Collegiate Schools of Busi-
ness (AACSB) to increase the flexibility and responsive-
ness to student needs (AACSB, 2003; Hatfield & Taylor,
CONTACT Qin Sun qin.sun@trident.edu Trident University International, College of Business Administration, 5757 Plaza Dr., Suite 100, Cypress, CA
90630, USA.
© 2017 Taylor & Francis Group, LLC
JOURNAL OF EDUCATION FOR BUSINESS
http://dx.doi.org/10.1080/08832323.2016.1274711

3. 1998). For example, professors can use discussion
forums, messaging and emails to create an interactive
community to facilitate and encourage experiential
learning among students (Wood et al., 2008), or design
interactive spreadsheets to enhance students’ problem
solving skills (Bertheussen & Myrland, 2016).
Online teaching and learning can be customized when
the assignments are interactive. Previous researchers
have identified different types of interactivity that can
occur online: learner-content interaction, learner-
instructor interaction, and learner-learner interaction
(Moore, 1989) as well as student-learning management
system interaction (Davidson-Shivers, 2009). Previous
studies have revealed that online courses with great levels
of interactivity combine higher levels of student motiva-
tion, enhanced learning outcomes, and satisfaction over
interactive learning environments (Espasa & Meneses,
2010). Perceived overall interactivity also positively influ-
ences learner satisfaction (Fulford & Zhang, 1993). In the
broader context of interactive teaching, it’s less clear as to
whether adaptive learning systems should fit into exist-
ing online or on-campus courses.
In this study, we intend to examine how the integra-
tion of an online interactive learning tool (i.e., Learn-
Smart) could enhance student learning and thus learning
effectiveness. To the best of our knowledge, there is little
peer-reviewed work examining this new learning tool in
the business fields. In this study, we describe the imple-
mentation of LearnSmart in introductory marketing and
management courses at a West Coast public university.
Literature review
Student learning is the central focus of higher education.
However, student learning is context dependent, and
various factors influence student learning effectiveness
such as students’ own motivation, classroom climate,
teaching methods, and course level (Comer et al., 2015).
In particular, interaction is considered as a determining
factor to promote student learning effectiveness, not
only in traditional classroom setting but also in online
education (Rovai & Barnum, 2003; Swan, 2003). The
importance of interactions that learners engage in has
been highlighted in the adaptive teaching and learning
pedagogy. According to Oxman and Wong (2014), adap-
tive learning refers to a learning process where the con-
tent taught adapts based on the responses of the
individual. Adaptive learning systems can help personal-
ize the instruction based on an individual learning model
(Kinshukan, 2003; VanLehn, 2006). Individualized pac-
ing used in adaptive learning technology demonstrated
more positive impacts than those with class-based or a
mixed form of pacing, and courses with adaptive
learning technologies showed better learning outcomes
than nonadaptive ones (Means, Peters, & Zheng, 2014).
LearnSmart is one adaptive learning system that can be
used on top of fully online or on-campus courses at any
university. It gives a new geometry to the interactivity
between students, the instructor and the course content.
LearnSmart is an adaptive learning tool that evaluates
students’ knowledge levels by tracking the topics stu-
dents have mastered, thus identifies the areas that need
further instruction and practice. Depending on student
progress, LearnSmart automatically adapts the learning
contents based on their knowledge strengths and weak-
nesses, and their confidence level around that knowledge
(Norman, 2011). So LearnSmart is tailored to the specific
needs of each student with the continuous evaluation of
a student’s knowledge on the concepts covered in each
chapter (McGraw-Hill Higher Education, 2012).
LearnSmart’s adaptive technology also identifies the
concepts that students are most likely to forget over the
course of the semester—by considering those that they
had been weakest on or least confident with—and encour-
ages periodic review by the students to ensure that con-
cepts are truly learned and retained. As a result, it goes
beyond systems that simply help students study for a test
or exam, and helps students with true concept retention
and learning. LearnSmart also generates dynamic reports
to document progress and suggests areas for additional
reinforcement, offering students real-time feedback on
their content mastery. By monitoring student progress,
professors can instantly assess the level of understanding
and mastery for an entire class or an individual student at
any given time, and adapt the teaching in classroom
(McGraw-Hill Education, 2014; Norman, 2011).
Statement/significance of the problem
Akin (1981) discussed that marketing college students
should learn not only marketing, but skills that will help
them in the future in the marketing field, such as the skill
of knowing how to learn. Kember, Charlesworth, Davies,
McKay, and Stott (1997) also emphasized the impor-
tance of enhancing meaningful independent learning
among college students. On one hand, college students
need to digest and comprehend the materials; on the
other hand, students should be able to apply those course
materials for academic assessment and future profes-
sional development (Richardson, 1994). The adaptive
teaching method could help meet individual student
needs and thus enhance student learning effectiveness
(Zhang, Zhou, Briggs, & Nunamaker, 2006). Student
learning effectiveness refers to the learning value as per-
ceived by students (Ganesh, Paswan, & Sun, 2015).
