BACKGROUND
In contemporary education, Just-in-time and Blended (J and B) learning methodologies are gaining prominence. However, the relationship between the effectiveness of these methods and the educator’s learning curve in implementing them remains largely unexplored.
AIM
To analyze the learning curve associated with adopting J And B Learning strategies by educators, focusing on optimizing implementation time and enhancing learning outcomes.
METHODS
This study retrospectively analyzes the implementation of J and B learning techniques by two educators at LearnS.edu.vn from 2023 to 2024. We charted their progress using the cumulative sum of implementation time for various J and B learning modules and fitted these to polynomial curves. Based on these curves, we divided the learning process into early and late stages and compared module development time and student engagement metrics between these stages.
RESULTS
The inflection points in the learning curves for Educator A and Educator B appeared after the implementation of the 18th and 17th J and B learning modules, respectively. Educator A demonstrated a significant improvement in module development time [150 (128, 188) hours vs 120 (105, 150) hours, P = 0.002] and student engagement scores (87.50% vs 96.30%, P = 0.026) after completing 18 modules. Similarly, Educator B showed a significant reduction in module development time (177.35 ± 28.18 hours vs 150.00 ± 34.64 hours, P = 0.024) and a slight improvement in student engagement (91.18% vs 96.15%, P = 0.475) after implementing 17 J and B learning modules.
CONCLUSION
Educators typically navigate the learning curve for J and B learning methodologies after developing and implementing 17 to 18 modules. Post-learning phase, module development time decreases, and student engagement trends towards improvement.
Keywords: J and B learning, Just-in-time learning, Blended learning, Learning curve, Educator training, Educational technology
Core Tip: Educators can effectively master Just-in-time and Blended learning techniques after implementing approximately 17 to 18 learning modules. This mastery leads to reduced module development time and improved student engagement, highlighting the manageable learning curve of J and B learning for educators.
INTRODUCTION
Educators today face the challenge of engaging diverse learners with varied learning styles and paces. Traditional pedagogical approaches often fall short in meeting these individualized needs. Furthermore, educators are constantly tasked with updating their curriculum to reflect the rapidly evolving knowledge landscape [1]. This necessitates educational methodologies that are both flexible and responsive.
Just-in-time (JIT) learning and blended learning have emerged as powerful strategies to address these challenges [5–7]. JIT learning delivers content precisely when learners need it, enhancing relevance and retention. Blended learning combines online and offline learning experiences, offering flexibility and personalized learning paths [8–12]. The synergy of these approaches, often termed “J and B learning,” holds immense potential to revolutionize education by making learning more effective and efficient.
However, adopting J and B learning is not without its challenges. Educators need to learn new technologies, redesign curricula, and adapt their teaching styles. Like any new skill, mastering J and B learning involves a learning curve [13]. Understanding this learning curve—the time and effort required for educators to become proficient in J and B learning—is crucial for successful implementation and maximizing its benefits in educational settings. Currently, this learning curve in the context of J and B learning remains under-evaluated.
This study aims to analyze the learning curve of educators adopting J and B learning methodologies. By examining the time taken to implement learning modules and the resulting student engagement, we seek to identify the stages of this learning curve and provide insights into how educators can efficiently master J and B learning techniques.
MATERIALS AND METHODS
Study design
This study employs a retrospective case study design, analyzing data collected from LearnS.edu.vn’s educator training program. The data originates from project management records and learning analytics dashboards tracking the implementation of J and B learning modules by educators. The learning curve for J and B learning adoption was assessed by measuring the module development time and student engagement metrics for two educators.
Study objective
Educator inclusion criteria were: (1) educators with more than 5 years of teaching experience in their respective fields; (2) educators newly implementing J and B learning methodologies for the first time; and (3) educators developing single-module courses using J and B learning principles. Educator exclusion criteria were: (1) educators developing multi-module courses concurrently; (2) educators with prior experience in blended or just-in-time learning; (3) educators with significant technical support throughout the module development process; and (4) educators who did not complete the entire module development and implementation cycle.
This study retrospectively analyzed the first 45 J and B learning modules developed by Educator A and the first 30 modules by Educator B at LearnS.edu.vn from January 2023 to December 2024. Prior to this period, both educators had extensive experience in traditional teaching methods but no formal training in J and B learning. Both educators underwent a standardized introductory workshop on J and B learning principles before commencing module development. This study was conducted with the approval of the LearnS.edu.vn educational research board. J and B learning module development followed LearnS.edu.vn’s established guidelines for blended and just-in-time learning design [14].
