Contributed by:
Gurjinder Singh
Determinants of effective online tutoring programs were modeled and elucidated in this report. It was aimed at clarifying influential factors, how and in what behaviors they were interrelated observed by Universitas Terbuka students. Exploratory-design was adopted, it was qualitatively ascertained first that conceptually five foremost factors were involved.
1. Turkish Online Journal of Distance Education-TOJDE July 2018 ISSN 1302-6488 Volume: 19 Number: 3 Article 9
Dr. Maximus Gorky SEMBIRING
Faculty of Education and Teacher Training
Universitas Terbuka
Tangerang Selatan, Indonesia
Determinants of effective online tutoring program were modelled and elucidated in this
report. It was aimed at clarifying influential factors, how and in what behaviors they were
interrelated observed by Universitas Terbuka students. Exploratory-design was adopted;
it was qualitatively ascertained first that conceptually five foremost factors involved.
They were operationally instigated as: perception of technology, rational for using
Internet, perception of media support; tutor learning strategy; effective online tutoring
support. Quantitatively, they were independent, moderating and dependent variables
respectively. Instruments in the form of list of queries and questionnaires for qualitative
and quantitative purposes respectively were elaborated related to those five variables
involved. Respondents were proportionally selected by distributing 600 questionnaires to
5,500 students under scholarship scheme, 283 were finally completed. Seven out of nine
hypotheses established were validated using structural-equation model (SEM). It was
detected that effective online tutorial was influenced by tutor learning strategy followed
by rational for using Internet and perception of technology. Tutor strategy was influenced
by rational using Internet and perception of technology. Perceptions of technology and
media support were influenced by rational using Internet. Inattentively, qualitative
approach was improperly verified by quantitative, since effective tutoring and tutor
strategy were not influenced by media perception.
Keywords: Exploratory-design, online tutorial, tutor learning strategy, SEM.
This study is an augmentation of comparable piece of work on the determinants of
effectual online tutorial support in Open Distance Learning (ODL) setting as reported by
Rustam, Haliman, Susanty, and Sembiring (2015) with modified initial operational
framework and different respondents. This kind of study is still pertinent to be sustained
as global stream of an online learning and its advantage as a result of how Internet
technologies integrated well with ODL are extensively explored within this couple of
decades. With an exponential advancement in information and communication
technology (ICT), online tutorial has become increasingly well-liked approach for most of
distance learners (Zang, Perris & Yeung, 2005). This represents various prospects for the
continued progression of ODL by providing current prospective students with larger
flexibility and prospect for obtaining quality education (Devine & Lokuge, 2012).
Integration of Internet technologies will potentially enhance student connectivity in ODL
ambience and strengthen the learning environment with emerging accepted technologies
and tutor contributions (Susilo, 2014; Price, Richardson & Jelfs, 2007), including on the
online technology self-efficacy related to the type of media used (Kobayashi, 2017) .
Up to the mid of 1990’s, including in Indonesia milieu, learner isolation issues have been
revealed as a common problem in ODL framework and it is ordinarily remarked as the
driving force of student dropout or attrition (Bean, 1985; Tinto, 1993; Sembiring, 2014).
2. By a fastidious understanding, approaching to the end of 1990’s, Universitas Terbuka
familiarized the online tutorial services to bridge student gap in accessing and acquiring
information, services, and academic supplies. The University has endorsed prominent
roles within the country as well as in the neighboring regions by offering 179 courses
through the online tutoring regularities as the beginning of a real online learning scheme.
Since then more than 800 courses totaling of plausible courses offered have been
switched into entirely or partly incorporated into online delivery mode (Universitas
Terbuka, 2017). These numbers are complied harmoniously with respect to what was
quantified in the University’s strategic and operational plan earlier (Universitas Terbuka,
Student body in 2016 for instance was totaled to 297,897. Given those facts, it is more
likely that the number of students participating in this service would probably be
approaching to a million student-course. These numbers come from calculating student
takes three to four courses per semester. In realization nonetheless, around 600,000
student-course were participated during 2016 academic year (Universitas Terbuka,
2017). All the same, the final grade was contributed up to 30% from the online tutorial
activities. It implies that the participation rate on this service was considered to be low.