2 Q. SUN ET AL.

4. Constructivism posits that students who are engaged
with interactive activities would have more effective
learning than those who are not (Leidner & Jarvenpaa,
1995). Therefore, it is important for business professors
to emphasize the engaging learning experience among
college students. LearnSmart offers an interactive and
adaptive way for students to read digital textbook chap-
ters while engaging with online practice problems and
quizzes. In comparison, students who read hard copy
textbooks would not have the opportunity to apply these
concepts; thus, the learning might be less effective.
Measurement of student learning effectiveness can be
assessed based on the objective performance such as
course grades, as well as subjective evaluation such as
student perception of LearnSmart and their satisfaction
with its use (Swan, 2003). Griff and Matter (2013) looked
at test scores of students in six schools, but did not find a
significant impact of LearnSmart on student grade per-
formance. However, there is no study to explore the
potential influence of this adaptive online learning tool
on student perceived learning effectiveness. This study
fills this literature gap and empirically tests the impact of
LearnSmart on several subjective evaluation factors of
learning effectiveness, that is, satisfaction with Learn-
Smart and perceived value, considering several relevant
factors in the literature such as perceived competence,
perceived challenge, and instructors (Ganesh et al.,
2015).
Perceived competence
Perceived competence is defined as the students’ percep-
tion of mastering their school work (Pintrich & De
Groot, 1990). Those students with higher perceived com-
petence have been found to be more intrinsically moti-
vated, have higher test performance than those with
lower perceived competence (Bicen & Laverie, 2009). In
addition, students with higher perceived competence
tend to give better course evaluations, thus indicating
more effective student learning (Clayson, 2009; Ganesh
et al., 2015). LearnSmart intends to improve student
learning by automatically adapting the practices and tests
with respect to student understanding of course contents,
thus enhancing their perceived competence. Students
have the opportunity to do multiple practices to under-
stand the same concepts if they did not fully understand
them at the beginning. Since perceived learning value
can be used to measure student learning effectiveness
(Ganesh et al., 2015), we would expect the students
would perceive high value from LearnSmart. In this way,
students would be more satisfied with LearnSmart as the
learning effectiveness improves. Therefore, we formu-
lated the following hypotheses:
Hypothesis 1a (H1a): Perceived competence would be
positively associated with perceived value.
H1b: Perceived competence would be positively associ-
ated with satisfaction with LearnSmart.
Perceived challenge
Perceived challenge refers to the student perception of
the workload in the class and the extent of difficulty of
the class or assignment. Extant literature shows the
direct association between perceived challenge and the
overall evaluation of class (Ganesh et al., 2015; Parayi-
tam, Desai, & Phelps, 2007). LearnSmart incorporates
various practices and quizzes into each chapter, which
requires much more time for students to finish the
chapter than just reading a textbook. Students may get
frustrated at the beginning due to the extra work and
thus perceive some challenge. With the extra learning
and improved understanding of course content, the stu-
dents would feel more competent and less challenged
over time. Taking these findings into account, we would
expect the students to perceive more value even though
it is perceived to be more challenging since they may
feel that they have become more competent. By the
same token, we expected that the increased value per-
ception of this adaptive learning tool would lead to
more satisfaction with LearnSmart (Ledden, Kalafatis, &
Samouel, 2007). As a result, it is reasonable to assume
the following:
H2a: Perceived challenge would be positively associ-
ated with perceived value.
2b: Perceived challenge would be positively associated
with satisfaction with LearnSmart.
Mediation role of perceived value
Perceived value by the students refers to the overall eval-
uation of the utility of the service or learning tool and
higher perceived value lead to satisfaction with the edu-
cation (Ledden et al., 2007; Dlacic, Arslanagic, Kadic-
Maglajlic, Mrkovic, Raspor, 2014). As students per-
ceive some value from the use of LearnSmart, they may
become more satisfied with the tool. In addition, because
perceived competency and perceived challenge are
expected to impact the perceived value of using Learn-
Smart, it is logical to propose that perceived value would
mediate the connection between perceived competency
and satisfaction with LearnSmart, as well as the relation
between perceived challenge and satisfaction with Learn-
Smart. Consequently, we propose that the following:
H3a: Perceived value would mediate the relation
between perceived competency and satisfaction
with LearnSmart.
