Distance Education
Vol. 29, No. 3, November 2008, 211–229
Effectiveness of using learner-centered principles on student retention in
distance education courses in rural schools
Wallace H. Hannuma*, Matthew J. Irvina, Pui-Wa Leib and Thomas W. Farmerb
aUniversity
of North Carolina at Chapel Hill, USA; bPennsylvania State University, USA
(Received 25 May 2008; final version received 6 August 2008)
Taylor and Francis
10.1080/01587910802395763
CDIE_A_339743.sgm
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0158-7919
hannum@unc.edu
Distance
Research
Open
302008
29
WallaceHannum
00000November
andEducation
Article
Distance
(print)/1475-0198
2008
Learning Association
(online)
of Australia, Inc.
This article examines the effectiveness of training facilitators in secondary schools to
follow APA learner-centered principles to support learners in distance education. The
study was a cluster-randomized control trial with 36 match pairs of schools and 246
students in the rural USA. The schools were selected at random and assigned at
random to treatment condition. Instructors were blind to the treatment condition as
were the local facilitators and schools. Data on length of time spent in the course and
whether students completed the semester were analyzed. The results indicated that
students in the intervention condition completed the first semester at a statistically
higher rate than control students where facilitators did not have this training. The
number of weeks students remained in the course was likewise statistically different
with students in the intervention condition staying in the course more weeks holding
instructor constant.
Keywords: learner-centered distance education; facilitator training; student retention;
rural education
Introduction
The use of distance education has been increasing dramatically in recent years. As in the
past, the growth in distance education reflects the need for courses among students who
are not able to participate in traditional face-to-face courses (Beldarrain, 2006). Bates
(2005) documented the rise in distance education worldwide and traced the history of
several distance education institutions including the British Open University, the University of Catalonia in Spain, Indira Gandhi National Open University in India, and the
Instituto Tecnologico y de Estudios Superiores de Monterrey in Mexico to demonstrate
both the growth and range of operations in distance education institutions worldwide.
Although the greatest growth and use of online learning is at the tertiary level, the impact
of distance education is also being felt in secondary schools. Among secondary school
students, grades 9–12, in the USA 12% have taken a distance education course at their
school and 8% have taken a distance education course on their own (Henke, 2008).
Watson and Ryan (2007) reported that 42 of the 50 states in the USA now have significant online learning programs in their schools that are either supplemental to the regular
face-to-face instruction or in which students take all, or almost all, of their courses online.
These programs are also growing, with 40% of them reporting annual growth of over 25%
(Watson & Ryan, 2007).
*Corresponding author. Email: hannum@unc.edu
ISSN 0158-7919 print/ISSN 1475-0198 online
© 2008 Open and Distance Learning Association of Australia, Inc.
DOI: 10.1080/01587910802395763
http://www.informaworld.com
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The incidence of distance education in high schools in rural areas of the USA is higher
than the national average. Hannum et al. (2006) reported that 69.8% of rural districts are
using distance education courses, most often in the form of online courses. Over 84.5% of
rural schools had used distance education, and the majority indicated a need for additional
distance education courses to meet their curriculum requirements. A recent report on
distance education in the USA also indicated that the proportion of rural districts with
students taking online courses is nearly twice that of urban and suburban districts (Setzer &
Lewis, 2005). Because many rural schools are small, often they do not have enough students
wanting to take a specific course to justify creating a class and assigning a teacher, and they
have difficulty attracting teachers especially in certain subject areas. Distance education via
the Internet has become an acceptable option in rural schools as a result of improvements
in the necessary infrastructure, affordability of technology, and near universal connectivity
(Hobbs, 2004; Jimerson, 2006; Malecki, 2003).
Effectiveness of distance education
Research supports that distance education in the form of online learning is at least as effective as traditional classes in terms of learning outcomes and often more effective (Bernard
et al., 2004; Cavanaugh, 2001; Cavanaugh, Gillan, Kromrey, Hess, & Blomeyer, 2004;
Cradler, McNabb, Freeman, & Burchett, 2002; Hobbs, 2004; Tallent-Runnels et al., 2006;
Waxman, Lin, & Michko, 2003). In a meta-analysis of distance education studies, Zhao,
Lei, Yan, Lai, and Tan (2005) reported an overall conclusion of equal effectiveness when
all studies were taken into account but noted that in more recently published studies
distance education was found to be more effective than face-to-face education. Although
studies support the effectiveness of distance education when compared with traditional
classroom instruction in terms of student achievement, students often fail to complete
online courses. Some report that 50–70% of students do not complete online courses or
programs (Carr, 2000; Roblyer, 2006; Rovai & Wighting, 2005; Simpson, 2004). Despite
this being a recognized concomitant of online courses, little research has examined and
tested factors that may improve course completion. The purpose of this article is to examine the initial effects of an intervention designed to increase secondary school students’
completion of an advanced online course by preparing local facilitators to support online
learners.
