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REVIEW 5 major objections 6 minor 33 references

From Formulas to Figures: How Visual Elements Impact User Interactions in Educational Videos

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Text-heavy video moments drive pauses, rewinds, dropouts

desk verdict The taxonomy and real-world dataset are the real contributions, but the permutation test's failure to preserve autocorrelation undermines the headline p-values as reported. read the letter →

arxiv 2505.01753 v1 pith:2SQJTNGN submitted 2025-05-03 cs.HC

Wolfgang Gritz , Hewi Salih , Anett Hoppe , Ralph Ewerth This is my paper · ORCID
classification cs.HC
keywords userinteractionsbehaviorvisualcomplexityeducationalvideosSTEMeducationdynamictimewarpingpermutationtestingvideoanalytics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the visual complexity of educational videos—defined as the number of visually distinguishable, meaningful elements on screen, especially textual ones like equations and formulas—changes how real viewers watch. Using anonymized viewing logs from 8,180 sessions across 25 physics and chemistry videos, it reports that sections where many textual elements appear trigger significantly more pauses, rewinds, and dropouts, while graphical elements do not. The authors interpret this through cognitive-load theory: text-heavy bursts demand extra processing time, and when the demand is too high, learners stop the video or leave it. If true, the result gives video creators a measurable design lever—reduce text spikes or break them into smaller steps—rather than relying on intuition about what makes a lesson hard to follow.

What carries the argument

The load-bearing machinery is a two-part pipeline. First is the paper's fine-grained taxonomy of visual objects in STEM videos, which classifies annotated elements into textual categories (equations, terms, lines of text) and information-visualization categories (diagrams, tables, charts, technical drawings), making visual complexity a concrete count of distinguishable meaningful objects per frame. Second is a modified Dynamic Time Warping (DTW) procedure used as the test statistic in a permutation test: it aligns the visual-complexity time series with each user-interaction time series while enforcing a causality constraint that interactions can only map to preceding visual changes and a reaction-window constraint that limits mappings to about ten seconds (two five-second bins) after a stimulus. Per-video p-values are aggregated with Fisher's method, and a permutation effect size (PES) is computed as the standardized distance between the observed DTW cost and the null distribution.

What would settle it

Independently re-annotate all 25 videos, or a large random sample of them, with a second annotator or an automated object detector using the same taxonomy, recompute the visual-complexity time series, and rerun the permutation tests; if the reported associations ($p = 0.0$, PES $= 0.89$ for pausing; $p = 0.017$, PES $= 0.47$ for dropouts; $p = 0.001$, PES $= 0.79$ for rewinds) do not replicate with the independent series, the original result reflects the single annotator's labeling habits rather than a property of the videos.

Watch

Extended reading notes

Core claim

The paper's central claim is that visual complexity, measured as the number of newly appearing annotated objects per five-second bin, is statistically associated with user behavior in authentic viewing logs, and the association is driven by textual elements. It reports that users paused more frequently in sections with high textual change counts ($p = 0.0$, PES $= 0.89$), paused more under overall visual complexity ($p = 0.009$, PES $= 0.64$), rewound away from text-heavy sections more often ($p = 0.001$, PES $= 0.79$), and dropped out of videos more often at textually complex points ($p = 0.017$, PES $= 0.47$). The binary version of the complexity signal—whether any change occurred—was weaker or non-significant for dropouts and rewinds, which the authors take to mean the magnitude of textual change, not the mere occurrence, drives behavior. No significant effects were found for graphical or visualization elements, leading to the conclusion that on-screen text imposes higher cognitive load than graphics in real learning settings.

Load-bearing premise

The entire visual-complexity time series for all 25 videos rests on annotations by a single primary annotator, and agreement with a second annotator was measured on only five frames; if that annotator's judgments drift over the full corpus or the taxonomy misses relevant object classes, the predictor series is biased and every reported association could be an artifact of the labeling rather than the video content.