JOURNAL OF EDUCATION FOR BUSINESS 3

5. 3b: Perceived value would mediate the relation
between perceived challenge and satisfaction with
LearnSmart.
Moderating role of instructor
Adaptive teaching tries to match teaching methods with
student learning style and instructor plays a significant role
in this adaptive learning process (Fleming, 2001; Sandman,
2014). As a result, we also explored the role of instructor in
the student teaching effectiveness, specifically a student’s
perceived value of LearnSmart. We assume that the more
experienced instructors would provide better guidance
regarding student use of LearnSmart and in-class instruc-
tion, which alleviate perceived challenge of using Learn-
Smart and thus increase their perceived competency of
learning. As a result, students would perceive higher value
from LearnSmart from experienced instructors than less
experienced ones, which leads to the following hypotheses:
H4a: The instructor would moderate the relation
between perceived competency and perceived
value.
H4b: The instructor would moderate the relation
between perceived challenge and perceived value.
Research methodology
The research context for this study was four undergradu-
ate marketing and management courses, offered at a pub-
lic university in the western United States. LearnSmart is
a part of the McGraw-Hill Connect course, which is
required for students to study each chapter of the course.
It is designed to improve student’s understanding of
course contents through its online interactive platform.
Over the 15-week semester, students were asked to com-
plete the LearnSmart assignment either before or after the
instructor finished the lecture in class. They were given
one week to finish LearnSmart and then take a quiz for
that chapter. The instructors can check the assignment
statistics to get details on student performance and the
time each student spent to finish LearnSmart assign-
ments. Then the instructors can adapt in-class teaching
with respect to the student LearnSmart performance.
Survey instrument
We also borrowed and adapted existing scales to measure
each construct, based on the qualitative feedback from stu-
dents in a marketing discussion forum (Table 1). Perceived
competency, perceived challenge, and satisfaction with
LearnSmart were measured with a 7-point Likert-type
scale with responses ranging from 1 (strongly disagree) to
7 (strongly agree; Ganesh et al., 2015). Ganesh et al. devel-
oped these scales based on faculty evaluation instruments
commonly used in higher education and validated these
scales with acceptable reliability, as well as convergent and
discriminant validity. Perceived value looks at the compar-
ative value of LearnSmart with respect to reading a text-
book and the 7-point Likert-type scale has been adapted
from Dlacic et al. (2014), which has showed acceptable
reliability, as well as convergent and discriminant validity
of this scale for perceived value. Demographic questions
such as age, gender, ethnicity, and employment status
were included at the end of questionnaire.
A pretest on the survey instrument was conducted to
ensure the face validity of instrument. Several experienced
researchers consented to review the clarity of the wording,
coherence, and logic order and possible ambiguity of the
questionnaire. The questionnaire was refined as a result.
Data collection
Data for this research were collected using an online
survey. Students were offered a course grade incentive
Table 1. Constructs scales.
Constructs Scale item M SD
Perceived
competency
LearnSmart made me more confident
in learning course concepts.
5.55 1.402
LearnSmart made me more confident
in applying course concepts.
5.41 1.422
LearnSmart improved my critical
thinking ability.
5.18 1.502
LearnSmart taught me tools for
decision making.
5.02 1.532
LearnSmart taught me skills useful for
life.
4.79 1.561
Perceived value LearnSmart provides greater learning
value than just reading a textbook.
5.64 1.48
LearnSmart provides greater learning
value than the class lectures.
5.01 1.578
LearnSmart pushes me to peak
performance compared to just
reading a textbook.
5.29 1.529
LearnSmart helps me to earn a higher
grade than just reading a textbook.
5.49 1.494
Perceived
challenge
LearnSmart requires more work than
just reading a textbook.
5.48 1.636
LearnSmart takes too much time than
just reading a textbook.
4.74 1.773
LearnSmart is more challenging than
just reading a textbook.
4.62 1.805
It is more frustrating to do LearnSmart
than just reading a textbook.
4.03 1.865
Satisfaction with
LearnSmart
I would like to continue using
LearnSmart for other courses.
5.31 1.59
I have no regrets about using
LearnSmart.
5.30 1.591
I am satisfied with the learning
effectiveness of LearnSmart.
5.38 1.458
I would recommend the use of
LearnSmart in other courses.
