Supercourse Experience. How Much do Students Remember?
Brent E. Faught*, Madelyn
Law, Michelle Zahradnik
Department of Health Sciences, Faculty of Applied Health Sciences,
Brock University, Canada
*Corresponding author: Dr. Brent E. Faught,
Department of Health Sciences, Faculty of Applied Health Sciences, Brock
University, Niagara Region, 1812 Sir Isaac Brock Way, St. Catharine’s, ON, L2S
3A1, Canada. Email: bfaught@brocku.ca
Citation:
Faught BE (2019) Supercourse Experience. How Much do
Students Remember? Educ Res Appl 6: 159. DOI: 10.29011/ERCA-159/100159
Abstract
Accelerated courses continue to be part of the changing
landscape in higher education despite limited evidence to support their
efficacy in relation to knowledge retention. The current study incorporated a
longitudinal cohort design to determine if a difference in knowledge retention
over time exists between students enrolled in traditional versus accelerated
undergraduate courses. Knowledge retention was assessed at four time points
(baseline, three, six and 12 months) for students completing a first-year course
(N=207) and a fourth-year course (N=63) delivered in both formats. A
significant main effect of traditional versus accelerated course format on
retention of knowledge over time was not found in either first- or fourth-year
courses. The non-significant estimate for course format indicates that students
in the first and fourth year traditional and accelerated courses had similar
knowledge retention levels on the quizzes at three, six and 12 months compared
to their baseline assessment. The positive and significant estimate for time
point demonstrated that the success of the retention quizzes decreased over
time in both the traditional and accelerated course formats. This study
concluded that the accelerated course format does not compromise short- and long-term
knowledge retention in first or fourth year undergraduate students.
Keywords: Supercourse; knowledge retention; longitudinal cohort;
undergraduate studies
Introduction
Accelerated teaching and learning is not new to higher
education. Marques and Luna [1] suggest that accelerated teaching is among the
most profound educational discoveries of the past century. Wlodkowski [2]
stated that accelerated learning programs have contributed to enhancing higher
education and predicted that one-quarter of all students would be engaged in
accelerated learning over the next 25 years. Studies have reported student
satisfaction following accelerated learning as equal or higher to that of
traditional format learning [3,4]. Nevertheless, educators have contested the
utility of accelerated teaching for years [5-7]. While not all the factors
contributing to some educators’ and administrators’ skepticism are clear, a
constant and largely unfounded criticism is that compressed courses compromise
quality of teaching, although there is evidence to the contrary [8,9].
Subsequently, concern exists by faculty that compacted time for instruction
could lead to compromised reflective learning, as students may require
significant time to engage with the course material [7]. Essentially, critics
question how well students learn in a short period of time and whether they are
disadvantaged in retaining knowledge over time once they have completed a
course. However, limited research comparing long-term knowledge retention
between accelerated and traditional course formats served as the impetus for
the current study, which addresses this gap in the literature. At Brock
University, an accelerated course is known as a Supercourse.
The structure of higher education is rapidly changing in response
to government funding, financial restrictions, and altered student demographics
and demands [10]. Accelerated courses, sometimes also termed “intensive
teaching formats,” “time shortened courses,” “block format,” “intensive modes
of delivery” or “compressed courses,” were developed in response to these
changes with the intent of offering courses in a shortened and focused format
with no significant loss in content or student contact time [11]. Whereas
traditional courses are offered during an academic semester with one to three
hours of lecture offered per week, accelerated courses are designed to cover
the same course material in a shortened time, but with the same amount of
student contact [12]. While course structure varies by institution, accelerated
courses are generally compressed anywhere from one to eight weeks, with
individual class sessions lasting four hours or more [6].
Accelerated courses
offer several practical benefits when compared to traditional course formats.