It is therefore right to anticipate how the online tutorial service did support a flexible and
rich learning environment to deliver a high quality and efficient ODL operations through
Universitas Terbuka tradition. Additionally, there is a limited revelation in how to create
and upgrade those tutorial supports substantively by means of online technologies so that
the supports are entirely effective, accessible, and beneficial to all students as users. The
study was therefore aimed at explicating variables and dimensions engaged that
influencing to the effectiveness of the online tutoring scheme. It was also important to
distinguish how all factors engaged were interrelated one another and in what behaviors.
Investigation is guesstimated utilizing mixed methods, i.e., exploratory-design.
The significance of long-established traditional teaching and learning schemes has been
challenged ever since as more educationalists are searching for alternate approaches of
presenting learning materials, engaging more students, and concurrently increasing their
academic performances. As a consequence, the use of both computers and Internet had
become an integral part of teaching and learning process. This prologue is recapitulated
by Guy and Lownes-Jackson (2012) and also adding to which that computers and
Internet had facilitated the growth of online tutoring support as the media of learning for
student across countless branches of disciplines.
Referring to the objective of the study, factors and associated dimensions of effective
online tutorial supports are expansively investigated. Effectiveness of online tutorial is in
general determined by various factors. Qualitatively, it was limited to four main factors,
partially and comparatively inspired by Zhang, Perris and Yeung (2005), Rossel-Aguilar
(2007), Shin and Kang (2015) and Mbatha (2015). They are: perception of technology,
rational for using Internet, perception of media support and tutor learning strategy.
Effectual online tutorial support is referred to as having prearranged design (Mitchel,
2005), highly interactive (MacKinnon & Williams, 2006), with quick-response feature
(Varnhagen & Digdon, 2002) and positively contributive to students final grade (Wilson &
Harris, 2002). Perception of technology is denoted of possessing comfortable and gainful
procedure (Sweeny & Ingram, 2004), promptness (Lee, Cortney & Balassi, 2010),
accuracy (Koch & Gobel, 1999) and embeded traceability trait (Bliwise, 2005).
Rational for using Internet is described as retaining effectiveness or efficacy (Bolliger &
Supanakorn, 2011), accessibility (Jain, 2006), communication (Elicker, O’Malle &
Williams, 2008) and simplicity or easiness when utilized by students (Osborn, 2010).
3. Perception of media support is expressed as preserving aspects on availability and
friendliness (MacKinnon & Williams, 2006), integration or methodical (Lenz, 2010) and
connectedness (Talmadge & Chitester, 2010). Tutor learning strategy is extracted to
conserving discussion (Dawson, 1998), initiating group work (Cheng & Swanson, 2010),
the style of tutor in teaching and learning process (Benham, 2002; Keefe, 1979), and
providing related referral sources (Talmadge & Chitester, 2010) for rendering students
appreciating the online tutoring sessions.
Having acknowledged and amalgamated the results as the follow up of interviews and
focus group discussions with respect to the literature review accomplished in advance (as
part of qualitative process), it comes to rectify them comprehensively. They are
systematized to ease the establishment of the research framework as illustrated in Table
1. The ultimate of these processes will be ending up with the research framework and
hypotheses that will be taken care of statistically afterward. This table is used as the
basis of establishing the operational framework of the study for quantitative method.
Table 1. Variables and dimensions of the study
No Variables Dimensions No Variables Dimensions Notes
Y1 : Contributive Perception X11 : Promptness
Y2 : Interactive of X12 : Accuracy Each
1 tutorial 2
Y3 : Responsive technology X13 : Traceability dimension is
Y4 : Prearranged X1 X14 : Gainful measured by
3 questions;
Rational for X21 : Accessibility
12 questions
using X22 : Easiness
3 X31 : Methodical for each
Internet X23 : Expansiveness Perception
X32 : Friendly variable
X2 X24 : Efficacy of media
4 X33 : Availability
Learning X41 : Style support
X34 : Connected- Total
strategy of X42 : Group work X3
5 ness questions: 60
tutor X43 : Referral source
X4 X44 : Discussion
Having considered the summary exhibited in Table 1, it is now on the right step to
establish the operational framework and hypotheses of the study. The framework, as
illustrated in Figure 1, will be analyzed afterwards under quantitative procedures with the
help of structural-equation model (SEM). SEM is a multivariate statistical analysis
technique that is utilized to analyze structural relationships. SEM is the combination of
factor analysis and multiple regression analysis. It is applied to analyze structural
relationship between measured variables and latent constructs. It is preferred by most
researcher for it estimates the multiple and interrelated dependence in a single analysis.
