Educational Research Applications (ISSN: 2575-7032)

Article / Research Article

"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

Received Date: 26 January 2019; Accepted Date: 25 February 2019; Published Date: 04 March 2019

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

-

-

-

-

Table 5: MEM results of knowledge retention in first-year course.  Note: The reference for the variable time point is the final assessment at 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

Table 7: MEM results of knowledge retention in fourth-year course.  Note: The reference for the variable time point is the final assessment at 12 months.

 

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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Citation: Faught BE (2019) Supercourse Experience. How Much do Students Remember? Educ Res Appl 6: 159. DOI: 10.29011/ERCA-159/100159