Volume 17, Issue 2 p. 274-286
RESEARCH REPORT
Open Access

Transfer of learning in histology: Insights from a longitudinal study

Dogus Darici

Corresponding Author

Dogus Darici

Institute of Anatomy and Molecular Neurobiology, University of Münster, Münster, Germany

Correspondence

Dr. Dogus Darici, Institute of Anatomy and Molecular Neurobiology, University of Münster, Vesaliusweg 2-4, Münster 48149, Germany.

Email: [email protected]

Search for more papers by this author
Kristina Flägel

Kristina Flägel

Institute of Family Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany

Search for more papers by this author
Katharina Sternecker

Katharina Sternecker

Chair of Neuroanatomy, Institute of Anatomy, Faculty of Medicine, Ludwig-Maximilians-University Munich, Munich, Germany

Search for more papers by this author
Markus Missler

Markus Missler

Institute of Anatomy and Molecular Neurobiology, University of Münster, Münster, Germany

Search for more papers by this author
First published: 29 December 2023

Abstract

All anatomical educators hope that students apply past training to both similar and new tasks. This two-group longitudinal study investigated the development of such transfer of learning in a histology course. After 0, 10, and 20 sessions of the 10-week-long course, medical students completed theoretical tasks, examined histological slides trained in the course (retention task), and unfamiliar histological slides (transfer task). The results showed that students in the histology group gradually outperformed the control group in all tasks, especially in the second half of the course, η2 = 0.268 (p < 0.001). The best predictor of final transfer performance was students’ retention performance after 10 sessions, β = 0.32 (p = 0.028), and theoretical knowledge after 20 sessions, β = 0.46 (p = 0.003). Results of eye tracking methodology further revealed that the histology group engaged in greater “visual activity” when solving transfer tasks, as indicated by an increase in the total fixation count, η2 = 0.103 (p = 0.014). This longitudinal study provides evidence that medical students can use what they learn in histology courses to solve unfamiliar problems but cautions that positive transfer effects develop relatively late in the course. Thus, course time and the complex relationship between theory, retention, and transfer holds critical implications for anatomical curricula seeking to foster the transfer of learning.

INTRODUCTION

In their daily medical routines, doctors must flexibly respond to unanticipated challenges. These challenges may occur, for example, in situations where an anatomical or surgical finding forces a deviation from the standard procedure (Zijderveld et al., 2008). Moreover, the recent Covid-19 pandemic has demonstrated that sudden and drastic changes can occur throughout the healthcare system, for which there may not be readily available solutions (Kinsella et al., 2020). Making the right decisions under such conditions of uncertainty, complexity, and time pressure is known to be difficult (Page, 2008).

To overcome these challenges, doctors rely on the knowledge, skills, and experience they have acquired throughout many years of medical and postgraduate training (Flägel et al., 2022). However, mere acquisition is not enough. In unfamiliar situations, these proficiencies need to be activated and rearranged anew to make the best clinical decision possible (Patel & Cranton, 1983).

This behavior has been named transfer of learning and is one of the oldest topics of interest in education (e.g., Patel & Cranton, 1983; Perkins & Salomon, 1992; Hager & Hodkinson, 2009; Dyre et al., 2016; Kulasegaram et al., 2017; Hajian, 2019).

Even though the transfer of learning is thought to be the “ultimate aim of teaching”, only a limited amount of research has been devoted to understanding transfer in undergraduate histology education (Nivala et al., 2013; Hortsch, 2022). The current study sought to explore the transfer of learning in a regular histology curriculum. Understanding these processes is critical to guide future curriculum development efforts and enable them to reach their full potential.

What is transfer of learning?

Transfer refers to the extent to which learning is applied from one context (i.e., medical school) to another (i.e., clinical case) (Patel & Cranton, 1983). There is no strict line between the transfer of learning and ordinary learning; however, the transfer becomes interesting when it occurs beyond the original context of learning. For example, a learner who has profound knowledge of gross anatomy may rapidly comprehend ultrasound images in radiology (Darici et al., 2023a). Comparably, a learner who has taken a dissection course may manage unexpected anatomical variations (MacPherson & Lisk, 2021).

In any case, professionals trust that whatever is learned will be retained and reused in appropriate situations that arise (Ripple & Drinkwater, 1982). In contrast, transfer problems occur when learners fail to apply the existing knowledge to new situations. Thus, the professional may solve familiar problems (retention task) but not problems when they appear in another context (transfer task). A related concept has been postulated for expert performances distinguishing between routine and adaptive expertise (Kua et al., 2021). Adaptive experts invent new procedures for solving unfamiliar problems, whereas regular experts may “simply” apply mastered procedures.

Although the transfer of learning in medical education research is genuinely a central theme, there has been little explicit research into this topic. Patel and Cranton (1983) were pioneers in this field, conducting research on final-year medical students as part of their Clerkship programs. They observed that learning outcomes cannot simply be generalized from one discipline to another, and emphasized the diversity and complexity of learning environments and situational contexts. Twenty years later, the construct experienced a revival in the research of simulation training, with the hypothesis that particularly realistic simulations (“high-fidelity”) would improve the transfer performance of medical students (e.g., Hariri et al., 2004; Norman et al., 2012; Boet et al., 2014; Grierson, 2014). However, the results are ambiguous. For example, Hariri et al. (2004) found that students who trained with a surgical simulator were able to transfer their knowledge to recognizing anatomical images. On the contrary, Norman et al. (2012) found no benefit of high-fidelity simulations on students' transfer performances. Similarly, several studies have investigated the transfer of basic science knowledge to clinical problem-solving. By analyzing 15 articles from different professions and disciplines, Castillo et al. (2018) suggested that instruction in the basic sciences may have a positive impact on clinical reasoning. Kulasegaram et al. (2015), however, found no immediate effects of anatomy education on memory or diagnostic tests.

