L05 — Atom Browser
L01 (57)
L02 (42)
L03 (103)
L04 (36)
L05 (103)
L06 (56)
L07 (44)
L08 (40)
MP01 (61)
MP02 (32)
MP03 (65)
MP04 (48)
MP05 (68)
MP06 (45)
103 Atoms
| ID | Name | Family | Definition | File |
|---|---|---|---|---|
L05-GEN-MIAHANDOFFVALID |
MIA Handoff Validation | GEN | Validates that MIA output contains all required fields for handoff to L06 assessment agents | L05-GEN-MIAHANDOFFVALID.json |
L05-GEN-MMC04TIMEAVAIL |
MMC04 Time Availability Constraint | GEN | Multimodal Learning Theory, Cognitive Load Theory | L05-GEN-MMC04TIMEAVAIL.json |
L05-GEN-MMS09CYCLICALP |
MMS09 Cyclical Progression | GEN | Multimodal Learning Theory, Cognitive Load Theory | L05-GEN-MMS09CYCLICALP.json |
L05-GEN-MODALITYMAPPIN |
modality_mapping_json_contract | GEN | Multimodal Learning Theory, Cognitive Load Theory | L05-GEN-MODALITYMAPPIN.json |
L05-MCG-ATTRIBUTION |
Content Attribution Compliance | MCG | Measures compliance with attribution requirements for curated content (CC-BY, creator credit, source linking) | L05-MCG-ATTRIBUTION.json |
L05-MCG-AUDIOGENQUAL |
Audio Content Generation Quality | MCG | Measures the quality of AI-generated audio content including narration clarity, pacing, pronunciation accuracy, and audio fidelity | L05-MCG-AUDIOGENQUAL.json |
L05-MCG-CONTENTACCESS |
Curated Content Accessibility Check | MCG | Verifies that discovered content (YouTube video, OER resource) is still accessible and not removed, made private, or geo-blocked | L05-MCG-CONTENTACCESS.json |
L05-MCG-GAPIDENTIFY |
Objective Gap Identification | MCG | Measures the degree to which learning objectives NOT covered by curated content have been identified for supplementary generation | L05-MCG-GAPIDENTIFY.json |
L05-MCG-IMAGEGENQUAL |
Image Content Generation Quality | MCG | Measures the quality of AI-generated images including relevance to content, visual clarity, educational value, and accessibility | L05-MCG-IMAGEGENQUAL.json |
L05-MCG-INTEGRATEQUAL |
Curated-Generated Integration Quality | MCG | Measures how seamlessly curated content integrates with generated supplements (style consistency, transition quality, coherent narrative) | L05-MCG-INTEGRATEQUAL.json |
L05-MCG-INTERACTGENQUAL |
Interactive Content Generation Quality | MCG | Measures the quality of AI-generated interactive elements including quizzes, simulations, drag-drop activities, and clickable diagrams | L05-MCG-INTERACTGENQUAL.json |
L05-MCG-MSACOMPLI |
MSA Modality Specification Compliance | MCG | Measures compliance with the modality selections, constraints, and accessibility requirements specified by MSA | L05-MCG-MSACOMPLI.json |
L05-MCG-OBJCOVERAGE |
Learning Objective Coverage | MCG | Measures the degree to which generated content covers all specified learning objectives from the LODA output | L05-MCG-OBJCOVERAGE.json |
L05-MCG-OVERLAYQUAL |
Accessibility Overlay Quality | MCG | Measures the quality of accessibility overlays (captions, transcripts, alt-text, audio descriptions) added to curated content | L05-MCG-OVERLAYQUAL.json |
L05-MCG-PROMPTALIGN |
Generation Prompt Alignment | MCG | Measures how well the generated content aligns with the MSA modality specification and generation prompt requirements | L05-MCG-PROMPTALIGN.json |
L05-MCG-TEXTGENQUAL |
Text Content Generation Quality | MCG | Measures the quality of AI-generated textual content including clarity, accuracy, pedagogical appropriateness, and reading level alignment | L05-MCG-TEXTGENQUAL.json |
L05-MCO-MULTICHANNELCO |
Multi-Channel Concurrency Demand | MCO | Evaluates the requirement for simultaneous processing of multiple information channels (e.g., audio, visual text, and visual graphics) within a single modality configuration. It measures the structura | L05-MCO-MULTICHANNELCO.json |
L05-MCO-PRIMARYSENSORY |
Primary Sensory Channel Dominance | MCO | Evaluates the degree to which a modality configuration relies on a single dominant sensory channel (e.g., visual, auditory, or haptic) for information delivery. It measures the representational constr | L05-MCO-PRIMARYSENSORY.json |