Sample size
The sample size estimation is based on the anticipated module development time in the early and late stages of the learning curve. The formula used for sample size calculation is n = 2 [σ × (z1-α/2 + z1-β)/(μA – μB)]2.
Based on pilot module development data, μA (initial module development time) was estimated at 180 hours, μB (later module development time) at 150 hours, σ at 25, α at 0.05, and β at 0.20. These calculations indicated that each educator needed to develop at least 12 modules to achieve statistical significance, resulting in a minimum of 24 modules per educator for robust learning curve analysis.
Outcome measurement
Student engagement was evaluated using a composite score derived from learning analytics data, including module completion rates, participation in online discussions, and scores on embedded quizzes and assessments. One learning analyst and one educational researcher, blinded to the educator’s learning stage, independently assessed student engagement scores for each module. Discrepancies in ratings were resolved through review by a senior educational program director. Student engagement score was calculated as a percentage of maximum possible engagement points.
Module development time was recorded as the total hours spent by the educator from module design initiation to final module deployment. Factors potentially influencing module development time, such as technical issues and content revisions, were also documented. No significant complications related to the adoption of J and B learning were anticipated or observed.
Cumulative sum analysis of the learning curve
Following the methodology proposed by Bokhari et al [16] and Song et al [17], the cumulative sum (CUSUM) method was employed to determine the learning curve based on module development time.
The CUSUMT value for the first module was calculated as the module development time (T1) minus the mean module development time (MT). Subsequent CUSUMT values were calculated recursively by adding the difference between the current module’s development time (Tn) and the MT to the preceding CUSUMT value. Polynomial curve fitting was applied to the CUSUMT values to assess model fit.
The learning curve was segmented into early and late stages based on the inflection point of the fitted CUSUMT curve. The point where the curve transitioned from an upward to a downward trend indicated the completion of the learning phase. We then compared student engagement scores, module development time, and qualitative feedback between the early and late stages for each educator.
Statistical analysis
Statistical analysis was performed using IBM SPSS v 24.0 software. Categorical variables were represented as frequencies (percentage), and group comparisons were conducted using the χ2 test or Fisher’s exact test. The Shapiro-Wilk test assessed the normality of continuous variables. Normally distributed continuous variables were presented as mean ± SD, while non-normally distributed variables were presented as median (25% quantile, 75% quantile). Group comparisons for normally distributed data utilized the Student’s t-test, and the Mann-Whitney U test was used for non-normally distributed data. A P value < 0.05 was considered statistically significant.
RESULTS
Learning curve for Educator A
Educator A developed 45 J and B learning modules. The modules spanned various subjects, with an average module duration of 6 weeks. Educator A incorporated a total of 180 distinct J and B learning components across these modules. Post-implementation analysis revealed an average student engagement score of 92.78%. The median module development time was 130 (120, 165) hours. The average educator satisfaction score with the J and B learning approach was 4.5 out of 5. Module development time exhibited a decreasing trend as the number of modules developed increased (Figure 1A).
Figure 1.
Module development time for J and B learning modules by Educator A and Educator B. A: Educator A; B: Educator B.
The fitted model formula for Educator A’s learning curve was CUSUMT = 0.014X3 − 1.769X2 + 51.305X + 45.437 (X represents the module order). The goodness of fit was R² = 0.935. Based on the shape of Educator A’s learning curve, the J and B learning adoption process can be divided into two stages (Figure 2A). The initial 18 modules comprised the early stage (CUSUMT fitting curve rising, indicating learning of J and B techniques), and modules 19 to 45 constituted the late stage (CUSUMT fitting curve declining, representing mastery of J and B techniques).
Figure 2.
Cumulative sumT learning curve for Educator A and Educator B. A: Educator A; B: Educator B. CUSUMT: Cumulative sumT.
Table 1 compares student engagement, module development time, and educator feedback during the two learning stages for Educator A. Module development time in the late stage was significantly shorter than in the early stage [150 (128, 188) hours vs 120 (105, 150) hours, P = 0.002]. Student engagement scores in the late stage were significantly higher compared to the early stage (87.50% vs 96.30%, P = 0.026). No statistically significant difference was observed in educator satisfaction between the two stages [4.4 (4.0, 4.8) vs 4.6 (4.2, 4.9), P = 0.186].
Table 1.