To conclude, a successful transfer is not a matter of course. Abundant evidence indicates that educational institutions have regularly “failed to achieve transfer of learning” (e.g., Haskell, 2001; Kulasegaram et al., 2015; Jurjus et al., 2016). For example, students who learned gross anatomy with multimedia struggled to transfer to cadaver-based measures (Saltarelli et al., 2014). In histology, a similar observation can be made: while histological specimens used in the course are often retained reliably and quickly, the identification of non-course slides is often overcharging novice learners (García et al., 2018). Hence, the skills acquired in the course context are regularly not applicable to a new context. Consequently, such inactive and inapplicable skills remain inert (Renkl et al., 1996), and must be avoided.

Despite being an old topic, this construct is becoming even more relevant in the current medical education landscape. While many medical procedures still rely on reproducing information, technological advancements especially in the fields of artificial intelligence are about to gradually automate those that involve repetitive routine tasks (Lazarus et al., 2022). Some authors postulate that “AI could alter the physician's role similar to how general automation altered the pilot's role several decades ago” (Kundu, 2021). In addition, medical knowledge is advancing exponentially (Densen, 2011), making current knowledge outdated by the time of graduation and necessitating lifelong learning for adaptation. The creative application of basic science knowledge to solve complex problems is becoming thus more relevant, as evidenced in the current COVID-19 pandemic (Kinsella et al., 2020). These trends are likely to raise the importance of adaptive expertise and knowledge transfer in medical practice. Consequently, the transfer of learning is most likely to play an increasingly significant role for medical students who will become the doctors of tomorrow.

In any case, the potential lack of knowledge or skill transfer seriously questions the usefulness of traditional forms of teaching, and thus warrants further clarification. One way to minimize this threat is to understand if and when the transfer occurs, and what conditions may influence this process.

Perspectives on transfer in histology

Histology is the study of the microscopic structure of tissues. It involves examining tissue samples under a microscope to identify the different types of cells and their characteristics, as well as the organization and arrangement of these cells within tissues. Histology serves as the central intersection of all basic sciences, as it enables a coherent discussion of structure and function. Therefore, the ability to transfer histological competencies can help students develop a more comprehensive understanding of biological systems. In addition, histology and histopathology is a rapidly evolving field, and new techniques and technologies are constantly being developed (Kinsella et al., 2020). Thus, the ability to transfer learning from previous histology courses or experiences to new situations is crucial for students to stay current and adapt to new technologies and techniques in the field (Gribbin et al., 2021).

When investigating transfer, the question arises as to what retention and transfer in histology actually mean. Based on Perkins and Salomon (1992) who popularized the concepts of near and far transfer—two poles of a continuum referring to the degree of similarity or dissimilarity between two contexts—it may be conceptualized as follows:

Students who learn to identify simple columnar epithelium in a cross-section of the jejunum may easily identify the same epithelium in another area of the same slide. Since both stimuli are almost the same, this could be a retention task. Thus, retention tasks measure how well the learner remembers the presented course material. Moreover, students might also identify such epithelium in the longitudinal section of another gastrointestinal organ with a different staining. Since two important elements would have been changed in this example (i.e., orientation and staining), this could be a near-transfer task. Ultimately, students might identify an abnormality of such epithelium in a completely unfamiliar specimen. Since many elements would be different in this case (i.e., orientation, staining, pathology, unfamiliar specimen), one could consider this a far-transfer task.

Thus, the transfer of learning in histology may refer to the ability to use acquired knowledge to analyze and understand new images that were not covered in the course's didactic section. Finally, there are three different combinations that arise from retention and transfer test results (Mayer, 2010): No learning is indicated by low results in retention and transfer. Rote learning means that high results are obtained in retention, but low in transfer. Meaningful learning, on the other hand, is characterized by high performances in both tests.

Undergraduate histology teachers would argue that when one moves farther away from the original learning context (i.e., the particular course slide that has been trained on in virtual microscopy), the extent of retention performance decreases, and transfer performance increases. Likewise, the tasks would get more abstract and thus difficult to solve as students would need to generalize their theoretical knowledge and visual skills to another context.

However, different forms of similarity/dissimilarity or near/far transfer tend to be defined pragmatically. For example, histopathologists with a focus on clinical cases and pathological conditions may conceptualize all the above-mentioned examples of healthy tissue as a retention task or “just” as ordinary learning. In a clinical context, a histological transfer could be conceptualized as the application of basic science skills to solve a complex and uncommon clinical case.

To conclude, the concept of transfer depends on the context in which it is used, and on the perspective. Yet, such conceptual relativity is problematic for scientific inquiry and may cause misunderstandings.

To use more perspective-independent criteria for transfer, this study refers to the Principle of Identical Elements of Thorndike and Woodworth (1901). The authors stated that the level of transfer depends on the level of similarity or dissimilarity between training and performance context. In other words, the more the elements of one context are identical to the elements of another context, the nearer the transfer. Following this principle, this study seeks to investigate transfer by systematically varying elements from the original learning context. The exact procedure will be explained in the methods section.