This study is conducted following mixed methods, i.e., exploratory-design (Creswell &
Clark, 2011). The research is predetermined instigated using qualitative procedure first
and then followed by quantitative series. Two kinds of instruments are prepared. The first
is in the form of list of systematic and unified questions for in-depth interviews and focus
group discussions for qualitative purpose. The second is an instrument in the form of
questionnaire for quantitative purpose to gather required data from eligible respondents
by survey. The ultimate objective of qualitative series is to establish the operational
framework and hypotheses of the study as illustrated in Figure 1.
Figure 1, as an auxiliary elaboration of Table 1, authorized highlights of four identified
variables influencing effective online tutorial support (as the dependent variable, Y). They
are: perception of technology (X1), rational for using internet (X2), perception of media
support (X3), and tutor learning strategy (X 4); where X1-3 and X4 are the independent and
moderating variables respectively.
4. The quantitative instrument consisted of 60 questions; Likert Scale 1-5 and related to the
five variables engaged. It implies that each variable has four dimensions and each
dimension is measured by three questions. This approach is meant to quantitatively
address the conceptual framework in operational level to be better organized on the
model, design, hypotheses, survey and sampling techniques, data collection and analysis,
and inferring the final remarks.
Figure 1. The operational framework
Variables are explored through questionnaire following Tjiptono and Chandra (2011). Six
hundred questionnaires were provided and distributed to 5,500 Universitas Terbuka
students under scholarship program through entirely 39 operating regional offices all
over Indonesia. A survey is started to assemble acquired data (Fowler, 2014). A purposive
sampling technique for qualitative procedure was chosen to determine eligible resource
persons as experts in the study. Proportional sampling technique was preferred to
determine eligible respondents for quantitative purpose by providing 600 questionnaires
(Cochran, 1977; Sugijono, 2012). Each regional office acquires 15 set of questionnaires to
be completed by selected students during March 2017.
After cautiously verifying, 283 out of 600 distributed questionnaires are finally completed
and processed. SEM is then applied to distinguish the pattern, power and behavioral
relations amongst all variables and dimensions engaged as the reflection of those 283
complete returned questionnaires (Hair, Black, Babin & Anderson, 2009). Processed data
were then arranged in the form of related figure and table completed under Lisrel version
8.80 as the end upshots of the study (Wijayanto, 2008).
The study therefore scrutinizes nine hypotheses (Figure 1). They are: effective online
tutorial is influenced by perception of technology (H1), tutor learning strategy (H2),
rational for using Internet (H3), and perception of media support (H4). Additionally, tutor
learning strategy is influenced by perception of technology (H 5), rational for using
Internet (H6), and perception of media support (H7); perception of technology (H8) and
perception of media support (H9) are also influenced by rational for using Internet.
Before depicting the final quantitative results, it is valuable to show the characteristics of
respondents as illustrated in Table 2. This will amplify our understanding related to the
qualitative and quantitative procedures utilized sequentially. The results of analyses are
detailed in the following interpretation accordingly with relevant figure and table.
5. Table 2. Respondent characteristics
Social & Political Mathematics & Total 100%
Faculty Economics=38%
Science=30% Science=32% [283 students]
Regional Sumatra [10]=10 Java-Bali[13]=13 Borneo [5]=4 Celebes [5]=5
Offices 38/40 Papua [2]=2 Nusa T [2]=2 Molluca [2]=2 Overseas [1]=0
0.00-0.99=0% 1.00-1.49=2% 1.50-1.99=8% 2,00-2,49=32%
2016 GPA
2.50-2.99=23% 3.00-3.49=14% 3.50-3.99=15% 4.00=6%
Age(Year) ≤20=43% 21-23=46% 24-26=11% ≥27=0.00
Length of
≤2-year=23% 3-year=46% 4 year=31% ≥5 year=0%
Marrital Yes=9% Yes=18%
If yes, children
Status No=91% No=82%
Occupation Full time=17% Part time=12% None=61%
Gender Female=61% Male=39%
Having observed the characteristics of respondents as illustrated in Table 2, we are now
moving to the results of SEM analysis especially on the results of hypotheses assessment
and the loading factors analysis as illustrated in Figure 2.
