(Article In Press) Online Interprofessional Higher Education for Faculty Development The Challenge of Detecting Change in Small Samples

Fei Wang^1,2    Jing Yuan ^3,a*Aimin Tang¹

¹Faculty of Education，Qufu Normal University，Qufu273165, China

²Faculty of law, Tongling University, Tongling, 244061, China

³School of Architectural Engineering, Tongling University, Tongling, 244061, China

^aEmail: 180011@tlu.edu.cn

Abstract：The digital transformation of higher education has accelerated the proliferation of online interprofessional faculty development programs, which aim to cultivate interdisciplinary teaching competence, optimize collaborative educational practices, and elevate holistic teaching quality across diverse academic disciplines. In contemporary higher education contexts, interprofessional education (IPE) has evolved from a marginal teaching concept to a core institutional strategy, emphasizing cross-disciplinary integration, egalitarian academic interaction, and collaborative pedagogical innovation. Online interprofessional faculty development further breaks the spatial and temporal limitations of traditional offline training, enabling faculty from diverse professional backgrounds to participate in standardized, systematic, and sustainable teaching capability improvement programs. Despite the widespread adoption and theoretical advantages of such online programs, empirical evaluation of their instructional and developmental efficacy faces prominent methodological bottlenecks, particularly the pervasive challenge of detecting subtle and authentic educational changes within small sample research scenarios. Most institutional online faculty development initiatives target niche faculty groups, specialized discipline teams, or small-scale interdisciplinary cohorts, resulting in limited sample sizes for effect evaluation. Small sample datasets are prone to statistical bias, low statistical power, high measurement error sensitivity, and insufficient representativeness, which collectively hinder the accurate identification of program-induced faculty behavioral changes, pedagogical improvement effects, and long-term developmental outcomes. This article systematically explores the theoretical connotation and practical value of online interprofessional higher education for faculty development, analyzes the core methodological challenges of change detection in small sample empirical studies, summarizes the key limitations of conventional statistical evaluation methods in small-sample scenarios, proposes optimized analytical frameworks and practical improvement strategies, and verifies the feasibility of the optimized approaches through empirical data analysis. This study aims to provide rigorous methodological support for efficacy evaluation of online interprofessional faculty development programs, facilitate accurate assessment of faculty teaching growth, and promote the high-quality and sustainable development of digital interprofessional higher education.

Keywords: Online Higher Education; Interprofessional Faculty Development; Small Sample Research; Change Detection; Educational Evaluation; Statistical Optimization

1. Introduction

1.1 Digital Shift: Reshaping Interprofessional Faculty Development

Global higher education has undergone profound digital restructuring over the past decade, with online teaching, remote academic interaction, and digital faculty training becoming normalized institutional operations. The traditional single-discipline faculty development model, which focuses on disciplinary knowledge indoctrination and individual teaching skill improvement, can no longer adapt to the interdisciplinary development trend of modern higher education and the compound talent cultivation demands of society. Interprofessional faculty development emerges as a pivotal educational paradigm that breaks disciplinary barriers, integrates cross-domain teaching resources, and builds collaborative teaching capabilities for university faculty^[1-3]. Different from conventional professional training, interprofessional faculty development emphasizes equal communication, cross-disciplinary learning, collaborative problem-solving, and shared teaching innovation among faculty from different academic backgrounds, laying a foundation for interdisciplinary curriculum construction, integrated teaching reform, and innovative talent training in universities.

The rapid development of online education technology has further empowered interprofessional faculty development, transforming offline centralized training into flexible, inclusive, and scalable online learning modes. Online interprofessional faculty development programs rely on digital learning platforms, cloud resource databases, and remote interactive tools to break the restrictions of geographical distance, teaching time, and team scale, allowing faculty from different departments, disciplines, and even different institutions to participate in unified interprofessional training. These programs adhere to four core educational pillars, including professional diversity, academic egalitarianism, online blended learning, and active pedagogical practice, to comprehensively improve faculty’s interdisciplinary teaching awareness, collaborative teaching ability, and digital teaching literacy^[4]. In the context of global educational digitization, online interprofessional faculty development has become an essential strategy for universities to optimize teaching teams, promote educational innovation, and enhance comprehensive educational competitiveness.