Finally, histology allows the application of an independent measure of proficiency, and this can be complemented by eye-tracking techniques that monitor the eye movements of learners during task completion. Multiple studies have analyzed the eye movement patterns of both novice and expert diagnosticians (e.g., Jaarsma et al., 2015; Brunyé et al., 2020), with results suggesting that differences in eye movements are linked to varying levels of medical proficiency and may be predictive of diagnostic accuracy. Therefore, this study used the eye tracking methodology as an additional objective methodology to validate the findings.

Aim of this study

The current study investigated how to transfer performance developed during a histology course and explored the relationship between theoretical knowledge, retention performance, and transfer. It was hypothesized that theoretical knowledge in histology would help students to apply their microscopy skills to unfamiliar situations, thus increasing their transfer performance on unfamiliar histological slides.

METHODS

To address these research questions, a longitudinal study was conducted during the summer term of 2022 at the Westfälische Wilhelms-University of Münster, Germany. Medical students were tested after 0, 10, and 20 three-hour histology laboratory sessions—timepoint 0 (t0), timepoint 1 (t1), and timepoint 2 (t2). While t0 represents students' prior knowledge at the beginning of the histology course (=baseline), t2 reflects the knowledge and skills they have acquired throughout the whole course. Random assignment to in-university curricula was not possible because researchers cannot assign students to classes. Thus, a quasi-experimental approach was used, and a control sample of second-semester medical students at the same university, who did not enroll in the histology course, was tested simultaneously. The control group were not entirely inexperienced in histology but had already received an introduction to the histology of the four basic tissues (epithelium, connective, muscle, nerve) in their first semester. Thus, they were not completely naïve to histological images. This study protocol was approved by the ethics committee of the university (“Ethik-Kommission der Ärztekammer Westfalen-Lippe und der Westfälischen Wilhelms-Universität”) under the reference 2022-370-f-N and was carried out in accordance with the Declaration of Helsinki, and its later amendments.

Participants

Medical students were recruited in the second, and third semesters voluntarily, and received no incentives for their participation. Data collection was anonymous. Digital informed consent was received from all participants. Data generated or analyzed during the study are available from the corresponding author upon request.

The histology curriculum in Münster

Anatomy for medical students is being taught over the first four semesters at the University of Münster. A lecture on general anatomy and embryology is given in the first semester, followed by macroscopic (gross) anatomy in the second semester with a full dissection course and small group seminars. The third semester offers courses on the central nervous system and histology. The histology courses encompass theoretical introductions and guided microscopic lab (virtual microscopy) sessions plus teamwork in small groups. The fourth semester offers an anatomy and imaging course in small group seminars with peer teaching. In Germany, national board examinations with a focus on anatomy, physiology, and biochemistry are conducted after the fourth semester.

Histology classes took place in a regular face-to-face setting on campus. During the Covid-19 pandemic, the course was temporarily offered online only (Darici et al., 2021) but the cohort in the summer term of 2022 examined in this study attended face-to-face classes as in the pre-Covid-19 era. Approximately 150 medical students and 50 dentistry students participated in the course with 20 course days of 3 h each. Two course days were scheduled per week (Tuesday and Thursday morning). Course attendance (>85%) was mandatory. One week after the last course day, students took a 50-question written single-choice exam, which they must have passed with ≥60% of the maximum score. Of these questions, at least 25 were image questions, i.e., the questions related to a histological image that was included. Students had permanent in-class and off-campus access to virtual microscopy software, which they could use for training. More details about the course content can be obtained from Darici et al. (2021).

In-class strategies to foster the transfer performance

Didactically, the course relied on a “Sandwich Principle” as proposed by Kadmon et al. (2008), and integrated individual and collective learning phases, as well as a wide range of didactic methods. For example, multiple-choice questions were used at the beginning of the course to activate the students' prior knowledge. Following brief introductions to the subject matter, there was a virtual microscopy phase that included practical training, as well as peer-teacher-supported small group discussions. According to the ICAP paradigm (Chi & Wylie, 2014), this phase aimed to prevent passivity and to increase cognitive engagement with the learning materials through interaction and co-construction. Next, the instructors presented and annotated the course slides to provide expert modeling examples. The goal was to provide a step-by-step solution to a task, and a clear picture in the students’ minds. Afterward, students practiced another task with similar features to consolidate their skills. Finally, students were shown new and unfamiliar histological slides to demonstrate transfer to a different context.

Such indirect and direct instruction elements, as well as problem–example–problem sequences were repeated and varied several times during a three-hour course day. These methods aimed at improving the transfer skills of the students. Throughout the course, the theory was introduced by up to five different lecturers in turn.

Study procedure

At three time points during the course, theoretical knowledge, retention, and transfer performance were measured. Because participants' concentration and motivation drop dramatically during prolonged time-on-task (Ackerman & Kanfer, 2009), the length of each measurement was held short to approximately 15 min. The histology group and control group performed the tasks separately in a large multimedia room to prevent spill-over effects (i.e. contamination) between both groups. Each participant had an own workstation with a computer, webcam, keyboard, and mouse. The stimuli were displayed using the software “RealEye” running under a university license (Wisiecka et al., 2022). This software allows the standardized presentation of the stimuli for a predetermined duration achieving a high degree of objectivity and an equal time-on-task duration.