Distance education model
A common model for distance education at the secondary school level features an instructor
teaching at a distance from the students using some course management system to deliver
an asynchronous online course. In the local schools, students who are taking the course have
a regular class period assigned for the purpose of signing on and completing required course
assignments. Several students may be in the same room during this class period although
they can be taking different online courses. An adult facilitator who is responsible for insuring that everything is working smoothly and order is maintained usually monitors the
students but does not teach the course. This approach differs from the more independent
learning model in tertiary education in which students typically complete online courses
working on their own without any teacher or facilitator physically present. The distance
education model guiding this study holds that with proper training, a school-based facilitator
can be a source of support necessary for secondary students’ success in online distance
education courses.
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Need for school-based facilitators
Rural educators and policymakers have indicated that student support in distance education
is vital and can include having school-based facilitators (e.g., Hobbs, 2004). A report by the
US Department of Education (2007) found that school districts that are using online courses,
as well as providers of these courses, indicate that the more support students receive at
school, as well as online, the better chances they have for success. There are suggestions in
the research that a school-based facilitator may be essential (e.g., Kirby & Driscoll, 1997)
to student success. Smith, Clark, and Blomeyer (2005) indicated that pairing a highly qualified online instructor with a classroom aid in the local school can work at least as well as
or ‘arguably better’ than traditional classroom instruction with a less qualified teacher
(p. 68). Barr and Parrett (2001) indicated that having courses facilitated by competent,
caring adults is important with at-risk students. Davis and Rose (2007) support the presence
of trained onsite facilitators to coach and advocate for K-12 students in online courses. This
confirms the role local facilitators can play in the success of distance education, even when
they do not have the same qualifications as the online instructor.
Student needs in distance education
Kim (2004) identified the issue of time and self-pacing as playing a role in student attrition
in distance education. Further, student motivation combined with these factors appears to be
a primary contributor to attrition in distance education. Simpson (2004) concluded that
retention rates have always been lower in distance education when compared with traditional education. He examined several interventions based on contacting students as well as
the timing of interventions during the course to determine whether it was possible to reduce
the oft reported dropout problem in distance education. Contact with students during the
course increased the number of students completing courses. Passey (2000) cautioned that
distance education courses should reflect the need to have strong social supports rather than
an approach that lowers social interactions. In a study of students in the UK participating in
an online Master of Education program, Motteram and Forrester (2005) found that students
had concerns about managing their workloads, keeping up with readings, and maintaining
sufficient motivation to be successful. Abel (2005) examined factors associated with
success in Internet-supported learning in tertiary institutions and found that along with
strong institutional commitment and motivation to be successful, the degree of support for
faculty and students predicts success. He reported that students in successful programs were
well supported including student orientation to online learning, a single point of contact for
students, and frequent feedback to students.
Students have reported less cohesiveness and involvement in their distance education
courses than in traditional face-to-face courses (Hughes, McLeod, Brown, Maeda, & Choi,
2005). Studies have shown that motivation factors (e.g., self-efficacy, locus of control, risk
taking, organization, and self-regulation) are important in distance education courses at the
secondary level (Rice, 2006). Some have criticized distance education for its lack of
personal contact (Frank, Reich, & Humphreys, 2003). Others have noted the sense of isolation students feel in distance education courses (Abrami & Bures, 1996).
Support for learning is an element of effective pedagogy often missing in distance
education courses (Bonk & Dennen, 1999; McCombs & Vakili, 2005). Distance education
courses often fail to do this as a result of their very nature. Many distance education courses
push content to learners via the Internet, but fail to provide students with necessary support
for learning. Having someone physically present with the learner, who knows the learner
and fully understands the local context of the learning, can be beneficial. Many distance
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education courses ignore this human element. Likely this is why so many students drop out
of distance education courses and rate them poorly. Important pieces essential for learning
are missing, and students recognize this.
Learner-centered view
The starting point in a more learner-centered view is attention to empirically based knowledge about learners and learning to identify practices that will likely enhance the
outcomes from distance learning. A set of such principles is embodied in the learnercentered psychological principles (LCPs) (APA Task Force on Psychology in Education,
1993; APA Work Group of the Board of Educational Affairs, 1997) that are based on a
comprehensive review and analysis of the literature on learning. The LCPs consist of 14
principles based on theory and research that focuses on human thinking, learning, motivation, and social processes and on personal and interpersonal relationships, beliefs, and
perceptions that are affected by and/or supported by the educational system as a whole.
These research-validated LCPs provide a framework for designing distance education
environments and practices that attend holistically and systemically to the needs of all
learners.
How the LCPs address learners and learning
The 14 LCPs are organized into four categories, or domains: (1) cognitive/metacognitive
factors, (2) motivational/affective factors, (3) developmental/social factors, and (4) individual difference factors. The 14 specific principles within these categories define much of
what is known about learning and learners as a result of research into both. Many of these
principles are consistent with recent discoveries from psychology relating to positive
youth development and prevention interventions (Blum, McNeely, & Rinehart, 2002;
Catalano, Haggerty, Oesterle, Flemming, & Hawkins, 2004; Libbey, 2004; Seligman &
Csikszentmihalyi, 2000). These principles are robust, represent the best knowledge about
human learning and development, and are applicable to all levels and types of school
learning including distance education (Hannum & McCombs, 2008; McCombs & Vakili,
2005). The LCPs are shown in Table 1.
