Feedback Instrument for Rescue Force Development – Exercises for independent work (FIRE-EV)

1. Abstract

The Feedback Instrument for Rescue Force Development – Exercises for independent work (commonly abbreviated as FIRE-EV, from the German Feedback-Instrument zur Rettungskräfte-Entwicklung – Eigenverantwortliches Arbeiten) is a standardized, psychometrically validated evaluation scale designed to assess the perceived pedagogical quality, structural adequacy, and didactic utility of autonomous learning tasks within rescue service and emergency leadership training. Conceptualized and validated through an institutional research collaboration between the Institute of the North Rhine-Westphalia Fire Brigade (Institut der Feuerwehr Nordrhein-Westfalen; IdF NRW) and the Department of Organizational and Business Psychology at the University of Münster (Westfälische Wilhelms-Universität Münster), the instrument serves as a dedicated, modular extension to the broader FIRE core evaluation framework (Schulte & Thielsch, 2019; Schulte et al., 2019).

The scale consists of four unidimensional, positively worded items evaluated on a seven-point Likert-type response scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”), alongside an explicit non-substantive option (“cannot be meaningfully answered”). Psychometric validation conducted across diverse cohorts of operational incident commanders (e.g., group leaders [Gruppenführer] and platoon leaders [Zugführer]; cumulative calibration samples encompassing N = 242 and N = 364 emergency personnel) revealed an exemplary unidimensional factor structure via confirmatory factor analysis (χ²(2) = 2.91, p = .234, CFI = .998, TLI = .995, RMSEA = .04, SRMR = .01). The instrument demonstrates solid internal consistency across samples (Cronbach’s α = .73 to .77; McDonald’s ω = .75 to .78) and strong evidence of construct, convergent, criterion-related, and discriminant validity. Empirical correlations verify robust associations with instructor behavior (r = .51), self-reported competence acquisition (r = .40), transfer of learning (r = .39), and overall course satisfaction (r = .50), while demonstrating theoretical divergence from general incoming pre-course test performance (r = .03) and minimal confounding by concurrent affective mood states (r = .25). The FIRE-EV provides an efficient, one-minute diagnostic instrument for instructional developers, emergency service academies, and educational researchers seeking to optimize independent learning paradigms in high-stakes training environments.

2. Keywords

FIRE-EV, rescue forces, emergency services, firefighter training, independent work, self-regulated learning, didactic evaluation, instructional quality, vocational education, confirmatory factor analysis, psychometrics, autonomous learning tasks

3. Authors

The FIRE-EV instrument was developed, calibrated, and validated through an academic-practitioner partnership between the Institute of the North Rhine-Westphalia Fire Brigade (IdF NRW) and the University of Münster:

• Meinald T. Thielsch, Ph.D. — Professor of Organizational and Business Psychology, Institute of Psychology, University of Münster (WWU Münster), Münster, Germany. Research specializations include educational evaluation, online assessment, human-computer interaction, and high-reliability team training.
• Ann-Kathrin Schulte, M.Sc. — Organizational and Business Psychology, Institute of Psychology, University of Münster, Münster, Germany. Lead psychometrician in the foundational FIRE framework research group.
• Collaborating Psychometricians & Didactic Contributors: Additional developmental and empirical contributions to the modular FIRE framework are credited to instructional methodology researchers and institutional partners at the IdF NRW, including Christian Röseler, Sarah Kleinstück, and Patrick Niemann.
• Institutional Affiliations:
  • Department of Psychology, Organizational and Business Psychology, University of Münster (WWU), Fliednerstraße 21, 48149 Münster, Germany. Institutional Webpage: go.wwu.de/fire.
  • Institut der Feuerwehr Nordrhein-Westfalen (IdF NRW), Wolbecker Str. 237, 48155 Münster, Germany.

4. Purpose

In high-reliability organizations (HROs) such as municipal fire departments, disaster management authorities, humanitarian medical units, and emergency rescue services, vocational education must satisfy demanding professional standards. Responders operating in command, tactical, and leadership positions are routinely required to make high-stakes, time-critical decisions under severe environmental stressors, extreme uncertainty, cognitive overload, and physical danger. The pedagogical curricula designed to prepare incident commanders must therefore impart a dense foundation of theoretical principles spanning engineering, fluid dynamics, chemistry, hazardous materials response, emergency medicine, civil protection doctrine, and administrative law. Because tactical command scenarios do not permit leaders to pause operations to consult textbooks, review checklists, or deliberate endlessly with staff, instructional curricula place heavy emphasis on theoretical mastery that can be rapidly mobilized into situational tactical decisions.