To measure participants’ retention and transfer, 20 histological images were chosen as stimuli (5 identical, 5 retention, and 10 transfer images). A group of three educators with a combined expertise in histology teaching for more than 15 years worked together to select these images. To keep the difficulty of these stimuli constant and to ensure comparability, the same images were used for all three time points. At each time point, the images were presented in a randomized order to prevent primacy or recency effects. The instruction for all images was identical: “Identify the following organ”. To capture fast processing, the images were only presented for a short time of 15 s per image. Students were then automatically redirected to a query asking “Which organ did you identify?”. Skipping back was not possible. To prevent idle time, students who solved the task faster than 15 s could skip directly to the query by pressing the spacebar. During the task completion, webcam eye tracking methodology was used to capture the students' eye movements. Figure 1 summarizes the tasks.

Details are in the caption following the image
Identical, retention, and transfer tasks were used in this study as exemplarily shown for the thymus organ. While the identical stimuli showed a replica of images in the course script, the retention task presented a familiar histological image retrieved from the virtual microscopy software that shared many identical visual elements with the original course slides. In contrast, the transfer task presented an unfamiliar histological image of the same organ that was retrieved from other university databases, and which shared few identical elements with the original course slide (i.e., different staining, perspective, and magnification). The same stimuli were used for all three time points to ensure comparability. They were presented in a randomized order to prevent rank effects.

Measures

Along with sociodemographics (age, gender), the following instruments were used.

Identical and retention performance

Stimuli were chosen that had a high similarity to the original learning context (the particular course slide learnt during virtual microscopy). For this purpose, ten image questions were chosen that showed five different organs (lung, spleen, thymus, trachea, adrenal gland). Of note, the organ slides were integrated in the regular learning activities during the course sessions 6–9 (see Table S1 and Figure S1). As a result, participants were expected to have prior knowledge at points t1, and t2. Five of those images were exact replicas of the sample images the histology group received during the course (identical task). An additional five images of the same five organs were created from the course slides in virtual microscopy software (retention task). Items were aggregated into an overall retention score by summarizing. The internal consistency of the scale was α = 0.76 indicating fairly high reliability.

Transfer performance

According to the Principle of Identical Elements (Thorndike & Woodwort, 1901), the transfer stimuli had to differ from the original learning context in many elements. For this purpose, 10 histological images were obtained from two different university databases (the University of Bern and the University of Homburg) showing the same organs but with different staining, magnification, and perspective. To maintain consistency in the difficulty of the organ diagnoses, the same organs as in the retention tasks were chosen as stimuli (i.e., lung, spleen, thymus, trachea, and adrenal gland). Each organ was tested twice in the transfer task. Using this strategy, it was intended to compare retention and transfer performance properly. Items were aggregated into an overall transfer score by summarizing. Internal consistency of the scale was borderline acceptable with α = 0.58, which is sufficient for such a broad construct (Taber, 2017).

Theoretical knowledge

Theoretical knowledge was assessed with eight single-choice items. These items were drawn from a question pool that had previously performed excellently in written histology examinations and subsequent item analyses (i.e., high discriminatory power and medium difficulty). Items included questions on general biomedical principles (i.e., mucociliary clearance), differential diagnostic knowledge (i.e., histology of groin vs. field skin), and staining behavior of histologic tissue (i.e., eosin stains collagen fibers). A sample question was: “Which staining technique stains collagen fibers red?”. There was no time limit for answering the theory questions. Items were aggregated into an overall theoretical score by summarizing. Internal consistency of the scale showed moderate reliability with α = 0.61, which was considered acceptable given the limited number of test items (Taber, 2017).

Eye-tracking methodology

The most commonly used eye movement metric fixation number (i.e., the number of times the eye fixated) was recorded using webcam eye-tracking methodology with appropriate HD webcams BCC950 (Logitech, Apples, Switzerland) (Papoutsaki et al. 2017). Fixation count indicates the frequency with which participants fixated on the screen (Darici et al., 2023b). A heatmap was created to illustrate, where on the slides the participants fixated.

Handling the missing data

Longitudinal studies typically have missing values because not all students are present at all time points. However, it is important to consider the underlying mechanism of the missing data when selecting an appropriate method for handling it. The exclusion of nonrandom missing values may affect the validity of the results.

In the current study, the percentage of missing data was less than 10%. Little's MCAR test indicated that the data were missing completely at random, χ2(85) = 22.386, p = 1.000. Therefore, multiple imputations with five imputations were performed to replace the missing data, and to increase the statistical power of the study.

Data analyses

The data collected from the experiments were analyzed using SPSS software, version 28 (IBM Corp., Armonk, NY). Descriptive statistics, including the mean and standard deviation, were calculated for all variables. The normal distribution of the data was assessed using the Shapiro–Wilk test.

To examine the effect of the histology course on theoretical knowledge, retention performance, and transfer performance, a repeated-measures ANOVA test was conducted with time (0, 10, and 20 course sessions) as the within-subjects factor and group (control, histology) as the between-subjects factor. Age and gender were included as covariates. Follow-up pairwise comparisons were conducted using the Bonferroni correction to adjust for multiple comparisons. Confidence intervals and effect sizes were calculated.