The ability to offer compressed courses in a college or university’s spring or summer term
is viewed as an attractive academic extension of the typical fall and winter
term course offerings. This can offer students the opportunity to enroll in
courses that did not align with their Fall or Winter term schedules, lighten
the course load during the academic year, make up for poor academic performance
or allow for the completion of a university degree in less than four years
[12]. Additionally, students are often enrolled in one accelerated course at a
time and are not distracted by an abundance of information and responsibilities
from several courses. Students are able to focus on a single subject area and
reduce poor habits such as procrastination due to the short duration of the course.
Lastly, the extended length of class sessions can result in lower absentee
rates since a significant amount of information is missed with each absence
[6].
Given the potential benefits stated above, accelerated courses
are becoming increasingly popular in postsecondary institutions [6,7]. In the
last decade, postsecondary institutions have begun incorporating accelerated
courses into their standard fall and winter term curricula [12,2]. Although
accelerated course formats have been developed and applied in several colleges
and universities, the debate surrounding the effectiveness and academic
legitimacy of accelerated courses is ongoing [6,7]. Despite limited evidence on
either side of the debate, detractors argue that accelerated courses offer
convenience over substance and rigor [2,13]. The compressed course’s ability to
deliver the breadth and depth of information offered in traditional course
formats is questioned [2]. A substantial number of comparative studies
examining accelerated and traditional courses have been conducted with the
objective of identifying the advantages of each course format.
The primary outcomes of interest for analyses comparing
traditional and accelerated course formats have been performance and student
learning assessed immediately following course completion [7]. Previous
research has indicated that the accelerated course format structure is equally
if not more effective in terms of the outcomes of interest [2,6,7,14,15]. A
comprehensive review of 100 comparative studies identified that most findings
supported both similar and improved learning and performance outcomes in
accelerated courses compared with traditional course formats [15]. A limited
number of studies demonstrated improved outcomes in traditional courses. More research
that is recent has confirmed previous findings across a variety of academic
disciplines, including fine arts, foreign languages, humanities, natural
science and social sciences [6].
The success of the accelerated format is often attributed to the
demographic composition, specifically the age of students, enrolled in the
various course formats [6]. Adult learners are more likely than traditional
university or college aged (18-24 years) students to enrol in accelerated
courses. Adult learners have demonstrated superior performance in a variety of
learning formats [16] and it has been suggested that their maturity and life
experience has benefited them in accelerated courses [6]. Furthermore, adult
students in accelerated courses are thought to possess improved self-direction
and motivation compared with younger students [13,17,18]. Nevertheless, similar
research comparing accelerated and traditional course formats between students
of comparable age demonstrated both equal and improved short-term performance
outcomes in expedited courses [6,14,19].
The effectiveness of
accelerated courses is primarily supported by the similarity in short-term
performance and student learning with the conventional course format. Despite
the observed short-term benefits associated with compressed courses, it is
equally important that students retain content over time. Educators might
suggest that a traditional course would advantage students in retaining
knowledge over time compared to the accelerated format because students would
have more time to process the course content. To date, research has yet to
demonstrate any long-term differences between accelerated and traditional
course formats [14]. Research involving comparative analysis of the long-term
impact of course format on learning is limited. A review of literature
examining the long-term outcomes of accelerated courses indicated that despite
findings on student learning immediately following the completion of
traditional and accelerated courses, long-term knowledge retention has not been
observed to differ by course format [7,14]. The most recent comparative
analysis of long-term outcomes involved students in accelerated and traditional
formats of a psychology course offered at a graduate level [14]. Students in
the accelerated course performed significantly better at the end of the course
compared with students in the traditional course. Nevertheless, a three-year
follow-up with post-tests of course content demonstrated no difference in
knowledge retention [14]. Despite the observed similarity between course
formats, the study was restricted to a graduate-level course (offered in both
three- and 15-week formats) and the sample size at the three-year follow-up
involved only nine individuals in the intensive course and six in the
traditional course [14]. Due to the limited number of studies and their
associated methodological limitations, additional research comparing long-term
outcomes was deemed necessary to ascertain the effects of a variety of
accelerated course formats [14,15].
