Münster Questionnaire for Evaluation – Additional Module Digital Teaching (MFE-ZdL)

Abstract

The Münster Questionnaire for Evaluation – Additional Module Digital Teaching (German: Münsteraner Fragebogen zur Evaluation – Zusatzmodul digitale Lehre; abbreviated as MFE-ZdL) is a specialized, multidimensional evaluation instrument developed at the Institute of Psychology at the University of Münster (Westfälische Wilhelms-Universität Münster). Engineered during the abrupt transition to remote higher education prompted by the COVID-19 pandemic, the MFE-ZdL was systematically implemented across the Summer Semester 2020, Winter Semester 2020/21, and Summer Semester 2021 to capture student appraisals of emergency remote teaching and enduring digital instructional elements. The instrument comprises an economical 10-item battery designed for administration via Computer-Assisted Self-Interviewing (CASI) or paper-based formats, requiring an average completion time of only 1 to 2 minutes. The module functions as a modular extension to established course evaluation batteries, notably the Münster Questionnaire for the Evaluation of Seminars (MFE-Sr) and Lectures (MFE-Vr).

Structurally, the MFE-ZdL combines inventory tracking, psychometric rating scales, and open-ended qualitative inquiry: Item 1 assesses specific deployed digital media (e.g., synchronous video conferencing, asynchronous recordings, live streams, discussion forums) alongside an open-response inventory (Item 2); Items 3 through 8 operationalize core pedagogical dimensions—instructional fit, curriculum equivalence, student-to-student reflective exchange, collaborative efficacy, technical cognitive load, and instructor reachability—via a 7-point Likert-type scale ranging from 1 (stimme gar nicht zu / strongly disagree) to 7 (stimme vollkommen zu / strongly agree), complemented by an explicit missing-value category (nicht sinnvoll beantwortbar / not meaningfully answerable); Item 9 measures behavioral and self-regulatory consequences (time management, autonomy, procrastination, superficial engagement); and Item 10 captures qualitative student suggestions. Psychometric evaluation across 5,154 course evaluations (nested within 3,895 individual students) revealed that the rating items capture distinct instructional facets rather than a monolithic unidimensional trait. While Cronbach’s alpha is reported for descriptive transparency (α = .73), classical internal consistency is theoretically inappropriate due to intentional construct heterogeneity. Multilevel random-intercept models demonstrated meaningful course-level discrimination, with intraclass correlation coefficients (ICC) ranging from .08 to .18 and all ΔAIC values exceeding 10. The MFE-ZdL serves as an empirical tool for institutional quality assurance, instructional feedback, and higher education research.

Keywords

higher education evaluation, digital teaching, online learning, student evaluation of teaching, course evaluation, COVID-19 educational impact, educational measurement, MFE-ZdL, instructional quality, self-regulated learning, psychometrics, blended learning, higher education didactics, computer-assisted evaluation

Authors

The Münster Questionnaire for Evaluation – Additional Module Digital Teaching (MFE-ZdL) was conceived, standardized, and validated by the Teaching Evaluation Team within the Institute of Psychology at the University of Münster (Westfälische Wilhelms-Universität Münster), Germany:

• Evaluation Team of the Institute of Psychology, Westfälische Wilhelms-Universität Münster, Germany.
• Meinald T. Thielsch, apl. Professor of Psychology, Department of Psychology, Westfälische Wilhelms-Universität Münster (Foundational developer of the broader Münsteraner Fragebogen zur Evaluation [MFE] framework).
• Gerrit Hirschfeld, Professor of Quantitative Methods, Faculty of Business and Health, Bielefeld University of Applied Sciences (Foundational co-developer of the original MFE framework).
• Institutional Oversight: Dean of Studies and Teaching Evaluation Taskforce, Department of Psychology, WWU Münster, Fliednerstraße 21, 48149 Münster, Germany.
• Official Portal: University of Münster Psychology Evaluation Portal (PsyEval)