To investigate the relationship between participants’ theoretical knowledge and their performance on the retention or transfer of learning tasks, Pearson's correlation coefficient was calculated. To predict transfer performance (dependent variable) with retention performance and theoretical knowledge (independent variables), a multiple linear regression analysis was performed for each time point. Age and gender were added as covariates. One benefit of regression analysis is that it may result in a more accurate and precise understanding of how each individual factor is associated with the outcome.

Furthermore, the variance inflation factor (VIF) was calculated to identify if there is multicollinearity among the predictor variables in a multiple regression model, which can affect the reliability and interpretability of the model (norm value VIF < 5). All statistical analyses were conducted at an alpha level of 0.05.

RESULTS

Sociodemographic characteristics of the participants

Of around 150 enrolled in the histology course, 47 third-semester medical students (mean age = 22.0 ± 2.8, 26 females, 21 males) participated in this study (histology group). A control sample was recruited during the concurrent dissection course and comprised 27 second-semester medical students (mean age = 19.81 ± 1.33, 15 females, 12 males) (control group). As expected, third-semester participants in the histology group were older than the second-semester participants in the control group, t(72) = −3.87, p < 0.001. The male-to-female ratio was equal in both groups, χ2(1) = 0.11, p = 0.917.

Transfer development over the course-span

A repeated-measures ANOVA was performed to assess how student performance had changed over the three time points (Table 1).

TABLE 1. Performance results after 0, 10, and 20 histology course sessions.
Performance Histology group (n = 47) Control group (n = 27)
Mean % (SD) Mean % (SD)
After 0 course sessions (t0)
Theory 35 (16) 40 (23)
Identical stimuli 27 (24) 23 (30)
Retention stimuli 29 (20) 21 (16)
Transfer stimuli 23 (14) 32 (21)
After 10 course sessions (t1)
Theory 53 (15) *** 36 (16)
Identical stimuli 38 (22) *** 19 (20)
Retention stimuli 35 (20) ** 16 (16)
Transfer stimuli 35 (16) *** 22 (14)
After 20 course sessions (t2)
Theory 54 (23) *** 29 (12)
Identical stimuli 68 (38) *** 19 (19)
Retention stimuli 65 (29) *** 32 (22)
Transfer stimuli 60 (25) *** 20 (11)
  • Note: The mean correct answers per task type are shown as percentages. Significant differences between both groups are highlighted in bold.
  • Abbreviation: SD, standard deviation.
  • ** p < 0.01;
  • *** p < 0.001.

The results showed an effect of time, F(8) = 2.14, p = 0.033, η2 = 0.058, indicating that the students progressed significantly over time. This effect was stronger when taking the group assignment into account (time × group), F(8) = 12.56, p < 0.001, η2 = 0.268, showing that both groups developed differently. While the performances of the control group were similar across all three measurement time points, the histology group progressed in all four different tasks.

Specifically, at t0, the performances were the same between the two groups, indicating equal prior knowledge and skills. A direct comparison of the main effects with Bonferroni correction revealed that the histology group performed significantly better than the control group in all four tasks at t1, as well as at t2. While their theoretical knowledge increased primarily in the first half of the course (mean difference t1t0 = +17.96%, p < 0.001), their greatest improvement in the other tasks occurred in the second half (e.g., mean difference t2t1 = +25.13%, p < 0.001).

In order to provide further evidence of the progress made by the histology group, the eye tracking data recorded during completion of the tasks were analyzed.

The results revealed that the eye movements of the histology group and the control group differed over the course of the study, with the histology group showing a greater increase in fixation counts compared to the control group, effect of time × group, p = 0.014, η2 = 0.103.

Relationship between the tasks

A correlation analysis was performed to examine the association between task performance at various stages (t0, t1, t2) and transfer performance after the course (t2) in a sample of participants from the histology group.

The results in Table 2 show that there was a significant relationship between the performance of the theory task and transfer performance. This relationship was strongest after 20 histology course sessions (t2), with a correlation coefficient of 0.46 (p < 0.001). Performance on both the identical task and the retention task at t0 and t1 showed a significant relationship with transfer performance at t2. However, performance on the identical task and retention task at t2 was not significantly correlated with transfer performance, with correlation coefficients of −0.17 and −0.08 respectively (ps > 0.05). Of all tasks, identical task performance at t0 showed the highest correlation with transfer performance at t2, with a correlation coefficient of 0.50 (p < 0.001).

TABLE 2. Intercorrelation matrix (n = 47).
Theory task performance Identical task performance Retention task performance
t 0 t 1 t 2 t 0 t 1 t 2 t 0 t 1 t 2
Transfer task performance at t2 0.17 0.39 ** 0.46 *** 0.50 *** 0.34 ** −0.17 0.29 * 0.38 ** −0.08
  • Note: Pearson's correlations are shown.
  • Abbreviations: SD, standard deviation; t0, after 0 course sessions; t1, after 10 course sessions; t2, after 20 course sessions.
  • * p < 0.05;
  • ** p < 0.01;
  • *** p < 0.001.

Predicting the transfer performance

A multiple regression analysis with data from the histology group was performed where the three performances of theory, identical, and retention tasks at each time point were included together as independent variables in the equation to predict transfer performance at t2 (Table 3). Of note, the VIF was below 5, indicating no problems with the multicollinearity of the variables.