Additionally, there is
a lack of evidence outlining which types of accelerated courses and student
characteristics influence short- and long-term student learning. Factors such
as course level (i.e., year 1-4 undergraduate, graduate) and type (i.e., required
vs. elective) are at least a few considerations when determining whether
accelerated learning jeopardizes or enhances knowledge retention of course
content. A greater understanding of the benefits associated with accelerated
courses will provide university stakeholders and administrators with evidence
to determine whether accelerated courses should be pursued largely in the
postsecondary environment. A longitudinal cohort design was developed with the objective of
addressing the following research question: Does
a difference in knowledge retention over time exist between students enrolled
in a traditional versus an accelerated Supercourse undergraduate course?
Methods
Study Design
A longitudinal cohort study over 21 months (course
delivery=9 months and follow-up=12 months) was conducted to compare knowledge
retention between students in first- and fourth-year courses. Analysis
of knowledge retention over time was stratified by year of study.
Three knowledge
retention quizzes were administered electronically at three times points
following the completion of the courses. Each quiz included 15 unique retention
quiz questions in addition to five non-repeat questions selected from the
course’s final exam. The selection of retention quiz questions was limited to
those answered correctly on the final exam by the entire class within a range
of the 10th to 90th percentile, thus
eliminating the easiest and most difficult questions on the extremes of the
distribution. Accordingly, baseline knowledge included in the analysis reflects
20 questions answered correctly by each student at the completion of the
course. Evaluating
students on retention of knowledge using the exact same material (i.e., repeat
questions) has been demonstrated to be a valid measurement technique [20].
Nevertheless, each set of the repeated 15 follow-up
questions was randomly distributed across the three times points to control for
memory recall of question order.
Retention quizzes were
distributed at three-, six- and 12-month periods for examining a knowledge
trajectory over time. These are considered valid and reasonable timelines for assessing
knowledge retention over an appreciable period of time [21]. At
each time point, students were provided with an email directing them to the
online quiz. Students were allotted seven days to complete the retention quiz
and were asked not to prepare in advance or use course material while
completing the quiz.
Knowledge retention
was determined separately for the repeat and non-repeat questions. Knowledge
retention on the non-repeat questions was evaluated by summing the number of
questions that were successfully answered at each time point. The definition of
success on the repeat retention quiz question responses at each time point is
summarized in Table 1. Baseline success was considered 100% for each student,
since the 15 questions selected for the retention quiz were questions answered
correctly during the final examination. Success at three months was evaluated
by comparing responses with baseline. That is, a question was considered a
success if answered correctly at both baseline and three months. Similarly, a
successfully answered question at six months was defined by questions answered
correctly at baseline, three months and six months. Finally, success at 12
months followed the same definition, with the inclusion of questions answered
correctly at baseline, three months, six months, and 12 months. The method of
defining success (i.e., correctly answered questions at each time point) was
implemented to ensure that success accurately represented knowledge retention
over time.
In addition to
short-term (i.e., three-month follow-up) and long-term (i.e., six- and 12-month
follow-up) knowledge retention, we compared the final course grade for students
enrolled in the traditional course with those enrolled in the Supercourse for both
first- and fourth-year courses using the Independent t test.
Study Population
The four groups of participants in the study included a sample
of students who successfully completed one of the following courses:
traditional first year (T1), Supercourse first year (S1), traditional fourth
year (T4) and Supercourse fourth year (S4). More specifically, students
included in the final study sample were those who a) successfully passed their
course, b) provided informed consent, and c) completed the baseline survey as
well as the three-, six- and 12-month follow-up quizzes.
Within an academic year, first- and fourth-year courses were
delivered in both traditional and Supercourse formats. The first-year
‘Introduction to Health Sciences’ course was taught in the traditional format
between September and April (24 weeks) and delivered again as a Supercourse
during the first two weeks in May. The fourth-year
‘Clinical Epidemiology’ course was taught in the traditional format between
January and April (12 weeks) and again as a Supercourse over one week in May.
