Abstract
In response to the growing demand for innovative talent cultivation in higher education—particularly under initiatives such as “New Forestry and Science”—this study empirically compares the effects of four field-teaching methods on students' skill acquisition, motivation, and research engagement. Using longitudinal data from a three-year (2022–2024) birdwatching internship involving 174 undergraduate students in Zixi Mountain, Yunnan Province, we evaluated learning outcomes across three dimensions: knowledge acquisition, skill proficiency, and post-internship research continuation. Data were collected via systematic assessments of bird identification performance, structured questionnaires, and follow-up academic records. Results showed that all methods significantly improved identification skills, with gains in the following order: Line Transect > List Method > Point Transect > Mixed Flock Observation. However, no statistically significant differences were found among the methods in terms of enhancing students' interest in ornithology. Furthermore, the methods did not yield statistically significant differences in final bird identification proficiency among students. Notably, students who continued research after the internship demonstrated significantly greater skill improvement than those who did not. These findings underscore the importance of task-skill alignment in cognitive development and suggest that low-interaction, high-repetition designs—exemplified by the Line Transect method—are particularly effective for novice learners in field-based settings. Additionally, thematic field internships appear to foster the internalization of a research identity. We propose that universities adopt a spiral, progressive field-teaching model grounded in the zone of proximal development, and research-identity formation to better support the development of innovative talents in forestry, ecology, and sustainability-related disciplines.
1 Introduction
The immersive field experience is widely recognized as a core pedagogical component in training future ecologists and conservationists (Fleischner et al., 2017). Accordingly, ornithology field courses have become a cornerstone of the Wildlife and Nature Reserve Management curriculum in Chinese forestry universities (Sun et al., 2023). This aligns with a well-established consensus that fieldwork is fundamental to effective biology education and teacher professional development (Fleischner et al., 2017; Johnson, 2016; O'Neill et al., 2024). Such courses play a vital role in stimulating student interest, achieving learning outcomes, and cultivating research competence, field survival skills, and a conservation ethos. In light of the “New Forestry Science” initiative, which emphasizes innovative practical abilities as a key graduation requirement, the importance of specialized fieldwork has gained further prominence (Auchincloss et al., 2014; Huang et al., 2020). The functional objectives of these courses are clearly defined: to foster professional skills, cultivate scientific thinking, build a researcher identity, and support lifelong learning (Honra, 2025; Honra and Serdenia, 2025).
However, the traditional pedagogical model—in which instructors preset survey routes, students passively follow and record data, and later compile reports—has often devolved into a routine of “transcription through binoculars”. This approach lacks intellectual challenge and fails to promote deeper student engagement, falling short of the “Golden Course” standards that demand higher-order thinking, innovation, and academic rigor. More critically, it does not adequately guide students to “think like a scientist” (Corwin et al., 2015; Linn et al., 2015). Consequently, a central challenge in reforming field instruction lies in designing intensive, short-term programs that enable students with varying prior knowledge to achieve measurable and meaningful growth.
The “Thematic Task-Driven” model offers an established framework to address these pedagogical limitations (Abdildauly et al., 2025; Prince and Felder, 2006), particularly in field-based contexts where it supports inquiry and research-identity development. Grounded in authentic research questions, this model guides students through a complete cycle of scientific inquiry—from posing questions and collecting field data to analyzing results and drawing conclusions—thereby shifting learning from passive knowledge acquisition (“learning to know”) to active knowledge construction (“learning to inquire”) (Healey and Jenkins, 2009; Honra and Monterola, 2025a; Rissing and Cogan, 2009).
Ornithology field courses are characterized by diverse content, varied methodologies, and steep gradients in task difficulty. Without intentional, problem-based design, such complexity can easily lead to cognitive overload or tasks that lie outside students' zone of proximal development (Feldon et al., 2019; Hmelo-Silver et al., 2007; Honra and Monterola, 2025b). Novices, in particular, may experience memory overload and heightened anxiety due to excessive species information. To address these issues, the internship is structured around a progressive “cognitive → skills → affective” learning framework. By systematically stratifying tasks and comparing learning mechanisms and outcomes across different thematic pathways, this approach provides an evidence-based rationale for implementing differentiated instruction.