TABLE 3. Regression coefficients for predicting the transfer performance after 20 course sessions (n = 47).
Predictor Unstandardized coefficient Standardized coefficient t p value Fit
B 95% CI [LL, UL] β
t 0 Radj2 = 0.011
Age −0.174 [−0.42, 0.07] −0.22 −1.44 0.157
Gender 0.075 [−1.52, 1.67] 0.02 0.09 0.925
Theoretical task performance 0.309 [−0.30, 0.92] 0.16 1.03 0.311
Identical task performance −0.406 [−1.05, 0.24] −0.19 −1.27 0.211
Retention task performance 0.194 [−0.58, 0.96] 0.08 0.51 0.612
t 1 Radj2 = 0.237
Age −0.171 [−0.39, 0.05] −0.22 −1.56 0.127
Gender −0.048 [−1.42, 1.32] −0.01 −0.07 0.944
Theoretical task performance 0.517 [−0.06, 1.09] 0.25 1.81 0.078
Identical task performance 0.238 [−0.31, 0.97] 0.15 1.04 0.306
Retention task performance 0.808 [0.09, 1.52] 0.32 2.28 0.028 *
t 2 Radj2 = 0.169
Age −0.126 [−0.36, 0.10] −0.16 −1.10 0.275
Gender −0.443 [−1.91, 1.03] −0.09 −0.60 0.546
Theoretical task performance 0.623 [0.22, 1.02] 0.46 3.12 0.003 **
Identical task performance −0.029 [−0.54, 0.48] −0.02 −0.12 0.909
Retention task performance −0.246 [−0.91, 0.42] −0.14 −0.75 0.460
  • Note: Pearson's correlations are shown.
  • Abbreviations: LL, lower limits; Radj2, coefficient of determination adjusted for the number of variables, SD, standard deviation; t0, after 0 course sessions; t1, after 10 course sessions; t2, after 20 course sessions; UL, upper limits.
  • * p < 0.05;
  • ** p < 0.01.

The results showed that none of the variables at t0 could predict transfer performance at t2. Similarly, there were no significant age or gender effects at any time point.

In contrast, retention stimuli performance at t1 was shown to be a good predictor of transfer performance at t2, with a standardized coefficient of β = 0.32 (p = 0.028). The overall model at t1 was able to explain 23.7% of the transfer performance variance at t2 (Radj2 = 0.237).

However, this pattern changed substantially at t2. Here, retention performance showed no independent predictive value, β = −0.14 (p = 0.460). The best predictor of transfer performance at t2 was now theory performance, β = 0.46 (p = 0.003). This means that a person's performance on a task that tests their understanding of the underlying concepts is a better predictor of their ability to apply that knowledge to a new task than their performance on a task that they have previously learned.

DISCUSSION

The current longitudinal study investigated the development of transfer of learning in histology and explored the complex relationship between theoretical knowledge, retention performance, and transfer performance over various time points.

Three key findings emerged from this study. First, the findings in Table 1 and Figure 2 revealed that the students displayed improved retention and transfer performances, with particularly significant increases observed in the second half of the course. Second, the correlation between theory and transfer performance increased over time, while it decreased for the retention and transfer performance (Table 2). Finally, retention performance, and theoretical knowledge both predicted final transfer performance of the study participants (Table 3). As the students achieved good results in both retention and transfer performance, the overall learning outcome in histology can be classified as meaningful (Mayer, 2010).

Details are in the caption following the image
The gaze behavior of participants during task completion was analyzed using eye-tracking methodology. Fixation count, which indicates the frequency with which participants fixated on the screen, is shown for different tasks and over 0, 10, and 20 course sessions, along with 95% confidence intervals. Furthermore, a heatmap indicates where the students directed their gaze. The left diagram displays the mean fixation count for the retention tasks, while the right diagram shows the mean fixation count for the transfer tasks. The histology group is represented with red lines, and the control group with blue lines.

Students develop transfer competencies in histology

Given the rapidly changing demands in the healthcare sector, the competency to transfer knowledge and skills to new and unfamiliar situations is critical. The core competency to transfer learning learning in biomedical sciences have been crucial in managing the response to the COVID-19 pandemic, and maybe even more relevant in potential future challenges in the health system (Kinsella et al., 2020). Therefore, understanding and fostering transfer of learning is a key educational goal.

The current study found that after an histology training, students were indeed able to identify not only histology course slides familiar to them but also unfamiliar slides they have not seen before. According to the principle of identical elements (Thorndike & Woodwort, 1901), these stimuli shared few similar elements with the original course slides in terms of staining, magnification etc.

This result is not trivial because the interpretation of unseen histological slides is a difficult task, especially for novices (Johnson et al., 2015). Histological slides demonstrate a two-dimensional equivalent of a three-dimensional structure and can appear similar to the untrained eye, especially with variable appearances in different sections and at different magnifications (Hortsch & Mangrulkar, 2015). In addition, meaningful structures must be distinguished from various histological artifacts, which often occur during specimen preparation. Furthermore, the large variety of staining techniques, each of which stains different structures in the tissue, provides an almost infinite number of possible combinations, making the task even more difficult. Accordingly, Garcia et al. (2018) argued that histology is “intrinsically difficult” due to the complexity of the subject, and learners’ lack of experience with the interpretation of histological images.

To conclude, for recognizing and interpreting unfamiliar slides in histology, learners must generalize their knowledge of structure and function, as well as their visual spatial abilities (Helle et al., 2010) to identify unfamiliar histological slides. The study results suggest that by attending a histology course as part of their basic sciences curriculum, students may successfully develop such transfer competencies.