Purpose
This study sought to ascertain whether having an adult facilitator trained in applying LCPs
to support secondary school students as they complete an online distance education course
is an effective way to enhance the engagement of students and increase their rate of completion of an online course. The goal was to determine if preparing local facilitators in using
LCPs would increase students’ progress in and completion of online distance education
courses more than was possible if facilitators did not have this training. The null hypotheses
were:
H1:
H2:
There will be no differences in persistence in a distance education course measured in
weeks between students whose facilitators were trained in applying LCPs and those
whose facilitators did not have such training.
There will be no differences in completion rates in a distance education course between
students whose facilitators were trained in applying LCPs and those whose facilitators
did not have such training.
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Table 1.
215
Learner-centered principles (McCombs, 2001, p. 187).
Cognitive and metacognitive factors
Principle 1: Nature of the learning process
The learning of complex subject matter is most effective when it is an intentional
process of constructing meaning from information and experience.
Principle 2: Goals of the learning process
The successful learner, over time and with support and instructional guidance, can
create meaningful, coherent representations of knowledge.
Principle 3: Construction of knowledge
The successful learner can link new information with existing knowledge in
meaningful ways.
Principle 4: Strategic thinking
The successful learner can create and use a repertoire of thinking and reasoning
strategies to achieve complex learning goals
Principle 5: Thinking about thinking
Higher order strategies for selecting and monitoring mental operations facilitate
creative and critical thinking.
Principle 6: Context of learning
Learning is influenced by environmental factors, including culture, technology, and
instructional practices.
Motivational and affective factors
Principle 7: Motivational and emotional influences on learning
What and how much is learned is influenced by the learner’s motivation. Motivation to
learn, in turn, is influenced by the individual’s emotional states, beliefs, interests and
goals, and habits of thinking.
Principle 8: Intrinsic motivation to learn
The learner’s creativity, higher order thinking, and natural curiosity all contribute to
motivation to learn. Intrinsic motivation is stimulated by tasks of optimal novelty and
difficulty, relevant to personal interests, and providing for personal choice and control.
Principle 9: Effects of motivation on effort
Acquisition of complex knowledge and skills requires extended learner effort and
guided practice. Without learners’ motivation to learn, the willingness to exert this
effort is unlikely without coercion.
Developmental and social factors
Principle 10: Developmental influence on learning
As individuals develop, they encounter different opportunities and experience different
constraints for learning. Learning is most effective when differential development within
and across physical, intellectual, emotional, and social domains is taken into account.
Principle 11: Social influences on learning
Learning is influenced by social interactions, interpersonal relations, and
communication with others.
Individual differences factors
Principle 12: Individual differences in learning
Learners have different strategies, approaches, and capabilities for learning that are a
function of prior experience and heredity.
Principle 13: Learning and diversity
Learning is most effective when differences in learners’ linguistic, cultural, and social
backgrounds are taken into account.
(continued)
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Table 1.
W.H. Hannum et al.
(Continued).
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Principle 14: Standards and assessment
Setting appropriately high and challenging standards and assessing the learner and
learning progress – including diagnostic, process, and outcome assessment – are
integral parts of the learning process.
Method
This study was a cluster-randomized control trial. Specifically, schools were matched and
then schools within each matched pair were randomly assigned to the intervention and control
condition. Students took an Advanced Placement (AP) English Literature and Composition
course that was taught by two instructors. This course was designed to teach the skills
necessary to encounter and understand a broad range of literature and to prepare students
for the AP English Literature and Composition exam. One focus was to expose students to
a broad range of literature including short works and poems, a chronological survey of British
literature, and novels that will challenge and edify readers. A second focus in the course was
to develop composition skills that range from essays to original poetry to literary responses.
The assignment of schools in each matched pair to control or intervention condition was done
by random, then each matched pair of schools was assigned to one of the two course instructors in such a fashion as to balance the overall number of students each instructor had. This
allowed control and intervention conditions to be nested within instructors. The students
completed the course working during a regularly scheduled school period with a facilitator
in the room with them. The study employed an experimental design because this is often the
most powerful approach to determining if an intervention has an effect (Shadish, Cook, &
Campbell, 2002). Yet, experimental designs have rarely been used in distance education
research (Abrami & Bernard, 2006).
Participants
This study selected rural schools at random from all rural schools in the USA using the
National Center for Educational Statistics (n.d.) database and following the Rural Education
Achievement Program (US Department of Education, n.d.) definition of rural. Random
selection of schools was undertaken to ensure a better representation of all rural schools and
to ensure generalizability of results. Each school was paired with a similar school in terms
of several demographic characteristics including proximity to each other, community and
school size, similarity in terms of resources, and academic backgrounds of students. One
school in each pair was assigned to the intervention condition and the other school was
assigned to the control condition by a random process. Students took an AP English
Literature and Composition course that was taught by two instructors. The design was
double-blind in that the schools did not know which condition they were in, nor did the
distance education instructors know which condition their students were in. The study
consisted of 36 schools (18 match pairs) and a total of 246 students. In order to maximize
sample size and power, an additional non-matched school that was in the control condition
was also included in analyses.