5.4 1.553
4 Q. SUN ET AL.

6. for their voluntary participation. This hardly made any
difference to the grade outcome, yet was spectacularly
successful in encouraging response (Ganesh et al.,
2015). About 215 students were invited to participate in
this study and we received 197 valid responses. Table 2
shows the demographic characteristics of sample; 52.8%
of the participants (107) were women and 46.2% (92)
were men. The largest age group of the respondents
was 21–25 years old (50.8%), followed by 26–30 years
old (21.1%), 36 years old or older (12.1%), and 18–
20 years old (5.5%). About half of the respondents
(47.2%) were Hispanic, while African American, Asian,
and Caucasian participants each were about 15% of the
sample. The majority of the respondents were
employed: 40.7% with full time employment, 35.7%
with part-time employment, and only 22.6% with no
employment.
Findings
The exploratory factor analysis (EFA) for the con-
structs was conducted to evaluate the dimensionality
of each construct. Only one factor was extracted to
show the unidimensionality of each construct. Based
on the Cronbach’s alpha values for the constructs in
this study (Table 3), ranging from .818 to .943, all
latent constructs used in the hypothesized model have
acceptable reliability (Churchill, 1979). The average
variance extracted (AVE) were all above 0.50, with
perceived competency at 0.797, perceived value at
0.821, perceived challenge at 0.649, and satisfaction
with LearnSmart at 0.857 (Table 3), indicating accept-
able convergent validity (McDonald Ho, 2002). In
addition, as the square root value of AVE per factor
(from 0.806 to 0.926) is more than the inter-factor
correlations (from 0.018 to 0.794), the constructs are
considered to have adequate discriminant validity
(Table 4; Fornell Larcker, 1981).
The hypothesized mediation relationships were tested
using three-step multiple regressions proposed by Baron
and Kenny (1986) and Sobel (1982). Three multiple
regressions were run to test the direct and indirect rela-
tionship between perceived competency, perceived value
and learning satisfaction with LearnSmart. The first
regression (Table 5) showed that perceived competency
is significant related to satisfaction with LearnSmart
(b D .794, p D .000) and the second regression also indi-
cated the significant impact of perceived competency on
perceived value (b D .772, p D .000). Therefore, H1a and
H1b are supported. The third regression found that both
perceived competency (b D .491, p D .000) and per-
ceived value (b D .392, p D .000) are positively related to
satisfaction with LearnSmart. In addition, the beta value
of perceived competency in the third regression is
smaller than that in the first regression, showing partial
mediation of perceived value. The Sobel test results sup-
ported the significance of partial mediation (Z D 5.891,
p D .000). As a result, H3a is confirmed.
By the same token, three multiple regressions were
used to test the direct and indirect relationship
between perceived challenge, perceived value and
Table 2. Sample demographic characteristics.
Variables Category n %
Gender Male 92 46.2
Female 105 52.8
Age (years) 18–20 11 5.5
21–25 101 50.8
26–30 42 21.1
31–35 19 9.5
36 or older 24 12.1
Ethnicity African American 27 13.6
Asian 29 14.6
Caucasian 30 15.1
Hispanics 94 47.2
Others 17 8.5
Employment Full time 81 40.7
Part time 71 35.7
No employment 45 22.6
Table 3. Factor loadings and AVE.
Constructs Items Factor loading AVE Square root of AVE
PC PC1 0.885
PC2 0.891
PC3 0.911
PC4 0.908
PC5 0.868 0.797 0.893
PV PV1 0.92
PV2 0.85
PV3 0.933
PV4 0.92 0.821 0.906
PCh PCh1 0.749
PCh2 0.827
PCh3 0.877
PCh4 0.764 0.649 0.806
SwLS SwLS1 0.947
SwLS2 0.872
SwLS3 0.933
SwLS4 0.949 0.857 0.926
Note. AVE D average variance extracted. PC D perceived competency; PCh D
perceived challenge; PV D perceived value; SwLS D satisfaction with
LearnSmart.
Table 4. Convergent and discriminant validity.
PC PV PCh SwLS
PC .936
PV .772ÃÃ
.926
PCh .175Ã
.134 .818
SwLS .794ÃÃ
.771ÃÃ
.018 .943
Note. Cronbach’s alpha is italicized diagonally and the correlations are off the
diagonal. PC D perceived competency; PCh D perceived challenge; PV D
perceived value; SwLS D satisfaction with LearnSmart.
Ã
p .05 level (two tailed).
ÃÃ
p .01 level (two tailed).
JOURNAL OF EDUCATION FOR BUSINESS 5

7. learning satisfaction with LearnSmart. The first regres-
sion (Table 6) showed that perceived challenge is not
significant related to satisfaction with LearnSmart
(b D .018, p D .799), but the second regression indi-
cated the significant impact of perceived challenge on
perceived value (b D .134, p D .059). Therefore, H2a
is supported whereas H2b is rejected. Although the
third regression found that both perceived challenge
(b D ¡.087, p D .057) and perceived value (b D .783,
p D .000) are significantly related to satisfaction with
LearnSmart, there is no mediation effect of perceived
value (Z D 0.803, p D .250). As a result, H3b is not
supported.