1.2 Practical Dilemma: Small Sample Barriers to Change Detection

While online interprofessional faculty development programs possess prominent theoretical and practical advantages, their efficacy evaluation work still faces unresolved methodological challenges, among which the difficulty of detecting educational changes in small sample scenarios is the most prominent and influential. In practical institutional operations, online interprofessional faculty development is mostly carried out in the form of small-scale cohort training, targeted discipline team improvement, and special teaching reform group cultivation. Different from large-scale public educational intervention experiments, institutional faculty development activities usually involve limited participants, resulting in small sample sizes for subsequent effect evaluation research. Small sample research is inevitable in faculty development evaluation due to the constraints of faculty team scale, training cost, institutional arrangement, and professional niche characteristics^[5-7].

However, conventional educational statistical evaluation methods are mostly designed based on large-sample statistical principles, relying on sufficient sample capacity to eliminate random errors, ensure data representativeness, and verify significant intervention effects. When applied to small sample scenarios of online interprofessional faculty development, these methods often expose obvious defects, including low statistical test power, high false-negative rate of change detection, susceptibility to individual outlier interference, and difficulty in distinguishing random fluctuation from authentic program-induced changes. Many existing online interprofessional faculty development studies fail to accurately capture subtle improvements in faculty teaching behaviors, incremental changes in teaching literacy, and long-term cumulative developmental effects due to small sample limitations, leading to inconsistent evaluation results, underestimated program efficacy, and insufficient empirical support for program optimization and promotion. This dilemma seriously restricts the scientific evaluation, iterative improvement, and standardized promotion of online interprofessional faculty development programs^[8].

1.3 Research Objectives and Structural Layout

This research focuses on the core contradiction between the practical value of online interprofessional higher education for faculty development and the methodological limitations of small-sample change detection. The primary research objectives include three core dimensions: first, to systematically elaborate the theoretical framework, operational modes, and practical values of modern online interprofessional faculty development; second, to deeply analyze the inherent barriers and key problems of change detection in small sample faculty development evaluation; third, to optimize the statistical evaluation framework and propose feasible operational strategies for small-sample change detection, so as to improve the accuracy and scientificity of online interprofessional faculty development efficacy evaluation. Additionally, this research intends to provide empirical reference and methodological guidance for institutional teaching training reform, educational evaluation innovation, and interprofessional education quality improvement^[9].

The overall structural layout of this paper follows the logical sequence of theoretical elaboration, problem analysis, empirical verification, strategy optimization, and conclusion prospect. The subsequent chapters will sort out the theoretical basis and current situation of online interprofessional faculty development, summarize the characteristics and classification of small sample research in this field, analyze the core challenges of small-sample change detection, conduct empirical comparative analysis through sample data, put forward targeted optimization strategies for evaluation methods, and finally summarize research conclusions and propose future research prospects.

2. Theoretical Foundation and Current Situation of Online Interprofessional Faculty Development

2.1 Theoretical Connotation: Core Logic of Interprofessional Faculty Development

Interprofessional faculty development is rooted in interdisciplinary education theory, collaborative learning theory, and professional sustainable development theory, with the core logic of breaking disciplinary isolation and realizing collaborative educational growth. Traditional university faculty training is discipline-centric, which takes professional knowledge improvement and single teaching skill upgrading as the core goals. This model leads to fragmented faculty professional development, isolated disciplinary teaching resources, and difficulty in adapting to the integrated development of modern academic disciplines and the compound talent training needs. Interprofessional faculty development breaks this single-dimensional development mode, taking cross-disciplinary interaction, collaborative teaching practice, and shared educational innovation as the core orientation, aiming to shape faculty’s interdisciplinary teaching thinking, collaborative teaching ability, and inclusive educational concept.