Although schools were matched on several characteristics, we conducted pre-intervention
assessments within the content area using items from the AP English Literature and
Composition examination that had been used in prior years. We limited instructor-related
variability in the following ways. Each instructor taught one subject area course with separate
class sections for intervention students and control students. This also reduced or eliminated
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the possibility of students in different treatment conditions coming into contact with and
interacting with each other during the study. Class sections were limited to approximately
20 students each. The instructors remained blind to the intervention status of their sections
as did the facilitators and the schools. In addition, instructors kept logs of their contact with
students and school personnel (i.e., facilitators and other school contacts).
Role of facilitator
The basic role of the facilitators was to support and guide students. Specifically, facilitators
dealt with various issues such as troubleshooting computer problems, coordinating efforts
with instructors and course administrators, monitoring student attendance, collecting some
homework assignments, proctoring exams, and helping students with any scheduling questions or problems students feel they cannot discuss with the instructors. The role of the
facilitator differed from that of the distance education instructor in several respects. Facilitators were physically present in the room with students when students were online during
the scheduled daily period for students to be in the distance education class. The course
instructors were not physically present and may or may not have been online or directly
accessible during that time since the course was asynchronous. Instructors and students
communicated through mediated means such as posting assignments, comments, and reflections through threaded discussion boards in the course platform software (e.g., Blackboard
Academic Suite) and by email. An additional difference was that facilitators, unlike the
course instructors, did not teach content. Indeed, facilitators were not expected to have the
requisite knowledge or skills to do so. The instructors alone were responsible for course
design and delivery of all content. Instructors may have asked facilitators to help check the
completion of an assignment, but the facilitators did not teach the content. In this investigation, it was stipulated that facilitators need only be an adult employee of the school with a
college education, not a teacher. Some facilitators who participated in the project were
teachers. However, principals, secretaries, librarians, and teachers of other subjects or
coaches also served in the facilitator role.
Facilitator training intervention
Facilitators in both conditions received basic training in the mechanics of using the distance
education software as part of their standard training. Specifically, all facilitators received
training on how to register students, how to assign/change passwords, how students can
access the courses on the Web, how students handle the mechanics of uploading and downloading files, how to send postings in the discussion forum, and how to check student
grades. The standard training provided facilitators with information about the basic or
standard role of facilitators, which was to support and guide students by helping them with
various problems. The training was delivered online via Blackboard Academic Suite and
made available one week before the course began.
In addition to this standard training, intervention facilitators were provided knowledge
about LCPs and ways of applying these to deal with common issues students encounter in
distance education. This was also accomplished by an online training which began a week
before the course started.
Following the overview of the LCPs, the training provided facilitators in the intervention
group with scenarios depicting common issues and problems that arise for students in
distance education. These scenarios were derived from our findings and experiences in a
pilot study with rural students and schools. These were intended to provide facilitators a
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better idea of what to expect and how the LCPs may help them deal with these situations.
The scenarios were provided over a period of several weeks starting before the course began
and continued over the first few weeks of the class. The scenarios were delivered in a multiple media format that included text, audio clips, and images. Each scenario featured one or
more students with a problem to which a model facilitator responded. These included the
following issues and topics:
!
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!
!
!
!
!
!
!
First day of school. This scenario presented strategies for introductions and icebreakers. The goal was to model the creation of an atmosphere that allows students to
collaborate, problem solve, and openly discuss anything course-related.
Discussing assignments. This scenario modeled a facilitator setting aside a class
period in the first week of the year to go over course logistics. Facilitators were asked
to encourage students to brainstorm strategies for specific problems relating to
technology, grades, and feeling overwhelmed or confused over an assignment.
Student fears. This scenario depicted a reluctant student having a conversation with
the facilitator concerning the student’s fears about the course. The facilitator modeled
some strategies that might be used for dealing with such issues.
Time management. This scenario featured a student who tended to procrastinate and
is discussing this with the facilitator. The facilitator covered a number of strategies for
effectively organizing the workload.
Helping students help themselves. This scenario was designed to encourage students
to interact with their online peers.
Too much work. In this scenario, a student is overwhelmed by the workload and is far
less confident than he/she initially appeared and believed. The facilitator modeled
several strategies that address these issues.
Disengaged. A student who was at the top of the class during the first weeks of the
course but later had falling grades and seemed increasingly disengaged was portrayed
in this scenario. The facilitator discussed this with the student to find out more about
where the student was having difficulties and what may have been the source.
Worries about grades. In this scenario a student was considering dropping the course
because of concerns about the grade in the online course being lower than grades in
other courses and the adverse affect on his/her grade point average. The student was
also having difficulties managing the workload. The facilitator attempted to allay the
student’s anxieties by discussing college expectations and the benefits of taking
challenging courses.
Intervention facilitators were provided an online discussion board related to each
scenario and were asked to post their comments about the scenario, which LCPs they
believed were evident, what they thought the LCPs suggested could be done in the situation,
and any comments about other facilitators’ postings. The intervention was also designed to
form a community of support among intervention facilitators. Intervention facilitators were
informed that the discussion board was where they could post questions to other facilitators
and use this to support each other.