L05-MCP-COHERENCE |
Mayer Coherence Principle Compliance | MCP | Measures the degree to which generated content excludes extraneous words, graphics, and sounds that do not support the learning objective | L05-MCP-COHERENCE.json |
L05-MCP-IMAGE |
Mayer Image Principle Compliance | MCP | Measures appropriate use of speaker/instructor images - present when beneficial, absent when distracting | L05-MCP-IMAGE.json |
L05-MCP-MODALITY |
Mayer Modality Principle Compliance | MCP | Measures the preference for spoken narration over on-screen text when graphics are present | L05-MCP-MODALITY.json |
L05-MCP-MULTIMEDIA |
Mayer Multimedia Principle Compliance | MCP | Measures the integration of visual elements (pictures, diagrams, animations) with textual content | L05-MCP-MULTIMEDIA.json |
L05-MCP-PERSONALIZATION |
Mayer Personalization Principle Compliance | MCP | Measures the degree to which content uses conversational, first/second person language rather than formal third-person style | L05-MCP-PERSONALIZATION.json |
L05-MCP-PRETRAINING |
Mayer Pre-training Principle Compliance | MCP | Measures the presence and quality of pre-training content that introduces key concepts before the main lesson | L05-MCP-PRETRAINING.json |
L05-MCP-REDUNDANCY |
Mayer Redundancy Principle Compliance | MCP | Measures absence of redundant on-screen text that duplicates narration when graphics are present | L05-MCP-REDUNDANCY.json |
L05-MCP-SEGMENTING |
Mayer Segmenting Principle Compliance | MCP | Measures the degree to which content is broken into manageable, student-paced segments with clear boundaries | L05-MCP-SEGMENTING.json |
L05-MCP-SIGNALING |
Mayer Signaling Principle Compliance | MCP | Measures the presence and effectiveness of cues (headings, arrows, highlights, emphasis) that guide attention to essential information | L05-MCP-SIGNALING.json |
L05-MCP-SPATIALCONTIG |
Mayer Spatial Contiguity Principle Compliance | MCP | Measures the spatial proximity of related text and graphics elements in generated content | L05-MCP-SPATIALCONTIG.json |
L05-MCP-TEMPORALCONTIG |
Mayer Temporal Contiguity Principle Compliance | MCP | Measures the temporal synchronization of related narration and graphics in generated content | L05-MCP-TEMPORALCONTIG.json |
L05-MCP-VOICE |
Mayer Voice Principle Compliance | MCP | Measures the humanness and friendliness of audio narration voice quality | L05-MCP-VOICE.json |
L05-MMA-ADAPTATIONAUDI |
Adaptation Audit Trail Verification | MMA | Evaluates whether every executed modality switch or adaptation event is accompanied by an immutable audit log recording the specific causal trigger, the logic rule applied, the timestamp, and the stat | L05-MMA-ADAPTATIONAUDI.json |
L05-MMA-ADAPTATIONDONO |
Adaptation 'Do No Harm' (Extraneous Load) Verification | MMA | Evaluates whether a proposed modality switch or adaptive intervention risks increasing extraneous cognitive load (distraction, interface friction, split attention) without a guaranteed pedagogical ben | L05-MMA-ADAPTATIONDONO.json |
L05-MMA-ADAPTATIONEFFE |
Adaptation Effectiveness Evidence Tracking | MMA | Evaluates whether the adaptive system actively tracks and correlates post-adaptation student outcomes against pre-adaptation baselines to validate the efficacy of the modality switch, ensuring the eng | L05-MMA-ADAPTATIONEFFE.json |
L05-MMA-ADAPTATIONLEAR |
Adaptation Student Agency Verification | MMA | Evaluates whether the adaptive logic respects student agency by requiring explicit confirmation, providing an opt-in/opt-out mechanism, or offering a negotiation step before executing a significant mo | L05-MMA-ADAPTATIONLEAR.json |
L05-MMA-ADAPTATIONOVER |
Adaptation Overfitting Prevention (Signal Persistence) | MMA | Evaluates whether the adaptive logic includes mechanisms for signal smoothing, hysteresis, or persistence checking to prevent 'overfitting' the instructional path to short-term, isolated, or noisy dat | L05-MMA-ADAPTATIONOVER.json |
L05-MMA-ADAPTATIONRESP |