Online interprofessional faculty development further integrates digital education theory and distance collaborative learning theory on the basis of traditional interprofessional training. It takes digital online platforms as the carrier, breaks the spatial and temporal barriers of offline training, and realizes the organic combination of synchronous interactive teaching and asynchronous autonomous learning. Its core connotation is reflected in three dimensions: equality of professional communication, diversity of learning resources, and sustainability of developmental growth. In online interprofessional learning environments, faculty from different professional backgrounds abandon disciplinary hierarchical differences, carry out equal academic exchanges and teaching discussions, share teaching experience and curriculum reform results, and jointly solve interdisciplinary teaching difficulties. At the same time, the online model integrates massive cross-disciplinary teaching resources, digital teaching cases, and innovative teaching methods, providing comprehensive and multi-dimensional support for faculty’s professional growth^[10].

2.2 Operational Modes: Practical Forms of Online Developmental Programs

Through sorting out the practical experience of online interprofessional faculty development in domestic and foreign universities, this paper summarizes three mainstream operational modes, covering short-term special training, medium-term systematic cultivation, and long-term iterative growth, forming a hierarchical and progressive developmental system. Each mode has distinct application scenarios, participant characteristics, and training focuses, adapting to different institutional faculty development needs.

The first mode is short-term special online training, which focuses on targeted teaching skill improvement and interdisciplinary concept popularization. This mode usually lasts 1–4 weeks, with compact training content and flexible learning arrangements, mainly targeting all faculty newly participating in interprofessional teaching reform. The training content focuses on basic interdisciplinary teaching theories, online teaching tool application, and simple cross-disciplinary curriculum design, aiming to quickly establish faculty’s interprofessional teaching awareness and improve basic digital teaching capabilities^[11].

The second mode is medium-term systematic online cultivation, which focuses on systematic improvement of interdisciplinary teaching competence and collaborative innovation ability. Lasting 1–6 months, this mode adopts the combination of online lectures, group collaborative discussions, interdisciplinary teaching practice, and regular achievement evaluation, targeting backbone faculty engaged in long-term interdisciplinary teaching and curriculum construction. It focuses on cultivating faculty’s ability to design interdisciplinary courses, organize collaborative teaching activities, and solve complex teaching problems.

The third mode is long-term iterative online growth, which focuses on sustainable professional development and educational innovation exploration. This mode takes academic years as the unit, builds a long-term online interprofessional learning community, and carries out continuous resource sharing, teaching cooperation, and research collaboration. It targets core teaching teams and teaching reform leaders, committed to promoting institutional interdisciplinary teaching system optimization and educational innovation development.

2.3 Current Status: Developmental Achievements and Existing Defects

In recent years, online interprofessional faculty development has achieved remarkable developmental results in global higher education institutions. First, the coverage of training programs has been continuously expanded, realizing the transformation from single-discipline sporadic training to multi-disciplinary full coverage, and basically forming a cross-department and cross-major faculty development system. Second, the digital training system has been gradually improved, with perfect online learning platforms, rich digital teaching resources, and standardized training management mechanisms, ensuring the orderly development of faculty development activities^[12]. Third, the training effect has been initially highlighted, effectively improving faculty’s digital teaching literacy and interdisciplinary teaching ability, and promoting the steady progress of university interdisciplinary teaching reform.

However, the current development work still has prominent defects, among which the imperfect efficacy evaluation system is the most prominent restriction factor. At present, most institutional online interprofessional faculty development evaluations adopt simple questionnaire surveys, academic score statistics, and subjective effect evaluation, lacking scientific, rigorous, and quantitative long-term evaluation mechanisms. In particular, for small-scale customized training programs commonly used in universities, there is a lack of targeted change detection methods, resulting in the inability to accurately quantify faculty’s teaching growth, identify subtle developmental changes, and evaluate the actual value of training programs. The lag of evaluation technology seriously restricts the iterative optimization and high-quality development of online interprofessional faculty development work.

3. Characteristics and Classification of Small Sample Research in Faculty Development Evaluation

3.1 Definition and Basic Characteristics of Small Sample Research

In the field of online interprofessional faculty development evaluation, small sample research refers to empirical research with a limited number of research participants, usually with a sample size of less than 50, which is formed based on the practical scale of institutional training activities. Different from large-sample educational research that pursues population representativeness and universal statistical significance, small sample faculty development research has distinct practical characteristics, including limited sample scale, homogeneous sample background, targeted research objects, and individualized developmental changes.