For both Supercourses, one day of instruction was equivalent to two weeks of
traditional format instruction. Course content and evaluation criteria remained
the same between the traditional and Supercourses for both first-year and
fourth-year courses. The instructors were the same for both first- and
fourth-year courses, regardless of the course format.
All students in the four courses were provided with the
opportunity to participate in this study. Students were informed of the study
on the final day of course delivery and were then provided with an email linked
to an online survey with a letter of invitation and consent. Participants
completed an online form indicating consent to participating in the study.
Consent was inferred by their selecting the consent box, providing their
student name, University email and completing the baseline survey. This
research project was granted approval through the Brock University Research
Ethics Board prior to conducting the study.
Statistical Analyses
All statistical analyses were performed using SPSS (IBM SPSS
Statistics for Windows, Version 20.0). Descriptive statistics were
calculated, including mean and standard deviation for continuous variables and
frequency and percentages for categorical variables. Comparison of descriptive
statistics for students in the traditional and Supercourses was stratified by
course year. Continuous and categorical variables were compared using the
Independent t test and Chi-square/Fisher exact test, respectively, between
students in the traditional and Supercourses within each of the first- and
fourth-year courses.
The difference in the trajectory of knowledge retention over
four points in time (baseline, three, six and 12 months) between students in
the traditional and Supercourse was determined using mixed effect modeling.
Since same-subject observations of knowledge retention closer in time have a
greater correlation than those farther apart, the first-order autoregressive
covariance structure was specified [22,23]. To test whether trajectories of
knowledge retention over the four times points differed between students in the
traditional and Supercourses, the Mixed Effect Model (MEM) examined the main
effects of course format and time point (i.e., period of retention assessment)
on knowledge retention. The interaction between course and period of assessment
was also considered. Two separate MEMs for the first- and fourth-year courses
were developed. Several covariates previously outlined in the literature were
considered during the modeling process, including gender, age, year
of study (i.e., year 1-4 of an undergraduate degree), course type (i.e.,
required vs. elective) and time spent completing the retention quiz. Covariates
significant at p<0.05 were included in the final multivariate mixed effects
analysis. Mixed effects modeling is an effective statistical approach to
analyze longitudinal data [22]. Unlike repeated measures analysis of
covariance, which is limited to continuous covariates that do not change over
time, MEMs incorporate time-dependent continuous covariates within the model
[22]. Additionally, the lack of independence observed in repeated measures on
the same subject is accepted in MEMs without influencing the validity of the
results [22].
Multiple Linear Regression (MLR) was utilized to compare the
effect of course on knowledge retention at each time point for the repeat
questions. MLR models were stratified by year of study. The covariates were
considered during the modeling process. Covariates significant at p<0.05
were included in the final MLR analysis.
Results
Participant
The complete study sample included 270 participants who met the
inclusion criteria. Table 2 outlines participant attrition at each time point
due to incomplete quizzes. Both T1 (n=187) and T4 (n=35) had larger samples
compared with the S1 (n=20) and S4 (n=28) groups due to greater enrolment
during the fall (September to December) and winter (January to April) academic
terms. T1 had a significantly larger sample size because of the first-year
course being offered during the fall/winter terms as well as the absence of
prerequisites. Participant characteristics including gender, degree
concentration, year of study, presence of learning disability preference for
learning and time to complete retention quizzes for all four groups are
outlined in Table 3.
Knowledge retention in first year traditional and accelerated
courses
The success of the retention quizzes at each time point are
displayed in Table 4. A significant main effect of course format (i.e.,
traditional vs. Supercourse) on the retention of knowledge over time (β=-0.369,
p=0.343) for the repeat questions was not found (Table 5). The non-significant
estimate for course format indicates that students in the first-year
traditional and Supercourse had similar knowledge retention levels on the
quizzes at three, six and 12 months following the baseline assessment. However,
a significant main effect of time (i.e., the time point of the knowledge
retention assessment) was observed. The reference for the time point variable
was the final assessment that took place at 12 months. The positive and
significant estimate for time point at baseline, three months and six months
demonstrated that the success of the retention quizzes decreased over time. Not
surprisingly, the greatest difference in knowledge retention was observed
between baseline and 12 months (β=9.202, p<0.0001), followed by three months
(β=3.412, p<0.0001) and six months (β=1.253, p <0.0001). The interaction
between time and course (p=0.632), in addition to several covariates including
gender (p=0.