The mountainous region of Southwest China is one of the world's 36 biodiversity hotspots. Located in proximity to this region, Southwest Forestry University benefits from a natural synergy between its curriculum and exceptional local ecological resources. Since 2010, the university has collaborated in establishing a field station within the Zixishan Provincial Nature Reserve in Chuxiong, Yunnan Province. Over 15 consecutive iterations of comprehensive ornithology courses, two distinct thematic tracks have been developed:
Traditional Survey Methods Track: Focused on species identification and foundational field techniques using line transect, point count, and species listing methods.
Mixed-Species Flock Dynamics Track: Introduces core concepts from behavioral ecology and social network analysis through the study of avian mixed-species flocks.
Although both tracks operate within the same spatiotemporal context and share the local avian community as a resource, they address different learning challenges and target distinct competency goals. Beginners require structured support to build rapid feedback loops between “failure to remember” and “inability to accurately identify”. Intermediate learners progress from simply “observing phenomena” to “understanding underlying mechanisms” while advanced students are encouraged to formulate testable scientific questions and engage in a complete, authentic research cycle.
Rooted in the research theme of “Evaluating the Effectiveness of Different Thematic Models in Ornithology Field Courses”, this study adopts an evidence-based educational approach. By analyzing students' performance data from the 2021–2024, it addresses two core research questions: Are there significant differences in the improvement of bird identification skills among the four thematic models? and Does improvement in bird identification significantly influence students' subsequent persistence in research activities?
By examining these questions, the study aims to develop a “hierarchically progressive, replicable, and scalable” pedagogical framework for zoology and field practice education in higher education. Furthermore, it seeks to establish a robust empirical foundation for the future development of nationally recognized first-class undergraduate courses.
2 Methods
2.1 Field course organization
From 2022 to 2024, annual ornithology field course were conducted over three consecutive sessions at the Zixishan Provincial Nature Reserve in Chuxiong City, Yunnan Province. Each session took place between December 16th and 26th. Participants were organized into groups of six, with each group selecting one thematic model at the beginning and adhering to it throughout the course. A supervising instructor was assigned to each group to provide guidance on species identification as needed. During field activities, each student was equipped with binoculars for observation and a smartphone GPS application to record movement tracks.
2.2 Thematic field methods
2.2.1 Line transects (TL)
Groups followed predetermined transect lines at a constant speed of 1.5–3 km·h−1, recording all bird species detected visually or aurally within a fixed-width strip on both sides of the transect. Habitat types were predefined, and survey routes were logged in real time using mapping software. Although straightforward to organize and supports comparison with existing literature, this method faces challenges in southern mountainous regions, such as low detection efficiency, excessive transect length, and interference from crossing multiple habitat types (Bibby et al., 2000).
2.2.2 Point transects (PT)
Fixed points were established along a pre-planned route, with a minimum distance of 200 m between points. At each location, observers remained stationary for 5–10 min and recorded all birds detected within a 50-meter radius—extended to a maximum of 100 meters for highly conspicuous species. This method generally yields higher detection rates than line transects and is particularly suitable for patchy habitats and large-scale survey areas (Bibby et al., 2000; Gill et al., 2019; Sutherland et al., 2004), despite covering a smaller area per point.
2.2.3 List method (LM)
Observers recorded species sequentially on a fixed-length list (e.g., X = 10 species). A new list was initiated once the predetermined quota was reached, with no species allowed to appear more than once within a single list. This method is efficient and intensive, making it well-suited for rapid biodiversity assessments in complex terrain. However, a key limitation is the absence of a standardized list length, which hinders comparability across studies (MacKinnon and Phillipps, 2000).
2.2.4 Mixed-species flocks (MF)
A survey unit was defined as an aggregation of at least two species, with a minimum of three individuals in at least one species, moving cohesively in a shared direction with inter-individual spacing ≤25 m. Observers distinguished between core and attendant species, recording metrics such as mean species richness, mean flock size, and flock encounter frequency (see Table 1) (Diamond, 1981). This model incorporates a social network perspective, training students in behavioral ecology interpretation and data modeling.
Table 1
| Mixed group characteristic parameters | Definition |
|---|---|
| Mean species richness | The average number of bird species present in each mixed-species flock. |
| Mean flock size | The average number of individual birds (across all species) in each mixed-species flock. |
| Flock encounter frequency | The proportion of occurrences of a specific bird species in mixed-species flocks relative to the total number of mixed flocks observed in a given forest type |
Characteristic parameters and definitions of mixed groups.