Editorial extensions

If this is right

  • Video creators can use the number of newly appearing text objects per time window as a risk score: text spikes mark where viewers will pause, rewind, or leave, so those segments can be redesigned or broken up before release.
  • Because graphical elements such as diagrams and charts showed no significant negative associations, shifting information from dense text to visual representations should reduce disruptive interactions without losing content.
  • The weaker binary-change results imply that the magnitude of a text burst matters more than the mere fact of change, so incremental text reveals are less disruptive than large simultaneous text additions.
  • The pattern supports adding system-determined pauses or interactive prompts at text-heavy points in the video, building on the segmenting-effect findings the paper cites.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper does not run: replace equation-heavy text segments with narrated visualizations of the same content and compare pause, rewind, and dropout rates; the paper's text-vs-graphics asymmetry predicts fewer disruptions in the visualization condition.
  • Varying the 10-second reaction window in the DTW analysis could produce a temporal response curve showing how quickly viewers pause or rewind after a text spike, distinguishing immediate overload from delayed comprehension attempts.
  • Automating the taxonomy with an object detector would let the same analysis scale from 25 videos to thousands and turn the correlational finding into a practical pre-publication screening tool for video producers.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper studies whether visual complexity in STEM educational videos is associated with user interactions in real-world viewing logs. The authors develop a fine-grained taxonomy of visual objects, annotate 25 videos from the TIB AV-Portal (SciFox channel), and construct per-video time series of the number of newly appearing visual objects (textual vs. information-visualization categories) in 5-second bins. They relate these to binned user interactions (pauses, rewinds, skips, dropouts) using a modified dynamic time warping (DTW) test statistic with a causality constraint and a 10-second reaction window, and assess significance with a permutation test aggregated across videos via Fisher's method. The headline findings are that textual visual complexity is associated with more pauses (p=0.0, PES=0.89), rewinds (p=0.001, PES=0.79), and dropouts (p=0.017, PES=0.47), while information-visualization elements show no such effects.

Significance. The study addresses a genuinely understudied question with authentic interaction data, which is a strength relative to laboratory studies. The taxonomy is a useful contribution, and the public code and careful preprocessing (e.g., normalizing by active sessions, unique-session counting) support reproducibility. If the statistical analysis is validated, the findings would have practical implications for educational video design, suggesting that text-heavy segments impose processing costs that manifest in pausing, rewinding, and dropout. However, the current statistical machinery has serious validity concerns (permutation null, reaction-window selection, multiple comparisons, annotation reliability), so the empirical contribution is not yet established at the level claimed.