The greater increase in fixation count of the histology group compared to the control group suggests that the histology group engaged in greater “visual activity” and exhibited altered visual behavior, confirming the progress in their abilities.

The results of the eye tracking methodology further indicate that training and prolonged exposure to visual stimuli in a histology course context can induce adaptive changes in visual behavior (see Figure 2). Specifically, the increase in total fixation count in the histology group was observed across multiple tasks, such as identical, retention, and transfer task, suggesting a general change in visual behavior rather than task-specific or stimulus-specific effects. These results indicate that visual perceptual processes are equally trained within the context of a histology course, which help learners orient themselves in unfamiliar images. The findings corroborate with previous eye tracking research (e.g., Jaarsma et al., 2015).

However, most of the improvement in students' transfer performance occured in the second half of the course (i.e., between 30 and 60 in-class hours of training) since the retention stimuli at t1 was a good predictor of transfer performance at t2. This suggests that students who were proficient at identifying familiar histological elements in the middle of the course had better transfer performance at the end of the course. While there were minor differences between the histology group and the untrained control group following 10 course sessions, these discrepancies became more pronounced after 20 h of training, as indicated in Table 1. Thus, there was a continuous improvement in transfer performance throughout a histology course. This is clearly not the sole result of the course design but most likely the product of complex developmental processes occurring inside and outside of the course. Additionally, the findings indicate that there may be still room for improvement even after 60 h of in-class training, as a steady-state plateau could not be achieved (see Table 1). Therefore, increasing the course time for histology may have beneficial effects on students’ transfer performances in basic science curricula. This observation is consistent with the self-assessment of students who wish to increase teaching time for histology (García et al., 2018). The late development of students’ transfer skills implies that much hands-on practice with microscopy, as well as a profound theoretical and biomedical foundation, is necessary for positive transfer to occur (Hortsch, 2022).

In any case, the results suggest that further reduction in histology hours could be fatal to this development. This observation may be true for anatomy or other biomedical sciences in general, whose curricular teaching time has decreased by up to 70% (Chan et al., 2020). The truncation of class periods may foster the production of superficial, context-specific, and inert knowledge among students (Renkl et al., 1996), rather than facilitating the development of transferable understanding of the human body. Achieving this goal is of particular importance in medical education, given the changing landscape of modern medicine, and the potentially beneficial “spill-over” effects of histological and biomedical knowledge on clinical decision-making (Woods et al., 2005, 2006; Woods, 2007; Nivala et al., 2013).

Theory and practice affect the transfer performance

Histology education in the present day is characterized by a balance between theoretical concepts and hands-on experience, specifically a combination of biomedical principles and visual expertise (Hortsch, 2022). This teaching approach aims to foster a deep understanding of the relationship between structure and function. Histology is considered the “most difficult subject in biomedical curricula” (Sherer et al., 2014), especially due to its objective of establishing interconnections across the fields of anatomy, physiology, and biochemistry.

The current study showed that students who performed well on baseline measures in retention tasks demonstrated stronger transfer performances at the end of the course. One explanation could be that students put in extra preparation before starting the course, leading to their early success. This perspective supports the old insight that deliberate practice leads to academic success (Ericsson et al., 1993).

Correlating the task performance at various stages and the transfer performance, the results indicate that the performance on the theory may be a good predictor of transfer task performance in the histology group, while the relationship between performance on the identical task, retention task and transfer appear to be less clear. It seems that the impact of theoretical knowledge on transfer performance increases over time, while it decreases for the other tasks.

However, this could also be explained by inherent traits or interests that helped these students in visual image processing, such as mental rotation skills (Roach et al., 2018) or an artistic learning (Tyler & Likova, 2012). This view is supported by research showing that some inherent traits, such as spatial relationship awareness (i.e., the ability to perceive objects in relation to each other), or conscientiousness (i.e., the personality trait of being careful, and diligent) may predict pre-test histology performances (Helle et al., 2010). Future studies on personality traits and visual expertise could build upon these observations, and investigate how these traits interact with transfer of learning, as the methods of activating and developing these traits to enhance transfer remain unclear.

Second, it was shown that both theoretical and practical aspects play an important role in promoting transfer performance, supporting the study hypothesis. Surprisingly, they became active at different points in time (see Table 3). In the middle of the course, for example, visual skills were more strongly associated with transfer performance than theoretical knowledge. Contrary, in the end of the course, theoretical knowledge was the only independent predictor of transfer performance. The results suggest a shift as the course progresses, with visual skills being more important early on, and theoretical knowledge becoming more critical toward the end of the course. During the first half of the course, students may have primarily relied on their ability to visually recognize elements or simply remember similar histological elements, which falls under Bloom's taxonomy's “remember” category, involving the recollection of facts and fundamental concepts. Toward the end of the course, however, students were able to integrate their basic biomedical knowledge into their decision-making, resulting in improved transfer performance as they were able to connect new information to existing knowledge. This achievement corresponds to Bloom's taxonomy's higher learning goals, such as “analyze” (i.e., establish connections among ideas) and “evaluate” (i.e., make a judgment).

To sum up, effective transfer performances in histology education may be enhanced by the incorporation of both theoretical and practical elements.

Conditions to promote the transfer of learning in histology

Medical students usually process a large amount of information during the first years of their studies. Therefore, suitable measures must be found to enhance the transfer performance of students in histology education without further increasing the overall workload. Several recommendations can be drawn from the literature.