Table 7 shows the results of hierarchical regressions
with instructor as an independent variable in model 1
and as a moderator in model 2. The results show the sig-
nificant moderating effect of instructor with respect to
the relation between perceived competency and per-
ceived value (b D .383, p D .066), and that between per-
ceived challenge and perceived value (b D ¡.455, p D
.015). Three instructors taught these four courses. One
had 5 years of experience with LearnSmart and the other
two were using LearnSmart for the first time. The post
hoc Scheffe test shows that the student in the class taught
by the instructor with several years of LearnSmart per-
ceived more value than those in the classes taught by the
instructors who just started to use LearnSmart. There-
fore, H4a and H4b are supported.
Conclusion
In this study we intended to examine the influence of an
online interactive learning tool LearnSmart on student
evaluation of this tool, the course, and their learning
effectiveness. Perceived competence and perceived chal-
lenge were also investigated to test their potential
impacts on student learning effectiveness by using Learn-
Smart. The findings show that the use of LearnSmart
improves student’s perceived competency, thus increas-
ing their perceived value of using LearnSmart, as well as
their satisfaction with LearnSmart. Perceived value was
also found to mediate the impact of perceived compe-
tency on satisfaction with LearnSmart. At the same time,
the instructor was found to play a significant role to facil-
itate student learning and improve student learning
effectiveness.
Perceived challenge was found in this study to impact
student’s perceived value of using LearnSmart, while it
does not influence satisfaction with LearnSmart. How-
ever, experienced instructors could help students
improve their perceived value of using LearnSmart by
adapting their teaching to student learning style. The
results of this study help evaluate the effectiveness of
using LearnSmart to enhance student learning effective-
ness and make recommendations on its future use. The
results also add new knowledge to marketing education
literature regarding the employment of a new informa-
tion technology in the course design.
Recommendations for additional research
There are some limits of this study. First of all, the objec-
tive measures of student performance such as student
Table 5. Mediation test with perceived competency as IV.
DV IV b t p R2
Z value
Regression 1 Satisfaction with LS Perceived competency .794 18.325 .000 .630
Regression 2 Perceived value Perceived competency .772 17.038 .000 .596
Regression 3 Satisfaction with LS Perceived competency .491 7.882 .000 .693
Perceived value .392 6.300 .000
Sobel test .000 5.891
Table 6. Mediation test with perceived challenge as IV.
DV IV b t p R2
Z
Regression 1 Satisfaction with LS Perceived challenge .018 0.256 .799 .000
Regression 2 Perceived value Perceived challenge .134 1.901 .059 .018
Regression 3 Satisfaction with LS Perceived challenge ¡.087 ¡1.912 .057 .602
Perceived value .783 17.230 .000
Sobel test .057 1.900
Table 7. Hierarchical regression with instructor as moderator.
Variables Model 1 Model 2
PC .790ÃÃÃ
.578ÃÃÃ
PCh .002 .315ÃÃ
Ins ¡.124ÃÃ
¡.106
InteractionPCIns .383y
InteractionPChIns ¡.455ÃÃ
R2
.611 .626
Note. Ins D instruction; PC D perceived competency; PCh D perceived chal-
lenge; dependent variable (DV) D perceived value.
y
p .10 (two tailed). ÃÃ
p .01 (two tailed). ÃÃÃ
p .001 (two tailed).
6 Q. SUN ET AL.

8. quiz grades and final grades are not included in the
study. Although a previous study shows an insignificant
impact of using LearnSmart on student’s test perfor-
mance, there is a need to consider both objective and
subjective measures in the same study. Second, the cross
sectional data is used in this study and longitudinal anal-
ysis is needed in future study to provide richer insights.
In addition, other factors such as student experience
with technology and their attitudes toward technology
could impact their use of online learning tool such as
LearnSmart and future researchers could explore the
impact of these factors on student learning performance.
More robust mediation analysis using structural equa-
tion modeling can be conducted to further test the
hypotheses in this study.
Of the three types of interaction, learner-learner inter-
action can be limited within LearnSmart, which can chal-
lenge the implementation of one type of interaction in an
online course. The ability of business instructors to com-
bine LearnSmart with other tools available in the online
environment such as blackboard is critical. The ability to
integrate learning needs and preferences for different
types of interactivity to increase satisfaction would be
valuable to see if there can be any teaching effectiveness
correlation. To generalize the findings of this first study,
a new survey will be undertaken in order to increase our
sample size with a second set of data.
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