Similarly, a non-significant association between course format
and success at three months (p=0.152), six months (p=0.386) and 12 months
(p=0.187) was observed for the non-repeat questions (Table 6). Several
covariates, including gender (p>0.05), age (p>0.05), course type
(p<0.05), year of study, (p>0.05) and time spent completing the quiz
(p>0.05) were considered during the modeling process at each time point.
With the exception of course type, the covariates were not significant and
therefore not included in the final models. Figure 2 represents the trajectory
of knowledge retention of non-repeat questions for the traditional and
Supercourse formats of the first-year courses.
Knowledge retention in fourth year traditional and accelerated
courses
Similar to the outcomes of the first-year model, a significant
main effect of course format on knowledge retention (β=-0.341, p=0.410) was not
observed, while the time point of assessment was significant (Table 7). As
observed with the first-year course, the greatest difference in knowledge
retention was observed between baseline and 12 months (β=10.595, p<0.0001),
followed by three months (β=3.864, p<0.0001) and six months (β=1.180,
p<0.0001). The interaction between time and course (p=0.780), in addition to
several covariates including gender (p=0.479), age (p>0.05), concentration
(p=0.562), year of study (p=0.835) and time spent completing the quiz
(p=0.024), were also considered during the modeling process. The
non-significant covariates were excluded from the final model. The variable
time spent completing the quiz was significant and included in the final model.
The observed significance indicates that the length of time spent completing
the quiz, impacted success on the retention quizzes. Figure 3 displays the
trajectory of knowledge retention of the repeat questions for the fourth-year
traditional and Supercourses.
A non-significant association between course format and success
at three months (p=0.279), six months (p=0.386) and 12 months (p=0.526) was
observed for the non-repeat questions (Table 8). Several covariates, including
gender (p>0.05), age (p>0.05), type of course (p>0.05), year of study
(p<0.05) and time spent completing the quiz (p>0.05), were considered
during the modeling process at each time point. Apart from year of study, the
covariates were not significant and therefore not included in the final model.
(Figure 4 represents the trajectory of knowledge retention of non-repeat
questions for the traditional and Supercourse formats of the first-year
courses.
Discussion
Regardless of course level, students enrolled in our
Supercourses overwhelmingly preferred (>95%) to have completed their course
in an accelerated format. While not statistically significant, it is noteworthy
that a relatively large number of students enrolled in the traditional
first-year (17%) and fourth-year (25%) courses expressed preference for
accelerated courses. Students prefer accelerated courses because they allow
them to learn subject material in a quicker and more convenient format [5].
Accelerated courses allow students to be immersed in the subject content of one
course, without having to manage the demands of multiple courses [6]. As a
result, students tend to be more self-motivated and actively engaged in their
education [24]. Furthermore, students tend to enroll in accelerated courses as
a strategy to balance work and education, since compressed courses often lead
to less absence from employment [5]. This would be especially relevant to
students enrolled in spring courses, which was the case in our study. Finally,
Rood [25] found that employers are equally accepting of their employees
enrolling in accelerated degree programs and that employers that were more
knowledgeable of accelerated programs demonstrated a stronger preference for
these programs compared to employers with less knowledge for accelerated
programs.