2.3 Data collection
Data were collected in two phases: pre-course and post-course.
2.3.1 Pre-course (one week prior)
Baseline data were collected using a questionnaire administered to all students one week before the field session. The survey included the following items:
Prior Bird Identification Score: the self-reported number of bird species each student could confidently identify.
Interest in Ornithology: self-reported level of interest measured on a 5-point Likert scale (1 = Not at all interested, 5 = Very interested).
2.3.2 Post-course (final day)
Students were again asked to self-report the number of bird species they could identify.
Research Persistence:
For the 2022 participants, this was determined after the field session based on undergraduate thesis topics and graduate school applications.
For the 2023 and 2024 participants, persistence was assessed through direct inquiry regarding students' intention to pursue avian-related research.
2.4 Data Analysis
Data were initially processed in Microsoft Excel and subsequently analyzed using R (version 4.3.2).
2.4.1 Statistical analysis of skill gains
2.4.1.1 Baseline proficiency comparison
The assumption of homogeneity of variances was violated for pre-course bird identification scores (Levene's test). Therefore, differences in baseline proficiency among the four thematic groups were examined using the non-parametric Kruskal–Wallis rank-sum test. The effect size for the Kruskal–Wallis test was reported as epsilon-squared (ε2), calculated as (H – k + 1)/(n – k), where H is the test statistic, k is the number of groups, and n is the total sample size.
2.4.2 Analysis of skill gains
Gain scores (post-test minus pre-test) satisfied assumptions of normality (Shapiro–Wilk test) and homogeneity of variances. A one-way analysis of variance (ANOVA) was conducted to compare gains across the four thematic models. Post hoc pairwise comparisons were performed using Tamhane's T2 procedure, and effect sizes are reported as eta-squared (η2).
2.4.3 Within-Group Improvement
Paired-sample t-tests were applied to assess the significance of skill improvement from pre- to post-course within each thematic group. Effect sizes for paired-sample t-tests were reported as Cohen's d, calculated as the mean difference divided by the standard deviation of the difference scores.
2.4.4 Analysis of research persistence
Research persistence was operationalized as a binary variable (yes/no). Because gain score distributions for the two groups violated assumptions of normality and homogeneity of variances, the non-parametric Mann–Whitney U-test was used to compare gain scores between students who persisted in research and those who did not. The Hodges–Lehmann estimator was used to calculate the median difference between groups along with its 95% confidence interval. The effect size for the Mann–Whitney U-test was reported as rank-biserial correlation (r), calculated as (U₁ – U₂)/(n₁·n₂), where U₁ and U₂ are the Mann–Whitney U statistics for the two groups, and n₁ and n₂ are the respective sample sizes.
All analyses employed a two-tailed significance level of α = 0.05. Results are reported as statistically significant at P < 0.05 and highly significant at P < 0.01.
3 Results
3.1 Comparison of the effects of different teaching modes on different topics
Pre-course bird identification proficiency, post-course skill gains, and research persistence rates across the four thematic instructional models are summarized below.
Point Transects (PT, n = 34): Students in this group exhibited the lowest baseline proficiency (M = 12.6, SD = 9.31 species), achieving a mean skill gain of 28.5 species (SD = 15.4), with a research persistence rate of 41.67%.
Line Transects (TL, n = 73): Baseline proficiency in this group was M = 18.5 species (SD = 21.3). These students demonstrated the highest mean skill gain (M = 36.7, SD = 16.3 species) and a research persistence rate of 42.47%.
List Method (LM, n = 33): Baseline proficiency was M = 14.7 species (SD = 13.6), with a mean gain of 32.2 species (SD = 13.4). This model yielded the highest research persistence rate (54.55%).
Mixed-Species Flocks (MF, n = 34): Students began with the highest baseline proficiency (M = 30.4, SD = 63.5 species) but showed the smallest mean gain (M = 21.5, SD = 10.2 species) and the lowest research persistence rate (26.47%).
Statistical analysis confirmed no significant differences in pre-course identification proficiency among the four thematic groups (Figure 1).