Figure 2. Results of hypotheses and the loading factors
Before describing and interpretating the results, it was confirmed under SEM output that
the data is statistically valid and reliable. This implies that further step can be performed.
Figure 2 obviously exposes that two out of nine hypotheses scrutinized are not validated
by the analysis. They are: (1) perception of media support to effective online tutorial (X 3
to Y where H4=-0.78) and (2) perception of media support to tutor learning strategy (X 3
to X4 where H7=0.88).These two hypotheses are not authenticated by the analysis since
the tvalues≤ 1.96 (for α=5%). This means that statistically the effectiveness of an online
tutorial scheme and tutor learning strategy are not positively and significantly influenced
by perception of media support respectively. We will observe later whether or not the gap
found in this stage is in a highly contradictory degree; whether they differ in conceptual
and/or operational frameworks or partly only in the rank of the dimensions (in a lower
level of meaning).
6. In additions, the other seven hypotheses are positively and quite significantly validated
by the analysis. They are: (1) H1=3.59 (perception of technology to effective online
tutorial; X1-Y), (2) H2=4.41 (tutor learning strategy to effective online tutorial; X 4-Y), (3)
H3=2.99 (rational for using Internet to effective online tutorial; X 2-Y), (4) H5=2.32
(perception of technology to tutor learning strategy; X1-X4), (5) H6=2.51 (rational for
using Internet to tutor learning strategy; X2-X4), (6) H8=6.39 (rational for using Internet
to perception of technology; X2-X1) and (7) H9=5.42 (rationale for using Internet to
perception of media support, X2-X3), since all of the tvalues≥ 1.96 (for α=5%). These imply
that from statistical outlooks effective online tutorial support is significantly influenced
by perception of technology, tutor learning strategy and rational for using Internet.
Besides, tutor learning strategy is influenced by perception of technology and rational for
using Internet; rational for using Internet influences both perception of technology and
perception of media support.
Having confirmed the results of hypotheses testing, we are now in the position of relating
the loading factors behavior. They are applied to tangibly discern the relations power of
each of the participating variables and their comportments. They are accomplished under
SEM, in the frame of factors affecting effective online tutorial support, to work out the
end results by following Wijayanto (2008) and Hair, Black, Babin and Anderson (2009).
Now, let us focus again on Figure 2. There are five details need to be methodically
elaborated prior to concluding the final ends under the quantitative approach.
The first is related to the utmost influential factor to the effective online tutorial variable.
The analysis obviously confirmed that tutor learning strategy (X 4 to Y=0.68) is the most
influential factor to effective online tutoring program then successively followed by
perception of technology (X1 to Y=0.44) and rational for using Internet (X2 to Y=0.32);
whereas perception of media support was excluded by the analysis. This means that most
respondents considered strategy of tutor and how they managed activities in each and
every session of tutorial session was exceptionally a big deal. This also implies that
effective online tutorial support to certain extent was forced by external force and out of
student control; as rational for using Internet, perception of technology, and perception
of media factors are intrinsically within student controls.
Besides, the dimensions within tutor learning strategy placed style (X41=0.89) by most
respondents as the highest aspect in controlling tutor learning strategy. This is consistent
to what was previously obtained from the qualitative inquiry. The other three dimensions
are successively described as follows: group work (X42=0.85), discussion (X44=0.82) and
referral source (X43=0.72). These results imply that most of respondents believed tutor
strategy, especially the style, was able to motivate students to be more involved in the
group work, induce discussion among themselves and search for related academic source
by their own way and available time. Reasonably, these are also the general impressions
obtained from the preceding qualitative inquiry.
The second is associated with factors affecting tutor learning strategy (X4). Conceptually,
it was influenced by perception of technology (X1), rational for using Internet (X2), and
perception of media support (X3). Having carefully assessed, however, perception of
media support (X3) had no effect on the tutor learning strategy (X4). Tutor learning
strategy was influenced by rational for using Internet (X2=0.29) and then followed by
perception of technology (X1=0.27). This means that rational for using Internet clearly
had more effects than that of perception of technology with respect to the tutor learning
strategy. Students positioned rational for using Internet is more likely to have influenced
on the online tutorial support directly and/or indirectly as compared to the perception on
the technology.