Adaptation Response Distinction (Support vs. Progression) | MMA | Evaluates whether the adaptive logic explicitly categorizes its output actions into 'Support' (remedial, scaffolding, slowing down) versus 'Progression' (acceleration, enrichment, advancing), ensuring | L05-MMA-ADAPTATIONRESP.json |
L05-MMA-ADAPTATIONSTAB |
Adaptation Stabilization Period Verification (Anti-Oscillation) | MMA | Evaluates whether the adaptive logic enforces a mandatory 'stabilization period' or 'cool-down window' following a modality switch, preventing rapid oscillation between states (flip-flopping) and ensu | L05-MMA-ADAPTATIONSTAB.json |
L05-MMA-ADAPTATIONTRIG |
Adaptation Trigger Definition Verification | MMA | Evaluates whether the adaptive instructional system has explicitly defined, measurable trigger conditions (e.g., performance thresholds, engagement drops, error patterns, time-on-task variances) that | L05-MMA-ADAPTATIONTRIG.json |
L05-MMA-MODALITYSWITCH |
Modality Switch Cost Estimation | MMA | Evaluates the estimated 'transaction cost' (cognitive re-orientation, temporal latency, technical friction) of a proposed modality switch against the projected pedagogical benefit, preventing adaptive | L05-MMA-MODALITYSWITCH.json |
L05-MMC-ACCESSIBILITYC |
Accessibility Constraint Verification | MMC | Evaluates whether the selected modality complies with the explicit accessibility requirements defined in the student's profile (e.g., WCAG standards, assistive technology support), acting as a hard fi | L05-MMC-ACCESSIBILITYC.json |
L05-MMC-BANDWIDTHANDNE |
Bandwidth and Network Stability Adaptation | MMC | Evaluates whether the selected modality and its specific bitrate/format are compatible with the student's current estimated network bandwidth and stability, ensuring that high-latency or low-bandwidth | L05-MMC-BANDWIDTHANDNE.json |
L05-MMC-CULTURALCOMMUN |
Cultural Communication Norm Verification | MMC | Evaluates whether the interaction patterns and communication styles required by the selected modality align with the student's declared cultural communication norms (e.g., power distance, collectivism | L05-MMC-CULTURALCOMMUN.json |
L05-MMC-DEVICECAPABILI |
Device Capability Constraint Verification | MMC | Evaluates whether the hardware and software requirements of the selected modality (e.g., screen real estate, processing power, audio output) are compatible with the detected or declared capabilities o | L05-MMC-DEVICECAPABILI.json |
L05-MMC-DYNAMICCONTEXT |
Dynamic Context Re-evaluation Verification | MMC | Evaluates whether the instructional delivery system maintains active triggers for re-evaluating modality suitability upon significant changes in context (e.g., bandwidth drop, environment shift), prev | L05-MMC-DYNAMICCONTEXT.json |
L05-MMC-INSTITUTIONALR |
Institutional Resource and Licensing Constraint Verification | MMC | Evaluates whether the specific tools, software licenses, physical facilities, or equipment required by the selected modality are currently available, licensed, and accessible within the student's inst | L05-MMC-INSTITUTIONALR.json |
L05-MMC-LINGUISTICCAPA |
Linguistic Capability and Load Verification | MMC | Evaluates whether the linguistic complexity and language settings of the selected modality align with the student's declared language proficiency (L1/L2 status), triggering support mechanisms or modal | L05-MMC-LINGUISTICCAPA.json |
L05-MMC-PHYSICALENVIRO |
Physical Environment Constraint Verification | MMC | Evaluates whether the student's current physical environment (e.g., noise levels, privacy, lighting, shared space) is compatible with the sensory and interaction demands of the selected modality (e.g. | L05-MMC-PHYSICALENVIRO.json |
L05-MME-ALGORITHMICBIA |
Algorithmic Bias and Fairness Verification | MME | Evaluates the modality recommendation logic for potential bias patterns, ensuring that decisions are not skewed by protected attributes (e.g., gender, ethnicity) or prohibited proxy variables, and ver | L05-MME-ALGORITHMICBIA.json |
L05-MME-DATAMINIMISATI |