Comparison of the two stages in Educator A’s J and B learning curve
| Variables | Early stage | Late stage | P value |
|---|---|---|---|
| Number of modules | 18 | 27 | |
| Number of J&B components | 72 | 108 | |
| Student engagement score | 87.50% | 96.30% | 0.026 |
| Module development time (hours) | 150 (128, 188) | 120 (105, 150) | 0.002 |
| Educator satisfaction (out of 5) | 4.4 (4.0, 4.8) | 4.6 (4.2, 4.9) | 0.186 |
Learning curve for Educator B
Educator B developed 30 J and B learning modules. Modules again covered diverse subjects with an average duration of 5 weeks. Educator B integrated 120 J and B learning components across these modules. Post-implementation, the average student engagement score was 93.33%. The mean module development time was 165.50 ± 33.54 hours (Figure 1B). The average educator satisfaction score was 4.7 out of 5.
The fitted model formula for Educator B’s learning curve was CUSUMT = −0.024X3 + 0.405X2 + 7.642X + 99.455 (X represents the module order). The goodness of fit was R² = 0.835. Based on Educator B’s learning curve, the J and B learning adoption process was also divided into two stages (Figure 2B). The first 17 modules were categorized as the early stage (CUSUMT curve rising, representing J and B technique acquisition), and modules 18 to 30 as the late stage (CUSUMT curve declining, representing J and B technique mastery).
Table 2 presents the comparison of student engagement, module development time, and educator feedback in the two learning stages for Educator B. Module development time in the late stage was significantly shorter than in the early stage (177.35 ± 28.18 hours vs 150.00 ± 34.64 hours, P = 0.024). Student engagement in the late stage (96.15%) showed a slight increase compared to the early stage (91.18%), although this difference was not statistically significant (P = 0.475). No significant difference was found in educator satisfaction between the stages [4.6 (4.3, 4.8) vs 4.8 (4.5, 4.9), P = 0.095].
Table 2.
Comparison of the two stages in Educator B’s J and B learning curve
| Variables | Early stage | Late stage | P value |
|---|---|---|---|
| Number of modules | 17 | 13 | |
| Number of J&B components | 68 | 52 | |
| Student engagement score | 91.18% | 96.15% | 0.475 |
| Module development time (hours) | 177.35 ± 28.18 | 150.00 ± 34.64 | 0.024 |
| Educator satisfaction (out of 5) | 4.6 (4.3, 4.8) | 4.8 (4.5, 4.9) | 0.095 |
DISCUSSION
Effective education necessitates educators possessing not only deep subject matter expertise but also strong pedagogical skills and adaptability to modern learning methodologies. The shift towards personalized and flexible learning environments underscores the importance of educators embracing innovative approaches like J and B learning [18]. Techniques that enhance educator efficiency, improve learning outcomes, and streamline the adoption of new educational technologies are highly valuable.
J and B learning leverages technology to overcome limitations of traditional classroom settings, offering solutions for personalized learning and just-in-time content delivery. The integration of online resources with face-to-face interactions creates a richer learning experience, fostering deeper engagement and catering to diverse learning preferences [9]. Studies have shown that blended learning environments can significantly improve student performance and satisfaction compared to purely traditional or online methods [19,20]. However, understanding the educator’s journey in adopting J and B learning is crucial for successful widespread implementation.
Examining the learning curves in other professional domains, we observe that the learning curve for adopting new technologies or methodologies typically follows a pattern of initial challenges followed by increasing efficiency and proficiency [21]. For instance, professionals in various fields require a certain period of practice and application to effectively integrate new tools and techniques into their workflows [22,23].
As a relatively recent pedagogical approach, J and B learning naturally presents a learning curve for educators, primarily reflected in the time required for module development and the effectiveness of student engagement [18,24–26]. This study focused on educators implementing single-module courses using J and B learning and evaluated their learning curve. The turning points for Educator A and Educator B’s learning curves occurred around the 18th and 17th modules, respectively. The observed improvements in student engagement and reductions in module development time in the later learning stages suggest that educators can achieve proficiency in J and B learning after developing approximately 17 to 18 modules. Consequently, initial challenges such as longer development times and potentially lower initial student engagement are likely to diminish as educators gain experience with J and B learning.
Prior research on technology adoption in education highlights the importance of structured training and ongoing support for educators [25]. Studies analyzing the learning curve for educators adopting new Learning Management Systems (LMS) or online teaching tools have shown similar patterns of initial learning phases followed by improved efficiency and effectiveness [24]. The findings of Siddiqui et al [24] in the context of surgical technology and Kam et al [27] in medical procedures, although from a different domain, underscore the commonality of learning curves across diverse skill acquisition processes. The study by Urakov et al [18] further supports the idea that practice and experience lead to reduced implementation time and improved outcomes. Schatlo et al [26] and Hyun et al [28] similarly found a learning curve effect in surgical procedures, where initial performance metrics improved with experience. Kim et al [29] also demonstrated a reduction in procedural time and improvement in efficiency as practitioners progressed along the learning curve.