Some respondents not only agreed upon quantitative effects at the variable level but also
in the ranks of the dimensions. Respondents concurred that by having a good sense on
rational for using Internet will certainly help them to get hold the easiness (X22=0.82),
7. access (X21=0.80), expansion (X23=0.80), and efficacy features (X24=0.79) related to the
chances of obtaining interactive and contributive online tutorial support. Moreover,
respondents viewed accuracy (X12=0.84) as the most influential dimension of rational for
using Internet and then followed by gainful (X14=0.81), traceability (X13=0.77), and
promptness respectively (X11=0.76) in conjunction with an interactive and contributive
online tutoring support.
The third consequence, on the rational for using Internet which affected perception of
technology (X2 to X1=0.63) and perception of media support (X2 to X3=0.56). These two
outputs show that perception of technology was much more affected by rational for using
Internet rather than that of perception of media support. Most of respondents learned
that to get advantages of interactive and contributive online tutorial support are more
likely to have achieved by having a good rational for using Internet as compared to
perception of media support. They were true in most cases. Given that to certain extent
the ICT were characterized by various advantages, student would be more entertained by
the online tutorial support. It is essentially critical to possess the perception of media
support but in fact it had no effect on the online tutorial in this inquiry.
The fourth is relationships between perception of media support with tutor learning
strategy and effective online tutorial scheme. It was actually disturbing that under
statistical procedure the perception of media support has no effects to both tutor learning
strategy as the moderating and effective online tutorial scheme as the dependent
variables. Theoretically, at least perception of media support influenced either effective
online tutorial supports directly or indirectly through the moderating variable, i.e., tutor
learning strategy. It seems that further prudent inquiry is necessary to envisage how and
why it unpredictably just goes like that.
The fifth is a gap between qualitative versus quantitative results. Initially, it was
established four main variables associated with the effective online tutorial support.
Based upon that basis, the conceptual framework was developed to be quantitatively
validated. Nine hypotheses were statistically scrutinized. After all, two of them were not
statistically validated by the analysis. This implies that the quantitative results were not
comparatively harmonious with the qualitative structure as formerly established.
Having perceived the quantitative and qualitative upshots, the results were distinct and
somewhat contradicting one another. Is it so? Effective online tutorial support was not
directly influenced by one of independent variable. Under quantitative routines, there are
three motives why it might happen so. The first is on the elaboration process of the
variables. The second is on the transformation process of variables into dimensions as the
bases to construct questionnaires. The third is on the data collection processes. These
deduce that further guarded inquest under quantitative procedure is significant
implemented by noticing those three aspects previously explained. Despite the distinctive
ends did take place, the quantitative outcomes are still useful (tutor learning strategy is
positively a hint to effective online tutorial support). From qualitative procedure, it
implies that the online tutorial can be firmly disclosed as summarized in Table 1.
Prior to justifying the closing line from qualitative versus quantitative results, it is
reasonable to think over whether the SEM output is in ‘good fit’ category or not. If yes, it
is dependable to consider the hypotheses and engender the loading factors to confirm the
power of all behavioral interrelations. The analysis confirmed that they actually are in the
‘good fit’ category except for Normal Fit Index (NFI) as illustrated in Table 3; NFI was
however in a marginal fit category. This implies that the quantitative model validated is
methodically dependable. The conceptual and operational framework implied having a
substantial and technical harmony in theoretical and methodological outlooks.
8. Table 3. Goodness of fit of the tested framework
Goodness of Fit Cut-off Value Results Notes
RMSEA Root Mean Square Error Approximation ≤ 0.08 0.08 Good Fit
RMSR Root Mean Square Residual < 0.05 or < 0.10 0.74 Good Fit
GFI Goodness of Fit ≥ 0.90 0.91 Good Fit
AGFI Adjusted Goodness of Fit Index ≥ 0.90 0.94 Good Fit
CFI Comparative Fit Index ≥ 0.90 0.96 Good Fit
NFI Normal Fit Index ≥ 0.95 0.94 Marginal Fit
RFI Relative Fit Index ≥ 0.90 0.93 Good Fit
Despite one goodness of fit is in marginal category (the NFI; Table 1), it is still helpful to
utilize them as a point of reference to bridge understanding between the qualitative and
quantitative endings. Three underlying evaluations need to be opened up to make use of
the corollaries. The first consequence is the dispute on the different results under
exploratory-design used. The second is reason adjacent to respondent characteristics.