Data Minimisation Compliance Verification | MME | Evaluates whether the set of student data signals requested for modality adaptation is strictly limited to variables that have a defined influence on the decision logic, identifying and blocking 'over | L05-MME-DATAMINIMISATI.json |
L05-MME-EQUITYANDLOWRE |
Equity and Low-Resource Pathway Verification | MME | Evaluates whether the instructional design supports digital equity by maintaining at least one viable 'low-resource' or 'offline-capable' pathway (e.g., text, audio-only, PDF) alongside high-bandwidth | L05-MME-EQUITYANDLOWRE.json |
L05-MME-STUDENTOVERRID |
Student Override and Opt-Out Mechanism Verification | MME | Evaluates whether the instructional delivery interface provides a functional, accessible, and non-punitive mechanism for the student to override, opt-out of, or manually alter the system-generated mod | L05-MME-LEARNEROVERRID.json |
L05-MME-MODALITYRECOMM |
Modality Recommendation Explainability Verification | MME | Evaluates whether every algorithmic modality recommendation, adaptation, or restriction is accompanied by a technically accessible and human-readable rationale (local explanation), ensuring the system | L05-MME-MODALITYRECOMM.json |
L05-MME-PEDAGOGICALOPT |
Pedagogical Optimisation Target Legitimacy | MME | Evaluates whether the optimization function driving the modality personalization engine targets valid pedagogical outcomes (e.g., mastery, retention, skill transfer) rather than narrow behavioral prox | L05-MME-PEDAGOGICALOPT.json |
L05-MME-PERSONALIZATIO |
Personalization Data Consent Verification | MME | Evaluates whether a valid, active, and specific consent basis exists for accessing and processing the student's personal data (e.g., traits, preferences, history) within the modality selection logic, | L05-MME-PERSONALIZATIO.json |
L05-MME-STEREOTYPEREIN |
Stereotype Reinforcement Avoidance | MME | Evaluates the modality adaptation logic to detect and flag deterministic mapping rules that rely on demographic stereotypes (e.g., age, gender, culture) to assign specific content types, preventing th | L05-MME-STEREOTYPEREIN.json |
L05-MME-SURVEILLANCEAN |
Surveillance and Biometric Constraint Verification | MME | Evaluates whether the instructional delivery system impermissibly mandates invasive surveillance signals (e.g., always-on webcam, gaze tracking, biometric authentication) as a condition for accessing | L05-MME-SURVEILLANCEAN.json |
L05-MML-CHALLENGEMODAL |
Challenge Modality Exposure (Anti-Narrowing) | MML | Evaluates whether the longitudinal modality pathway includes periodic 'challenge modality' exposures (formats outside the student's primary preference zone) to prevent skill atrophy and representation | L05-MML-CHALLENGEMODAL.json |
L05-MML-STUDENTPREFERE |
Student Preference Data Collection | MML | Evaluates whether the system possesses a record of the student's stated (explicit) or demonstrated (implicit) preference for specific instructional modalities, creating the data basis for agency-orien | L05-MML-LEARNERPREFERE.json |
L05-MML-MESHINGHYPOTHE |
Meshing Hypothesis Rejection | MML | Evaluates whether the modality assignment logic implicitly or explicitly relies on the scientifically unsupported 'meshing hypothesis' (that matching instructional mode to a preferred learning style i | L05-MML-MESHINGHYPOTHE.json |
L05-MML-METACOGNITIVER |
Metacognitive Rationale for Preference Choices | MML | Evaluates whether student-facing modality choices are accompanied by explicit rationales, affordance summaries, or content previews that support metacognitive calibration, ensuring that preference dec | L05-MML-METACOGNITIVER.json |
L05-MML-MODALITYOPTION |
Modality Option Provision (Agency Check) | MML | Evaluates whether the instructional design provides a choice set of at least two valid modality options to the student, safeguarding student agency and self-determination unless specific prohibitive c | L05-MML-MODALITYOPTION.json |
L05-MML-PREFERENCEOBJE |