Our study followed the definition of accelerated teaching and
learning outlined by Kretovics and colleagues [12], whereby the Supercourses
were scheduled with the same number of contact hours as traditional courses,
but the duration period was shortened to one-twelfth the time. Our study found
that students enrolled in an accelerated course were not disadvantaged with
respect to knowledge attained upon completion of the course, such that no
significant difference existed in the final course average, regardless of
course year. Other studies also demonstrated no discernable difference in
learning as determined by final course average between accelerated and
traditional courses [5,13]. Changes in course content or student evaluations
can alter a final course average. Furthermore, it is common for faculty members
to adjust their assignments and methods of assessment in compressed courses
[12]. However, we implemented identical course content and student evaluations
by the same instructors for both modes of delivery and in both courses.
Although not measured, the lack of significant difference in final course
average could be attributed to greater student engagement, time management, and
ability to focus on one subject in our Supercourses, which would counteract the
challenges of a compressed course duration. Kasworm [26] reported that students
enrolled in an accelerated degree indicated that they benefited from learning
one subject at a time rather than experiencing the ‘focus overload’ that is
characteristic of being enrolled in multiple courses simultaneously.
Student knowledge retention during our longitudinal study
diminished progressively at follow-up time points of three, six and 12 months
regardless of course format, course level, age, gender, degree concentration
and year of study. Previous studies have shown that long-term knowledge
retention is not mediated by the number of course contact hours [14,19] age,
gender [5] or year of study [27]. Similarly, the decreased knowledge retention
trajectory in our study was comparable for repeated and non-repeated questions.
Both types of questions were evaluated in our study to determine the influence
of familiarity of question content because of repeated questions. Our results
indicate that the repeated questions over 12 months did not advantage a
student’s knowledge retention over time, such that a similar decreased
knowledge retention over time was found for repeated and non-repeated
questions.
A limitation in our study was the inability to control for the
environment with respect to follow-up quizzes. Follow-up quizzes were completed
online by all students. While instructions were provided prior to each
follow-up quiz to complete each question without assistance from course
material or another person, it was not possible to authenticate whether the
subjects did so. Nevertheless, the average time taken to complete the follow-up
quizzes was considered reasonable for both courses. Furthermore, except for the
three-month follow-up quiz in the first-year course, the average time to
complete the quizzes was similar between the groups for both courses.
Conclusion
Accelerated courses continue to gain popularity because of the
convenience they afford students, administrators and faculty members. It is
important that accelerated courses be crafted and organized so that the same
material that would be covered in a traditional course is properly conveyed to
students. If an accelerated course is structured to reflect its traditional
course, it is expected that knowledge attained in the course and knowledge
retention over time following the course should be the same for both formats.
Knowing that accelerated courses do not compromise learning adds another
motivation for students in higher education to enroll in such courses and
programs. University and college administration and academic faculty should
continue to endorse accelerated learning opportunities. A properly designed
accelerated course can facilitate the academic requirements of a traditional
course, but can also enable students to schedule their curricular pursuits more
efficiently within their non-academic commitments [10]. Accelerated courses
could be offered during fall or spring reading weeks or scheduled in a block
sequence over any term to facilitate the preference of students to focus on one
course at a time. Regardless of the approach, our study concluded that the
accelerated course format does not compromise short- and long-term knowledge
retention in first-year or fourth-year undergraduate students. Thus,
accelerated courses are a practical and feasible option for students in higher
education.
Figure
1: Knowledge trajectory of repeat questions in the first-year
courses. Note: Knowledge trajectory of T1 (red) and S1 (blue) courses.
Figure 2:
Knowledge trajectory of non-repeat questions in the first-year courses. Note:
Knowledge trajectory of T1 (red) and S1 (blue) courses.
Figure 3: Knowledge trajectory of repeat questions in the fourth-year
courses. Note: Knowledge trajectory of T4 (red) and S4
(blue) courses.