The attempt to form a community among the intervention facilitators was intended to
provide the professional support and collegial interaction they may have been accustomed
to from their experience in small rural schools. The community of intervention facilitators
was also intended to be a resource – a group of individuals who were experiencing similar
issues, and with whom facilitators might be more apt to identify. Given that this was for
many a new role and context, having this support and awareness among facilitators may
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have been reassuring. In addition, facilitators may draw on each other to solve problems or
resolve issues they may encounter. Control facilitators also had access to a discussion board
that they could use as needed. However, control facilitators were not provided the overview
of the LCPs, scenarios, or directed to discussion boards to discuss the scenarios or to form
a community with other facilitators.
Another key feature of the intervention was the use of data to provide feedback to and
professional development for facilitators in the intervention condition during the semester.
Specifically, students and facilitators completed rating scales assessing relevant LCPs and
related factors including beliefs about learning, motivation, class experiences, and facilitator support. The results of this survey were aggregated at the classroom level and provided
to intervention facilitators. Members of the project staff then conducted brief feedback
sessions over the telephone with intervention facilitators to discuss and interpret the results.
A main focus of the survey and feedback session was how students perceived their distance
education setting in terms of several LCPs and related factors. In addition, the implications
from these results and suggested steps facilitators in the intervention condition might have
taken were provided.
Outcome measures
There were two principal outcomes of interest in this investigation. The first was the length
of time that students stayed in the online course. The second outcome variable was whether
students stayed in the course or dropped out at some time during the course.
Length of course participation
The length of students’ participation in the online course was measured in the number of
weeks. Facilitators were directed in several written documents and communications to
inform research staff when a student decided to no longer continue in the course as soon as
this decision had been reached. Research staff documented the date when any student
decided to withdraw. The length of course participation was calculated as the number of
weeks students were in the online course and this included the week the decision to no
longer continue was made.
Dropout
The next principal outcome variable was student dropout. This was a dummy coded variable
with students who dropped out of the course receiving a value of 1 and those who did not
receiving a value of 0.
Covariate measures
Several baseline or pretest covariates were obtained. These included several self-report
measures of facilitators’ beliefs and reflective self-awareness. Students also completed
measures of their prior course-related achievement and several self-report motivation
scales. In addition, a variable representing instructors was included.
Facilitator beliefs and reflective self-awareness
Information regarding facilitators’ beliefs and reflective self-awareness was measured by
several subscales and adapted from the assessment of learner-centered practices (ALCP)
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battery (McCombs & Lauer, 1997). The ALCP consists of several surveys developed to
help teachers identify students’ needs and changes in areas of instruction that can meet these
needs. The original ALCP battery included surveys for teachers, students, and administrators. The ALCP surveys have been validated with more than 5000 K-20 teachers and more
than 25,000 students taught by those teachers. Different versions have been validated for
grades K-3, 4–8, 9–12, and college (McCombs, 2003; McCombs & Lauer, 1997).
For the secondary school level, the ALCP battery assesses four domains of classroom
practice that are most predictive of student motivation and achievement (i.e., establishing
positive relationships and classroom climate, providing motivational support for learning,
facilitating students’ learning and thinking skills, and honoring student voice and providing
individual choice and challenge). The secondary teacher survey also includes several
measures of the following beliefs: teachers can influence adolescents’ learning (Can influence difficult stages) or cannot impact adolescent learning (Difficult stage), teachers can
best support learning by a moderate degree of control (Moderately controlling) or a high
level of control (Highly controlling), and teachers can best support learning by providing a
moderate level of student choice and control (Moderately autonomy supportive) or a high
level (Highly autonomy supportive). In addition, the reflective self-awareness subscale
assesses the degree to which teachers are aware of the influence of thoughts and feelings on
actions and tend to analyze and reflect on relevant experiences.
For this investigation, the secondary teacher scales assessing beliefs and reflective selfawareness were modified by changing the point of view and wording from that of a teacher
to that of a facilitator. These adapted measures were administered via an online survey.
Facilitators completed the survey as their first activity and immediately before starting to go
through the appropriate training materials. The measures of classroom practice were likewise
modified but were not administered at baseline because the course was not yet underway.
Student motivation
The student measures of the ALCP battery also contained several scales that assess different
dimensions of student motivation. These included beliefs in learning competency (selfefficacy), knowledge-seeking learning curiosity (state epistemic curiosity), use of actively
engaged learning strategies (active learning strategies), use of learning strategies that avoid
effort (effort avoidance strategies), intrinsic motivation and mastering goals (task mastery
goals), extrinsic motivation and achievement-driven goals (performance oriented goals), and
motivation to avoid work (work avoidance goals). Students completed these scales via an
online survey at the start of the course as a baseline measure of their motivation. The student
ALCP battery also contained several measures of the same classroom practices teachers or
facilitators completed and were likewise modified but were not administered at baseline.
Student achievement
Prior student course-related achievement was measured with a previous multiple-choice
portion of an AP English Literature and Composition exam from a set of such exams
instructors are provided by the College Board (n.d.). Students completed the exam early in
the course over two days (i.e., the fourth and fifth days of academic instruction). The exam
was online and embedded in the course platform. In addition, students were given a time
limit each day for taking and having access to the exam. Instructors were able to do this
through the features of the course platform and this was done to control the amount of time
students had to take the exam as well as to simulate more realistic testing conditions.