Preference-Objective Conflict Resolution | MML | Evaluates the system's logic for resolving conflicts between a student's stated modality preference and the strict representational demands of the learning objective, ensuring that resolution involves | L05-MML-PREFERENCEOBJE.json |
L05-MML-PREFERENCEROLE |
Preference Role Definition (Engagement vs. Performance) | MML | Evaluates whether the system's usage of student preference data is explicitly scoped to influence engagement, satisfaction, or motivation metrics, rather than being treated as a causal mechanism for d | L05-MML-PREFERENCEROLE.json |
L05-MMO-COGNITIVECOMPL |
Cognitive Complexity (Bloom) Identification | MMO | Evaluates whether the target learning objective includes a defined cognitive complexity level (specifically aligned to Bloom's Taxonomy or a functional equivalent) to constrain modality selection. | L05-MMO-COGNITIVECOMPL.json |
L05-MMO-FORMATSUBSTITU |
Format Substitution vs. Affordance Shift | MMO | Evaluates whether a proposed alternative modality or modality switch represents a genuine shift in representational affordance (e.g., text to visual) versus a mere 'format substitution' (e.g., PDF to | L05-MMO-FORMATSUBSTITU.json |
L05-MMO-HIGHSOCIALENGA |
High Social Engagement Affordance Availability | MMO | Evaluates whether the instructional modality plan includes at least one 'high social engagement' affordance (e.g., synchronous chat, collaborative whiteboard, live audio) when the pedagogical strategy | L05-MMO-HIGHSOCIALENGA.json |
L05-MMO-STUDENTNOVELTY |
Student Novelty Tolerance Integration | MMO | Evaluates whether the student's estimated tolerance for novelty (or Openness to Experience) has been utilized to calibrate the complexity, unfamiliarity, or experimental nature of the selected modalit | L05-MMO-LEARNERNOVELTY.json |
L05-MMO-STUDENTSELFREG |
Student Self-Regulation to Structure Mapping | MMO | Evaluates whether the student's estimated self-regulation capacity has been utilized to determine the appropriate level of instructional guidance, navigational freedom, and structural scaffolding with | L05-MMO-LEARNERSELFREG.json |
L05-MMO-STUDENTSOCIABI |
Student Sociability Signal Integration | MMO | Evaluates whether an external student sociability or interaction-need signal has been successfully ingested and used to parameterize the social affordances of the selected modality (e.g., isolating vs | L05-MMO-LEARNERSOCIABI.json |
L05-MMO-LEARNINGOBJECT |
Learning Objective Domain Identification | MMO | Evaluates whether the target learning objective has been explicitly classified into a primary domain (cognitive, affective, or psychomotor) to support modality alignment decisions. | L05-MMO-LEARNINGOBJECT.json |
L05-MMO-LOWSOCIALEXPOS |
Low Social Exposure Pathway Availability | MMO | Evaluates whether the instructional modality plan includes at least one valid 'low social exposure' or independent study option when the primary modality involves high social visibility or collaborati | L05-MMO-LOWSOCIALEXPOS.json |
L05-MMO-MODALITYALLOCA |
Modality Allocation for Mixed Objectives | MMO | Evaluates whether instructional segments containing multiple or compound learning objectives (e.g., hybrid cognitive/psychomotor tasks) have explicitly allocated distinct modalities to each objective | L05-MMO-MODALITYALLOCA.json |
L05-MMO-MODALITYASSESS |
Modality-Assessment Alignment | MMO | Evaluates whether the functional capabilities of the selected modality support the specific interaction and output mechanisms required by the planned assessment strategy (e.g., performance demonstrati | L05-MMO-MODALITYASSESS.json |
L05-MMO-MODALITYREPRES |
Modality-Representation Alignment | MMO | Evaluates whether the intrinsic representational affordances of the selected modality (e.g., spatial, temporal, simulative) match the representational requirements explicitly declared by the learning | L05-MMO-MODALITYREPRES.json |
L05-MMO-MODALITYSUCCES |
Modality Success Criteria Definition | MMO | Evaluates whether the selected modality mapping includes an explicit, measurable criterion for success (e.g., completion status, interaction count, assessment score threshold) to distinguish between m | L05-MMO-MODALITYSUCCES.json |