The third is on the implication of findings for future consideration if conducting further
research with a comparable theme.
After completing the procedures, tutor learning strategy is mutually supporting with
rational for using Internet and perception of technology along with their dimensions (as
two independent variables). Likewise, moderating variable partly interconnected with
independent variables. Remarkably, despite perception of media support has no effect to
the online tutorial support, it was influenced by rational for using Internet. Fortunately,
independent variables are interrelated one another with significant power of relations.
This implies that the qualitative and quantitative results are considerably varies;
however, it is providential are not absolutely contradict each other.
Exploratory-design, as part of mixed methods, was conducted by collecting and analyzing
qualitative data first and then building the quantitative structure prior to interpretation
(Creswell & Clark, 2011). It aims at measuring qualitative exploratory findings. Prior to
building operational framework under quantitative procedure, the conceptual framework
(qualitative outcomes) should be first established as the framework of the study that will
be statistically scrutinized afterwards. Therefore, connecting the two strands with respect
to theoretical and/or instrumental elaboration become crucial details. In fact, the end
results show that two out of nine hypotheses established are imperfectly verified by the
analysis. Besides, the order of dimensions is partly not in harmony as well. Again, this is
to make more observable that the quantitative approach is still unable to perfectly
approve prior qualitative exploratory findings.
Referring to the respondent characteristics (Table 2), it can be definitely enlightened that
most of respondents are young and they were highly literate in ICT, full time students and
having a good academic performance (GPA) as well. These facts explain why most
respondents did not regard media support aspects, as one of independent variable, as a
chief clue leading to effective online tutorial support; as well as to the moderating
variable, i.e., tutor learning strategy. What is primarily critical to them as distance
learners and users is how tutors plan, organize and monitor all tutorial activities in a good
and well-regulated quality procedure primarily in providing and maintaining an online
tutoring program accordingly.
Anticipating corresponding research for further judgement is prominent to be explored.
The magnitude of respondents is not solely restricted to the scholarship students but also
by welcoming all other 297 thousand students. Having involved them, it will enlarge the
effects obtained with respect to the framework resulted from qualitative inquiry; related
to searching for the determinant of effective tutorial comprehensively under ODL setting.
Sensible insight is required to be identified to avoid restraint retrieving harmony between
9. qualitative and quantitative conclusion. Most of all, searching for and adopting
appropriate methods are certainly urgent to define an authentic determinant of effective
online tutorial service that mutually supporting each other, both under qualitative and
quantitative procedures.
The results of this inquiry encountered slight considerable variations between what had
been achieved from qualitative routines as opposed to the quantitative approach. Two out
of nine hypotheses assessed are not validated by the analysis. This implies that
established qualitative frame is imperfectly approved by the quantitative approach. Yet,
they only differ, not necessarily contradicting in a high influence. The end result is
therefore still useful for the University and related stakeholders in respecting critical
variables that should be carefully taken into account to provide effective online tutorial
scheme along with their dimensions in accordance with student needs.
It is worth to note that most respondents classified tutor learning strategy in the first
spot as a tip-off point. This is becoming more crucial, according to Lee and Martin (2017),
despite online discussions are a common component of most online courses, how to
engage students in online discussions has been an everlasting challenge. This entails that
the University should take this upshot by spotting imaginable constraints which might be
real, especially how to motivate students being much more active in a group work and
discussion through common acceptable style of tutors. The University is well-
recommended to get ahead on four dimensions in this variable so that tutors would have
comparable perception on that issue. Additionally, imagining this know-how is
unanimously typical in a wide-ranging of ODL ambiance, the management, faculty, and
tutors would then be well-advised to reflect on the variables discussed along with their
associated dimensions explained earlier. It is aimed at offering beliefs that tutor learning
strategy grows to be straightforward aptitude to endeavor great online tutoring scheme
as expected by students for their academic performance and persistence as distance
learners (Sembiring, 2015). Although online tutoring idea is phenomenal, primarily in
ODL environment, because of its flexibility and convenience, it is truly important to
address those issues that adversely impacting on retention with respect to the success of
the vast majority of students.