First, small sample research has the characteristic of scale limitation and scenario specificity. The sample size is constrained by the scale of university professional teams, the number of participants in special training programs, and the scope of teaching reform groups, which cannot reach the sample threshold required by conventional large-sample statistical tests. Second, small sample participants have high professional homogeneity and developmental pertinence. Most participants are faculty engaged in similar interdisciplinary teaching work, with consistent professional development foundations and similar training demands, making individual developmental changes more targeted but also more susceptible to individual differential interference. Third, small sample research focuses on subtle individual growth and incremental program effects, rather than overall population changes. The developmental changes of faculty in small sample scenarios are mostly subtle, cumulative, and individualized, lacking drastic overall changes, which puts forward higher requirements for the sensitivity and accuracy of change detection methods^[13].

3.2 Classification of Small Sample Scenarios in Developmental Evaluation

Combined with the practical operation mode of online interprofessional faculty development, this paper divides small sample research scenarios into three typical types, including niche discipline cohort scenarios, special reform team scenarios, and cross-institutional collaborative scenarios. Each scenario has unique sample characteristics, data characteristics, and change detection difficulties, as shown in Table 1.

3.3 Data Characteristics of Small Sample Developmental Evaluation

Compared with large-sample educational evaluation data, small sample online interprofessional faculty development data have unique structural characteristics, which fundamentally lead to the difficulty of change detection. First, the data has low statistical stability. Limited sample size makes the dataset unable to effectively eliminate random measurement errors and accidental individual fluctuations, resulting in unstable statistical results and poor repeatability of evaluation conclusions. Second, the data has obvious individual heterogeneity. Even in homogeneous discipline cohorts, faculty’s teaching foundation, learning ability, and developmental speed are different, leading to differentiated change trends, which cover up the overall program intervention effect. Third, the data has subtle change amplitude. The improvement of faculty’s teaching literacy and collaborative ability is a gradual cumulative process, and the short-term intervention effect of online training is mostly incremental and subtle, which is difficult to capture by conventional statistical methods. Fourth, the data has longitudinal variability. Long-term follow-up evaluation data of faculty development is susceptible to external factors such as institutional teaching policy adjustment, curriculum reform, and daily teaching work interference, increasing the complexity of change identification.

4. Core Challenges of Change Detection in Small Sample Faculty Development Evaluation

4.1 Statistical Limitations: Low Test Power and High Error Risk

Conventional educational change detection mainly relies on parametric statistical methods such as t-test, variance analysis, and regression analysis, which are based on the central limit theorem and large-sample asymptotic normality hypothesis. These methods require sufficient sample capacity to ensure the normality of data distribution, the effectiveness of parameter estimation, and the credibility of significance test results. In small sample scenarios, the data distribution often deviates from normal distribution, and the basic hypothesis of conventional statistical tests cannot be satisfied, resulting in significantly reduced statistical test power. Low test power directly leads to a sharp increase in the false-negative rate of change detection, that is, the actual effective training intervention cannot be statistically identified, and the real developmental changes of faculty are misjudged as no significant change.

In addition, small sample data is highly sensitive to individual outliers and random errors. A single faculty’s extreme learning performance, abnormal teaching evaluation data, or accidental missing data will significantly interfere with the overall statistical results, leading to biased parameter estimation and distorted change trend judgment. Different from large samples that can dilute individual abnormal interference through data superposition, small sample datasets lack error tolerance, and local data fluctuations will affect the overall evaluation conclusion, greatly reducing the accuracy and reliability of change detection.

4.2 Methodological Defects: Inadaptability of Conventional Evaluation Frameworks

The existing online interprofessional faculty development evaluation system is mostly designed for large-scale training projects, and the evaluation index system and analytical framework have obvious inadaptability to small sample scenarios. First, the conventional evaluation index system focuses on overall group changes and ignores individualized subtle growth. Most evaluation indicators take group average improvement, overall pass rate, and unified skill score change as the core measurement standards, lacking refined indicators for individual incremental changes and personalized developmental progress, which cannot reflect the diversified growth of small sample faculty.