To cultivate these advanced professional competencies without overburdening classroom instructional hours, vocational training academies increasingly rely on asynchronous, self-directed learning modules—known within German pedagogical traditions as Aufgaben zum eigenverantwortlichen Arbeiten (EVA tasks; exercises for independent work). EVA assignments constitute structured, self-regulated individual tasks completed by trainees outside of formal lecture hours (e.g., in evening study periods during residential courses). Trainees autonomously analyze complex scenario vignettes, evaluate fire protection blueprints, calculate hydraulic delivery capacities, and determine tactical task-force allocations. Despite the ubiquity of these exercises, standardized psychometric evaluation instruments specifically dedicated to measuring the didactic quality, scope, cognitive challenge, and comprehensibility of independent study tasks in emergency training settings were historically non-existent.

The primary purpose of the FIRE-EV is to fill this psychometric gap by providing an objective, brief, theoretically grounded evaluation scale. The scale addresses four fundamental diagnostic and practical applications:

• Targeted Quality Assurance: The scale enables emergency service training academies to systematically diagnose the functional strengths and deficiencies of self-study assignments. It prevents curricula developers from relying on generic course satisfaction indicators that confound classroom teaching with self-study material.
• Formative Instructional Optimization: By decomposing independent work into core didactic parameters—task volume (scope), conceptual enrichment, instruction clarity, and difficulty calibration—the instrument produces actionable feedback for faculty instructors. Instructors can ascertain whether low learning outcomes stem from confusing task formulations, excessive workload, or mismatched difficulty levels.
• Benchmarking and Curriculum Evolution: The scale permits programmatic comparisons across differing iterations of instructional modules, contrasting traditional paper-based tactical worksheets against digitized, interactive simulations or asynchronous e-learning modules.
• Empirical Research in Vocational and Adult Education: Beyond emergency command academies, the FIRE-EV offers educational psychologists and adult learning researchers a validated measure of instructional task design to examine how self-regulated task attributes correlate with knowledge retention, academic self-efficacy, and operational transfer in mission-critical professional education.

5. Psychological Construct

The construct assessed by the FIRE-EV is defined as the perceived pedagogical and instructional quality of exercises for independent work (Qualität von Aufgaben zum eigenverantwortlichen Arbeiten). Grounded in didactic design principles and cognitive load paradigms, this construct conceptualizes independent learning exercises not as isolated work products, but as an interactive instructional mechanism whose educational efficacy depends on specific psychological and structural characteristics.

Within educational psychometrics, instructional quality during independent study is manifested across four interdependent operational facets, which converge into a unified latent construct within the FIRE-EV:

1. Curricular and Quantitative Scope Adequacy (Workload Balance)

The first structural dimension reflects the participant’s appraisal of the task’s quantitative extent relative to available time constraints. In self-directed learning environments, excessive volume triggers cognitive exhaustion, strategic superficiality (e.g., skimming or copying solutions), and task aversion. Conversely, an insufficient scope fails to activate necessary cognitive elaboration strategies, leaving foundational concepts under-practiced. Adequacy of scope (measured via Item 1: “Der Umfang der EVA-Aufgaben war angemessen”) reflects the equilibrium wherein the assigned workload matches the learners’ temporal resources and cognitive capacities.

2. Cognitive Elaboration and Deep Conceptual Understanding

The second facet assesses the degree to which independent exercises succeed in fostering meaningful cognitive processing rather than rote repetition. Drawing upon cognitive theories of knowledge acquisition, effective independent work must facilitate the consolidation of episodic classroom instruction into durable, flexible mental models. High perceived quality on this dimension (measured via Item 2: “Die EVA-Aufgaben haben mein Verständnis der Inhalte weiter vertieft”) signifies that the exercises provoked higher-order cognitive operations, such as schema integration, relational thinking, and the application of theoretical principles to simulated operational problems.