Figure 4:
Knowledge
trajectory of non-repeat questions in the fourth-year courses. Note: Knowledge trajectory of T4 (red) and S4
(blue) courses.
|
Baseline |
Quiz 1 |
Quiz 2 |
Quiz 3 |
Success |
Baseline |
✓ |
|
|
|
✓ |
3 Months |
✓ |
✓ |
|
|
✓ |
6 Months |
✓ |
✓ |
✓ |
|
✓ |
12 Months |
✓ |
✓ |
✓ |
✓ |
✓ |
Table 1: Method of defining repeat question success at each
time point.
|
T1 |
S1 |
T4 |
S4 |
Baseline (N) |
238 |
28 |
45 |
30 |
3 Months |
204 (85.71) |
25 (89.29) |
37 (82.22) |
28 (93.33) |
6 Months |
195 (81.93) |
22 (78.57) |
37 (82.22) |
28 (93.33) |
12 Months |
187 (78.57) |
20 (71.43) |
35 (77.78) |
28 (93.33) |
Table 2: Participant attrition. Note: Values in brackets indicate the percent
of baseline sample remaining at each time point.
|
T1 (N=187) |
S1 (N=20) |
T4 (N=35) |
S4 (N=28) |
Mean age in years (SD) |
19.10 (2.08) |
20.50 (3.20) |
22.51 (1.98) |
21.96 (1.95) |
Gender (%) |
|
|
|
|
Male |
43 (23.00) |
8 (40.00) |
15 (42.90) |
12 (42.90) |
|
|
|
|
|
Female |
144 (77.00) |
12 (60.00) |
20 (57.10) |
16 (57.10) |
Concentration (%) |
† |
† |
|
|
Major |
160 (85.60) |
9 (45.00) |
33 (94.30) |
26 (92.90) |
Non-major |
27 (14.40) |
11 (55.00) |
2 (5.70) |
2 (7.10) |
Year of study (%) |
† |
† |
|
|
1 |
155 (82.90) |
11 (55.00) |
- |
- |
2 |
25 (13.40) |
3 (15.00) |
- |
- |
3 |
5 (2.70) |
5 (25.00) |
1 (2.90) |
6 (22.20) |
4 |
2 (1.10) |
1 (5.00) |
31 (88.60) |
20 (74.10) |
5 |
- |
- |
2 (5.70) |
1 (3.70) |
7 |
- |
- |
1 (2.90) |
- |
Identified Learning Disability (%) |
|
|
|
|
Yes |
4 (2.10) |
1 (5.00) |
1 (2.90) |
3 (10.70) |
No |
178 (95.20) |
19 (95.00) |
32 (91.40) |
25 (89.30) |
Unknown |
3 (1.60) |
3 (1.60) |
2 (5.70) |
- |
Undisclosed |
2 (1.10) |
2 (1.10) |
- |
- |
Preference (%) |
† |
† |
^ |
^ |
Traditional |
139 (74.30) |
1 (5.00) |
29 (82.90) |
1 (3.60) |
Supercourse |
48 (25.70) |
19 (95.00) |
6 (17.10) |
27 (96.40) |
Mean time to complete quiz in minutes (SD) |
|
|
|
|
3-month |
12.60 (9.00) † |
9.00 (3.96) † |
24.60 (15.00) |
21.60 (12.00) |
6-month |
9.6 (56.40) |
10.80 (9.60) |
19.20 (19.80) |
23.40 (14.40) |
12-month |
9.6 (6.60) |
9.60 (6.00) |
18.60 (12.60) |
17.40 (14.40) |
Final course average (SD) |
76.20 (6.63) |
74.65 (7.23) |
78.57 (6.92) |
78.18 (5.58) |
Table 3: Participant characteristics (N=270). Note: †= p<0.05 when comparing
T1 with S1; ^ = p<0.05 when comparing T4 with S4.