Second, conventional cross-sectional evaluation methods cannot capture longitudinal cumulative changes. Most existing evaluations adopt pre-test and post-test cross-sectional comparison, only detecting the short-term discrete changes before and after training, ignoring the continuous cumulative growth and delayed developmental effects of faculty’s interdisciplinary teaching ability. The online interprofessional faculty development effect has obvious lag and accumulation, and short-term cross-sectional detection is difficult to reflect the real long-term developmental changes, especially in small sample scenarios with subtle single-stage changes.

Third, conventional qualitative evaluation methods lack quantitative change identification standards. Subjective evaluation methods such as questionnaire surveys and expert interviews are greatly affected by human factors, lacking objective quantitative criteria for judging change amplitude and growth degree, resulting in ambiguous evaluation conclusions and difficulty in accurately distinguishing effective developmental changes from random data fluctuations.

4.3 Practical Barriers: Confounding Factors and Uncontrollable Interference

In the practical evaluation process of small sample online interprofessional faculty development, various uncontrollable confounding factors further increase the difficulty of change detection. First, individual developmental interference exists universally. Faculty’s professional growth is affected by multiple factors such as daily teaching accumulation, personal autonomous learning, and institutional regular training, not only dependent on the online interprofessional development program. In small sample scenarios, it is difficult to separate the program-induced effective changes from individual spontaneous growth, resulting in inaccurate evaluation of program net effect.

Second, external environmental interference is complex and diverse. Institutional teaching policy adjustment, interdisciplinary curriculum reform, teaching assessment standard update, and social educational environment changes will all affect faculty’s teaching behaviors and professional capabilities. These external interference factors are difficult to quantify and eliminate in small sample evaluation, covering up the real intervention effect of online development programs.

Third, the online learning environment brings unique interference variables. Faculty’s online learning participation, learning duration, interactive frequency, and resource utilization efficiency have obvious individual differences. Some faculty may have low online participation and insufficient learning investment, resulting in insignificant personal growth, while others have active learning and significant improvement. The uneven learning investment in small samples leads to discrete change effects, making it difficult to form stable and detectable overall change rules^[14].

5. Empirical Analysis of Small Sample Change Detection: Data Comparison and Result Verification

5.1 Empirical Research Design

To intuitively verify the limitations of conventional methods and the effectiveness of optimized strategies in small sample change detection, this paper takes a university’s online interprofessional faculty development program as the research object, carries out empirical comparative analysis. The program is a medium-term systematic online cultivation project targeting interdisciplinary teaching backbone faculty, with a total of 32 participants, belonging to a typical small sample research scenario. The training cycle is 3 months, focusing on improving faculty’s interdisciplinary curriculum design ability, collaborative teaching organization ability, and online interactive teaching literacy.

This research sets up two evaluation groups for comparative analysis, including conventional statistical evaluation group and optimized small-sample detection group. The evaluation indicators cover three core dimensions: theoretical literacy improvement, practical teaching ability growth, and collaborative innovation capability enhancement, with a total of 12 secondary refined indicators. The research collects longitudinal data of pre-training, mid-training, post-training and one-month follow-up, forming continuous panel data for change trend analysis. This paper compares the detection sensitivity, result accuracy, and conclusion stability of different methods, so as to clarify the optimal path of small sample change detection.

5.2 Index System and Data Collection

Based on the connotation of online interprofessional faculty development, this research constructs a three-dimensional refined evaluation index system, covering theoretical cognition, practical ability, and collaborative literacy, realizing quantitative measurement of faculty’s all-round developmental changes. The index system and weight distribution are shown in Table 2.

In terms of data collection, this research adopts longitudinal tracking data collection, obtaining four stages of data: pre-training baseline data, mid-training process data, post-training immediate effect data, and one-month follow-up sustained effect data. All data are quantified by unified scoring standards, with a full score of 100 for each indicator, ensuring the comparability and continuity of data. After data sorting, eliminate invalid samples with missing data and abnormal values, and finally retain 32 valid sample datasets for subsequent comparative analysis.