Further comprehensive study should be aggressively and regularly organized in terms of
instructional design necessity and learning styles endorsement relative to the virtues of
online tutoring structure in ODL perspectives with much broader perspectives. All these
endeavors lead to satisfying student need and expectation.
Maximus Gorky SEMBIRING, a Doctor in Educational Management,
is a senior academic of Universitas Terbuka. He is currently the
Director of Research Center. His research interest mainly focuses
on student support services. He has been in the University for
more than 33 years. In the last six years, he authored 12 articles
published in the international journals and/or presented in the
International (world) Conferences. He was awarded four Best
Paper Awards by the Asian Association of Open Universities (2014,
2015, 2016 and 2017). He was also awarded two Best Paper
10. Awards by the International Council for Open and Distance Education (2013 and 2015).
Moreover, in 2017, he was awarded three Best Paper Awards, i.e., two from the
Educational Technology World Conference, Indonesia and one from the International
Conference on Open and Innovative Education hosted by the Open University of Hong
Dr. Maximus Gorky SEMBIRING
Faculty of Education and Teacher Training, Universitas Terbuka
Jalan Cabe Raya, Pondok Cabe,Pamulang, Tangerang Selatan, INDONESIA 15418
Phone: +62 816878444
E-mail: [email protected]
Bean, J. P. (1985). Interaction effects based on class level in an exploratory model of
college student dropout syndrome. American Educational Research Journal,
22(1), 35-64.
Benham, C. (2002). Training effectiveness, on-line delivery, and the influence of learning
style. Proceedings of the 2002 ACM Special Interest Group on Computer
Personnel Research Conference, 41-46. Kristiansand, Norway.
Bliwise, N. (2005). Web-based tutorials for teaching introductory statistics. Journal of
Educational Computing Research, 33(3), 309-325.
Bolliger, D., & Supanakorn, S. (2011). Learning styles and student perceptions of the use
of interactive online tutorials. British Journal of Educational Technology, 42(3),
Cheng, J., & Swanson, Z. (2011). An examination of the effects of web-based tutorials on
accounting student learning outcomes. Review of Higher Education and Self-
Learning, 4(10), 14-28.
Cochran, W. G. (1977). Sampling techniques. 3rd Ed. New York: John Wiley & Sons.
Creswell, J. W. & Clark, V. L. P. (2011). Designing and conducting mixed-methods
Research. 2nd Ed. Los Angles: Sage Publication.
Devine, J., & Lokuge, W. (2012). Engaging distance students through online tutorials.
Proceedings of 2012 AAEE Conference, Melbourne, Australia.
Dawson, S. (1998). Effective tutorial teaching. Melbourne: RMIT Publishing.
Elicker, J., O’Malle, A., & Williams, C. (2008). Does an interactive Web-CT site help
students learn? Teaching of Psychology, 35, 126-131.
Fowler, F. J. Jr. (2014). Survey research methods. 5th Ed. Los Angeles: Sage Publication.
Guy, R. S., & Lownes-Jackson, M. (2012). Assessing effectiveness of web-based tutorials
using pre-test and post-test measurements. Interdisciplinary Journal E-Learning
& Learning Objects, 8, 15-38.
Hair, Jr., J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data
analysis with readings. 7th Ed. New Jersey: Prentice Hall.
Jain, A. (2006). Students’ perceptions of workshop based introductory macro-economic
tutorials: A survey. Economic Papers, 25(3), 235-251.
Keefe, J. (1979). Learning style: An overview. In J. W. Keefe (Ed.), Learner learning
styles: Diagnosing and prescribing programs. Reston: NASSP.
Kobayashi, M. (2017). Students’ media preferences in online learning. Turkish Online
Journal of Distance Education, 18(3), 4-15.
11. Koch, C., & Gobell, J. (1999). A hypertext based tutorial with links to the Web for teaching
statistics and research methods. Behavior Research methods, Instruments &
Computers, 31, 7-13.
Lee, J., & Martin, L. (2017). Investigating Students’ Perceptions of Motivating Factors of
Online Class Discussions. International Review of Research in Open and
Distributed Learning, 18(5). DOI:
(Accessed 8 September 2017).