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Instructor
Two instructors taught all sections of the online course. In order to explore and control for
possible instructor effects, a dummy coded variable representing one of the two instructors
was included in analyses.
Analysis plan
The purpose of the study was to determine if there was any treatment effect on the length
of time students persisted in a distance education course (hypothesis 1) and whether they
completed the first semester (hypothesis 2). Due to the multilevel structure of the data, that
is, students were nested within schools (or facilitators), a two-level unconditional model
with random intercept (model 1) was first run using the SAS MIXED procedure to estimate
the amount of explainable variance at each level (Raudenbush & Bryk, 2002; Snijders &
Bosker, 1999). Based on experimenter observations instructor and treatment appeared to
have an effect on length of participation, dummy coded instructor and treatment variables
were entered at level 2 to explain school level variance. To explore significant covariates
for each level, stepwise ordinary least squares regression was used to liberally select predictors (among group mean centered level 1 student motivation scales and achievement pretest
scores as well as grand mean centered level 2 average school achievement pretest and
student motivation scores and facilitator scale scores) that were significant at the 0.25 level.
The selected and potentially helpful covariates were entered into the two-level mixed model
to obtain more precise effect and standard error estimates (model 2). Statistically nonsignificant predictors at the 0.05 level were removed from the model (model 3). The last model
(model 4) was the most parsimonious, and retained only significant predictors at the 0.05
level. For all mixed model estimations, the restricted maximum likelihood (REML) estimation method, model-based fixed effects standard error method, and between–within degrees
of freedom method were requested (Kreft & de Leeuw, 1998).
To explain dropout from the study, similar analytical strategies were followed. Because
the outcome measure was dichotomous (1 = dropout during semester; 0 = remained in the
study at the end of semester), stepwise logistic regression was used to select significant
covariates (at the 0.05 level) instead. Moreover, generalized linear mixed models (the SAS
GLIMMIX procedure) were used to take into account the multilevel data structure in
estimating the predictor effects. Because dropout status at the end of the semester and length
of participation contain essentially the same information, results from the two modeling
approaches are expected to be similar.
Results
The purpose of this study was to determine if training local facilitators in supporting online
learners would increase the rate at which they persisted in and completed an online course.
The principal finding was that students who had facilitators trained in a learner-centered
approach to provide support for students stayed in an online course more weeks and
completed the course at a higher rate than a control group who had facilitators without this
training.
Table 2 summarizes the internal consistency reliabilities for student and facilitator
self-report scales.
Appendix 1 presents the fixed and random effect estimates on length of course participation. There was a statistically significant amount of variation in average length of participation across schools (estimated level-2 variance = 23.82, p < 0.01). After adding the school
222
Table 2.
W.H. Hannum et al.
Coefficient alphas for student and facilitator self-report baseline measures.
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Coefficient alphas
Student measures
• Effort avoidance strategies
0.67
Facilitator measures
• Learner-centered beliefs
• Non-learner-centered beliefs about learners
• Can influence adolescents
• Adolescence difficult stage
• Moderately controlling
• Highly controlling
• Moderately autonomy supportive
0.74
0.60
0.31 (0.48)
0.74 (0.78)
0.53
0.53
0.72
Note: Values in parentheses are alphas after dropping an item with a negative point biserial correlation.
level predictors selected by stepwise OLS regression, school mean variance was reduced to
9.33 (i.e., about 61% of school level variance was accounted for by the set of predictors).
Among the predictors entered in the second model, only the treatment, instructor, and facilitator beliefs that adolescence is a difficult stage were statistically significant at the 0.05 level.
After removing all the nonsignificant predictors from the model, treatment and instructor
effects remained statistically significant but the difficult stage facilitator variable became
nonsignificant in model 3. These three predictors accounted for about 39% of the school level
variance. After further removing the nonsignifcant difficult stage facilitator variable in the
fourth model, both treatment and instructor effects remained statistically significant and they
account for about 33% of the school level variance in length of participation in the first semester. Controlling for instructor difference, students in experimental schools on average
remained in the study 4.69 weeks longer than students in control schools. Thus, hypothesis
1, which stated that there will be no differences in persistence in a distance education course
measured in weeks between students whose facilitators were trained in applying LCPs and
those whose facilitators did not have such training, was rejected. Students whose facilitators
had training in applying LCPs remained in the course more weeks. Student within school
variance was large and practically unexplained by the school level predictors.
Appendix 2 shows the fixed and random effect estimates on dropout. Among the set of
predictors selected by logistic regression and entered in the first model, only treatment,
instructor, and facilitator beliefs that can influence difficult stages were statistically significant at the 0.05 level. These variables remained significant after removing the nonsignificant predictors from the second model. Because the measure of facilitator beliefs that can
influence difficult stages was not very reliable (coefficient alpha = 0.48), it was removed
from the third model to see whether the effects of treatment and instructor were affected.
Treatment and instructor effects were statistically significant at the 0.05 level with (model
2) or without the facilitator beliefs that can influence difficult stages covariate (model 3).