Purpose

The primary purpose of the MFE-ZdL is to provide a brief, standardized, and empirically grounded measurement system for student evaluations of digital and blended higher education courses. Historically, student evaluations of teaching (SET) focused on traditional face-to-face seminar and lecture dynamics, capturing instructor clarity, blackboard or slide readability, classroom atmosphere, and didactic engagement (Thielsch & Hirschfeld, 2010a, 2010b). The unprecedented global disruption caused by the COVID-19 pandemic in early 2020 necessitated an emergency transition from in-person instruction to purely remote learning environments. University administrators and instructors were confronted with urgent pedagogical challenges: selecting appropriate communication technologies, maintaining academic rigor, preserving peer-to-peer discourse, and preventing student isolation.

Standard course evaluation instruments proved structurally insufficient for capturing these specific dynamics. Instruments designed for physical lecture halls could not assess bandwidth limitations, digital tool proliferation, asynchronous communication delays, or the unique psychological toll of prolonged isolation. Consequently, the MFE-ZdL was developed to measure:

• Technological Implementation: The specific typology of synchronous and asynchronous instructional media deployed by instructors (e.g., lecture recordings, real-time Zoom sessions, discussion boards).
• Instructional Coherence and Fit: The subjective harmony between the selected digital media and the academic subject matter.
• Curricular Equivalence: The perceived degree to which digital delivery successfully imparted the learning objectives that would traditionally have been taught in a physical classroom.
• Collaborative and Reflective Dialogue: The adequacy of student-to-student interaction, collective discourse, and collaborative group tasks within virtual environments.
• Frictional Burden and Usability: The extraneous cognitive and temporal burden placed on students by technical infrastructures, platform friction, and software demands.
• Instructor Accessibility: The perceived reachability and responsive presence of faculty across virtual, asynchronous, or mediated communication channels.
• Self-Regulatory and Behavioral Impacts: The proximal consequences of digital learning on student study patterns, differentiating adaptive outcomes (enhanced autonomy, efficient time management) from maladaptive vulnerabilities (academic procrastination, cognitive disengagement).

In addition to formative feedback for individual instructors, the MFE-ZdL serves macro-level quality management purposes within academic faculties and university senates. Aggregated cross-course analyses enable steering committees to identify technological bottlenecks, assess the effectiveness of faculty-development workshops on digital didactics, and allocate institutional resources toward optimal digital teaching infrastructures.

Psychological Construct

The overarching construct captured by the MFE-ZdL is student perception and behavioral adaptation regarding digital higher education. Rather than representing an indivisible, highly cohesive personality trait or a singular latent factor, this construct is fundamentally formative and multidimensional. It comprises several conceptually distinct yet interacting psychological, instructional, and behavioral dimensions:

1. Didactic Technology-Task Fit (Item 3)

Grounded in the broader psychological framework of Task-Technology Fit (TTF), this facet assesses the perceived congruence between the educational objectives of a specific academic course and the digital communication technologies selected by the instructor. Digital tools cannot be effectively deployed in an agnostic manner; what succeeds in a highly quantitative statistics lab (e.g., screen shares, interactive coding notebooks) may fail in an advanced clinical psychology case conference (which demands nuanced socio-emotional reading and group reflection). This facet measures whether students perceive the digital tools as organic facilitators of instructional objectives rather than arbitrary administrative burdens.

2. Perceived Instructional and Curricular Equivalence (Item 4)

This dimension operationalizes students’ comparative appraisal of learning efficacy between digital modalities and traditional face-to-face instruction. Students evaluate whether structural transitions to digital delivery caused a dilution of curricular depth or whether the totality of the planned academic curriculum was comprehensively conveyed. This appraisal reflects both cognitive mastery and perceived equity in educational quality during periods of educational disruption or blended learning execution.