5.3 Comparative Analysis of Detection Results

This research uses independent sample t-test (conventional method) and optimized small-sample longitudinal change detection method (optimized method) to analyze the sample data respectively, and compares the change detection effects of the two methods in terms of overall change significance, individual subtle change identification, and sustained effect capture. The comparative results of core detection indicators are shown in Table 3.

5.4 Empirical Result Discussion

The empirical comparison results fully verify the inherent defects of conventional statistical methods in small sample faculty development change detection and the significant advantages of optimized methods. First, conventional t-test methods have low detection sensitivity in small sample scenarios, unable to effectively identify subtle overall changes and individual incremental growth, resulting in serious false-negative detection results, which greatly underestimates the actual efficacy of online interprofessional faculty development programs. Second, conventional methods have poor anti-interference ability to small sample abnormal data and random fluctuations, leading to unstable detection results and poor repeatability of evaluation conclusions, which cannot provide reliable empirical support for program effect evaluation.

In contrast, the optimized small-sample detection method significantly improves the accuracy and sensitivity of change detection, can effectively capture the subtle cumulative growth of faculty’s professional abilities, accurately identify long-term sustained developmental effects, and eliminate the interference of individual abnormal data and external confounding factors. The empirical results also confirm that the difficulty of small sample change detection in online interprofessional faculty development is not unsolvable, and targeted methodological optimization and framework improvement can effectively break through the small sample data limitations and realize scientific and accurate evaluation of developmental effects.

6. Optimization Strategies for Small Sample Change Detection in Faculty Development Evaluation

6.1 Statistical Method Optimization: Adapting to Small Sample Data Characteristics

Aiming at the low statistical power and high error risk of conventional large-sample statistical methods, this paper proposes to adopt small-sample adaptive statistical methods to replace traditional parametric tests. First, introduce non-parametric statistical methods such as Wilcoxon signed-rank test and Kruskal-Wallis test, which do not rely on normal distribution hypothesis, can effectively adapt to small sample non-normal data distribution, and improve the accuracy of significance test. Second, apply bootstrap resampling technology to expand effective sample capacity through repeated sampling simulation, eliminate random error interference, improve data stability and statistical test power, and solve the problem of insufficient small sample statistical representativeness.

Third, construct longitudinal panel data analysis model, focus on tracking continuous change trends of individual faculty in multiple stages, replace single cross-sectional comparison with multi-stage dynamic comparison, effectively capture cumulative incremental changes and delayed developmental effects, and make up for the defect that conventional methods cannot identify subtle long-term changes^[15]. Fourth, introduce effect size evaluation indicators to replace single significance test, take change amplitude, growth rate and effect durability as core evaluation standards, avoid misjudging valid small-amplitude changes as invalid changes due to insufficient statistical significance, and improve the rationality of small sample change detection.

6.2 Evaluation Framework Improvement: Building Refined Small-Sample Evaluation System

To solve the inadaptability of conventional evaluation framework to small sample scenarios, it is necessary to build a refined, individualized and longitudinal online interprofessional faculty development evaluation system. First, optimize the evaluation index system, add individualized growth indicators and incremental change indicators on the basis of existing overall indicators, refine the measurement standards of subtle ability improvement, and realize the quantitative identification of personalized developmental changes of small sample faculty.

Second, change the single cross-sectional evaluation mode to multi-stage longitudinal tracking evaluation mode, set up pre-training baseline, mid-training process, post-training immediate effect, and follow-up sustained effect multi-dimensional evaluation nodes, record the continuous growth track of faculty’s professional ability, and accurately capture cumulative developmental changes. Third, integrate quantitative statistics and qualitative analysis, combine objective data detection such as test scores and behavioral statistics with subjective evaluation such as expert interviews and teaching reflection analysis, complement the advantages of different evaluation methods, and improve the comprehensiveness and accuracy of small sample change detection.

Fourth, build individualized growth benchmarking mechanism, set personalized development baselines for each faculty according to their pre-training professional foundation, judge individual growth amplitude and change degree based on vertical self-comparison, rather than single horizontal group comparison, effectively avoid the problem that individual differences cover up developmental changes in small samples, and realize accurate evaluation of individualized growth effects.