L05-MMO-OBJECTIVEBASED |
Objective-Based Modality Justification | MMO | Evaluates whether the selected instructional modality is explicitly justified by the pedagogical demands of the learning objective (e.g., representational requirements, complexity) rather than relying | L05-MMO-OBJECTIVEBASED.json |
L05-MMO-TRAITADAPTATIO |
Trait Adaptation Framing Verification | MMO | Evaluates whether trait-informed modality recommendations are presented to the student as navigable 'support options' or suggestions, rather than as deterministic 'fixed labels' or rigid diagnoses, th | L05-MMO-TRAITADAPTATIO.json |
L05-MMO-TRAITBASEDEXCL |
Trait-Based Exclusionary Constraint Check | MMO | Evaluates whether student trait signals are being used to impermissibly exclude, hide, or block access to valid instructional modalities (hard gating), enforcing the principle that traits should infor | L05-MMO-TRAITBASEDEXCL.json |
L05-MMS-HIGHGUIDANCEEN |
High Guidance Entry Verification (Novice Scaffolding) | MMS | Evaluates whether the initial instructional segments of a sequence provide high levels of guidance (e.g., worked examples, modeling, explicit instruction) when the student is identified as a novice in | L05-MMS-HIGHGUIDANCEEN.json |
L05-MMS-METACOGNITIVER |
Metacognitive Reflection Step Verification | MMS | Evaluates whether the instructional sequence includes explicit, dedicated nodes or interaction prompts designed to trigger metacognitive consolidation (e.g., self-explanation, summary generation, conf | L05-MMS-METACOGNITIVER.json |
L05-MMS-MODALITYSEQUEN |
Modality Sequence Explicit Specification | MMS | Evaluates whether the collection of instructional modalities is organized into an explicit, directed sequence (e.g., linear, branching, or graph-based), distinguishing a designed learning path from an | L05-MMS-MODALITYSEQUEN.json |
L05-MMS-PREMATUREABSTR |
Premature Abstraction Avoidance Verification | MMS | Evaluates whether abstract, symbolic, or theoretical representations are structurally 'gated' or preceded by concrete, sensory, or grounded representations within the sequence, preventing the introduc | L05-MMS-PREMATUREABSTR.json |
L05-MMS-REMEDIATIONBRA |
Remediation Branch Verification (Anti-Looping) | MMS | Evaluates whether the instructional sequence structure includes explicit branching logic for remediation (alternative instructional pathways) triggered by performance breakdown, ensuring that students | L05-MMS-REMEDIATIONBRA.json |
L05-MMS-REPRESENTATION |
Representational Practice Alignment Verification | MMS | Evaluates whether the instructional sequence includes active practice opportunities or interaction nodes that are explicitly aligned to and paired with preceding representational (informational) steps | L05-MMS-REPRESENTATION.json |
L05-MMS-RETRIEVALPRACT |
Retrieval Practice Opportunity Verification | MMS | Evaluates whether the instructional sequence includes specific interaction nodes designed to trigger 'retrieval practice' (active recall from memory) rather than mere 'review' (passive re-exposure), e | L05-MMS-RETRIEVALPRACT.json |
L05-MMS-SCAFFOLDINGWIT |
Scaffolding Withdrawal (Guidance Fading) Verification | MMS | Evaluates whether the instructional sequence includes specific logic or rules for 'fading' (reducing) guidance and support structures as student competence increases, ensuring that modalities shift fr | L05-MMS-SCAFFOLDINGWIT.json |
L05-MMS-TRANSFERTASKVE |
Transfer Task Verification (Novel Context Application) | MMS | Evaluates whether the instructional sequence includes a 'transfer task'—an application activity situated in a novel context distinct from the initial instruction—to verify that the student can general | L05-MMS-TRANSFERTASKVE.json |
L05-MMT-ABSTRACTCONTEN |
Abstract Content Scaffolding Verification | MMT | Evaluates whether content classified as highly abstract, theoretical, or conceptual is accompanied by required scaffolding representations—specifically concrete examples, analogies, or visualizations— | L05-MMT-ABSTRACTCONTEN.json |