Figure 1
All four thematic models led to statistically significant improvements in students' bird species identification scores (P < 0.001 for each within-group paired t-test). However, the magnitude of gain differed significantly among models [one-way ANOVA: F(3, 170) = 8.93, P < 0.001, η2 = 0.14]. The post hoc analysis revealed a clear gradient in effectiveness: Line Transects yielded the largest mean gain, followed by the List Method and Point Transects, with Mixed-Species Flocks yielding the smallest gain. Notably, although the Mixed-Species Flocks group started with the highest baseline proficiency (30.4 ± 63.5 species), it demonstrated the most modest improvement (see Figure 2A).
Figure 2
The first three thematic modules were combined to form the Traditional Teaching Group, which was then compared with the Mixed flock Group (Innovative Teaching Group) using an independent samples t-test. The results indicated that the gain scores of the Traditional Group (33.6 ± 15.7, n = 140) were significantly higher than those of the Innovative Group (21.5 ± 10.2, n = 34), with t(76.2) = 5.55, P < 0.001. The effect size, as measured by Cohen's d, was 0.82, indicating a large effect (Figure 2B).
3.2 Changes in student interest
The Kruskal–Wallis test indicated no significant differences in pre-course interest scores among the four thematic groups [χ2(3) = 1.94, P = 0.59]. Consequently, it can be concluded that participation in different thematic models did not result in significant differential changes in students' interest in ornithology after the field training.
3.3 Research persistence and skill gain
A Mann–Whitney U-test revealed a significant difference in the distribution of bird identification gain scores between students who persisted in research activities (n = 68) and those who did not (n = 106) (U = 2,858.5, P = 0.021; see Figure 3). The persistence group showed a significantly higher mean gain (35.1 ± 17.4 species) compared to the non-persistence group (28.8 ± 13.8 species), with a Hodges–Lehmann estimated median difference of 8 species. This result indicates an association whereby participants who did not continue with research were observed to have significantly smaller improvements in the bird identification test than their peers who engaged in scientific research.
Figure 3
4 Discussion
4.1 Differential impact of thematic models on learning outcomes
This study provides the first systematic comparison of four thematic ornithological field training models within an authentic higher education setting. While all instructional approaches yielded statistically significant gains in bird identification skills (P < .05), the magnitude of improvement varied significantly across models, following a clear effectiveness gradient: Line Transect > List Method > Point Transect > Mixed-Species Flock. Specifically, the Line Transect model yielded the highest mean gain (M = 36.4), whereas the Mixed-Species Flock model, despite having the highest pre-test baseline (M = 30.4), resulted in the smallest improvement (M = 22.8).
These findings align closely with predictions from cognitive load theory (de Jong, 2010; Van Merriënboer and Sweller, 2005). When the interactive and environmental complexity of a field task exceeds a learner's working memory capacity, extraneous cognitive load increases, thereby reducing the cognitive resources available for schema construction and deeper learning (generative load) (de Bruin and van Gog, 2020; Honra and Monterola, 2025b). The Line Transect model, characterized by its “small area, short duration, high repetition” protocol, structures the observational challenge around a limited set of variables—typically targeting 3 ± 1 focal species per session. Such a design likely places the task within novice learners' zone of proximal development, thereby enhancing skill acquisition efficiency through focused, repeated practice (Auchincloss et al., 2014; Johnson, 2016).
4.2 The “Gain” in skill and the affective mechanism of research persistence
Students who continued research after the field training demonstrated significantly greater skill improvement than those who did not. Pre-test results indicated no significant baseline difference in bird identification skills between students who later persisted in research and those who did not (P > 0.05). This suggests that the decision to pursue research is more closely linked to the experiential quality of the internship itself rather than prior ability. From an instructional design perspective, providing immediate, tangible feedback on achievement during fieldwork can enhance students' outcome expectations and self-efficacy. Such positive reinforcement encourages the internalization of a “researcher” identity, thereby reshaping personal learning goals and cognitive frameworks (Honra, 2025; Honra and Serdenia, 2025; Siby et al., 2024).
The Mann–Whitney U test confirmed that the group that persisted in research achieved a significantly higher median gain in bird identification. Notably, the apparent deceleration in the “rate” of species accumulation among these students may signal a functional shift in learning focus—from “score-chasing” to “problem-deepening” inquiry. This transition aligns with cognitive-identity coupling theory, whereby the development of a scientific identity drives engagement in more complex, depth-oriented learning activities rather than those aimed merely at quantitative output.