3. Instructional Transparency and Task Comprehensibility

Self-directed learning tasks require high transparency of instructions because learners cannot immediately request clarification from a physically present instructor. Task ambiguity introduces extrinsic cognitive load, forcing learners to expend mental effort deciphering what is required rather than mastering the content. Clarity of task phrasing and unambiguous expectations (measured via Item 3: “Die Aufgabenstellungen der EVA-Aufgaben waren verständlich”) constitute a prerequisite for autonomous task engagement and self-efficacy.

4. Calibrated Difficulty Level (Zone of Proximal Development)

The fourth facet assesses the balance between task demands and learner competencies. Aligned with Vygotskian perspectives on learning and Csíkszentmihályi’s flow model, optimal learning occurs when task challenges align with the upper boundary of learner capability. Tasks that are excessively difficult induce helplessness, cognitive overload, and academic demotivation; tasks that are overly simplistic induce boredom and cognitive disengagement. An appropriate difficulty level (measured via Item 4: “Der Schwierigkeitsgrad der EVA-Aufgaben war angemessen”) ensures productive cognitive friction that sustains motivation while supporting progressive mastery.

6. Theoretical Framework

The psychometric architecture of the FIRE-EV is anchored in three theoretical paradigms from instructional psychology, cognitive science, and educational sociology:

Didactic Social Forms and Autonomous Learning

In classical instructional design frameworks, pedagogical interactions are categorized into four fundamental social forms (Sozialformen des Unterrichts): frontal/direct instruction, partner work, cooperative group work, and individual work (Siemer, 2007; Ulrich, 2016). Individual work (Einzelarbeit) serves the distinct psychological purpose of autonomous acquisition, cognitive synthesis, and deliberate consolidation of curriculum content. In military, paramilitary, and civil protection academies, individual study tasks bridge the gap between classroom theory and real-time operational simulation. Pluntke (2013) emphasizes that for independent work to succeed, instructional designers must meet three conditions: allocating sufficient time, providing unambiguous operational prompts, and precisely balancing difficulty to avoid under-challenge or overwhelming frustration.

Self-Regulated Learning (SRL) Theory

Independent study tasks inherently operate within models of self-regulated learning (e.g., Boekaerts, 1997; Zimmerman, 2000; Sitzmann & Ely, 2011). In an EVA scenario, the learner transitions from a passive consumer of information to an active executive controller of their cognitive, metacognitive, and motivational processes. Autonomous tasks require planning competence, sustained volitional control, dynamic self-monitoring, and diagnostic error recognition (Dietrich et al., 1999). When tasks lack pedagogical clarity or clear relevance, learners experience self-regulatory failure, manifested as academic procrastination, task abandonment, or superficial compliance (Ackerman & Gross, 2005). Conversely, well-designed independent tasks stimulate intrinsic motivation, prompt mastery-oriented self-reflection, and enhance objective academic performance, a pattern documented across secondary, tertiary, and professional education (Boekaerts, 1997; Sitzmann & Ely, 2011).

Cognitive Load Theory (CLT)

The structural composition of the FIRE-EV directly reflects the principles of Cognitive Load Theory (Sweller et al., 1998; Paas et al., 2003). CLT posits that human working memory is strictly limited in bandwidth and duration when processing novel information. Total cognitive load consists of three components:

• Intrinsic Cognitive Load: The inherent complexity and element interactivity of the operational subject matter (e.g., calculating structural collapse vectors or multi-casualty triage priorities).
• Extrinsic Cognitive Load: Mental effort generated by poor instructional design, confusing formulations, ambiguous diagrams, or disorganized task structures.
• Germane Cognitive Load: Productive mental effort dedicated to schema construction, conceptual reorganization, and long-term memory encoding.

Within this framework, the FIRE-EV acts as a diagnostic sensor for cognitive load alignment. Item 3 (clarity of instructions) measures the successful minimization of extrinsic cognitive load; Item 1 (appropriate volume) and Item 4 (calibrated difficulty) ensure that intrinsic load remains within working memory capacity; and Item 2 (deepened understanding) confirms the successful activation of germane cognitive load.