Lee, W., Courtney, R., & Balassi, S. (2010). Do online homework tools improve student
results in principles of microeconomics courses? The American Economic Review,
100(2), 286-287.
Lenz, L. (2010). The effect of a web-based homework system on student outcomes in the
1st year mathematics course. Journal of Computers in Mathematics & Science
Teaching, 293, 233-246.
MacKinnon, G., & Williams, P. (2006). Models for integrating technology in higher
education: The physics of sound. Journal of College Science Teaching, 35(7), 22-
Mbatha, B. (2015). A paradigm shift: adoption of disruptive learning innovations in an
ODL environment – the case of the University of South Africa. International
Review of Research in Open and Distributed Learning, 16(3), 218-232.
Mitchell, M., & Jolley, J. (1999). The correlation: A self-guided tutorial. Teaching of
Psychology, 26, 298-299.
Osborn, D. (2010). Do print, Web-based or blackboard integrated tutorial strategies
differentially influence student learning in an introductory psychology class?
Journal of Instructional Psychology, 37(3), 247-251.
Price, L., Richardson, J. T. E., & Jelfs, A. (2007). Face-to-face versus online tutoring
support in distance education. Studies in Higher Education, 31(1), 1-20.
Retna, K., Chong, E., & Cavana, R. (2009). Tutors and tutorials: Students’ perceptions in a
New Zealand university. Journal of Higher Education Policy and Management,
31(3), 251-260.
Rosell-Aguilar, F. (2007). Changing tutors roles in online tutorial support for ODL through
audio-graphic SCMC. The JALT CALL Journal, 3(1-2), 81-94.
Rustam, Haliman, H., Susanty, E., & Sembiring, M. G. (2015). Exposing the gist of
effectual an online tutorial support. Paper presented at the 29th Annual
Conference of the Asian Association of Open Universities (AAOU), hosted by Open
University Malaysia, Kuala Lumpur, 30 November-2 December 2015.
Sembiring, M. G. (2014). Modeling determinants of student retention in distance
education institutions. International Journal of Continuing Education & Lifelong
Learning, 6(2), 15-18.
Sembiring, M. G. (2015). Student satisfaction and persistence: Imperative features for
retention in ODL. Asian Association of Open University (AAOU) Journal, 10(1),
June 2015, 1-11.
Shin, W. S., & Kang, M. (2015). The use of a mobile learning management system at an
online university and its effect on learning satisfaction and achievement.
International Review of Research in Open and Distributed Learning, 16(3), June
2015, 110-130.
Sugiyono. (2012). Metode penelitian kombinasi. Bandung: Penerbit Alfa Beta.
Susilo, A. (2014). Emerging technologies acceptance in online tutorials: tutors’ and
students’ behavior intentions in higher education. Open Praxis, 6(3), Jul-Sep
2014, 257-274.
12. Sweeney, J., & Ingram, D. (2001). A comparison of traditional and web-based tutorials in
marketing education. Journal of Marketing Education, 23(1), 55-62.
Tallmadge, W., & Chitester, B. (2010). Integrating concepts using online tutorials in a
freshman chemistry course: Transformative Dialogues. Teaching and Learning
Journal, 4(2), 1-7.
Tinto, V. (1993). Leaving college: rethinking causes and cures of student attrition . 2nd Ed.
Chicago: University of Chicago.
Tjiptono, F., & Chandra, G. (2011). Service, quality & satisfaction. Yogyakarta: Andi.
Universitas Terbuka. (2015). Strategic and operational planning of Universitas Terbuka
2014–2021. Tangerang Selatan: Universitas Terbuka.
Universitas Terbuka. (2017). Rector’s office yearly report. Tangerang Selatan: Universitas
Varnhagen, C., & Digdon, N. (2002). Helping students read reports of empirical research.
Teaching of Psychology, 29, 160-164.
Wijayanto, S.H. (2008). Structural equation modeling — Lisrel 8.80. Yogyakarta: Penerbit
Graha Ilmu.
Wilson, S., & Harris, A. (2002). Evaluation of the Psychology Place: A web-based
instructional tool for psychology courses. Teaching of Psychology, 29, 165-168.
Zhang, W. Y., Perris, K., & Yeung, L. (2005). Online tutorial support in open and distance
learning: students’ perceptions. British Journal of Educational Technology, 36(5),