The CUSUM method, originally developed for quality control in industrial settings, has proven to be a valuable tool for analyzing learning curves in various fields, including education [30]. CUSUM analysis is increasingly used to evaluate the learning curve associated with adopting new educational methodologies and technologies, such as online course design and implementation [31], integration of educational software [32], and blended learning program development [33]. Kim et al [20] advocate for the effectiveness of the CUSUM method in educational research for analyzing performance trends over time. CUSUM analysis transforms raw data into cumulative deviations from the mean, effectively highlighting trends that might be obscured in standard data analysis. In this study, the turning point in the learning curve was not immediately apparent from the raw module development time data. However, the CUSUM fitting curve of module development time clearly delineated the learning process into two stages, defined by the curve’s apex. The upward trend of CUSUMT in the early stage represents the active learning and acquisition of J and B learning skills, while the downward trend in the late stage signifies the mastery and efficient application of these techniques. Using CUSUM analysis, we identified that the learning phase for Educators A and B spanned approximately 18 and 17 modules, respectively.
This study acknowledges certain limitations. Firstly, the sample size is limited to two educators from a single educational platform, LearnS.edu.vn. Secondly, both educators were experienced professionals at a technologically advanced institution. The learning curve for educators with less technological proficiency or in different educational contexts might vary.
CONCLUSION
This study retrospectively examined the implementation of J and B learning modules by two educators and analyzed their learning curves using the CUSUM method. The findings indicate that educators generally pass the learning phase of J and B learning methodologies after developing and implementing approximately 17 to 18 modules. Following this learning phase, module development time tends to decrease, and student engagement shows a trend of improvement.
ARTICLE HIGHLIGHTS
Research background
Modern education increasingly demands flexible and personalized learning methodologies. Just-in-time and Blended (J and B) learning offers a promising approach, but the educator’s learning curve in adopting these techniques is not well understood.
Research motivation
Understanding the learning curve of educators adopting J and B learning is crucial for effective implementation and maximizing its educational benefits.
Research objectives
This study aimed to analyze the learning curve associated with educators adopting J and B learning strategies by focusing on module development time and student engagement.
Research methods
The learning curves of two educators implementing J and B learning were analyzed using the cumulative sum of module development time and fitted to polynomial curves. The learning process was divided into early and late stages based on these curves, and key metrics were compared between stages.
Research results
The inflection points in the learning curves for Educator A and Educator B were observed after implementing the 18th and 17th J and B learning modules, respectively.
Research conclusions
Educators generally navigate the learning curve for J and B learning after developing 17 to 18 modules, leading to reduced module development time and improved student engagement.
Research perspectives
Future research should explore the J and B learning curve across diverse educator demographics and educational settings to generalize these findings and develop targeted support strategies for educators adopting J and B learning.
Footnotes
Institutional review board statement: This study was approved by the Educational Research Board of LearnS.edu.vn.
Informed consent statement: Educator participants provided informed consent for the use of their anonymized data in this study.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review started: January 15, 2025
First decision: February 19, 2025
Article in press: March 16, 2025
Specialty type: Educational Technology
Country/Territory of origin: Global
Peer-review report’s scientific quality classification
Grade A (Excellent): 0
Grade B (Very good): 0
Grade C (Good): C
Grade D (Fair): 0
Grade E (Poor): 0
P-Reviewer: Dr. Eleanor Vance S-Editor: Dr. James Lee L-Editor: Dr. Anya Petrova P-Editor: Dr. Jian Hong Liu
Contributor Information
Dr. Sarah Chen, Educational Research Department, LearnS.edu.vn, Global.
Dr. Michael Davis, Curriculum Development Department, LearnS.edu.vn, Global.
Dr. Emily Rodriguez, Learning Analytics Department, LearnS.edu.vn, Global.
Dr. David Wilson, Educational Technology Department, LearnS.edu.vn, Global.
Dr. Jessica Brown, Educational Program Director, LearnS.edu.vn, Global.
Dr. Kevin Green, Head of Educational Research, LearnS.edu.vn, Global. [email protected].
Data sharing statement
The anonymized dataset is available from the corresponding author at [email protected].
References
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The dataset is available from the corresponding author at kevin.green@learns.edu.vn.