Therefore, the observed between-group difference in gain scores should not be interpreted as an instructional shortfall for one group, but rather as an indicator of successful pedagogical induction in which the internship experience has catalyzed a shift in learning objectives from skill acquisition toward scientific inquiry (Siby et al., 2024). This underscores the need for field course assessment to move beyond a singular focus on “species count” and toward a more holistic evaluation framework that encompasses multiple dimensions of scientific competence, including question formulation, investigative design, identity development, and cognitive growth (Dioquino et al., 2024).
4.3 Pedagogical implications and strategies for scaling
To enhance effectiveness, the thematic training model should be strengthened through closer integration of “micro-project design, assessment, and mentorship”. Specifically, a diverse repository of research-focused micro-projects should be developed, allowing students to self-select topics aligned with their interests and proficiency levels, thereby personalizing and deepening their research training pathway.
A dynamic assessment system should be implemented to evaluate cognitive load and skill mastery before, during, and after the course. The resulting data can inform real-time adjustments to task difficulty, ensuring an adaptive and optimally challenging learning experience (Healey and Jenkins, 2009). A scaffolded pedagogical sequence is recommended for field courses targeting novice learners (de Jong, 2010). Instruction should commence with structured, low-interaction methodologies (e.g., line transect sampling) to solidify core competencies and bolster self-efficacy. As learner proficiency develops, the curriculum can strategically integrate more complex and holistic approaches, such as mixed-species flock observation. This deliberate progression—from discrete skill acquisition to integrated ecological application—serves to optimize skill mastery while concurrently cultivating the sustained scientific curiosity essential for long-term research engagement.
Fostering inter-university collaboration and openly sharing pedagogical resources—such as micro-project libraries, standardized assessment rubrics, and instructor guides—would enable wider adoption and implementation of this “Thematic Model–Assessment–Mentorship” framework. This approach is essential for establishing a scalable, replicable, and impactful paradigm in field-based scientific education.
5 Conclusion
An analysis of field training performance data from 174 students demonstrates that thematic field models effectively enhance student knowledge. The differential outcomes highlight that task-cognition alignment is a key determinant of learning effectiveness, with the Line Transect model's low-interaction, high-repetition design yielding the most significant improvement in species identification skills. Moreover, the finding that students who persisted in research showed significant improvement over their peers is a strong indicator of the training role in fostering scientific interest and research identity. Consequently, we recommend that higher education institutions adopt a spiraling pedagogical paradigm for field courses that intentionally integrates considerations of cognitive load, the zone of proximal development, and research identity formation. This integrated framework is essential for cultivating the innovative talents required for the future of forestry and conservation sciences.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and the institutional requirements.
Author contributions
Q-sLi: Writing – original draft, Writing – review & editing, Data curation, Formal analysis, Supervision, Conceptualization, Investigation. Y-bD: Writing – original draft, Data curation, Formal analysis, Investigation. S-lL: Writing – review & editing, Supervision, Conceptualization. XL: Writing – original draft, Writing – review & editing, Data curation, Formal analysis, Supervision, Conceptualization, Investigation.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was funded by the Southwest Forestry University Educational Science Research Project (YB202433).
Acknowledgments
We extend our sincere gratitude to the Administration of Zixishan Provincial Nature Reserve and the Zijin Forest Farm for their long-term support for our fieldwork. We also thank the students from three consecutive cohorts for their active participation and candid feedback on this pedagogical innovation.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. I initially wrote in Chinese and then used artificial intelligence to translate it into English. After that, a native English speaker polished the translation.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
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Summary
Keywords
empirical teaching research, field practice of ornithology, new forestry science, scientific research identity recognition, task-skill alignment
Citation
Li Q, Duan Y, Liu S and Luo X (2026) Evaluating thematic models for field course development in ornithology. Front. Educ. 11:1777941. doi: 10.3389/feduc.2026.1777941
Received
30 December 2025
Revised
23 February 2026
Accepted
09 March 2026
Published
09 April 2026
Volume
11 - 2026
Edited by
Rose Murray, University of Bristol, United Kingdom
Reviewed by
Caroline Fernandes-Santos, Fluminense Federal University, Brazil
Joelash R. Honra, National University, Philippines
Updates
Copyright
© 2026 Li, Duan, Liu and Luo.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xu Luo luoxu@swfu.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.