major comments (5)
  1. [§4.3, Table 1] The permutation test permutes the visual-complexity series VisCom_v without preserving its temporal autocorrelation, while the modified DTW test statistic with a two-bin warping window is sensitive to the autocorrelation structure of the series. Because both VisCom_v and the interaction series are bursty (visual changes cluster at slide transitions and interactions cluster in bursts), the original series can achieve a lower DTW cost than a randomly shuffled series that has been whitened, so the null distribution is shifted toward higher costs and the p-values in Table 1 are biased toward zero. This affects every row of Table 1, including the headline pause/T VisCom result (p=0.0, PES=0.89) and rewind-from/T (p=0.001, PES=0.79). Please re-run the analysis under a null that preserves the autocorrelation of VisCom_v (e.g., block permutation, circular shifting, or phase-randomized surrogates) and report the resulting p-values and PES.
  2. [§4.3, Algorithm 1] The causality constraint is described inconsistently with its stated intent. The text says that setting all costs below the main diagonal to infinity 'prevents future user interactions from being mapped to past visual complexity changes,' but in the matrix orientation implied by Algorithm 1 (s1 = visual complexity, s2 = user interactions, D[i,j] = (s1[j]-s2[i])^2), cells below the diagonal have j < i and correspond to mapping a current interaction to an earlier visual stimulus, which is precisely the desired causal direction. Either the orientation or the direction of the constraint is wrong. Please specify the exact indexing and verify the implementation in the public code, because a reversed constraint would change the test statistic into one that aligns interactions with future stimuli and invalidates the interpretation.
  3. [§4.3, Table 1] The paper reports 36 significance tests (6 actions × 3 modality groupings × 2 conditions) in Table 1 without any multiple-comparison correction. With a Bonferroni threshold of approximately 0.0014, only the pause/T VisCom (p=0.0) and rewind-from/T VisCom (p=0.001) results remain significant; the dropout/T result (p=0.017) would not survive. Please apply a suitable correction (e.g., Benjamini-Hochberg) or explicitly label the analysis as exploratory, and revise the conclusions to match the corrected outcome.
  4. [§3.2/§4.1] The predictor series is built from annotations by a single primary annotator (A1), with inter-annotator agreement measured on only five frames against A2 before A1 completed the full annotation. Because the visual-complexity time series is defined by the appearance of new objects across frames, the static IoU check does not validate the temporal tracking that determines 'new' objects, and any annotation drift or tracking error is propagated into every permutation test. Please report per-video or per-change-event annotation quality (e.g., a sample of change events re-annotated by A2) or otherwise demonstrate that the predictor series is reliable.
  5. [§4.3] The two-bin (10 s) reaction window and the 5 s bin size are chosen based on exploratory visual analysis of the same data that is later used for hypothesis testing. This post-hoc tuning of the test statistic's parameters can inflate the apparent significance because the window is selected to make the alignment cost small. Please report a sensitivity analysis over the window size (e.g., 1, 2, 3 bins) and, ideally, confirm the findings on a hold-out set or with pre-registered parameter choices.
minor comments (6)
  1. [§3.2] The phrase 'Reviewer guidelines were established and continuously adapted' appears to be a typo; it should presumably read 'Annotation guidelines were established and continuously adapted.'
  2. [§4.3, Eq. (2)] The sentence 'The negative sign ensures a positive PESv indicates a positive.' is incomplete; it should specify that a positive PESv indicates a positive relationship between visual complexity and the respective user interaction.
  3. [Table 1] The meaning of 'time' under the 'action' column is ambiguous; the labels 'from' and 'to' for rewind and skip should be defined in the caption (e.g., 'from' = source position of the seek, 'to' = target position).
  4. [Algorithm 1] The pseudocode declares a window parameter but does not specify its exact indexing; please state precisely which cells are set to infinity in terms of i and j, both for the causality constraint and the reaction window.
  5. [§4.3] The statement 'Based on exploratory visual analysis, we observed that user responses often occur up to 10 seconds after a visual stimulus' should be accompanied by a concrete description of the exploration (e.g., number of videos inspected and the procedure used to arrive at the 10 s threshold).
  6. [§5] The abbreviation 'CTLM' should be 'CTML' (Cognitive Theory of Multimedia Learning) to match the usage in the introduction.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims are empirical associations evaluated with a permutation test, not constructed from their inputs.

full rationale

The central results (pauses, rewinds, and dropouts following textual visual changes) are statistical findings from annotated videos and user logs, not quantities derived by definition from the input series. The visual-complexity series is constructed from bounding-box annotations via a stated taxonomy, and the user-interaction series comes from logged events; the DTW cost is a test statistic, and the p-values arise from permuting the visual-complexity series against fixed interaction series. No parameter is fitted to the outcome and then reported as a prediction: the 10-second reaction window is an exploratory modeling choice, and the binary-condition reanalysis is a robustness check rather than a fitted quantity. The self-citations (e.g., Merkt et al. 2022, which includes two coauthors of the present paper) are used for related work and to motivate bin size, not as a load-bearing proof or uniqueness theorem. The paper also states its own limitation in Section 6: 'our results are based on 25 videos, and the findings' generalizability requires further research.' The skeptic's concern that random permutation destroys autocorrelation and biases the null distribution is a statistical validity critique, not circularity; it does not make any equation of the paper equivalent to its own inputs. Therefore no pattern of self-definition, fitted-input-as-prediction, or load-bearing self-citation is present.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim is an empirical correlation built from annotation-derived time series and a small number of modeling choices (bin size, reaction window, edge trimming). No free parameter is fitted to the outcome in a way that would force the results.