As expected, the odds of dropping out for students in the control schools were higher
(about exp(2) = 7.39 times) than the odds for students in the experimental schools, holding
instructor constant. Thus, hypothesis 2, which stated that there will be no differences in
completion rates in a distance education course between students whose facilitators were
trained in applying LCPs and those whose facilitators did not have such training, was
rejected. Students whose facilitators had training in applying LCPs completed the course
more often than students whose facilitators did not have this training.
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223
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In summary, regardless of whether length of participation or dropout was analyzed,
treatment and instructor effects were statistically significant. Of particular interest in this
study was the treatment effect. Holding instructor constant, students in the treatment condition were more likely to persevere through the difficult AP course in the first semester than
students who were not. The difference in average length of participation was nontrivial
(about four weeks).
Limitations
This study is limited by the population studied, that is, rural secondary schools in the USA.
Generalizing beyond this population is not appropriate. The study is limited by the specific
course taught, that is, AP English Literature and Composition. It is possible that the findings
are unique to this course and that different results would be found for other courses such as
mathematics, science, or foreign language. The two course instructors had experience teaching this course online, but it is possible that the impact of the facilitator training may differ
in courses in which the instructors have more or less experience. The study is limited to
distance education models that have local facilitators physically present with students.
Generalizing to situations in which students work independently without a facilitator
present is not appropriate. This study focused on training local facilitators to support
students using a more learner-centered approach. Different results may be found if such
training in learner-centered approaches was given to distance education instructors rather
than facilitators. This is the first time the facilitator training program was developed and
used. The outcomes may be different during subsequent uses of the training program.
Students in rural secondary schools typically have good IT skills (Hannum et al., 2006).
Results of this study may have been different if students did not have good IT skills and thus
experienced difficulty using the technology. The data used in this study were collected over
a one semester time period. The outcomes may be different for longer time periods.
Conclusion and discussion
Research indicates that student learning in distance education courses is at least equal to that
in traditional classes if not better; however, distance education courses often have substantially lower rates of course completion. The purpose of this study was to explore the initial
efficacy of providing training to school-based facilitators in following more learnercentered practices to support secondary students taking an online course and determine the
impact on students’ persistence. Specifically, this study used a cluster-randomized control
trial to investigate the effectiveness of an intervention to improve students’ first semester
retention in and completion of a distance education course. The results indicated that
students in the intervention condition, where their facilitators were trained to follow a more
learner-centered approach to supporting students, completed the first semester at a statistically higher rate than did students in the control condition, where the facilitators did not
have this training. A similar measure to first semester retention, the number of weeks in the
course, was likewise statistically different with students in the intervention condition
staying in the course more weeks.
This research extended previous work on distance education by applying a randomized
control trial to investigate the impact of having trained facilitators support students taking a
distance education course. This intervention was based on the empirically validated LCPs
that had been shown to exert influence on learning in traditional face-to-face courses but had
not been used in distance education courses. Rather than comparing a distance education
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224
W.H. Hannum et al.
course with a traditional face-to-face course as has been done in so much research, this
study sought to explore different treatments within a distance education course that might
have promise for improving course completion rates. This follows the recommendations of
researchers such as Joy and Garcia (2000); Larreamendy-Joerns and Leinhardt (2006);
Lockee, Moore, and Burton (2001); and Lou, Bernard, and Abrami (2006). Furthermore,
this study followed the suggestions of Lou et al. (2006) by controlling for any potential
instructor effects, differences in instructional materials, and time on task.
Results indicated a significant difference in terms of student persistence when local
course facilitators were trained in use of the LCPs. The dropout rate for students in the
control condition (57%) in which their facilitators were not trained in using a learnercentered approach was within the range other researchers have reported (Carr, 2000;
Roblyer, 2006; Rovai & Wighting, 2005; Simpson, 2004). However, the dropout rates of
students in the intervention condition (34%) whose facilitators were trained in using a
learner-centered approach was significantly less.
Subsequent research should seek to explore the use of learner-centered approaches in
distance education courses, especially by examining how the interactions among facilitators
and students may differ when the facilitators have been trained in the use of such an
approach. Examining student perceptions of the different ways facilitators can support their
learning could be interesting as could exploring the impact of a more learner-centered
approach on student achievement as an outcome and potential intervening variable. This
research should also explore the use of learner-centered approach with a broader range of
students, not just rural secondary students who are taking AP courses.
The intervention in this study was aimed at local facilitators to enable them to support
online learners by applying learner-centered approaches. Students and facilitators had faceto-face contact on a regular basis during this course. This contact may have been an important component of the intervention that could not be accomplished as well at a distance. An
alternative approach would be to train the course instructors in applying learner-centered
approaches as part of their online teaching and see if this would have a similar impact on
student persistence and completion of the course.