7. Validity

The construct, criterion-related, and convergent validity of the FIRE-EV were systematically evaluated in a sequence of psychometric studies conducted at the Institute of the North Rhine-Westphalia Fire Brigade (IdF NRW), adhering to international psychometric conventions (Cohen, 1992; American Educational Research Association et al., 2014).

Content Validity and Expert Review (Study I)

Initial item generation adapted validated items from the Münster Evaluation Questionnaire Homework Module (Münsteraner Fragebogen zur Evaluation – Zusatzmodul Hausaufgaben; MFE-ZHa; Grötemeier & Thielsch, 2014), supplemented by context-specific didactic formulations. In Study I, a panel of N = 33 domain actors—comprising seven certified senior instructors (86% male, mean age = 38.9 years, SD = 6.0) and 26 operational commanders participating in advanced leadership training courses (96% male, mean age = 30.5 years, SD = 6.9)—critically reviewed the initial item pool. Participants rated each item on linguistic comprehensibility, professional relevance, and instructional importance. No EVA item was judged incomprehensible by more than 3% of the sample. Furthermore, items addressing scope, understanding, phrasing, and difficulty achieved exceeding endorsement, with over 90% of both instructors and participants rating them as “important” or “highly important” for learning success. Qualitative feedback confirmed that the four final core items comprehensively represented the quality dimensions of autonomous vocational exercises.

Construct and Convergent Validity (Study III, N = 364)

To verify construct validity, the FIRE-EV was correlated with established dimensions of the overarching FIRE core questionnaire (Schulte & Thielsch, 2019) and associated instructional scales:

• Instructor Behavior (Dozentenverhalten, FIRE): The quality of EVA tasks displayed a strong, statistically significant positive correlation with instructor behavior (r = .51, p < .001). This confirms theoretical expectations: instructors design, introduce, and align independent exercises with direct classroom teaching. Effective instructor guidance provides the cognitive foundation necessary for successful independent completion.
• Group Work Evaluation (FIRE-G): Independent work quality correlated positively with the evaluation of cooperative group exercises (r = .39, p < .001). Both instructional formats represent student-centered, active learning social forms dependent upon the overarching didactic architecture of the training program.
• Competence Acquisition (Kompetenzerwerb, FIRE): High perceived EVA quality was strongly associated with self-reported acquisition of tactical and operational competencies (r = .40, p < .001).
• Transfer of Learning (Transfer, FIRE): Ratings of EVA tasks correlated significantly with the anticipated transferability of learned competencies to real-world fireground command scenarios (r = .39, p < .001).
• Overall Course Satisfaction & Grading: The FIRE-EV exhibited robust associations with general satisfaction with the training course (r = .50, p < .001) and overall course grades assigned by the students (Spearman’s ρ = .28, p < .001; inverted scale).

Criterion-Related and Divergent Validity

Criterion-related validity was corroborated through a significant inverse relationship with perceived instructional overload (Überforderung, FIRE; r = -.35, p < .001). When independent tasks are calibrated in difficulty, clear in instruction, and reasonable in volume, trainees experience significantly less cognitive and emotional overwhelm during the course.

Divergent validity was examined against objective baseline performance: scores achieved on the standardized entrance examination (Eingangsprüfung) administered upon arrival at the academy. If the FIRE-EV merely reflected participants’ prior academic aptitude or pre-existing technical knowledge, it would correlate strongly with baseline examination scores. Instead, the observed correlation was virtually zero and statistically non-significant (r = .03, p = .655, n = 221). This non-significant relationship demonstrates that the FIRE-EV measures the didactic quality of the instructional tasks within the course itself, independent of incoming trainee cognitive ability or operational background.

Assessment of Affective Bias

Educational evaluations can be vulnerable to affective mood confounding (Spiel, 2001). Trainee mood was assessed simultaneously using a validated five-point smiley facial-expression rating scale (Jäger, 2004). The correlation between the FIRE-EV and concurrent participant mood was modest (r = .25, p < .001). Because successful, rewarding learning experiences naturally induce positive affect, a small-to-moderate association is theoretically consistent; the absence of a large correlation confirmed that the scale was not compromised by mood-state halo effects.