3. Collaborative Reflection and Socio-Cognitive Exchange (Items 5 & 6)

Human learning is profoundly social. Drawing on Vygotskian social constructivism, these two items measure the degree to which digital formats facilitate or obstruct reflective peer dialogue and formal collaborative group work. Item 5 captures whether students experienced sufficient opportunities to deeply process and verbally deconstruct complex lecture materials with fellow students. Item 6 targets active digital collaboration (e.g., shared breakout rooms, collaborative document editing, peer-review pipelines), evaluating the technical and operational success of peer-to-peer instructional exercises.

4. Extraneous Usability Burden and Technological Friction (Item 7)

Digital learning introduces potential technological friction—software bugs, complicated login barriers, server latency, inconsistent platform layouts across courses, and unstable learning management systems. Drawing on Cognitive Load Theory, this item captures the subjective extraneous load experienced by the student. It explicitly measures whether navigating the course’s technological framework required additional effort, diverting finite temporal and cognitive resources away from actual academic study.

5. Mediated Instructor Reachability and Social Presence (Item 8)

In physical instructional settings, students frequently ask informal clarifying questions before or after lectures, engage in brief corridor exchanges, or utilize walk-in office hours. Purely digital settings eliminate spontaneous physical co-presence, shifting communication entirely to digital, asynchronous, or mediated channels (emails, chat tools, forum boards, formal Zoom appointments). This dimension gauges whether this communicative shift preserved an acceptable sense of instructor availability or created perceived interpersonal barriers and psychological distance.

6. Proximal Behavioral and Self-Regulatory Consequences (Item 9)

The sudden removal of mandatory physical presence profoundly impacts students’ self-regulated learning dynamics. This facet captures both positive adaptations (e.g., enhanced autonomy, more efficient personal time management due to flexible scheduling) and negative dysregulation (e.g., pronounced academic procrastination, diminished depth of cognitive engagement). This captures the divergent psychological reactions students exhibit when physical academic routines are replaced by autonomous digital learning.

Theoretical Framework

The construction of the MFE-ZdL is anchored in multiple foundational psychological and instructional theories that explain how individuals learn, communicate, and self-regulate within technology-mediated environments.

1. Self-Determination Theory (SDT)

According to Self-Determination Theory, proposed by Edward L. Deci and Richard M. Ryan, human psychological flourishing, intrinsic motivation, and academic perseverance require the ongoing satisfaction of three basic psychological needs:

• Autonomy: The experience of agency and self-endorsement of one’s actions. Digital learning can enhance autonomy by providing self-paced asynchronous content (captured in Item 9), yet severely reduce perceived autonomy if technological mandates are rigid and confusing (Item 7).
• Competence: Feeling effective within one’s learning environment. If digital platforms impede curricular acquisition (Item 4), perceived competence deteriorates.
• Relatedness: The feeling of being socially connected, cared for, and belonging to a broader community. Physical distancing directly imperils relatedness, elevating feelings of alienation. The MFE-ZdL operationalizes this vulnerability through Items 5, 6, and 8, assessing peer reflection, collaboration, and instructor accessibility.

2. Transactional Distance Theory

Formulated by Michael G. Moore (1993), Transactional Distance Theory posits that distance education is not merely a geographic separation, but a pedagogical phenomenon characterized by a psychological and communicative space across which misunderstandings can occur. Transactional distance is governed by three macro-variables: Dialogue (interaction between instructor and learner), Structure (the rigidity or responsiveness of course design), and Learner Autonomy. When structural flexibility is absent and two-way dialogue is curtailed, transactional distance expands, increasing student distress. Items 3, 5, and 8 directly evaluate whether the chosen digital format preserved sufficient dialogue to minimize transactional distance.

3. Social Presence Theory and the Community of Inquiry (CoI)

Originating from Short, Williams, and Christie (1976) and heavily expanded within digital education by Garrison, Anderson, and Archer (2000), the Community of Inquiry framework asserts that effective online educational experiences emerge from the confluence of three core elements: Cognitive Presence, Teaching Presence, and Social Presence. Social presence represents the ability of participants to identify with the community, communicate purposefully in a trusting environment, and develop interpersonal relationships. When teaching presence (Item 8) and social presence (Items 5 & 6) are maintained, deep cognitive processing of content occurs even in remote settings.