For example, Balemans et al. (2015) studied long-term retention in histology, and reported the beneficial effects of pen-and-pencil drawing. Drawing supported model-based reasoning (Van Meter et al., 2006; Quillin & Thomas, 2015; Backhouse et al., 2017; Pickering, 2017), metacognitive processes (Naug et al., 2011), and long-term retention (Balemans et al., 2015). Classroom clickers were also shown to promote the transfer of new information (Alexander et al., 2009). Furthermore, various micro-teaching options have been proposed to unlock inert knowledge, such as thorough and diverse practice and deliberate practice (Ericsson et al., 1993), explicit abstraction, active self-monitoring, arousing mindfulness, problem-based learning, use of metaphors, analogy, and examples (Jamrozik & Gentner, 2020). Additionally, the use of high-quality histology questions concerning Bloom's taxonomy may potentially foster the transfer of learning (Zaidi et al., 2017). However, the ideal point of time to implement these educational interventions into the curriculum remains unclear.

At the same time, technical innovations in the field of virtual reality are opening up new possibilities for promoting transfer of learning in histology. For example, the simulation of virtual tumor boards could create a clinical context that allows for vertical integration with histology. Additionally, Dyre et al. (2016) found positive effects on the transfer abilities of medical students through error management training. Future studies will need to show whether such training formats are also suitable for histology education.

Limitations of the study

There are several limitations to consider. First, the focus of this study was on near-transfer performance. Further studies with larger sample sizes are needed to extend the results to far-transfer scenarios, such as pathological conditions or other medical areas. Even though the control group was one semester behind the intervention cohort, and thus closely related in terms of previous knowledge, and other sociodemographics, minor differences were still evident. For example, the control group was composed of individuals who were younger than those in the intervention group, which may introduce a potential age-related bias. Plus, the control group was not actively engaging in histology in their current semester, potentially resulting in differing levels of motivation to tackle the same tasks repeatedly.

Another limitation derives from the fact that the participants solved the same images over the three time points. The limitation lies in the potential for practice or testing effects and the inability to assess the participants' ability to generalize their histology skills. When participants repeatedly encounter the same set of images, they may become more proficient in recognizing and analyzing those specific images due to practice, rather than demonstrating overall improvement in their histology skills. This could artificially inflate the perceived effectiveness of the intervention, making it challenging to ascertain whether the observed improvements are a result of the intervention itself or simply the consequence of repeated exposure to the same material. To control for testing effects, a larger number of items (28 items per measurement point) were used to make it difficult for learners to remember the items. In addition, the images were shown for a short time of 15 s each, conducted at time intervals of 10 course days, i.e., at least 4 weeks apart, and were presented in a randomized order. In addition, a control group was used. Finally, as a quasi-experimental design was conducted, other unknown confounders could have had an impact on the subjects. It is important to consider these limitations in the context of the present research and to acknowledge the need for further investigation to confirm and expand upon these findings.

CONCLUSIONS

The ability to transfer learned knowledge and skills to new contexts is a crucial aspect of anatomy education, allowing healthcare professionals to remain adaptable and capable of continuous learning in an increasingly dynamic environment (Hortsch, 2022). Transfer of learning opposes the inflexibility of current Artificial Intelligence systems (as noted by Cortes et al., 2022), and may play a significant role in modern medical education. It is expected that the medical professionals of the future will face increasingly complex challenges that deviate from typical routine requirements, thus necessitating the application of sophisticated adaptation abilities. However, research has demonstrated that students frequently struggle to effectively apply their knowledge beyond the end of a course (García et al., 2018).

This study provides new evidence that students may successfully apply biomedical competencies acquired in histology classes to solve unfamiliar tasks. Such positive transfer effects appear to develop late in the course, so further shortening of course times in histology is highly discouraged. To measure transfer performance, the Principle of Identical Elements was employed as a methodological framework in histology, enabling the objective measurement of the distance between diverse stimuli and rendering it open to scientific examination.

The relationship between theoretical knowledge, retention performance and transfer performance found in this study hold implications for anatomical curricula seeking to foster the transfer of learning.

ACKNOWLEDGMENTS

Open Access funding enabled and organized by Projekt DEAL.

    Biographies

    • Dogus Darici, M.Sc., M.D., is a postgraduate medical researcher and educational psychologist in the Anatomy, Institute of Anatomy and Molecular Neurobiology at Westfälische Wilhelms-University in Münster, Germany. His research interests are in the area of visual expertise and professional identity in medical education.

    • Kristina Flägel, M.D., is a medical researcher at the Institute of Family Medicine at the University Hospital Schleswig-Holstein in Lübeck, Germany. Her expertise includes digital solutions in medical education and postgraduate training.

    • Katharina Sternecker, Dr. rer. nat., is a postgraduate research associate at the Department of Anatomy II at the Ludwig-Maximilians-University in Munich and teaches microscopic anatomy, macroscopic anatomy as well as neuroanatomy. Her current research focuses on teaching digital microscopy online.

    • Markus Missler, M.D., is a university professor of anatomy and Director of the Institute of Anatomy and Molecular Neurobiology at Westfälische Wilhelms-University in Münster, Germany. He teaches anatomy, histology and embryology to pre-clinical medical and dental students. His research interests are in the molecular neurobiology of the synapse.