8. Reliability

The reliability of the FIRE-EV was examined across two independent samples of fire service command personnel, assessing internal consistency under different measurement model assumptions:

Measurement Invariance and Model Assumptions

To determine the mathematically appropriate internal consistency metric, competitive nested model testing was performed using chi-square difference testing (Δχ²):

• Sample II (EFA Cohort, N = 242): A chi-square test demonstrated that the essential tau-equivalent measurement model fit the data well (χ²(5) = 4.16, p = .526, CFI = 1.00, TLI = 1.00, RMSEA = .00 [.00, .08], SRMR = .05). Under conditions of essential tau-equivalence (where factor loadings are constrained to equality), Cronbach’s alpha represents an unbiased estimate of reliability.
• Sample III (CFA Cohort, N = 364): The four items conformed to a tau-congeneric measurement model, wherein factor loadings freely vary across indicators. Under tau-congeneric conditions, Cronbach’s alpha systematically underestimates true scale reliability, making McDonald’s omega (composite reliability / coefficient omega) the superior and mathematically rigorous metric.

Internal Consistency Coefficients

Both coefficients demonstrate that the four-item FIRE-EV scale achieves solid reliability for an ultra-brief instrument:

• Study II (N = 242): Cronbach’s α = .73; McDonald’s ω = .75.
• Study III (N = 364): Cronbach’s α = .77; McDonald’s ω = .78.

Given the brevity of the scale (four items) and its intended application as an aggregated organizational feedback metric, internal consistency estimates exceeding .75 satisfy established psychometric standards for group-level evaluation and diagnostic research.

9. Factor Analysis

The latent structural integrity of the FIRE-EV was developed using exploratory factor analysis (EFA) and confirmed via confirmatory factor analysis (CFA) across separate participant cohorts.

Exploratory Factor Analysis (Study II)

In Study II, an initial pool of six candidate items was administered to N = 242 course participants (after excluding 108 records due to withheld data-sharing consent, unengaged response sets, or incomplete data). Descriptive analysis revealed that Item EVA_6 (“Die EVA-Aufgaben wurden ausreichend nachbesprochen” / debriefing of tasks) exhibited inadequate corrected item-total correlation (r_it = .17), negative excess kurtosis (-1.15), and substantial floor effects (M = 3.69, SD = 1.79), prompting its exclusion prior to factor extraction.

An exploratory principal component analysis with oblimin rotation was conducted on the remaining items using the psych package in R (Revelle, 2021). The analysis extracted a distinct unidimensional factor explaining substantial variance. Factor loadings for all retained items were high (ranging from .70 to .76), with communalities (h²) between .48 and .58:

• EVA_1 (Sufficient time for tasks): loading = .70, h² = .48 (subsequently removed on theoretical grounds due to overlap with participant personal time management).
• EVA_2 (Appropriate scope): loading = .71, h² = .51.
• EVA_3 (Deepened understanding): loading = .71, h² = .50.
• EVA_4 (Clear task phrasing): loading = .73, h² = .53.
• EVA_5 (Appropriate difficulty level): loading = .76, h² = .58.

Confirmatory Factor Analysis (Study III)

To cross-validate the four-item scale (EVA_2 through EVA_5), a confirmatory factor analysis was performed on data from Study III (N = 364 incident command trainees) using the lavaan package in R (Rosseel, 2012). Robust maximum likelihood estimation (MLR) was employed to accommodate slight departures from multivariate normality.

The hypothesized unidimensional structural model demonstrated excellent fit to the empirical data across all recognized benchmark criteria (Hu & Bentler, 1999):

• Chi-Square: χ²(2) = 2.91, p = .234 (non-significant, indicating no substantial discrepancy between observed and model-implied covariance matrices).
• Comparative Fit Index (CFI): .998 (exceeding the ≥ .95 threshold for excellent fit).
• Tucker-Lewis Index (TLI): .995 (exceeding the ≥ .95 threshold).
• Root Mean Square Error of Approximation (RMSEA): .040 (90% CI [.000, .120]; point estimate well below the ≤ .06 benchmark).
• Standardized Root Mean Square Residual (SRMR): .010 (substantially below the ≤ .08 benchmark).