4. Cognitive Load Theory and Usability

John Sweller’s Cognitive Load Theory differentiates between intrinsic cognitive load (the inherent difficulty of the academic topic), germane cognitive load (the mental effort devoted to schema construction), and extraneous cognitive load (mental effort imposed by the manner in which information is presented). In digital teaching, poor user experience design, ambiguous file structures, and software instability generate high extraneous load. Item 7 directly assesses this load burden, determining whether students could engage with course contents without unnecessary technical overhead.

Validity

The validity of the MFE-ZdL has been established through several empirical and qualitative approaches within the context of university-wide teaching evaluations.

Content Validity

Content validity was established through an iterative, expert-driven deductive development process initiated in April 2020 at WWU Münster. A pool of 25 preliminary items was initially drafted by psychometricians and educational psychology staff within the evaluation team. This item pool systematically sampled the conceptual domains of technological tools, didactic fit, curriculum equivalence, peer contact, cognitive friction, instructor reachability, and behavioral self-regulation. These candidates were subjected to expert panel reviews involving the Vice Dean of Academic Affairs (Fachdekanin), experienced faculty lecturers, and student representatives. Items exhibiting redundancy, semantic ambiguity, or excessive length were eliminated or refined, yielding the streamlined 10-item module. This multi-stakeholder review confirmed that the final scale comprehensively and economically operationalized the quality dimensions of digital higher education.

Construct and Discriminant Validity via Method Effects

Construct validity analyses conducted across subsequent semesters provided psychometric evidence regarding the measurement architecture of the tool. In the initial Summer Semester 2020 implementation, three of the Likert items were framed negatively (e.g., “In contrast to a classroom course, learning content was lost due to digitization”). Subsequent exploratory factor analyses (EFA; Maximum Likelihood with Oblimin rotation) revealed that these three negatively keyed items loaded together onto a distinct factor despite addressing theoretically unrelated content (curriculum loss, missing peer interaction, and technical burden). This finding demonstrated a classic method artifact (negative wording bias).

To establish construct purity, the authors developed positively worded counterparts for these items and administered a 13-item experimental version throughout Winter Semester 2020/21 and Summer Semester 2021. Factor analytic comparisons demonstrated that converting the items to a uniform positive valence resolved the method artifact. Consequently, the scale was permanently standardized into the 10-item battery (Items 3–8 positively polarized), solidifying the structural validity of the items as distinct indicators.

Multilevel Criterion and Discriminant Discrimination

To establish that the MFE-ZdL measures course-specific teaching quality rather than individual student response tendencies, multilevel random-intercept models were fitted across 5,154 course evaluations nested within individual courses. As shown in empirical evaluations, all six Likert-type items demonstrated substantial and statistically meaningful between-course variance:

• Item 3 (Didactic Fit): $SD_{\text{within}} = 0.96$, $SD_{\text{between}} = 0.30$, $\text{ICC} = .09$, $\Delta\text{AIC} = 175.80$
• Item 4 (Content Equivalence): $SD_{\text{within}} = 1.30$, $SD_{\text{between}} = 0.38$, $\text{ICC} = .08$, $\Delta\text{AIC} = 106.85$
• Item 5 (Peer Reflection): $SD_{\text{within}} = 1.71$, $SD_{\text{between}} = 0.79$, $\text{ICC} = .18$, $\Delta\text{AIC} = 654.89$
• Item 6 (Digital Collaboration): $SD_{\text{within}} = 1.20$, $SD_{\text{between}} = 0.37$, $\text{ICC} = .09$, $\Delta\text{AIC} = 103.21$
• Item 7 (Technical Ease): $SD_{\text{within}} = 1.41$, $SD_{\text{between}} = 0.41$, $\text{ICC} = .08$, $\Delta\text{AIC} = 152.40$
• Item 8 (Instructor Reachability): $SD_{\text{within}} = 0.98$, $SD_{\text{between}} = 0.41$, $\text{ICC} = .15$, $\Delta\text{AIC} = 360.12$