6.3 Practical Control Optimization: Eliminating External Confounding Interference

In view of the complex confounding interference in small sample evaluation practice, targeted control and standardized optimization should be carried out from the aspects of research design, data collection and variable control. First, strengthen the standardization of research design, unify the training cycle, teaching resources, learning requirements and evaluation standards of online interprofessional development programs, reduce the interference of uneven training conditions on individual growth, and improve the consistency of sample change effects.

Second, carry out effective control of confounding variables, fully investigate and record external interference factors such as faculty’s daily teaching workload, personal autonomous learning situation and institutional policy changes in the evaluation cycle, and eliminate or deduct the interference of non-program factors on developmental changes through variable control technology, so as to accurately separate the net effect of online faculty development programs.

Third, standardize online learning process management, monitor faculty’s online learning participation, resource utilization and interactive participation in real time, screen valid learning samples with sufficient learning investment, exclude invalid samples with low participation and incomplete learning, reduce data discrete degree caused by uneven learning investment, and improve the quality and detection efficiency of small sample data.

7. Conclusion and Research Prospect

7.1 Main Research Conclusions

This paper systematically studies the core problem of change detection challenges in small sample evaluation of online interprofessional higher education for faculty development, sorting out the theoretical connotation, operational modes and current developmental status of online interprofessional faculty development, analyzing the basic characteristics and typical scenarios of small sample research in this field, exploring the core challenges of small sample change detection from statistical limitations, methodological defects and practical barriers, and verifying the effectiveness of optimized strategies through empirical data analysis. The main research conclusions are summarized as follows.

First, online interprofessional faculty development is an important digital educational innovation mode in modern higher education, which has prominent practical value in breaking disciplinary barriers, optimizing faculty teaching team construction and promoting interdisciplinary teaching reform. However, its efficacy evaluation work is severely restricted by small sample scenarios, and the difficulty of detecting subtle developmental changes has become a key bottleneck restricting the scientific development of this field.

Second, small sample research in online interprofessional faculty development evaluation has the characteristics of limited scale, obvious individual heterogeneity, subtle change amplitude and complex interference factors. Conventional large-sample statistical evaluation methods have prominent inadaptability in small sample scenarios, with low detection sensitivity, high false-negative rate and poor result stability, unable to accurately identify the real developmental effects of training programs.

Third, the optimized strategies based on statistical method improvement, evaluation framework reconstruction and practical interference control can effectively break through small sample limitations. The optimized detection method significantly improves the accuracy, sensitivity and stability of small sample change detection, can effectively capture individual subtle incremental changes and long-term sustained developmental effects, and realize scientific and rigorous evaluation of online interprofessional faculty development efficacy.

7.2 Research Deficiencies

This research still has some deficiencies that need further improvement. First, the empirical research sample scale is still limited, and the verification of the optimized method is only based on a single institutional small sample dataset, which needs more multi-institutional and multi-scenario sample data to further verify the universal applicability of the optimization strategy. Second, this research mainly focuses on short-term and medium-term developmental change detection, and the long-term sustainable growth effect of online interprofessional faculty development lacks longer cycle tracking analysis. Third, the individualized difference analysis of small sample faculty development is not deep enough, and the internal mechanism of individual differentiated growth needs further theoretical and empirical exploration.

7.3 Future Research Prospects

On the basis of this research, future research can be carried out from three core directions. First, expand multi-scenario empirical verification, collect small sample data of online interprofessional faculty development from different types of universities and different discipline backgrounds, further optimize and revise the small-sample change detection framework, and improve the universal applicability of the evaluation system.

Second, carry out long-term tracking research, build a long-cycle faculty developmental change monitoring mechanism, explore the long-term evolution law of online interprofessional faculty development effects, and reveal the sustained growth mechanism of faculty’s interdisciplinary teaching ability.

Third, integrate intelligent evaluation technology, combine big data analysis and machine learning algorithms with small sample change detection, build an intelligent and automatic small-sample developmental change identification model, further improve the refinement and intelligence level of faculty development efficacy evaluation, and provide more powerful methodological and technical support for the high-quality development of online interprofessional higher education for faculty development.

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