L05-MMT-CONTENTCOMPLEX |
Content Complexity and Density Management | MMT | Evaluates whether the estimated information density and complexity of the content are compatible with the information transmission rate and cognitive load profile of the selected modality, ensuring th | L05-MMT-CONTENTCOMPLEX.json |
L05-MMT-CONTENTSEGMENT |
Content Segmentation and Chunking Verification | MMT | Evaluates whether complex, long-form, or information-dense content is structurally divided into manageable segments (chunking) or offers user-controlled pacing breaks, preventing cognitive overload ca | L05-MMT-CONTENTSEGMENT.json |
L05-MMT-CONTENTTYPOLOG |
Content Typology Classification | MMT | Evaluates whether the instructional content asset has been explicitly classified into a fundamental typology (e.g., Conceptual, Procedural, Affective, or Hybrid) to enable affordance-based mapping and | L05-MMT-CONTENTTYPOLOG.json |
L05-MMT-MODALITYREDUND |
Modality Redundancy Risk Verification | MMT | Evaluates whether the simultaneous use of multiple modalities (e.g., Audio + Text) results in detrimental redundancy (the 'Redundancy Effect') where identical information is presented via competing ch | L05-MMT-MODALITYREDUND.json |
L05-MMT-MOTORANDPROCED |
Motor and Procedural Skill Affordance Verification | MMT | Evaluates whether content identified as requiring psychomotor skill acquisition or procedural execution is mapped to a modality that supports dynamic demonstration (modeling) or interactive practice ( | L05-MMT-MOTORANDPROCED.json |
L05-MMT-SIGNALINGANDAT |
Signaling and Attention Guidance Verification | MMT | Evaluates whether complex or visually/auditorily dense content includes explicit signaling cues (e.g., highlights, arrows, vocal emphasis, headers) to guide student attention to essential material, th | L05-MMT-SIGNALINGANDAT.json |
L05-MMT-SPATIALSTRUCTU |
Spatial Structure Affordance Verification | MMT | Evaluates whether content identified as possessing inherent spatial structure (e.g., topology, geometry, anatomy, geography) is mapped to a modality capable of visual, diagrammatic, or haptic represen | L05-MMT-SPATIALSTRUCTU.json |
L05-MMT-SPLITATTENTION |
Split-Attention Risk Verification | MMT | Evaluates whether the spatial layout or temporal synchronization of instructional elements forces the student to split their attention between disjoint, non-integrated sources (e.g., a diagram with a | L05-MMT-SPLITATTENTION.json |
L05-MMT-TEMPORALSEQUEN |
Temporal Sequencing Affordance Verification | MMT | Evaluates whether content identified as possessing inherent temporal properties (e.g., sequential processes, chronological history, rhythm, dynamic change over time) is mapped to a modality that suppo | L05-MMT-TEMPORALSEQUEN.json |
L05-MQS-ACCESSCOMPLI |
Accessibility Compliance Score | MQS | Measures compliance with accessibility standards including alt-text, captions, transcripts, contrast ratios, and screen reader compatibility | L05-MQS-ACCESSCOMPLI.json |
L05-MQS-COGLOAD |
Cognitive Load Assessment | MQS | Measures the estimated cognitive load of generated content, balancing intrinsic complexity with extraneous load minimization | L05-MQS-COGLOAD.json |
L05-MQS-ENGAGESCORE |
Engagement Potential Score | MQS | Measures the predicted engagement potential of generated content based on interactivity, variety, relevance, and interest factors | L05-MQS-ENGAGESCORE.json |
L05-MQS-FACTACCURACY |
Factual Accuracy Score | MQS | Measures the factual accuracy of generated content through fact-checking against authoritative sources | L05-MQS-FACTACCURACY.json |
L05-MQS-MAYEROVERALL |
Mayer Principles Overall Compliance | MQS | Aggregates compliance scores across all 12 Mayer principles to provide an overall multimedia learning quality score | L05-MQS-MAYEROVERALL.json |
L05-MQS-SENSORYBALANCE |
Sensory Channel Balance Score | MQS | Measures the balance of generated content across sensory channels (visual, auditory, kinesthetic/haptic) to avoid channel overload | L05-MQS-SENSORYBALANCE.json |