In every instance, the Akaike Information Criterion (AIC) of the random-intercept model improved by dramatically more than 10 points compared to fixed-intercept null models (Burnham & Anderson, 2004), confirming that the items discriminate between instructional settings. Notably, Item 5 demonstrated an ICC of .18, indicating that 18% of the total variance in reflective exchange was attributable to differences between courses (e.g., didactic seminar setups versus large broadcast lectures).

Reliability

Within classical psychometric testing, reliability is conventionally indexed via internal consistency metrics such as Cronbach’s alpha or McDonald’s omega. However, psychometric experts explicitly emphasize that calculating internal consistency across the MFE-ZdL rating items is theoretically and conceptually inappropriate. The items do not operationalize a singular, reflective latent trait (where high correlation across items is expected and desirable). Instead, the instrument functions as an index of heterogeneous, functionally independent facets of digital instructional execution.

For instance, an instructor may demonstrate outstanding reachability via email (high score on Item 8) while utilizing digital software that is plagued by server crashes and severe usability friction (low score on Item 7). Similarly, an asynchronous lecture recording might achieve comprehensive curricular equivalence (high score on Item 4) while offering zero peer reflection opportunities (low score on Item 5). Forcing these distinct instructional dimensions into a single summated scale obscures diagnostic clarity. Consequently, items are evaluated and reported as independent descriptive indicators.

Nevertheless, for transparent documentation across the large-scale validation sample ($N = 5,154$), Cronbach’s alpha across the six rating items (Items 3–8) was calculated at:

α = .73

This value reflects moderate overall positive inter-correlation among instructional strengths without excessive redundancy. Inter-item correlations among the rating items ranged from $r = .21$ (between Item 7 [technical ease] and Item 8 [instructor reachability]) to $r = .45$ (between Item 3 [didactic fit] and Item 4 [content equivalence], and between Item 5 [peer reflection] and Item 6 [digital collaboration]).

Test-retest reliability cannot be calculated within the operational deployment of the MFE-ZdL due to strict student anonymity protocols mandated by university evaluation policies, as well as the genuine temporal evolution of courses across an academic semester. Objectivity of administration, scoring, and interpretation is guaranteed through automated CASI web presentation, standardized numerical scoring, and automated descriptive reporting.

Factor Analysis

The factor structure of the MFE-ZdL was scrutinized using Exploratory Factor Analysis (EFA; Maximum Likelihood estimation with Oblimin oblique rotation) utilizing the statistical software R (version 4.2.0) with the psych package (Revelle, 2022). Factor analytic investigations were primarily undertaken during development to test for item redundancy, inspect latent clustering, and identify method variance.

Initial Development EFA (Summer Semester 2020)

In the initial version, which contained three negatively formulated items (Items 4, 5, and 7), EFA revealed a problematic two-factor solution. Rather than clustering along theoretical instructional lines, Factor 1 was composed of the positively worded items (Item 3, Item 6, Item 8), while Factor 2 was heavily defined by the three negatively worded items. Factor loadings on this secondary method factor were pronounced, obscuring genuine instructional relationships and confirming the presence of a construct-irrelevant method artifact.