Based on the results of this study, we suggest that having facilitators in the room with
secondary school students as they work on distance education courses can have a positive
impact on the students’ persistence in these courses and their completion of the courses. The
facilitators do not have to be teachers or familiar with the course content, as they do not have
any responsibilities for conveying course content. Rather their role is to help students with
their self-management, motivation, and other specific problems that students may encounter. The facilitator can also serve as the eyes and ears for distance education instructors who
are not physically present by passing information back to the instructors when students
encounter problems and stumble. Students taking AP courses through distance education
may experience more problems due to the demanding nature of AP courses. Having trained
local facilitators may be a way to alleviate or mitigate some of the issues students face in
AP courses, such as the higher workload and the more demanding nature of AP courses.
We believe that it is important to provide training for facilitators in LCPs of supporting
students in order to have benefits such as those found in this research. This study found
support for the position of Davis and Rose (2007) that professional development is necessary
for onsite facilitators to enable them to play a successful role in coaching and supporting
students taking online courses. When the training is based on helping facilitators use LCPs
to support students, those students who have facilitators with this training persist longer and
are less likely to drop out of online courses. Thus, we suggest facilitator training should
center on how to apply learner-centered approaches to support students taking online courses.
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225
Acknowledgement
This work was supported by a Research and Development Center grant (R305A04056) from the
Institute of Education Sciences to the National Research Center on Rural Education Support.
Notes on contributors
Wallace Hannum is an associate professor in the School of Education at the University of North
Carolina at Chapel Hill and the associate director of the National Research Center on Rural Education
Support. His research and development work focuses on instructional uses of technology, especially
distance education.
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Matthew Irvin is a research scientist at the National Research Center on Rural Education Support. His
research has focused on risk and resilience, student engagement and adjustment, and rural youth. He
has taught and developed online courses at the university level on child and adolescent development.
Pui-Wa Lei is an associate professor in the College of Education at Pennsylvania State University.
Her teaching and research interests are in the areas of advanced statistical methods and measurement
theories.
Tom Farmer is an associate professor in the College of Education at Pennsylvania State University.
He teaches courses in the etiology and characteristics of students with disabilities, practices and
research issues in the education and treatment of students with behavioral disorders, and the social
development of students with disabilities.
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Facilitator variables
• Learner-centered beliefs
• Non-learner-centered beliefs about learners
• Can influence adolescents
• Adolescence difficult stage
• Moderately controlling
• Highly controlling
• Moderately autonomy supportive
Random effects
School variance
Student variance
Model 1
(N = 240, 37 schools)
Model 2
(N = 224, 34 schools)
Model 3
(N = 236, 36 schools)
Model 4
(N = 240, 37 schools)
12.44***
–
–
(0.88)
–
–
13.94***
3.30*
−4.22*
(1.46)
(1.57)
(1.61)
12.81***
4.46**
−4.12**
–
(1.36)
(1.46)
(1.48)
–
12.27***
4.69**
−3.69*
–
(1.35)
(1.50)
(1.51)
–
–
–
−6.14
(3.70)
–
–
–
–
–
–
–
–
–
–
–
–
–
–
−3.34
−2.03
−3.04
−3.66*
−2.41
3.53
1.49
(2.36)
(1.83)
(2.07)
(1.74)
(2.33)
(1.92)
(2.09)
–
–
–
−2.09
–
–
–
–
–
–
(1.38)
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
23.82**
27.65***
(6.80)
(2.75)
9.33*
26.62***
(4.14)
(2.73)
14.55**
26.94***
(4.76)
(2.69)
15.97***
27.62***
(5.02)
(2.74)
Note: Table entries are mixed model effect estimates. Standard errors in parentheses. Cells with missing values are variables not included in model.
a
Significant grand mean centered school-level fixed effects selected by OLS stepwise regression.
*p < 0.05; **p < 0.01; ***p < 0.001.
W.H. Hannum et al.
Fixed effectsa
Intercept
Treatment
Instructor
Student variables
• Effort avoidance strategies
228
Appendix 1. Fixed and random effect estimates on length of course participation.
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Appendix 2. Fixed and random effect estimates on dropout.
Model 1
(N = 215, 33 schools,
generalized χ2/df = 0.78)
Model 2
(N = 236, 36 schools,
generalized χ2/df = 0.72)
Model 3
(N = 240, 37 schools,
generalized χ2/df = 0.69)
Fixed effectsa
Intercept
Treatment
Instructor
−0.60
−1.95*
2.13*
(0.70)
(0.79)
(0.84)
−0.42
−1.70*
1.82**
(0.61)
(0.67)
(0.69)
−0.10
−2.00**
1.52*
(0.64)
(0.72)
(0.73)
Student variables
• Course-related achievement
• Effort avoidance strategies
−0.69
3.76
(0.79)
(1.98)
–
–
–
–
–
–
–
–
Facilitator variables
• Learner-centered beliefs
• Non-learner-centered beliefs about learners
• Can influence adolescents
1.28
1.41
2.54**
(1.09)
(0.92)
(0.91)
–
–
2.55**
–
–
(0.92)
–
–
–
–
–
–
Random effects
School variance
2.13
(1.07)
2.51
(1.02)
3.28
(1.17)
Distance Education
Note: Table entries are GLIMMIX effect estimates. Standard errors in parentheses. Cells with missing values are variables not included in model.
a
Significant grand mean centered school-level fixed effects selected by stepwise logistic regression.
*p < 0.05; **p < 0.01; ***p < 0.001.
229