Standardized factor loadings (λ) on the single latent factor were all statistically significant (p < .001) and substantial: Item 1 (EVA_2) λ = .65; Item 2 (EVA_3) λ = .69; Item 3 (EVA_4) λ = .73; Item 4 (EVA_5) λ = .82.

Item Psychometric Parameters

Manifest item-level properties established in Study III (N = 364) documented strong discriminatory efficiency (corrected item-total correlations r_it from .59 to .76):

• Item 1 (EVA_2, Scope): Mean = 5.70, SD = 1.17, Skewness = -1.12, Kurtosis = 1.29, r_it = .59.
• Item 2 (EVA_3, Understanding): Mean = 5.65, SD = 0.98, Skewness = -0.85, Kurtosis = 1.36, r_it = .64.
• Item 3 (EVA_4, Phrasing): Mean = 5.53, SD = 1.09, Skewness = -0.88, Kurtosis = 0.74, r_it = .68.
• Item 4 (EVA_5, Difficulty): Mean = 5.71, SD = 0.99, Skewness = -0.93, Kurtosis = 0.88, r_it = .76.

10. Instrument / Measurement Tool

• Instrument Designation: Feedback Instrument for Rescue Force Development – Exercises for independent work (FIRE-EV; Feedback-Instrument zur Rettungskräfte-Entwicklung – Eigenverantwortliches Arbeiten).
• Scale Classification: Standardized pedagogical evaluation inventory / instructional self-report questionnaire.
• Target Population: Emergency services personnel, fire service officers, incident commanders, civil protection trainees, and students participating in vocational courses with autonomous learning assignments.
• Item Count: 4 core manifest items (unidimensional).
• Administration Modality: Paper-and-pencil self-administered interviewing (PASI) or computer-assisted self-interviewing (CASI / web-based).
• Estimated Completion Time: Approximately 1 minute.
• Response Scale: Seven-point Likert-type agreement scale:
  • 1 = stimme gar nicht zu (strongly disagree)
  • 2 = stimme nicht zu (disagree)
  • 3 = stimme eher nicht zu (somewhat disagree)
  • 4 = neutral (neutral)
  • 5 = stimme eher zu (somewhat agree)
  • 6 = stimme zu (agree)
  • 7 = stimme vollkommen zu (strongly agree)
  • Supplemental non-substantive option: nicht sinnvoll beantwortbar (cannot be meaningfully answered / not applicable).
• Scoring and Computational Rules:
  • Item Scoring: Direct score assignment from 1 to 7 corresponding to the selected rating. Responses designated as “cannot be meaningfully answered” receive no points and are treated as missing values for that individual.
  • Individual Scale Score (Research Application): The mean score of all valid completed items for a given respondent:
    
    Scale Score = (Sum of Valid Item Scores) / (Number of Valid Items Answered).
  • Course Evaluation Score (Institutional Reporting): Calculate the unweighted arithmetic mean for each item across all course respondents. The overall module evaluation score is the mean of these four item averages:
    
    Module Evaluation Mean = (Mean_Item1 + Mean_Item2 + Mean_Item3 + Mean_Item4) / 4.
  • Data Integrity & Anonymity Thresholds: In paper administrations, protocols with two or more omitted items should be excluded from scale-level computation. To ensure respondent anonymity and statistical stability, group feedback reports should only be generated when at least 8 completed protocols are available (or a minimum 50% response rate in small cohorts of 9–14 trainees; Thielsch & Weltzin, 2013).

11. Permissions & Fee and Test Year

• Year of Formal Publication/Validation: 2019–2021 (developed through research initiatives initiated in 2015–2017).
• Copyright Ownership: Institut der Feuerwehr Nordrhein-Westfalen (IdF NRW) and the Organizational and Business Psychology Research Group at the University of Münster (Meinald T. Thielsch et al.).
• Accessibility, Licensing, and Fee Structure: The FIRE-EV is made available free of charge for non-commercial academic research and educational quality assurance. Educational institutions, fire services, and public safety organizations may utilize the scale without royalty fees, provided appropriate scholarly citation is maintained. Commercial assessment providers or consulting entities must obtain prior authorization from the copyright holders.
• Official Documentation Repository: Central documentation, downloadable administration protocols, and modular extensions are accessible at the University of Münster FIRE platform: go.wwu.de/fire.

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13. Items of the Scale