Revised EFA on Final Positive Item Battery

Following the conversion of all rating items to positive wording, EFA demonstrated a clear dispersion of variance across the items. While an unconstrained factor analysis can extract a weak general factor reflecting broad student course satisfaction, individual item uniquenesses remain high, aligning with the conceptual framework that each item represents an independent facet. Inspection of the sample correlation matrix ($N = 5,154$) illustrates these moderate, differentiated associations:

Item	Item 3	Item 4	Item 5	Item 6	Item 7	Item 8
Item 3 (Didactic Fit)	1.00	.45	.32	.38	.31	.38
Item 4 (Curricular Equivalence)	.45	1.00	.39	.33	.38	.30
Item 5 (Peer Reflection)	.32	.39	1.00	.45	.26	.24
Item 6 (Digital Collaboration)	.38	.33	.45	1.00	.35	.28
Item 7 (Technical Ease)	.31	.38	.26	.35	1.00	.21
Item 8 (Instructor Reachability)	.38	.30	.24	.28	.21	1.00

Distributional analyses revealed that all rating items exhibited left-skewness and positive kurtosis (e.g., Item 8 mean $M = 6.29, SD = 1.06$, skewness $= -1.96$, excess kurtosis $= 4.38$; Item 3 mean $M = 6.23, SD = 1.00$, skewness $= -1.68$, excess kurtosis $= 3.63$). Item 5 demonstrated the widest spread and least skewness ($M = 4.48, SD = 1.91$, skewness $= -0.26$, excess kurtosis $= -1.16$), indicating that peer interaction was the most variably experienced facet across remote courses. Given this non-normality and formative design, researchers and practitioners are advised against collapsing the battery into subscales or computing a single composite sum score.

Instrument / Measurement Tool

• Instrument Name: Münster Questionnaire for Evaluation – Additional Module Digital Teaching (MFE-ZdL) [German: Münsteraner Fragebogen zur Evaluation – Zusatzmodul digitale Lehre].
• Primary Application: Higher education course evaluation, student evaluations of teaching (SET), blended learning assessment, educational quality assurance, and instructional psychological research.
• Administration Format: Standardized online Computer-Assisted Self-Interviewing (CASI); fully compatible with Paper-and-Self-Interviewing (PASI).
• Duration: Approximately 1 to 2 minutes of respondent processing time.
• Total Item Count: 10 items divided across three functional formats:
• Inventory Assessment: Items 1 and 2 capture the objective deployment of digital teaching formats via multiple-choice selection and an open-ended specification field.
• Instructional Quality Ratings: Items 3 through 8 assess core pedagogical dimensions using a 7-point Likert scale.
• Learning Impact and Feedback: Item 9 captures behavioral/self-regulatory adaptations via multiple-choice selection; Item 10 provides an open text field for qualitative recommendations.
• Response Formats:
  • Items 1 & 9: Multiple-choice checkboxes allowing multiple selections simultaneously.
  • Items 2 & 10: Open-ended narrative text fields.
  • Items 3 to 8: 7-point Likert-type rating scale with the verbal anchors:
    • 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)
    • NA = nicht sinnvoll beantwortbar (not meaningfully answerable / missing value)
• Scoring and Diagnostic Rules:
  • Items are evaluated individually at the course level. No composite total score or subscales are computed.
  • For Items 3–8: Calculate unweighted arithmetic mean ($M$) and standard deviation ($SD$) per individual course.
  • For Items 1 & 9: Calculate absolute and relative response frequencies (%) for each response option.
  • Missing data handling: Pairwise deletion is utilized for students endorsing “NA” or skipping an item.
  • Qualitative fields (Items 2 & 10): Returned directly to instructors for thematic analysis and instructional development.

Permissions & Fee and Test Year

The MFE-ZdL was developed in 2020 by the Teaching Evaluation Team of the Institute of Psychology at the University of Münster (WWU Münster). The instrument is published as an open-access psychometric tool for academic, institutional, and research applications. It is available free of charge for non-commercial educational evaluation, institutional quality management, and scientific research in pedagogy, psychology, and higher education didactics. The instrument and its foundational parent scales (MFE-Sr and MFE-Vr) are documented through the official University of Münster Psychology Evaluation Portal. Institutions wishing to integrate the items into automated institutional learning management systems or evaluation platforms are encouraged to cite the authors and primary institutional documentation.

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Items of the Scale