|
Baseline |
3 months |
6 months |
12 months |
T1 |
|
|
|
|
Repeat |
15 (100) |
9.25 (61.6) |
7.12 (47.5) |
5.86 (39.1) |
Non-repeat |
5 (100) |
3.07 (61.4) |
2.61 (52.2) |
2.39 (47.8) |
S1 |
|
|
|
|
Repeat |
15 (100) |
8.90 (59.3) |
6.60 (44.0) |
5.25 (35.0) |
Non-repeat |
5 (100) |
2.55 (51.0) |
2.40 (48.0) |
2.55 (51.0) |
T4 |
|
|
|
|
Repeat |
15 (100) |
8.94 (59.6) |
6.29 (41.93) |
4.89 (32.6) |
Non-repeat |
5 (100) |
3.09 (61.80) |
2.86 (57.2) |
2.29 (45.8) |
S4 |
|
|
|
|
Repeat |
15 (100) |
8.32 (55.46) |
5.64 (37.6) |
4.36 (29.1) |
Non-repeat |
5 (100) |
2.54 (50.8) |
2.71 (54.2) |
2.36 (47.2) |
Table 4: Success of retention quizzes at each time
point. Note: Values in brackets indicate
the success of retention quizzes expressed as a percentage.
|
Estimate |
Standard Error |
T-value |
p-value |
Intercept |
5.464 |
0.382 |
14.312 |
<0.0001 |
Course format (Supercourse) |
-0.369 |
0.389 |
-0.95 |
0.343 |
Time point |
|
|
|
|
Baseline |
9.202 |
0.176 |
52.305 |
<0.0001 |
3 months |
3.412 |
0.157 |
21.612 |
<0.0001 |
6 months |
1.253 |
0.124 |
10.121 |
<0.0001 |
12 months |
- |
- |
- |
- |
|
Estimate |
Standard error |
T-value |
p-value |
3 months |
|
|
|
|
Intercept |
3.634 |
0.329 |
11.057 |
<0.001 |
Course format (Supercourse) |
-0.424 |
0.295 |
-1.437 |
0.152 |
Type of course |
-0.033 |
0.19 |
-1.712 |
0.088 |
6 months |
|
|
|
|
Intercept |
2.824 |
0.3 |
9.4 |
<0.001 |
Course format (Supercourse) |
-0.234 |
0.269 |
-0.868 |
0.386 |
Type of course |
0.006 |
0.017 |
0.35 |
0.726 |
12 months |
|
|
|
|
Intercept |
2.581 |
0.33 |
7.824 |
<0.001 |
Course format (Supercourse) |
0.358 |
0.27 |
1.323 |
0.187 |
Type of course |
-0.482 |
0.206 |
-2.333 |
0.021 |
Table 6: MLR results of knowledge retention in first-year
course.
Estimate |
Standard error |
T-value |
p-value |
|
Intercept |
4.363 |
0.329 |
13.284 |
<0.0001 |
Course format (Supercourse) |
-0.341 |
0.412 |
-0.828 |
0.41 |
Time point |
||||
Baseline |
10.595 |
0.318 |
33.283 |
<0.0001 |
3 months |
3.864 |
0.304 |
12.69 |
<0.0001 |
6 months |
1.18 |
0.249 |
4.736 |
<0.0001 |
12 months |
- |
- |
- |
- |
Time spent (hours) |
11.048 |
4.963 |
- |
0.026 |
|
Estimate |
Standard error |
T-value |
p-value |
3 months |
|
|
|
|
Intercept |
-0.479 |
1.31 |
-0.366 |
0.716 |
Course format (Supercourse) |
0.33 |
0.302 |
1.092 |
0.279 |
Year of study |
0.546 |
0.27 |
2.018 |
0.048 |
6 months |
|
|
|
|
Intercept |
3.387 |
1.354 |
2.501 |
0.015 |
Course (Supercourse) |
0.271 |
0.312 |
0.868 |
0.389 |
Year of study |
-0.392 |
0.28 |
-1.403 |
0.166 |
12 months |
|
|
|
|
Intercept |
1.514 |
1.352 |
1.12 |
0.267 |
Course format (Supercourse) |
-0.199 |
0.311 |
-0.638 |
0.526 |
Year of study |
0.381 |
0.279 |
1.365 |
0.177 |
Table
8: MLR results of knowledge retention in fourth-year
course.
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