free parameters (4)
  • Reaction window size = 2 bins (10 s)
    Set after exploratory visual analysis of the same data (Section 4.3); limits DTW alignment to interactions within 10 s of a visual change.
  • Time bin size = 5 s
    Chosen following prior work [2,6] (Section 4.2); determines the granularity of both time series.
  • Trimmed edge bins = 2 bins at start and 2 at end
    First two and last two bins are removed due to startup effects and closing remarks (Section 4.2); a data-dependent decision.
  • Annotation agreement threshold = IoU 0.88
    Used to decide that annotation guidelines were stable (Section 3.2).
assumptions (5)
  • domain assumption User interactions (pause, rewind, skip, dropout) are caused by preceding visual content rather than by audio, difficulty, or other confounds.
    The DTW causality constraint maps interactions only to earlier visual changes; no confound variables are modeled (Section 4.3).
  • domain assumption Visual complexity is adequately measured by the number of newly appearing annotated elements between frames, not by static element counts.
    Section 4.1 defines complexity via tracking IDs of appearing bounding boxes.
  • domain assumption The taxonomy is complete and valid for physics and chemistry videos from the SciFox channel.
    The taxonomy was developed iteratively by the two annotators on this dataset (Section 3.2).
  • standard math Permutation of the visual complexity series yields a valid null distribution for the DTW cost statistic.
    Permutation testing assumes exchangeability under the null of no temporal association (Section 4.3).
  • domain assumption TIB AV-Portal logs with at least one play event reflect genuine viewing sessions.
    Sessions without play events are filtered out; data come from consenting users (Section 4.2).

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Cite this review

Pith. "Pith review of From Formulas to Figures: How Visual Elements Impact User Interactions in Educational Videos." pith.science (2026). https://pith.science/paper/2SQJTNGN

@misc{pith2026250501753,
  author       = {Pith},
  title        = {Pith review of: From Formulas to Figures: How Visual Elements Impact User Interactions in Educational Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2SQJTNGN}},
  note         = {Machine review of arXiv:2505.01753}
}
read the original abstract

Educational videos have become increasingly relevant in today's learning environments. While prior research in laboratory studies has provided valuable insights, analyzing real-world interaction data can enhance our understanding of authentic user behavior. Previous studies have investigated technical aspects, such as the influence of cuts on pausing behavior, but the impact of visual complexity remains understudied. In this paper, we address this gap and propose a novel approach centered on visual complexity, defined as the number of visually distinguishable and meaningful elements in a video frame, such as mathematical equations, chemical formulas, or graphical representations. Our study introduces a fine-grained taxonomy of visual objects in educational videos, expanding on previous classifications. Applying this taxonomy to 25 videos from physics and chemistry, we examine the relationship between visual complexity and user behavior, including pauses, in-video navigation, and session dropouts. The results indicate that increased visual complexity, especially of textual elements, correlates with more frequent pauses, rewinds, and dropouts. The results offer a deeper understanding of how video design affects user behavior in real-world scenarios. Our work has implications for optimizing educational videos, particularly in STEM fields. We make our code publicly available (https://github.com/TIBHannover/from_formulas_to_figures).

Figures

Figures reproduced from arXiv: 2505.01753 by the authors.

Figure 1
Figure 1. Shows the visualization of the developed taxonomy for visual objects in [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Annotated example frame (see 4:46 at https://av.tib.eu/media/15698) [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Overview of our data preprocessing (Section 4.2). [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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    van der Zee, T., Admiraal, W., Paas, F., Saab, N., Giesbers, B.: Effects of sub- titles, complexity, and language proficiency on learning from online education videos. Journal of Media Psychology29(1), 18–30 (2017). https://doi.org/10.1027/ 1864-1105/a000208

    2017

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.